From fbb5a30db6a1d53e9c38a0648353633995b62689 Mon Sep 17 00:00:00 2001 From: EO-Robotics-Team <22110240029@m.fudan.edu.cn> Date: Thu, 11 Sep 2025 19:19:41 +0800 Subject: [PATCH 01/13] This pull request sets up the initial GitHub repository configuration and automation for the EO-1 project (#5) (#6) * Add initial project structure with configuration files, datasets, and example scripts * Update .gitignore to include new demo data paths, modify pre-commit configuration to exclude additional directories, and enhance README with more examples and installation instructions. Adjust dataset handling in pipeline configuration and dataset classes for improved training flexibility. Remove deprecated demo scripts and refine evaluation scripts for clarity. * Update .gitignore to include demo data paths, enhance README with additional examples, and modify Libero benchmark configuration files for improved clarity and structure. Adjust training scripts and evaluation settings across various experiments for consistency. * Remove fast testing workflow configuration from GitHub Actions * Update pre-commit configuration to refine exclusions, enhance README with structured examples, and remove unused imports in the EO model script. Co-authored-by: dlqu_0010 --- .github/settings.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/settings.yml b/.github/settings.yml index fedc553..0163e46 100644 --- a/.github/settings.yml +++ b/.github/settings.yml @@ -2,7 +2,6 @@ repository: # See https://docs.github.com/en/rest/reference/repos#update-a-repository for all available settings. # The name of the repository. Changing this will rename the repository - # name: eo-1-dev # name: EO-1 # A short description of the repository that will show up on GitHub From 735ee5a11b6a812a8aab5d38a9eac906c13521f4 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Fri, 12 Sep 2025 23:23:09 +0800 Subject: [PATCH 02/13] Refactor model input handling for multimodal data, including image and video features --- .github/settings.yml | 127 --- .gitignore | 4 +- README.md | 179 ++-- demo_data/example1.jpg | Bin 0 -> 117078 bytes demo_data/example2.png | Bin 0 -> 412328 bytes eo/model/modeling_eo1.py | 166 ++-- eo/model/modeling_qwen2_5_vl.py | 1112 +++++++++--------------- experiments/1_demo/data-demo.yaml | 24 +- experiments/2_libero/data-libero.yaml | 2 +- getting_started/1_customize_data.ipynb | 0 scripts/eval_policy.py | 16 +- tools/test_hf_model.py | 54 ++ 12 files changed, 710 insertions(+), 974 deletions(-) delete mode 100644 .github/settings.yml create mode 100644 demo_data/example1.jpg create mode 100644 demo_data/example2.png delete mode 100644 getting_started/1_customize_data.ipynb create mode 100644 tools/test_hf_model.py diff --git a/.github/settings.yml b/.github/settings.yml deleted file mode 100644 index 0163e46..0000000 --- a/.github/settings.yml +++ /dev/null @@ -1,127 +0,0 @@ -repository: - # See https://docs.github.com/en/rest/reference/repos#update-a-repository for all available settings. - - # The name of the repository. Changing this will rename the repository - # name: EO-1 - - # A short description of the repository that will show up on GitHub - description: "EO: Open-source Unified Embodied Foundation Model Series" - - # A URL with more information about the repository - homepage: https://eo-robotics.ai - - # A comma-separated list of topics to set on the repository - topics: robot-control, generalist-robot-policy, vla, embodied-ai, large-embodied-model, unified-model - - # Either `true` to make the repository private, or `false` to make it public. - private: false - - # Either `true` to enable issues for this repository, `false` to disable them. - has_issues: true - - # Either `true` to enable projects for this repository, or `false` to disable them. - # If projects are disabled for the organization, passing `true` will cause an API error. - has_projects: false - - # Either `true` to enable the wiki for this repository, `false` to disable it. - has_wiki: false - - # Either `true` to enable downloads for this repository, `false` to disable them. - has_downloads: true - - # Updates the default branch for this repository. - default_branch: main - - # Either `true` to allow squash-merging pull requests, or `false` to prevent - # squash-merging. - allow_squash_merge: true - - # Either `true` to allow merging pull requests with a merge commit, or `false` - # to prevent merging pull requests with merge commits. - allow_merge_commit: false - - # Either `true` to allow rebase-merging pull requests, or `false` to prevent - # rebase-merging. - allow_rebase_merge: false - - # Either `true` to enable automatic deletion of branches on merge, or `false` to disable - delete_branch_on_merge: true - - # Either `true` to enable automated security fixes, or `false` to disable - # automated security fixes. - enable_automated_security_fixes: true - - # Either `true` to enable vulnerability alerts, or `false` to disable - # vulnerability alerts. - enable_vulnerability_alerts: true - -# Milestones: define milestones for Issues and Pull Requests -# milestones: -# - title: milestone-title -# description: milestone-description -# # The state of the milestone. Either `open` or `closed` -# state: open - -# Collaborators: give specific users access to this repository. -# See https://docs.github.com/en/rest/reference/repos#add-a-repository-collaborator for available options -# collaborators: - # - username: bkeepers - # permission: push - # - username: hubot - # permission: pull - - # Note: `permission` is only valid on organization-owned repositories. - # The permission to grant the collaborator. Can be one of: - # * `pull` - can pull, but not push to or administer this repository. - # * `push` - can pull and push, but not administer this repository. - # * `admin` - can pull, push and administer this repository. - # * `maintain` - Recommended for project managers who need to manage the repository without access to sensitive or destructive actions. - # * `triage` - Recommended for contributors who need to proactively manage issues and pull requests without write access. - -# See https://docs.github.com/en/rest/reference/teams#add-or-update-team-repository-permissions for available options -teams: - - name: core - # The permission to grant the team. Can be one of: - # * `pull` - can pull, but not push to or administer this repository. - # * `push` - can pull and push, but not administer this repository. - # * `admin` - can pull, push and administer this repository. - # * `maintain` - Recommended for project managers who need to manage the repository without access to sensitive or destructive actions. - # * `triage` - Recommended for contributors who need to proactively manage issues and pull requests without write access. - permission: admin - - name: docs - permission: push - -branches: - - name: main - # https://docs.github.com/en/rest/reference/repos#update-branch-protection - # Branch Protection settings. Set to null to disable - protection: - # Required. Require at least one approving review on a pull request, before merging. Set to null to disable. - required_pull_request_reviews: - # The number of approvals required. (1-6) - required_approving_review_count: 1 - # Dismiss approved reviews automatically when a new commit is pushed. - dismiss_stale_reviews: true - # Blocks merge until code owners have reviewed. - require_code_owner_reviews: true - - # Required. Require status checks to pass before merging. Set to null to disable - required_status_checks: - # Required. Require branches to be up to date before merging. - strict: true - # Required. The list of status checks to require in order to merge into this branch - contexts: - # - "test" - - "quality" - - "security" - - "release" - - # Required. Enforce all configured restrictions for administrators. Set to true to enforce required status checks for repository administrators. Set to null to disable. - enforce_admins: true - # Prevent merge commits from being pushed to matching branches - required_linear_history: true - # Required. Restrict who can push to this branch. Team and user restrictions are only available for organization-owned repositories. Set to null to disable. - restrictions: - apps: [] - users: [] - teams: [] diff --git a/.gitignore b/.gitignore index c8b1e51..c2cee81 100644 --- a/.gitignore +++ b/.gitignore @@ -226,9 +226,11 @@ source experiments/outputs scripts/env.sh -scripts/pre-commit.sh +pre-commit.sh tools/caption_video.html experiments/2_libero/logs experiments/2_libero/logs/videos demo_data/demos25 + +demo_data/libero_spatial_no_noops_1.0.0_lerobot diff --git a/README.md b/README.md index a6bf213..4691a8c 100644 --- a/README.md +++ b/README.md @@ -67,8 +67,8 @@ We introduce **EO-1** model, an open-source unified embodied foundation model co Clone the repository: ```bash -git clone https://github.com/EO-Robotics/EO.git -cd EO +git clone https://github.com/EO-Robotics/EO-1.git +cd EO-1 ``` Create a conda environment and install dependencies: @@ -101,8 +101,8 @@ pip install -e . ### Experiment Examples - [Demo Training](experiments/1_demo/) - Quick start with demo data and debug mode -- [Libero Benchmark](experiments/2_libero/) - Spatial reasoning tasks and evaluation -- [SimplerEnv Benchmark](experiments/3_simpler/) - Real-world deployment on WidowX and Google Robot +- [Libero Benchmark](experiments/2_libero/) - Tuning on Libero benchmark tasks +- [SimplerEnv Benchmark](experiments/3_simpler/) - Tuning on SimplerEnv benchmark, including WidowX and Google Robot - [SO101 Tasks](experiments/4_so101/) - SO100 collection manipulation tasks - [WidowX Platform](experiments/5_widowx/) - WidowX robot specific training and evaluation - [AgiBot Platform](experiments/6_agibot/) - AgiBot robot training and deployment @@ -116,27 +116,51 @@ pip install -e . ```python from transformers import AutoModel, AutoProcessor -# load the model and processor + +# load model and processor processor = AutoProcessor.from_pretrained("IPEC-COMMUNITY/EO-1-3B", trust_remote_code=True) model = AutoModel.from_pretrained( "IPEC-COMMUNITY/EO-1-3B", trust_remote_code=True, - torch_dtype=torch.bfloat16 + dtype=torch.bfloat16 ).eval().cuda() # prepare the model input batch = { - "observation.images.image": [img], # PIL.Image + "observation.images.image": [img], "observation.images.wrist_image": [wrist_img], "observation.state": [state], - "task": ["You are a helpful physical agent equipped with both reasoning and robotic control. \ - You see the Tic-Tac-Toe board, think strategically, act logically, and block threats."] + "task": ["Pick up a red piece and place it at (0, 2)."] } -# generate multimodal outputs -output = processor.generate(model, batch) -text = output.text -actions = output.action.numpy() +# 1. action sampling [robot control] +output = processor.select_action(model, batch) +print(output.action) + +# prepare conversation +messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": "demo_data/example2.png"}, + {"type": "text", "text": "You are a helpful physical agent equipped with both reasoning and robotic control. \ + You see the Tic-Tac-Toe board, think strategically, act logically, and block threats."}, + ], + }, +] +# 2. text generation [multimodal reasoning] +input_length = inputs["input_ids"].shape[1] +inputs = processor.apply_chat_template( + messages, + tokenize=True, + return_dict=True, + return_tensors="pt" +).to("cuda") + +outputs = model.generate(**inputs, max_new_tokens=1024, return_dict_in_generate=True) +generated_ids = outputs.sequences +text = processor.decode(generated_ids[0, input_length:]) +print(text) ``` ### Datasets @@ -145,22 +169,40 @@ We use [LeRobot](https://github.com/huggingface/lerobot) as the primary source f For Multimodal data, e.g., image, video, text, points and bounding boxes, we follow the [Qwen2.5-VL](https://colab.research.google.com/github/QwenLM/Qwen2.5-VL/blob/main/cookbooks/spatial_understanding.ipynb) and [Qwen2-VL-Finetune](https://github.com/2U1/Qwen2-VL-Finetune) recipes. In interleaved pretraining, we integrate the EO-Data1.5M dataset — a large-scale, high-quality embodied dataset designed to unify reasoning and control. Data are organized in a standardized format as shown below:

-Here, the `lerobot` and `view` fields connect actions with multimodal conversations, enabling the model to capture the rich temporal dynamics and causal dependencies among vision, language, and action modalities — a core requirement for robust performance in open-world embodied interactions. +Here, the `lerobot` and `view` fields connect actions with multimodal conversations, enabling the model to capture the rich temporal dynamics and causal dependencies among vision, language, and action modalities — a core requirement for robust performance in open-world embodied interactions. For more details, please refer to [getting_started/1_load_dataset](getting_started/1_load_dataset.ipynb). -To combine robot control data and multimodal data, we support a flexible YAML-based configuration, where each dataset can be assigned weights and sampling strategies. This makes it easy to balance embodied control trajectories with multimodal reasoning data for interleaved training. For example: +To combine robot control data and multimodal data, we support a [flexible YAML-based configuration](eo/data/schema.py), where each dataset can be assigned weights and sampling strategies. This makes it easy to balance embodied control trajectories with multimodal reasoning data for interleaved training. For example: ```yaml -# configs/example.yaml -mm_datasets: # optional - - json_path: LEROBOT_DATASET/bridge_interleaved_data.jsonl - sampling_strategy: random:5% +# @multimodal data config +mm_datasets: + - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file + vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL + sampling_strategy: random:10% # sampling strategy - - json_path: RefCOCO/refcoco.jsonl - sampling_strategy: random:10% + - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl +# @robot control config lerobot_datasets: - - repo_id: bridge - select_video_keys: [observation.images.image_0] + - repo_id: demo25 + root: ./demo_data + # Optional fields: + episodes: [1, 2, 3] # specific episodes to load (None = all) + train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task + delta_action: false # train with delta actions + state_mode: "MEAN_STD" # state normalization mode + select_video_keys: # which camera streams to load + [ + observation.images.head, + observation.images.hand_left, + observation.images.hand_right, + ] + select_state_keys: # proprioceptive states + [observation.states.joint.position, observation.states.effector.position] + select_action_keys: # action targets + [actions.joint.position, actions.effector.position] + effector_indices: [14, 15] # indices of effector channels in the flattened action vector + weight: 1.0 # dataset weight for sampling ``` ### 2. Fine-tuning on your dataset @@ -174,62 +216,64 @@ lerobot_datasets: To fine-tune **EO-1** on your own embodiment, you only need to adapt the configuration file. Specifically, convert your dataset into the LeRobot format, then define the fields that describe where your videos, states, and actions are located. The following YAML snippet shows a typical setup: ```yaml -# @multimodal corpora +# @multimodal data config +# leave empty if only robot control data mm_datasets: -# @robot control episodes lerobot_datasets: - - repo_id: AgiBotWorld-Beta/example001 # dataset identifier - root: /oss/vla_next/DATA # path to the dataset root directory - - # Optional fields: - train_subtask: mixture:0.9 # mix sub-task instructions and overall instructions with 90% sub-task - delta_action: false # train with delta actions + - repo_id: libero_spatial_no_noops_1.0.0_lerobot # replace with your dataset name + root: ./demo_data/ # replace with your dataset root path select_video_keys: [ - observation.images.head, - observation.images.hand_left, - observation.images.hand_right, - ] # which camera streams to load - select_state_keys: [ - observation.states.joint.position, - observation.states.effector.position, - ] # proprioceptive states - select_action_keys: [actions.joint.position, actions.effector.position] # the action targets to supervise during training - select_effector_keys: [actions.effector.position] # effector control channels - effector_indices: [14, 15] # indices of effector channels in the flattened action vector + observation.images.image, + observation.images.wrist_image, + ] # replace with your feature keys + select_state_keys: [observation.state] + select_action_keys: [action] + + - repo_id: libero_90_no_noops_lerobot + root: HF_LEROBOT_HOME + # If not specified, uses all keys by default ``` -Once your dataset is prepared and the configuration file (e.g., example.yaml) is set up, you can launch fine-tuning with the following command. We use torchrun to support distributed or multi-GPU training, while the arguments control training mode, optimization, and which model components to freeze or update. +Once your dataset is prepared and the configuration file (e.g., example.yaml) is set up, you can launch fine-tuning with the following command. We use torchrun to support distributed or multi-GPU training, while the arguments control training mode, optimization, and which model components to freeze or update. Please launch scripts to [experiments/1_demo](experiments/1_demo) and [experiments/2_libero](experiments/2_libero)to start a demo training. ```bash -torchrun $TORCH_RUN_ARGS onvisfm/train.py \ - ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ # load pre-trained model - --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ # load vlm backbone from Qwen2.5-VL-3B-Instruct - --train-lerobot-only True \ # w/o multimodal data - --data-path configs/example.yaml \ - --chunk-size 16 \ - --dataloader-num-workers 8 \ - --freeze-vision-tower False \ - --freeze-llm False \ - --freeze-merger False \ - --bf16 True \ - --tf32 True \ - --num-train-epochs 25 \ - --per-device-train-batch-size 64 \ - --learning-rate 5e-5 \ - --merger-lr 5e-5 \ - --vision-lr 1e-5 \ - --warmup-ratio 0.03 \ - --gradient-checkpointing True \ - --save-steps 2000 \ - --report-to wandb \ - --run-name bridge \ - --state-mode MAEN_STD +accelerate launch $ACCELERATE_ARGS scripts/train.py \ + ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ + ${deepspeed:+--deepspeed configs/${deepspeed}.json} \ + --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ + --train-lerobot-only ${lerobot_only} \ + --data-path ${dataset} \ + --chunk-size ${chunk_size} \ + --dataloader-num-workers ${data_num_workers} \ + --freeze-vision-tower False \ + --freeze-llm False \ + --freeze-merger False \ + --bf16 True \ + --tf32 True \ + --fp16 False \ + --num-train-epochs ${epoch} \ + --per-device-train-batch-size ${PER_DEVICE_BATCH_SIZE} \ + --gradient-accumulation-steps 1 \ + --learning-rate ${lr} \ + --merger-lr ${mlr} \ + --vision-lr ${vlr} \ + --weight-decay 0.1 \ + --warmup-ratio 0.03 \ + --lr-scheduler-type cosine \ + --logging-steps ${logging_steps} \ + --gradient-checkpointing True \ + --save-strategy steps \ + --save-steps ${save_steps} \ + --save-total-limit 3 \ + --report-to ${report} \ + --run-name ${run_name} \ + --attn-implementation flash_attention_2 ``` ## Benchmark -Mastering Diverse Manipulations on Multiple Embodiments +Mastering Diverse Manipulations on Multiple Embodiments. More details can be found in [experiments/2_libero](experiments/2_libero/), [experiments/3_simpler](experiments/3_simpler/), and [experiments/8_vllmeval](experiments/8_vllmeval/). | Model | Franka Pick-and-Place (7 Tasks) | AgiBot Long-horizon Dexterity (4 Tasks) | WidowX Out-of-Box (13 Tasks) | Reasoning Control (4 Tasks) | | ------------ | ------------------------------- | --------------------------------------- | ---------------------------- | --------------------------- | @@ -260,6 +304,7 @@ Robot Control Benchmark Results ## 📅 Roadmap +- [x] 🤖 Release [EO-1](https://huggingface.co/IPEC-COMMUNITY/EO-1-3B) pretraining, finetune scripts, and documentations. - [ ] 🤗 Release [pre-training models](https://huggingface.co/collections/IPEC-COMMUNITY/eo-robotics-68ac4ff30e1f746cac28ca14) and experiment finetune scripts. - [ ] 🔥 Release Interleaved Dataset `EO-Data1.5M`, benchmark `EO-Bench` and all detailed pre-training code. - [ ] ⚡️ Efficient LLM Inference over Long Sequences, Efficient 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EO1VisionFlowMatchingOutputWithPast: output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions @@ -217,74 +218,76 @@ def forward( output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) - if inputs_embeds is None: - inputs_embeds = self.model.embed_tokens(input_ids) - if pixel_values is not None: - pixel_values = pixel_values.type(self.visual.dtype) - image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, image_embeds, self.config.image_token_id - ) + inputs_embeds = self.get_input_embeddings()(input_ids) - if pixel_values_videos is not None: - pixel_values_videos = pixel_values_videos.type(self.visual.dtype) - video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, video_embeds, self.config.video_token_id - ) + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - if states is not None: - states = states.type(self.state_proj.weight.dtype) - state_embs = self.state_proj(states) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, state_embs, self.config.text_config.state_token_id - ) + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) - if actions is not None: - noise_mask = input_ids == self.config.text_config.action_token_id - pass_mask = input_ids == self.config.text_config.action_pass_id - mask = noise_mask | pass_mask # (b s) + if states is not None: + states = states.type(self.state_proj.weight.dtype) + state_embs = self.state_proj(states) + inputs_embeds, _ = self.replace_special_embeddings( + input_ids, inputs_embeds, state_embs, self.config.text_config.state_token_id + ) - pass_mask_in_action = pass_mask[mask] # (n, ) - pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1) + if actions is not None: + noise_mask = input_ids == self.config.text_config.action_token_id + pass_mask = input_ids == self.config.text_config.action_pass_id + mask = noise_mask | pass_mask # (b s) - time = sample_time(actions.shape[0], inputs_embeds.device) # (n,) - time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1) - time_expanded[pass_mask_in_action] = 0.0 + pass_mask_in_action = pass_mask[mask] # (n, ) + pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1) - noise = sample_noise(actions.shape, inputs_embeds.device) - x_t = time_expanded * noise + (1 - time_expanded) * actions - u_t = noise - actions + time = sample_time(actions.shape[0], inputs_embeds.device) # (n,) + time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1) + time_expanded[pass_mask_in_action] = 0.0 - time_embs = create_sinusoidal_pos_embedding( - time, - self.config.text_config.hidden_size, - device=inputs_embeds.device, - ) - time_embs = time_embs.type(inputs_embeds.dtype) + noise = sample_noise(actions.shape, inputs_embeds.device) + x_t = time_expanded * noise + (1 - time_expanded) * actions + u_t = noise - actions + + time_embs = create_sinusoidal_pos_embedding( + time, + self.config.text_config.hidden_size, + device=inputs_embeds.device, + ) + time_embs = time_embs.type(inputs_embeds.dtype) - x_t = x_t.type(self.action_in_proj.weight.dtype) - action_embs = self.action_in_proj(x_t) - time_embs = time_embs[:, None, :].expand_as(action_embs) + x_t = x_t.type(self.action_in_proj.weight.dtype) + action_embs = self.action_in_proj(x_t) + time_embs = time_embs[:, None, :].expand_as(action_embs) - action_time_embs = torch.cat([action_embs, time_embs], dim=2) - action_time_embs = self.action_time_mlp_in(action_time_embs) - action_time_embs = F.silu(action_time_embs) - action_time_embs = self.action_time_mlp_out(action_time_embs) + action_time_embs = torch.cat([action_embs, time_embs], dim=2) + action_time_embs = self.action_time_mlp_in(action_time_embs) + action_time_embs = F.silu(action_time_embs) + action_time_embs = self.action_time_mlp_out(action_time_embs) - num_actions = mask.sum().item() - num_action_features = action_time_embs.shape[0] * action_time_embs.shape[1] - assert num_actions == num_action_features, ( - f"action features and tokens do not match: {num_actions=}, {num_action_features=}" - ) + num_actions = mask.sum().item() + num_action_features = action_time_embs.shape[0] * action_time_embs.shape[1] + assert num_actions == num_action_features, ( + f"action features and tokens do not match: {num_actions=}, {num_action_features=}" + ) - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - action_mask = mask_expanded.to(inputs_embeds.device) + mask_unsqueezed = mask.unsqueeze(-1) + mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) + action_mask = mask_expanded.to(inputs_embeds.device) - action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs) + action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype) + inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs) if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) @@ -332,13 +335,21 @@ def forward( _actions = None if not (self.training or states is None) and actions is None and self._has_action_gen_seq(input_ids): + # action sampling _actions, outputs = self._sample_actions(input_ids=input_ids, **model_kwargs) - hidden_states = outputs[0] - logits = self.lm_head(hidden_states[:, -1]) + logits = torch.zeros( + inputs_embeds.shape[0], 1, self.config.text_config.vocab_size, device=inputs_embeds.device + ) + logits[..., self.config.text_config.eos_token_id] = 33.8125 # <|im_end|> else: + # text generation outputs = self.model(**model_kwargs) hidden_states = outputs[0] - logits = self.lm_head(hidden_states) + # only compute necessary logits, do not upcast to float if not computing loss + slice_indices = ( + slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + ) + logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None fm_loss = None @@ -346,9 +357,11 @@ def forward( if actions is not None: action_time_embs = hidden_states[action_mask[..., 0]] action_time_embs = action_time_embs.type(self.action_out_proj.dtype) + v_t = self.action_out_proj(action_time_embs) u_t = u_t.reshape(v_t.shape) v_t = v_t.type(u_t.dtype) + losses = F.mse_loss(u_t, v_t, reduction="none") if action_is_pad is not None: in_episode_bound = (~action_is_pad).reshape(-1, 1) @@ -361,15 +374,9 @@ def forward( ar_loss = None if labels is not None: - logits = logits.float() - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.vocab_size) - shift_labels = shift_labels.view(-1) - # enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - ar_loss = loss_fct(shift_logits, shift_labels) + ar_loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) loss = loss + ar_loss if loss is not None else ar_loss return EO1VisionFlowMatchingOutputWithPast( @@ -398,15 +405,15 @@ def _sample_actions( return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, ) -> Tensor: - """Sample actions from the model, break down into 2 steps to make a unified generation interface: - 1. pass the mm prefix to the model, and update kvcache - 2. perform denoising steps, with noise q and mm kvcache + """Sample actions from the model, break down into 3 steps to make a unified generation interface: input_ids: <|im_start|>user<|vision_start|><|image_pad|>...<|vision_end|><|state_start|><|state_pad|><|state_end|>task...<|vla|><|im_end|> -> AR kvcache <|im_start|>assistant<|action_start|><|action_pad|>...<|action_end|> -> FM denoising <|im_end|> -> AR """ chunksz_eoa = self.config.action_chunk_size + 1 + + # 1. pass the mm prefix to the model, and update kvcache mm_outputs = self.model( position_ids=position_ids[..., :-chunksz_eoa], attention_mask=attention_mask[:, :-chunksz_eoa], @@ -415,6 +422,8 @@ def _sample_actions( use_cache=use_cache, cache_position=cache_position[:-chunksz_eoa], ) + + # 2. perform denoising steps, with noise q and mm kvcache device = inputs_embeds.device x_t = sample_noise( [ @@ -469,6 +478,9 @@ def _sample_actions( # euler step x_t += dt * v_t.reshape(x_t.shape) time += dt + + # 3. get the final EOS token logits + outputs.last_hidden_state = torch.cat( [mm_outputs.last_hidden_state, outputs.last_hidden_state], dim=1 ) diff --git a/eo/model/modeling_qwen2_5_vl.py b/eo/model/modeling_qwen2_5_vl.py index d7a735b..2d9f979 100644 --- a/eo/model/modeling_qwen2_5_vl.py +++ b/eo/model/modeling_qwen2_5_vl.py @@ -24,17 +24,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -import math -from collections.abc import Callable from dataclasses import dataclass from typing import Any, Optional, Union +from collections.abc import Callable import torch import torch.nn as nn import torch.nn.functional as F # noqa: N812 -from torch.nn import CrossEntropyLoss +from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLTextConfig, Qwen2_5_VLVisionConfig from transformers.activations import ACT2FN -from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache +from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.generation.utils import ( GenerateNonBeamOutput, @@ -42,46 +41,25 @@ LogitsProcessorList, StoppingCriteriaList, ) -from transformers.modeling_attn_mask_utils import AttentionMaskConverter -from transformers.modeling_flash_attention_utils import ( - is_flash_attn_available, -) +from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask +from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel -from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import ( - Qwen2_5_VLConfig, - Qwen2_5_VLTextConfig, - Qwen2_5_VLVisionConfig, -) from transformers.processing_utils import Unpack +from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm from transformers.utils import ( - add_start_docstrings, - add_start_docstrings_to_model_forward, - is_torch_flex_attn_available, + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_torchdynamo_compiling, logging, - replace_return_docstrings, ) from transformers.utils.deprecation import deprecate_kwarg -if is_flash_attn_available(): - from flash_attn import flash_attn_varlen_func - from flash_attn.layers.rotary import apply_rotary_emb - from transformers.modeling_flash_attention_utils import FlashAttentionKwargs - - -if is_flash_attn_available(): - pass - -if is_torch_flex_attn_available(): - from torch.nn.attention.flex_attention import BlockMask - from transformers.integrations.flex_attention import make_flex_block_causal_mask - logger = logging.get_logger(__name__) -_CONFIG_FOR_DOC = "Qwen2_5_VLConfig" - class Qwen2_5_VLMLP(nn.Module): def __init__(self, config, bias: bool = False): @@ -124,6 +102,8 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class Qwen2_5_VisionRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) @@ -135,26 +115,6 @@ def forward(self, seqlen: int) -> torch.Tensor: return freqs -class Qwen2RMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - Qwen2RMSNorm is equivalent to T5LayerNorm - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - input_dtype = hidden_states.dtype - hidden_states = hidden_states.to(torch.float32) - variance = hidden_states.pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - return self.weight * hidden_states.to(input_dtype) - - def extra_repr(self): - return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" - - class Qwen2_5_VLPatchMerger(nn.Module): def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: super().__init__() @@ -171,58 +131,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x -def apply_rotary_pos_emb_flashatt( - q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor -) -> tuple[torch.Tensor, torch.Tensor]: - cos = cos.chunk(2, dim=-1)[0].contiguous() - sin = sin.chunk(2, dim=-1)[0].contiguous() - q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q) - k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k) - return q_embed, k_embed - - -class Qwen2_5_VLVisionFlashAttention2(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: - super().__init__() - self.num_heads = num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) - - def forward( - self, - hidden_states: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb: torch.Tensor | None = None, - position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - seq_length = hidden_states.shape[0] - q, k, v = ( - self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) - ) - if position_embeddings is None: - logger.warning_once( - "The attention layers in this model are transitioning from computing the RoPE embeddings internally " - "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " - "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " - "removed and `position_embeddings` will be mandatory." - ) - emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - cos = emb.cos() - sin = emb.sin() - else: - cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_flashatt(q.unsqueeze(0), k.unsqueeze(0), cos, sin) - q = q.squeeze(0) - k = k.squeeze(0) - - max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() - attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape( - seq_length, -1 - ) - attn_output = self.proj(attn_output) - return attn_output - - def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] @@ -244,6 +152,18 @@ def apply_rotary_pos_emb_vision( return q_embed, k_embed +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + def eager_attention_forward( module: nn.Module, query: torch.Tensor, @@ -271,12 +191,18 @@ def eager_attention_forward( class Qwen2_5_VLVisionAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: + def __init__(self, config: Qwen2_5_VLVisionConfig) -> None: super().__init__() - self.num_heads = num_heads - self.head_dim = dim // num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) + self.dim = config.hidden_size + self.num_heads = config.num_heads + self.head_dim = self.dim // self.num_heads + self.num_key_value_groups = 1 # needed for eager attention + self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + self.scaling = self.head_dim**-0.5 + self.config = config + self.attention_dropout = 0.0 + self.is_causal = False def forward( self, @@ -284,9 +210,10 @@ def forward( cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, ) -> torch.Tensor: seq_length = hidden_states.shape[0] - q, k, v = ( + query_states, key_states, value_states = ( self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) ) if position_embeddings is None: @@ -301,89 +228,69 @@ def forward( sin = emb.sin() else: cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) - - attention_mask = torch.full( - [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype - ) - for i in range(1, len(cu_seqlens)): - attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 - - q = q.transpose(0, 1) - k = k.transpose(0, 1) - v = v.transpose(0, 1) - attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) - attn_weights = attn_weights + attention_mask - attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) - attn_output = torch.matmul(attn_weights, v) - attn_output = attn_output.transpose(0, 1) - attn_output = attn_output.reshape(seq_length, -1) - attn_output = self.proj(attn_output) - return attn_output + query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) -class Qwen2_5_VLVisionSdpaAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: - super().__init__() - self.num_heads = num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) + attention_interface: Callable = eager_attention_forward + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] - def forward( - self, - hidden_states: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb: torch.Tensor | None = None, - position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - seq_length = hidden_states.shape[0] - q, k, v = ( - self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) - ) - if position_embeddings is None: - logger.warning_once( - "The attention layers in this model are transitioning from computing the RoPE embeddings internally " - "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " - "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " - "removed and `position_embeddings` will be mandatory." + if self.config._attn_implementation == "flash_attention_2": + # Flash Attention 2: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, ) - emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - cos = emb.cos() - sin = emb.sin() else: - cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) - - attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool) - for i in range(1, len(cu_seqlens)): - attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True - q = q.transpose(0, 1) - k = k.transpose(0, 1) - v = v.transpose(0, 1) - attn_output = F.scaled_dot_product_attention( - q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attention_mask, dropout_p=0.0 - ) - attn_output = attn_output.squeeze(0).transpose(0, 1) - attn_output = attn_output.reshape(seq_length, -1) + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) + for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + )[0] + for q, k, v in zip(*splits, strict=False) + ] + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() attn_output = self.proj(attn_output) return attn_output -QWEN2_5_VL_VISION_ATTENTION_CLASSES = { - "eager": Qwen2_5_VLVisionAttention, - "flash_attention_2": Qwen2_5_VLVisionFlashAttention2, - "sdpa": Qwen2_5_VLVisionSdpaAttention, -} - - -class Qwen2_5_VLVisionBlock(nn.Module): +class Qwen2_5_VLVisionBlock(GradientCheckpointingLayer): def __init__(self, config, attn_implementation: str = "sdpa") -> None: super().__init__() self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) - self.attn = QWEN2_5_VL_VISION_ATTENTION_CLASSES[attn_implementation]( - config.hidden_size, num_heads=config.num_heads - ) + self.attn = Qwen2_5_VLVisionAttention(config=config) self.mlp = Qwen2_5_VLMLP(config, bias=True) def forward( @@ -392,67 +299,35 @@ def forward( cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, ) -> torch.Tensor: hidden_states = hidden_states + self.attn( self.norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb, position_embeddings=position_embeddings, + **kwargs, ) hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) return hidden_states -Qwen2_5_VL_START_DOCSTRING = r""" - This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. - Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage - and behavior. - - Parameters: - config ([`Qwen2_5_VLConfig`]): - Model configuration class with all the parameters of the model. Initializing with a config file does not - load the weights associated with the model, only the configuration. Check out the - [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - - -@add_start_docstrings( - "The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top.", - Qwen2_5_VL_START_DOCSTRING, -) +@auto_docstring class Qwen2_5_VLPreTrainedModel(PreTrainedModel): - config_class = Qwen2_5_VLConfig + config: Qwen2_5_VLConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] _skip_keys_device_placement = "past_key_values" - _supports_flash_attn_2 = True + _supports_flash_attn = True _supports_sdpa = True - _supports_cache_class = True - _supports_static_cache = ( - False # TODO (joao): fix. torch.compile failing probably due to `cache_positions` - ) - def _init_weights(self, module): - std = self.config.get_text_config().initializer_range - if isinstance(module, (nn.Linear, nn.Conv3d)): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - elif isinstance(module, Qwen2RMSNorm): - module.weight.data.fill_(1.0) + _can_compile_fullgraph = True + _supports_attention_backend = True class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel): - config_class = Qwen2_5_VLVisionConfig + config: Qwen2_5_VLVisionConfig _no_split_modules = ["Qwen2_5_VLVisionBlock"] def __init__(self, config, *inputs, **kwargs) -> None: @@ -473,9 +348,7 @@ def __init__(self, config, *inputs, **kwargs) -> None: head_dim = config.hidden_size // config.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) - self.blocks = nn.ModuleList( - [Qwen2_5_VLVisionBlock(config, config._attn_implementation) for _ in range(config.depth)] - ) + self.blocks = nn.ModuleList([Qwen2_5_VLVisionBlock(config) for _ in range(config.depth)]) self.merger = Qwen2_5_VLPatchMerger( dim=config.out_hidden_size, context_dim=config.hidden_size, @@ -553,7 +426,7 @@ def get_window_index(self, grid_thw): return window_index, cu_window_seqlens - def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor: + def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor: """ Args: hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): @@ -601,14 +474,13 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch. cu_seqlens_now = cu_seqlens else: cu_seqlens_now = cu_window_seqlens - if self.gradient_checkpointing and self.training: - hidden_states = self._gradient_checkpointing_func( - blk.__call__, hidden_states, cu_seqlens_now, None, position_embeddings - ) - else: - hidden_states = blk( - hidden_states, cu_seqlens=cu_seqlens_now, position_embeddings=position_embeddings - ) + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + position_embeddings=position_embeddings, + **kwargs, + ) hidden_states = self.merger(hidden_states) reverse_indices = torch.argsort(window_index) @@ -617,7 +489,34 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch. return hidden_states +@dataclass +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +class Qwen2_5_VLModelOutputWithPast(ModelOutput): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + last_hidden_state: torch.FloatTensor = None + past_key_values: list[torch.FloatTensor] | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + class Qwen2_5_VLRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + def __init__(self, config: Qwen2_5_VLTextConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" @@ -714,18 +613,6 @@ def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim return q_embed, k_embed -def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: - """ - This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, - num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) - """ - batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) - return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) - - class Qwen2_5_VLAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer @@ -778,8 +665,9 @@ def forward( output_attentions: bool = False, use_cache: bool = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None - | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC + position_embeddings: None | ( + tuple[torch.Tensor, torch.Tensor] + ) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: bsz, q_len, _ = hidden_states.size() @@ -856,8 +744,9 @@ def forward( output_attentions: bool | None = False, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None - | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC + position_embeddings: None | ( + tuple[torch.Tensor, torch.Tensor] + ) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: """ @@ -914,12 +803,9 @@ def forward( return outputs -@add_start_docstrings( - "The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top.", - Qwen2_5_VL_START_DOCSTRING, -) +@auto_docstring class Qwen2_5_VLModel(Qwen2_5_VLPreTrainedModel): - config_class = Qwen2_5_VLTextConfig + config: Qwen2_5_VLTextConfig def __init__(self, config: Qwen2_5_VLTextConfig): super().__init__(config) @@ -933,6 +819,7 @@ def __init__(self, config: Qwen2_5_VLTextConfig): self._attn_implementation = config._attn_implementation self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types self.gradient_checkpointing = False # Initialize weights and apply final processing @@ -944,18 +831,20 @@ def get_input_embeddings(self): def set_input_embeddings(self, value): self.embed_tokens = value + @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, + **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple | BaseModelOutputWithPast: output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions @@ -979,7 +868,7 @@ def forward( # torch.jit.trace() doesn't support cache objects in the output if use_cache and past_key_values is None and not torch.jit.is_tracing(): - past_key_values = DynamicCache() + past_key_values = DynamicCache(config=self.config) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) @@ -993,12 +882,43 @@ def forward( # the hard coded `3` is for temporal, height and width. if position_ids is None: position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) - elif position_ids.dim() == 2: + elif position_ids.ndim == 2: position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) - causal_mask = self._update_causal_mask( - attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions - ) + # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions + # where each dim indicates visual spatial positions for temporal/height/width grids. + # There are two scenarios when FA2-like packed masking might be activated. + # 1. User specifically passed packed `position_ids` and no attention mask. + # In this case we expect the useer to create correct position ids for all 3 grids + # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len] + # 2. User runs forward with no attention mask and no position ids. In this case, position ids + # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are + # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass + # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation` + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + text_position_ids = position_ids[0] + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "input_embeds": inputs_embeds, + "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + "position_ids": text_position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) hidden_states = inputs_embeds @@ -1008,41 +928,25 @@ def forward( # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None - next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) - if self.gradient_checkpointing and self.training: - layer_outputs = self._gradient_checkpointing_func( - decoder_layer.__call__, - hidden_states, - causal_mask, - position_ids, - past_key_values, - output_attentions, - use_cache, - cache_position, - position_embeddings, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=causal_mask, - position_ids=position_ids, - past_key_value=past_key_values, - output_attentions=output_attentions, - use_cache=use_cache, - cache_position=cache_position, - position_embeddings=position_embeddings, - ) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[decoder_layer.attention_type], + position_ids=text_position_ids, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache = layer_outputs[2 if output_attentions else 1] - if output_attentions: all_self_attns += (layer_outputs[1],) @@ -1052,206 +956,40 @@ def forward( if output_hidden_states: all_hidden_states += (hidden_states,) - next_cache = next_decoder_cache if use_cache else None - if not return_dict: return tuple( - v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None + v + for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] + if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=next_cache, + past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, ) - def _update_causal_mask( - self, - attention_mask: Union[torch.Tensor, "BlockMask"], - input_tensor: torch.Tensor, - cache_position: torch.Tensor, - past_key_values: Cache, - output_attentions: bool = False, - ): - if self.config._attn_implementation == "flash_attention_2": - if attention_mask is not None and past_key_values is not None: - is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] - if is_padding_right: - raise ValueError( - "You are attempting to perform batched generation with padding_side='right'" - " this may lead to unexpected behaviour for Flash Attention version of Qwen2_5_VL. Make sure to " - " call `tokenizer.padding_side = 'left'` before tokenizing the input. " - ) - if attention_mask is not None and 0.0 in attention_mask: - return attention_mask - return None - if self.config._attn_implementation == "flex_attention": - if isinstance(attention_mask, torch.Tensor): - attention_mask = make_flex_block_causal_mask(attention_mask) - return attention_mask - - # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in - # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail - # to infer the attention mask. - past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 - using_static_cache = isinstance(past_key_values, StaticCache) - using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) - - # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward - if ( - self.config._attn_implementation == "sdpa" - and not (using_static_cache or using_sliding_window_cache) - and not output_attentions - ): - if AttentionMaskConverter._ignore_causal_mask_sdpa( - attention_mask, - inputs_embeds=input_tensor, - past_key_values_length=past_seen_tokens, - sliding_window=self.config.sliding_window, - is_training=self.training, - ): - return None - - dtype = input_tensor.dtype - min_dtype = torch.finfo(dtype).min - sequence_length = input_tensor.shape[1] - # SlidingWindowCache or StaticCache - if using_sliding_window_cache or using_static_cache: - target_length = past_key_values.get_max_cache_shape() - # DynamicCache or no cache - else: - target_length = ( - attention_mask.shape[-1] - if isinstance(attention_mask, torch.Tensor) - else past_seen_tokens + sequence_length + 1 - ) - - # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). - causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( - attention_mask, - sequence_length=sequence_length, - target_length=target_length, - dtype=dtype, - cache_position=cache_position, - batch_size=input_tensor.shape[0], - config=self.config, - past_key_values=past_key_values, - ) - - if ( - self.config._attn_implementation == "sdpa" - and attention_mask is not None - and attention_mask.device.type in ["cuda", "xpu", "npu"] - and not output_attentions - ): - # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when - # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. - # Details: https://github.com/pytorch/pytorch/issues/110213 - causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) - - return causal_mask - - @staticmethod - def _prepare_4d_causal_attention_mask_with_cache_position( - attention_mask: torch.Tensor, - sequence_length: int, - target_length: int, - dtype: torch.dtype, - cache_position: torch.Tensor, - batch_size: int, - config: Qwen2_5_VLConfig, - past_key_values: Cache, - ): - """ - Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape - `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. - - Args: - attention_mask (`torch.Tensor`): - A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. - sequence_length (`int`): - The sequence length being processed. - target_length (`int`): - The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. - dtype (`torch.dtype`): - The dtype to use for the 4D attention mask. - cache_position (`torch.Tensor`): - Indices depicting the position of the input sequence tokens in the sequence. - batch_size (`torch.Tensor`): - Batch size. - config (`Qwen2_5_VLConfig`): - The model's configuration class - past_key_values (`Cache`): - The cache class that is being used currently to generate - """ - if attention_mask is not None and attention_mask.dim() == 4: - # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. - causal_mask = attention_mask - else: - min_dtype = torch.finfo(dtype).min - causal_mask = torch.full( - (sequence_length, target_length), - fill_value=min_dtype, - dtype=dtype, - device=cache_position.device, - ) - diagonal_attend_mask = torch.arange( - target_length, device=cache_position.device - ) > cache_position.reshape(-1, 1) - if config.get_text_config().sliding_window is not None: - # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also - # the check is needed to verify is current checkpoint was trained with sliding window or not - if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: - sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= ( - cache_position.reshape(-1, 1) - config.get_text_config().sliding_window - ) - diagonal_attend_mask.bitwise_or_(sliding_attend_mask) - causal_mask *= diagonal_attend_mask - causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) - if attention_mask is not None: - causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit - if attention_mask.shape[-1] > target_length: - attention_mask = attention_mask[:, :target_length] - mask_length = attention_mask.shape[-1] - padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( - causal_mask.device - ) - padding_mask = padding_mask == 0 - causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( - padding_mask, min_dtype - ) - return causal_mask - @dataclass -class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): - """ +@auto_docstring( + custom_intro=""" Base class for Qwen2_5_VL causal language model (or autoregressive) outputs. - - Args: - loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): - Language modeling loss (for next-token prediction). - logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): - Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) - - Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see - `past_key_values` input) to speed up sequential decoding. - hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + - one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. - - Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. - - Attentions weights after the attention softmax, used to compute the weighted average in the self-attention - heads. - rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): - The rope index difference between sequence length and multimodal rope. + """ +) +class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. """ loss: torch.FloatTensor | None = None @@ -1262,87 +1000,13 @@ class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): rope_deltas: torch.LongTensor | None = None -QWEN2_5_VL_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide - it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see - `past_key_values`). - - If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] - and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more - information on the default strategy. - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, - config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape - `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention - blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - pixel_values (`torch.FloatTensor` of shape `(seq_length, num_channels * image_size * image_size)): - The tensors corresponding to the input images. Pixel values can be obtained using - [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses - [`Qwen2_5_VLImageProcessor`] for processing images. - pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)): - The tensors corresponding to the input videos. Pixel values can be obtained using - [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses - [`Qwen2_5_VLImageProcessor`] for processing videos. - image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): - The temporal, height and width of feature shape of each image in LLM. - video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): - The temporal, height and width of feature shape of each video in LLM. - rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): - The rope index difference between sequence length and multimodal rope. -""" - - class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] - config_class = Qwen2_5_VLConfig _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] + config = Qwen2_5_VLConfig + accepts_loss_kwargs = False + def __init__(self, config): super().__init__(config) self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config) @@ -1353,7 +1017,6 @@ def __init__(self, config): self.lm_head = nn.Linear(text_config.hidden_size, text_config.vocab_size, bias=False) self.rope_deltas = None # cache rope_deltas here - # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): @@ -1374,6 +1037,86 @@ def set_decoder(self, decoder): def get_decoder(self): return self.model + def get_video_features( + self, pixel_values_videos: torch.FloatTensor, video_grid_thw: torch.LongTensor | None = None + ): + """ + Encodes videos into continuous embeddings that can be forwarded to the language model. + + Args: + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + pixel_values_videos = pixel_values_videos.type(self.visual.dtype) + video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) + split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + video_embeds = torch.split(video_embeds, split_sizes) + return video_embeds + + def get_image_features( + self, pixel_values: torch.FloatTensor, image_grid_thw: torch.LongTensor | None = None + ): + """ + Encodes images into continuous embeddings that can be forwarded to the language model. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype) + image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) + split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + image_embeds = torch.split(image_embeds, split_sizes) + return image_embeds + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor = None, + video_features: torch.FloatTensor = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = ( + special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + ) + if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel(): + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}" + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = ( + special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + ) + if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel(): + raise ValueError( + f"Videos features and video tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}" + ) + + return special_image_mask, special_video_mask + def get_rope_index( self, input_ids: torch.LongTensor | None = None, @@ -1562,20 +1305,18 @@ def get_rope_index( return position_ids, mrope_position_deltas - @add_start_docstrings_to_model_forward(QWEN2_5_VL_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=Qwen2_5_VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + @can_return_tuple def forward( self, - input_ids: torch.LongTensor | None = None, + input_ids: torch.LongTensor = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, - return_dict: bool | None = None, pixel_values: torch.Tensor | None = None, pixel_values_videos: torch.FloatTensor | None = None, image_grid_thw: torch.LongTensor | None = None, @@ -1583,124 +1324,82 @@ def forward( rope_deltas: torch.LongTensor | None = None, cache_position: torch.LongTensor | None = None, second_per_grid_ts: torch.Tensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], ) -> tuple | Qwen2_5_VLCausalLMOutputWithPast: r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - - Returns: - - Example: - - ```python - >>> from PIL import Image - >>> import requests - >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration - - >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") - >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") - - >>> messages = [ - { - "role": "user", - "content": [ - {"type": "image"}, - {"type": "text", "text": "What is shown in this image?"}, - ], - }, - ] - >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" - >>> image = Image.open(requests.get(url, stream=True).raw) - - >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos]) - - >>> # Generate - >>> generate_ids = model.generate(inputs.input_ids, max_length=30) - >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..." + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. ```""" + output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is None: - inputs_embeds = self.model.embed_tokens(input_ids) - if pixel_values is not None: - pixel_values = pixel_values.type(self.visual.dtype) - image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) - n_image_tokens = (input_ids == self.config.image_token_id).sum().item() - n_image_features = image_embeds.shape[0] - if n_image_tokens != n_image_features: - raise ValueError( - f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" - ) + inputs_embeds = self.get_input_embeddings()(input_ids) - mask = input_ids == self.config.image_token_id - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - image_mask = mask_expanded.to(inputs_embeds.device) - - image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - - if pixel_values_videos is not None: - pixel_values_videos = pixel_values_videos.type(self.visual.dtype) - video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) - n_video_tokens = (input_ids == self.config.video_token_id).sum().item() - n_video_features = video_embeds.shape[0] - if n_video_tokens != n_video_features: - raise ValueError( - f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" - ) - - mask = input_ids == self.config.video_token_id - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - video_mask = mask_expanded.to(inputs_embeds.device) - - video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - if attention_mask is not None: - attention_mask = attention_mask.to(inputs_embeds.device) + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) - # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme - if position_ids is None and (attention_mask is None or attention_mask.ndim == 2): - # calculate RoPE index once per generation in the pre-fill stage only - if ( + if position_ids is None: + # Calculate RoPE index once per generation in the pre-fill stage only. + # When compiling, we can't check tensor values thus we check only input length + # It is safe to assume that `length!=1` means we're in pre-fill because compiled + # models currently cannot do asssisted decoding + prefill_compiled_stage = is_torchdynamo_compiling() and ( + (input_ids is not None and input_ids.shape[1] != 1) + or (inputs_embeds is not None and inputs_embeds.shape[1] != 1) + ) + prefill_noncompiled_stage = not is_torchdynamo_compiling() and ( (cache_position is not None and cache_position[0] == 0) - or self.rope_deltas is None or (past_key_values is None or past_key_values.get_seq_length() == 0) - ): + ) + if (prefill_compiled_stage or prefill_noncompiled_stage) or self.rope_deltas is None: position_ids, rope_deltas = self.get_rope_index( input_ids, image_grid_thw, video_grid_thw, - second_per_grid_ts, - attention_mask, + second_per_grid_ts=second_per_grid_ts, + attention_mask=attention_mask, ) self.rope_deltas = rope_deltas - # then use the prev pre-calculated rope-deltas to get the correct position ids else: batch_size, seq_length, _ = inputs_embeds.shape - delta = ( - (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) - if cache_position is not None - else 0 - ) position_ids = torch.arange(seq_length, device=inputs_embeds.device) - position_ids = position_ids.view(1, -1).expand(batch_size, -1) - if cache_position is not None: # otherwise `deltas` is an int `0` - delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0) - position_ids = position_ids.add(delta) - position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1) + if cache_position is not None: + delta = (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) + else: + delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device) + delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1) + position_ids += delta.to(position_ids.device) outputs = self.model( input_ids=None, @@ -1711,31 +1410,22 @@ def forward( use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, - return_dict=return_dict, + return_dict=True, cache_position=cache_position, + **kwargs, ) hidden_states = outputs[0] - logits = self.lm_head(hidden_states) + + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: - # Upcast to float if we need to compute the loss to avoid potential precision issues - logits = logits.float() - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.vocab_size) - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) return Qwen2_5_VLCausalLMOutputWithPast( loss=loss, @@ -1780,8 +1470,35 @@ def prepare_inputs_for_generation( **kwargs, ) - # Qwen2-5-VL position_ids are prepareed with rope_deltas in forward - model_inputs["position_ids"] = None + # Qwen2-5-VL position_ids are prepared with rope_deltas + if position_ids is None: + # Calculate RoPE index once per generation in the pre-fill stage only. + # When compiling, we can't check tensor values thus we check only input length + # It is safe to assume that `length!=1` means we're in pre-fill because compiled + # models currently cannot do asssisted decoding + if cache_position[0] == 0 or self.model.rope_deltas is None: + vision_positions, rope_deltas = self.get_rope_index( + model_inputs.get("input_ids", None), + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + attention_mask=attention_mask, + ) + self.model.rope_deltas = rope_deltas + # then use the prev pre-calculated rope-deltas to get the correct position ids + elif "position_ids" in model_inputs: + position_ids = model_inputs["position_ids"][None, ...] + delta = self.model.rope_deltas + delta = delta.repeat_interleave(position_ids.shape[1] // delta.shape[0], dim=0) + vision_positions = position_ids + delta.expand_as(position_ids) + vision_positions = vision_positions.expand(3, vision_positions.shape[1], -1) + + # Concatenate "text + vision" positions into [4, bs, seq-len] + if "position_ids" not in model_inputs: + text_positions = torch.arange(input_ids, device=input_ids.device)[None, None, :] + else: + text_positions = model_inputs["position_ids"][None, ...] + model_inputs["position_ids"] = torch.cat([text_positions, vision_positions], dim=0) if cache_position[0] != 0: model_inputs["pixel_values"] = None @@ -1792,6 +1509,7 @@ def prepare_inputs_for_generation( def _get_image_nums_and_video_nums( self, input_ids: torch.LongTensor | None, + inputs_embeds: torch.Tensor | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Get the number of images and videos for each sample to calculate the separation length of the sample tensor. @@ -1809,10 +1527,31 @@ def _get_image_nums_and_video_nums( video_token_id = self.config.video_token_id vision_start_token_id = self.config.vision_start_token_id - vision_start_mask = input_ids == vision_start_token_id + if inputs_embeds is not None: + vision_start_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(vision_start_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + image_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + video_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + else: + vision_start_mask = input_ids == vision_start_token_id + image_mask = input_ids == image_token_id + video_mask = input_ids == video_token_id + vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1) - image_mask = input_ids == image_token_id - video_mask = input_ids == video_token_id image_nums = torch.sum(vision_first_mask & image_mask, dim=1) video_nums = torch.sum(vision_first_mask & video_mask, dim=1) @@ -1844,7 +1583,9 @@ def _expand_inputs_for_generation( def _expand_dict_for_generation_visual(dict_to_expand): image_grid_thw = model_kwargs.get("image_grid_thw", None) video_grid_thw = model_kwargs.get("video_grid_thw", None) - image_nums, video_nums = self._get_image_nums_and_video_nums(input_ids) + image_nums, video_nums = self._get_image_nums_and_video_nums( + input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None) + ) def _repeat_interleave_samples(x, lengths, repeat_times): samples = torch.split(x, lengths) @@ -1879,14 +1620,9 @@ def _repeat_interleave_samples(x, lengths, repeat_times): dict_to_expand[key], lengths=lengths, repeat_times=expand_size ) elif key == "second_per_grid_ts": - if not isinstance(dict_to_expand[key], list): - raise TypeError( - f"Expected value for key '{key}' to be a list, but got {type(dict_to_expand[key])} instead." - ) - tensor = torch.tensor(dict_to_expand[key]) - lengths = list(video_nums) - tensor = _repeat_interleave_samples(tensor, lengths=lengths, repeat_times=expand_size) - dict_to_expand[key] = tensor.tolist() + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size + ) return dict_to_expand def _expand_dict_for_generation(dict_to_expand): @@ -1900,10 +1636,7 @@ def _expand_dict_for_generation(dict_to_expand): dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) return dict_to_expand - # input_ids is required for expanding visual inputs - # If input_ids is unavailable, visual inputs will not be used; therefore, there is no need to expand visual inputs. - if input_ids is not None and input_ids.numel() != 0: - model_kwargs = _expand_dict_for_generation_visual(model_kwargs) + model_kwargs = _expand_dict_for_generation_visual(model_kwargs) if input_ids is not None: input_ids = input_ids.repeat_interleave(expand_size, dim=0) @@ -2074,4 +1807,9 @@ class GenerateDecoderOnlyOutput(ModelOutput): actions: torch.FloatTensor | None = None -__all__ = ["Qwen2_5_VLForConditionalGeneration", "Qwen2_5_VLModel", "Qwen2_5_VLPreTrainedModel"] +__all__ = [ + "Qwen2_5_VLForConditionalGeneration", + "Qwen2_5_VLModel", + "Qwen2_5_VLPreTrainedModel", + "Qwen2_5_VLTextModel", +] diff --git a/experiments/1_demo/data-demo.yaml b/experiments/1_demo/data-demo.yaml index 76067a0..1027e97 100644 --- a/experiments/1_demo/data-demo.yaml +++ b/experiments/1_demo/data-demo.yaml @@ -1,8 +1,24 @@ mm_datasets: - - json_path: demo_data/libero_spatial_mmu.jsonl - # - json_path: demo_data/refcoco/refcoco.jsonl - # vision_base_path: demo_data/refcoco + - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file + vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL + sampling_strategy: random:10% # sampling strategy + - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl + +# @robot control config lerobot_datasets: - - repo_id: libero_spatial_no_noops_1.0.0_lerobot + - repo_id: demo25 root: ./demo_data + # Optional fields: + # episodes: [1, 2, 3] # specific episodes to load (None = all) + train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task + delta_action: false # train with delta actions + state_mode: "MEAN_STD" # state normalization mode + # which camera streams to load + select_video_keys: [observation.images.head, observation.images.hand_left, observation.images.hand_right] + # proprioceptive states + select_state_keys: [observation.states.joint.position, observation.states.effector.position] + # action targets + select_action_keys: [actions.joint.position, actions.effector.position] + effector_indices: [14, 15] # indices of effector channels in the flattened action vector + weight: 1.0 # dataset weight for sampling diff --git a/experiments/1_demo/train.sh b/experiments/1_demo/train.sh index f25c7ba..614afd5 100644 --- a/experiments/1_demo/train.sh +++ b/experiments/1_demo/train.sh @@ -1,28 +1,8 @@ -DEBUG=true -if [ "$DEBUG" = true ]; then - GPUS=2 - report=none - data_num_workers=0 - save_steps=200 - logging_steps=1 +GPUS=8 PER_DEVICE_BATCH_SIZE=64 - # ACCELERATE_ARGS="--num_machines 1 --num_processes 1 --dynamo_backend=no" - ACCELERATE_ARGS="--num_machines 1 --num_processes 2 --dynamo_backend=no --multi_gpu" -fi -# distributed settings -GPUS=${GPUS:-8} -GPUS_PER_NODE=${GPUS_PER_NODE:-8} -NODES=$((GPUS / GPUS_PER_NODE)) -PER_DEVICE_BATCH_SIZE=${PER_DEVICE_BATCH_SIZE:-64} -data_num_workers=${data_num_workers:-8} -report=${report:-wandb} -save_steps=${save_steps:-5000} -logging_steps=${logging_steps:-100} - -ACCELERATE_ARGS=${ACCELERATE_ARGS:-"--main_process_ip=\$MASTER_ADDR --main_process_port=\$MASTER_PORT \ - --num_machines ${NODES} --machine_rank 0 --num_processes=${GPUS} --multi_gpu \ - --mixed_precision=bf16 --dynamo_backend=no"} +ACCELERATE_ARGS="--main_process_ip=\$MASTER_ADDR --main_process_port=\$MASTER_PORT \ + --num_machines 1 --machine_rank 0 --num_processes=${GPUS} --multi_gpu" # * datasets dataset=experiments/1_demo/data-demo.yaml @@ -33,29 +13,21 @@ lr=1e-4 mlr=1e-4 vlr=2e-5 -chunk_size=16 +chunk_size=30 epoch=50 -lerobot_only=True -# fine-tuning -resume_path= model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} -echo $run_name . scripts/env.sh conda activate eo -export DATASET_NUM_PROCESSES=10 accelerate launch $ACCELERATE_ARGS scripts/train.py \ - ${resume_path:+--output-dir $resume_path} \ ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ - ${deepspeed:+--deepspeed configs/${deepspeed}.json} \ --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ - --train-lerobot-only ${lerobot_only} \ --data-path ${dataset} \ --chunk-size ${chunk_size} \ - --dataloader-num-workers ${data_num_workers} \ + --dataloader-num-workers 8 \ --freeze-vision-tower False \ --freeze-llm False \ --freeze-merger False \ @@ -64,18 +36,17 @@ accelerate launch $ACCELERATE_ARGS scripts/train.py \ --fp16 False \ --num-train-epochs ${epoch} \ --per-device-train-batch-size ${PER_DEVICE_BATCH_SIZE} \ - --gradient-accumulation-steps 1 \ --learning-rate ${lr} \ --merger-lr ${mlr} \ --vision-lr ${vlr} \ --weight-decay 0.1 \ --warmup-ratio 0.03 \ --lr-scheduler-type cosine \ - --logging-steps ${logging_steps} \ --gradient-checkpointing True \ --save-strategy steps \ - --save-steps ${save_steps} \ + --logging-steps 100 \ + --save-steps 5000 \ --save-total-limit 3 \ - --report-to ${report} \ + --report-to none \ --run-name ${run_name} \ --attn-implementation flash_attention_2 diff --git a/experiments/2_libero/data-libero.yaml b/experiments/2_libero/data-libero.yaml index fb08f31..ccafef4 100644 --- a/experiments/2_libero/data-libero.yaml +++ b/experiments/2_libero/data-libero.yaml @@ -3,7 +3,7 @@ mm_datasets: lerobot_datasets: - repo_id: libero_spatial_no_noops_1.0.0_lerobot - root: HF_LEROBOT_HOME + root: ./demo_data/ select_video_keys: [observation.images.image, observation.images.wrist_image] select_state_keys: [observation.state] select_action_keys: [action] diff --git a/getting_started/1_customize_data.ipynb b/getting_started/1_customize_data.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/scripts/eval_policy.py b/scripts/eval_policy.py index 3b2da18..3a2aca1 100644 --- a/scripts/eval_policy.py +++ b/scripts/eval_policy.py @@ -8,7 +8,7 @@ argparser.add_argument( "--model_path", type=str, - default="outputs/", + default="experiments/outputs/your_path", help="Path to the pretrained model", ) argparser.add_argument( @@ -22,19 +22,15 @@ def eval_policy(): # set the observation (image, state, etc.) - image0 = "test/demo/example1.png" - image1 = Image.open("test/demo/example1.png") + image0 = "demo_data/example.png" + image1 = Image.open("demo_data/example.png") - model = ( - AutoModel.from_pretrained(args.model_path, trust_remote_code=True, torch_dtype=torch.bfloat16) - .eval() - .cuda() - ) + model = AutoModel.from_pretrained(args.model_path, dtype=torch.bfloat16).eval().cuda() - processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True) + processor = AutoProcessor.from_pretrained(args.model_path) batch = { - "observation.images.image_0": [image0], + "observation.images.image": [image0], "observation.images.wrist_image": [image1], "observation.state": [torch.rand(8)], "task": ["put the object in the box."], diff --git a/tools/test_hf_model.py b/tools/test_hf_model.py new file mode 100644 index 0000000..d6bb120 --- /dev/null +++ b/tools/test_hf_model.py @@ -0,0 +1,54 @@ +from transformers import AutoProcessor + +from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration + +"""set model name or path""" +model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B +model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + model_name_or_path, + device_map="auto", + trust_remote_code=True, + # attn_implementation="flash_attention_2", +) + +processor = AutoProcessor.from_pretrained(model_name_or_path, trust_remote_code=True) + +messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": "demo_data/refcoco/images/COCO_train2014_000000168643_2.jpg"}, + { + "type": "text", + "text": "If the yellow robot gripper follows the yellow trajectory, what will happen? Choices: A. Robot puts the soda on the wooden steps. B. Robot moves the soda in front of the wooden steps. C. Robot moves the soda to the very top of the wooden steps. D. Robot picks up the soda can and moves it up. Please answer directly with only the letter of the correct option and nothing else.", + }, + ], + }, +] + +times = 0 +past_key_values = None + +while True: + if times > 0: + prompt = input("Enter your prompt: ") + if prompt == "q": + exit(0) + messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]}) + inputs = processor.apply_chat_template( + messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" + ).to("cuda") + + input_length = inputs["input_ids"].shape[1] + outputs = model.generate( + **inputs, max_new_tokens=1024, past_key_values=past_key_values, return_dict_in_generate=True + ) + + past_key_values = outputs.past_key_values + generated_ids = outputs.sequences + + completion = processor.decode(generated_ids[0, input_length:], skip_special_tokens=False) + print(completion) + + messages.append({"role": "assistant", "content": [{"type": "text", "text": completion}]}) + times += 1 From abd4aa93a566855cbcab891d645c8e724f6b4964 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sat, 13 Sep 2025 01:08:12 +0800 Subject: [PATCH 04/13] Refactor training scripts and configuration files for improved clarity and performance. --- eo/model/modeling_qwen2_5_vl.py | 1 - experiments/1_demo/train.sh | 7 +++---- experiments/2_libero/data-libero.yaml | 30 +++++++++++++-------------- experiments/2_libero/train.sh | 3 +-- 4 files changed, 19 insertions(+), 22 deletions(-) diff --git a/eo/model/modeling_qwen2_5_vl.py b/eo/model/modeling_qwen2_5_vl.py index 2d9f979..c3928b5 100644 --- a/eo/model/modeling_qwen2_5_vl.py +++ b/eo/model/modeling_qwen2_5_vl.py @@ -831,7 +831,6 @@ def get_input_embeddings(self): def set_input_embeddings(self, value): self.embed_tokens = value - @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, diff --git a/experiments/1_demo/train.sh b/experiments/1_demo/train.sh index 614afd5..9fced30 100644 --- a/experiments/1_demo/train.sh +++ b/experiments/1_demo/train.sh @@ -1,8 +1,7 @@ -GPUS=8 - PER_DEVICE_BATCH_SIZE=64 +GPUS=1 +PER_DEVICE_BATCH_SIZE=16 -ACCELERATE_ARGS="--main_process_ip=\$MASTER_ADDR --main_process_port=\$MASTER_PORT \ - --num_machines 1 --machine_rank 0 --num_processes=${GPUS} --multi_gpu" +ACCELERATE_ARGS="--num_machines 1 --machine_rank 0 --num_processes=${GPUS}" # * datasets dataset=experiments/1_demo/data-demo.yaml diff --git a/experiments/2_libero/data-libero.yaml b/experiments/2_libero/data-libero.yaml index ccafef4..a4ec8bf 100644 --- a/experiments/2_libero/data-libero.yaml +++ b/experiments/2_libero/data-libero.yaml @@ -8,20 +8,20 @@ lerobot_datasets: select_state_keys: [observation.state] select_action_keys: [action] - - repo_id: libero_90_no_noops_lerobot - root: HF_LEROBOT_HOME - select_video_keys: [observation.images.image, observation.images.wrist_image] - select_state_keys: [observation.state] - select_action_keys: [action] + # - repo_id: libero_90_no_noops_lerobot + # root: HF_LEROBOT_HOME + # select_video_keys: [observation.images.image, observation.images.wrist_image] + # select_state_keys: [observation.state] + # select_action_keys: [action] - - repo_id: libero_object_no_noops_1.0.0_lerobot - root: HF_LEROBOT_HOME - select_video_keys: [observation.images.image, observation.images.wrist_image] - select_state_keys: [observation.state] - select_action_keys: [action] + # - repo_id: libero_object_no_noops_1.0.0_lerobot + # root: HF_LEROBOT_HOME + # select_video_keys: [observation.images.image, observation.images.wrist_image] + # select_state_keys: [observation.state] + # select_action_keys: [action] - - repo_id: libero_10_no_noops_1.0.0_lerobot - root: HF_LEROBOT_HOME - select_video_keys: [observation.images.image, observation.images.wrist_image] - select_state_keys: [observation.state] - select_action_keys: [action] + # - repo_id: libero_10_no_noops_1.0.0_lerobot + # root: HF_LEROBOT_HOME + # select_video_keys: [observation.images.image, observation.images.wrist_image] + # select_state_keys: [observation.state] + # select_action_keys: [action] diff --git a/experiments/2_libero/train.sh b/experiments/2_libero/train.sh index 693d7bd..9df9f3f 100644 --- a/experiments/2_libero/train.sh +++ b/experiments/2_libero/train.sh @@ -1,8 +1,7 @@ GPUS=8 PER_DEVICE_BATCH_SIZE=64 -ACCELERATE_ARGS="--main_process_ip=\$MASTER_ADDR --main_process_port=\$MASTER_PORT \ - --num_machines 1 --machine_rank 0 --num_processes=${GPUS} --multi_gpu" +ACCELERATE_ARGS="--num_machines 1 --machine_rank 0 --num_processes=${GPUS} --multi_gpu" # datasets dataset=experiments/2_libero/data-libero.yaml From efd052792672c90b6fe87d024c8db19dd2092896 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sun, 14 Sep 2025 12:31:47 +0800 Subject: [PATCH 05/13] Update .gitignore to include new output paths, modify dataset configurations for improved clarity, and adjust training scripts for consistency across experiments. Enhance README documentation for better guidance on dataset preparation and training processes. --- .assets/openloop_example.png | Bin 0 -> 745883 bytes .gitignore | 2 + README.md | 10 +- demo_data/interleaved_demo.jsonl | 1132 ----------------- .../images/COCO_train2014_000000567396_13.jpg | Bin 0 -> 100682 bytes .../images/COCO_train2014_000000579299_4.jpg | Bin 0 -> 77557 bytes demo_data/refcoco/refcoco.jsonl | 10 +- eo/data/lerobot_dataset.py | 7 +- eo/model/configuration_eo1.py | 2 +- eo/model/{processor.py => processing_eo1.py} | 0 eo/train/pipeline_config.py | 2 +- experiments/1_demo/README.md | 128 ++ experiments/1_demo/data-demo.yaml | 2 +- experiments/1_demo/train.sh | 4 +- experiments/2_libero/README.md | 14 +- experiments/2_libero/data-libero.yaml | 32 +- experiments/3_simpler/README.md | 10 +- experiments/8_vllmeval/README.md | 14 +- getting_started/1_load_dataset.ipynb | 844 +++++++++--- getting_started/2_train_finetune.ipynb | 258 +++- pyproject.toml | 1 + scripts/eval_policy.py | 4 +- tools/test_hf_model.py => scripts/test_vlm.py | 11 +- scripts/train.py | 6 +- tools/openloop.py | 119 ++ 25 files changed, 1246 insertions(+), 1366 deletions(-) create mode 100644 .assets/openloop_example.png delete mode 100644 demo_data/interleaved_demo.jsonl create mode 100644 demo_data/refcoco/images/COCO_train2014_000000567396_13.jpg create mode 100644 demo_data/refcoco/images/COCO_train2014_000000579299_4.jpg rename eo/model/{processor.py => processing_eo1.py} (100%) rename tools/test_hf_model.py => scripts/test_vlm.py (84%) diff --git a/.assets/openloop_example.png b/.assets/openloop_example.png new file mode 100644 index 0000000000000000000000000000000000000000..26b3d966134f55710717a2a40c837064bc18bacf GIT binary patch literal 745883 zcmdSBXIN9&7dMLIC@KOfD$=D%6Oi7CN(TWI=_P~o8hQ%^EP!FABq5;~ zB_W{+zw!t08v%zu9s_?|aDA!t^uk!%Lx14ms*|$5D+vju>G{uvFQ9xI5;D`rD)LWW zd8MsSk|tp-(n+_|lQ;YZp!bpw4< 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zpFOx-RQs+8mq#L{hS$Xjm=#+ZYuA1M9be!PeJw7w1NPX&#Kd-P4`?M}0PF%30-S=6 zgHj8qMuO9WC=rjd|2Pg*EClB~eLFHe`=4>ho`b<{+MWs&QPEC6MfSeS6>q*;xw5hXTn2~l zA1@2R_PPen6`#XLY=J=0I+y-4q%=4Nv*r?b1qUdNmX;QkOUgpKgMznifBin`fj!-* z+t&w*VD0-7*mmIIckCuLaX$PtgipirN%Tf?j;f?&3y>|zEb;)&1KV5U_2Ne70M}r+ zz<{d89=bMq`&pe<~}fB#|%n H>f`?aPVwUo literal 0 HcmV?d00001 diff --git a/.gitignore b/.gitignore index c2cee81..47a7942 100644 --- a/.gitignore +++ b/.gitignore @@ -225,6 +225,7 @@ source .DS_Store experiments/outputs +outputs scripts/env.sh pre-commit.sh tools/caption_video.html @@ -234,3 +235,4 @@ experiments/2_libero/logs/videos demo_data/demos25 demo_data/libero_spatial_no_noops_1.0.0_lerobot +experiments/test diff --git a/README.md b/README.md index 4691a8c..012facc 100644 --- a/README.md +++ b/README.md @@ -148,6 +148,7 @@ messages = [ ], }, ] + # 2. text generation [multimodal reasoning] input_length = inputs["input_ids"].shape[1] inputs = processor.apply_chat_template( @@ -176,11 +177,13 @@ To combine robot control data and multimodal data, we support a [flexible YAML-b ```yaml # @multimodal data config mm_datasets: + # classical multimodal data - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL - sampling_strategy: random:10% # sampling strategy + sampling_strategy: random:100% # sampling strategy - - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl + # interleaved data jsonl, rely on `lerobot_datasets` to load robot control data + - json_path: demo_data/interleaved_demo.jsonl # @robot control config lerobot_datasets: @@ -305,8 +308,7 @@ Robot Control Benchmark Results ## 📅 Roadmap - [x] 🤖 Release [EO-1](https://huggingface.co/IPEC-COMMUNITY/EO-1-3B) pretraining, finetune scripts, and documentations. -- [ ] 🤗 Release [pre-training models](https://huggingface.co/collections/IPEC-COMMUNITY/eo-robotics-68ac4ff30e1f746cac28ca14) and experiment finetune scripts. -- [ ] 🔥 Release Interleaved Dataset `EO-Data1.5M`, benchmark `EO-Bench` and all detailed pre-training code. +- [ ] 🤗 Release [pre-training models](https://huggingface.co/collections/IPEC-COMMUNITY/eo-robotics-68ac4ff30e1f746cac28ca14), Interleaved Dataset `EO-Data1.5M` and benchmark `EO-Bench`. - [ ] ⚡️ Efficient LLM Inference over Long Sequences, Efficient KV-cache, etc. - [ ] 🤖 Integrate with human feedback fine-tuning. diff --git a/demo_data/interleaved_demo.jsonl b/demo_data/interleaved_demo.jsonl deleted file mode 100644 index 006dc27..0000000 --- a/demo_data/interleaved_demo.jsonl +++ /dev/null @@ -1,1132 +0,0 @@ -{"lerobot": ["demos25 3718 0", "demos25 3718 16"], "conversations": [{"from": "human", "value": "Describe the robotic arm's path to accomplish \"place the grasped large bag of potato chips into red canvas tote bag\", outputting trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped large bag of potato chips into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped large bag of potato chips into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4082 16"], "conversations": [{"from": "human", "value": "Generate trajectory points in json format detailing how the robotic arm must move to achieve \"place the grasped pink soap into red canvas tote bag\"."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [327, 112]},\n {\"point_2d\": [279, 145]},\n {\"point_2d\": [244, 158]},\n {\"point_2d\": [221, 169]},\n {\"point_2d\": [202, 177]},\n {\"point_2d\": [182, 184]}\n ],\n \"label\": \"place the grasped pink soap into red canvas tote bag\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [327, 112]},\n {\"point_2d\": [279, 145]},\n {\"point_2d\": [244, 158]},\n {\"point_2d\": [221, 169]},\n {\"point_2d\": [202, 177]},\n {\"point_2d\": [182, 184]}\n ],\n \"label\": \"place the grasped pink soap into red canvas tote bag\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4259 0", "demos25 4259 16", "demos25 4685 0"], "conversations": [{"from": "human", "value": "To successfully execute \"retrieve the bottled ad calcium milk from the table with the right arm\", define the robotic arm's movement. Output trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nretrieve the bottled ad calcium milk from the table with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nretrieve the bottled ad calcium milk from the table with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon the execution of grab the doubanjiang on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4417 0", "demos25 4417 16", "demos25 4685 0"], "conversations": [{"from": "human", "value": "Detail the robotic arm's motion to achieve \"place the grasped bottled ad calcium milk into red canvas tote bag\". Present the solution as trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped bottled ad calcium milk into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped bottled ad calcium milk into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon the execution of grab the doubanjiang on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4685 0", "demos25 4685 16"], "conversations": [{"from": "human", "value": "Considering the goal of \"place the grasped doubanjiang into red canvas tote bag with the right arm\", how should the robotic arm proceed? The output must include trajectory points."}, {"from": "gpt", "value": "```xml\nplace the grasped doubanjiang into red canvas tote bag with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped doubanjiang into red canvas tote bag with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7243 0", "demos25 7243 16", "demos25 8209 0"], "conversations": [{"from": "human", "value": "For achieving \"place the grasped large bag of potato chips into red canvas tote bag\", detail the robotic arm's course of action with an output of trajectory points."}, {"from": "gpt", "value": "```xml\nplace the grasped large bag of potato chips into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped large bag of potato chips into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Are we able to begin grasp the pink soap on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7558 16"], "conversations": [{"from": "human", "value": "What actions must the robotic arm take to achieve \"place the grasped pink soap into red canvas tote bag\"? Output trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped pink soap into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped pink soap into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7684 0", "demos25 7684 16", "demos25 8209 0"], "conversations": [{"from": "human", "value": "What is the required motion plan for the robotic arm to achieve \"retrieve the bottled ad calcium milk from the table with the right arm\"? Present trajectory points."}, {"from": "gpt", "value": "```xml\nretrieve the bottled ad calcium milk from the table with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nretrieve the bottled ad calcium milk from the table with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped doubanjiang into red canvas tote bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7830 0", "demos25 7830 16", "demos25 8209 0"], "conversations": [{"from": "human", "value": "What actions must the robotic arm take to achieve \"place the grasped bottled ad calcium milk into red canvas tote bag\"? Output trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [245, 144]},\n {\"point_2d\": [231, 154]},\n {\"point_2d\": [214, 166]},\n {\"point_2d\": [200, 176]},\n {\"point_2d\": [185, 185]},\n {\"point_2d\": [171, 196]}\n ],\n \"label\": \"place the grasped bottled ad calcium milk into red canvas tote bag\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [245, 144]},\n {\"point_2d\": [231, 154]},\n {\"point_2d\": [214, 166]},\n {\"point_2d\": [200, 176]},\n {\"point_2d\": [185, 185]},\n {\"point_2d\": [171, 196]}\n ],\n \"label\": \"place the grasped bottled ad calcium milk into red canvas tote bag\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can we undertake place the grasped doubanjiang into red canvas tote bag with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 8209 0", "demos25 8209 16"], "conversations": [{"from": "human", "value": "To successfully execute \"place the grasped doubanjiang into red canvas tote bag with the right arm\", define the robotic arm's movement. Output trajectory points."}, {"from": "gpt", "value": "```xml\nplace the grasped doubanjiang into red canvas tote bag with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped doubanjiang into red canvas tote bag with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11023 0", "demos25 11023 16"], "conversations": [{"from": "human", "value": "Provide the articulation sequence for the robotic arm to achieve \"place the grasped sunflower seeds into blue nylon bag\", giving trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [293, 134]},\n {\"point_2d\": [247, 148]},\n {\"point_2d\": [213, 160]},\n {\"point_2d\": [187, 170]},\n {\"point_2d\": [166, 179]},\n {\"point_2d\": [157, 188]}\n ],\n \"label\": \"place the grasped sunflower seeds into blue nylon bag\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [293, 134]},\n {\"point_2d\": [247, 148]},\n {\"point_2d\": [213, 160]},\n {\"point_2d\": [187, 170]},\n {\"point_2d\": [166, 179]},\n {\"point_2d\": [157, 188]}\n ],\n \"label\": \"place the grasped sunflower seeds into blue nylon bag\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11432 16"], "conversations": [{"from": "human", "value": "Explain the robotic arm's path to achieve \"place the grasped cookie biscuit into blue nylon bag\". Note: trajectory points in xml format are required for the output."}, {"from": "gpt", "value": "```xml\nplace the grasped cookie biscuit into blue nylon bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped cookie biscuit into blue nylon bag\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11620 0", "demos25 11620 16", "demos25 12071 0"], "conversations": [{"from": "human", "value": "What actions must the robotic arm take to achieve \"retrieve the cola from the table with the right arm\"? Output trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nretrieve the cola from the table with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nretrieve the cola from the table with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11769 0", "demos25 11769 16"], "conversations": [{"from": "human", "value": "Explain the robotic arm's path to achieve \"place the grasped cola into blue nylon bag\". Note: trajectory points in xml format are required for the output."}, {"from": "gpt", "value": "```xml\nplace the grasped cola into blue nylon bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped cola into blue nylon bag\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12071 0", "demos25 12071 16"], "conversations": [{"from": "human", "value": "Provide the articulation sequence for the robotic arm to achieve \"place the grasped facial cleanser into blue nylon bag with the right arm\", giving trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [274, 62]},\n {\"point_2d\": [269, 91]},\n {\"point_2d\": [235, 125]},\n {\"point_2d\": [210, 149]},\n {\"point_2d\": [185, 167]},\n {\"point_2d\": [160, 182]}\n ],\n \"label\": \"place the grasped facial cleanser into blue nylon bag with the right arm\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [274, 62]},\n {\"point_2d\": [269, 91]},\n {\"point_2d\": [235, 125]},\n {\"point_2d\": [210, 149]},\n {\"point_2d\": [185, 167]},\n {\"point_2d\": [160, 182]}\n ],\n \"label\": \"place the grasped facial cleanser into blue nylon bag with the right arm\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24775 0", "demos25 24775 16"], "conversations": [{"from": "human", "value": "What is the optimal way for the robotic arm to move to achieve \"place the grasped sandwich cookies into blue nylon bag\"? Deliver trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped sandwich cookies into blue nylon bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped sandwich cookies into blue nylon bag\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25072 16", "demos25 25736 0"], "conversations": [{"from": "human", "value": "Detail the robotic arm's motion to achieve \"place the grasped bath ball into blue nylon bag\". Present the solution as trajectory points."}, {"from": "gpt", "value": "```xml\nplace the grasped bath ball into blue nylon bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped bath ball into blue nylon bag\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped blue hand cream into blue nylon bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25282 0", "demos25 25282 16"], "conversations": [{"from": "human", "value": "For the robotic arm to perform \"retrieve the tea pi from the table with the right arm\", how should it be maneuvered? Specify trajectory points."}, {"from": "gpt", "value": "```xml\nretrieve the tea pi from the table with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nretrieve the tea pi from the table with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25443 0", "demos25 25443 16"], "conversations": [{"from": "human", "value": "Describe the robotic arm's path to accomplish \"place the grasped tea pi into blue nylon bag\", outputting trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [287, 94]},\n {\"point_2d\": [281, 112]},\n {\"point_2d\": [260, 131]},\n {\"point_2d\": [238, 150]},\n {\"point_2d\": [219, 165]},\n {\"point_2d\": [184, 180]}\n ],\n \"label\": \"place the grasped tea pi into blue nylon bag\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [287, 94]},\n {\"point_2d\": [281, 112]},\n {\"point_2d\": [260, 131]},\n {\"point_2d\": [238, 150]},\n {\"point_2d\": [219, 165]},\n {\"point_2d\": [184, 180]}\n ],\n \"label\": \"place the grasped tea pi into blue nylon bag\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25736 0", "demos25 25736 16"], "conversations": [{"from": "human", "value": "To achieve \"place the grasped blue hand cream into blue nylon bag with the right arm\", what sequence of movements should the robotic arm execute? Please provide trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [183, 26]},\n {\"point_2d\": [140, 60]},\n {\"point_2d\": [127, 82]},\n {\"point_2d\": [119, 105]},\n {\"point_2d\": [116, 128]},\n {\"point_2d\": [107, 154]}\n ],\n \"label\": \"place the grasped blue hand cream into blue nylon bag with the right arm\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [183, 26]},\n {\"point_2d\": [140, 60]},\n {\"point_2d\": [127, 82]},\n {\"point_2d\": [119, 105]},\n {\"point_2d\": [116, 128]},\n {\"point_2d\": [107, 154]}\n ],\n \"label\": \"place the grasped blue hand cream into blue nylon bag with the right arm\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41080 0", "demos25 41080 16", "demos25 42354 0"], "conversations": [{"from": "human", "value": "Considering the goal of \"place the grasped packaged ham sausage into red canvas tote bag\", how should the robotic arm proceed? The output must include trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [210, 91]},\n {\"point_2d\": [192, 112]},\n {\"point_2d\": [179, 140]},\n {\"point_2d\": [171, 166]},\n {\"point_2d\": [163, 193]},\n {\"point_2d\": [156, 219]}\n ],\n \"label\": \"place the grasped packaged ham sausage into red canvas tote bag\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [210, 91]},\n {\"point_2d\": [192, 112]},\n {\"point_2d\": [179, 140]},\n {\"point_2d\": [171, 166]},\n {\"point_2d\": [163, 193]},\n {\"point_2d\": [156, 219]}\n ],\n \"label\": \"place the grasped packaged ham sausage into red canvas tote bag\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the most probable development after grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41566 16", "demos25 42354 0"], "conversations": [{"from": "human", "value": "Articulate the robotic arm's planned movement to achieve \"place the grasped hair conditioner into red canvas tote bag\". As for the output, list trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped hair conditioner into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped hair conditioner into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is place the grasped packaged ham sausage into red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41832 0", "demos25 41832 16", "demos25 42354 0"], "conversations": [{"from": "human", "value": "To successfully execute \"retrieve the sunflower seeds from the table with the right arm\", define the robotic arm's movement. Output trajectory points in json format."}, {"from": "gpt", "value": "```json\n{\n \"points\": [\n {\"point_2d\": [234, 153]},\n {\"point_2d\": [245, 151]},\n {\"point_2d\": [258, 150]},\n {\"point_2d\": [270, 150]},\n {\"point_2d\": [282, 150]},\n {\"point_2d\": [293, 150]}\n ],\n \"label\": \"retrieve the sunflower seeds from the table with the right arm\"\n}\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```json\n{\n \"points\": [\n {\"point_2d\": [234, 153]},\n {\"point_2d\": [245, 151]},\n {\"point_2d\": [258, 150]},\n {\"point_2d\": [270, 150]},\n {\"point_2d\": [282, 150]},\n {\"point_2d\": [293, 150]}\n ],\n \"label\": \"retrieve the sunflower seeds from the table with the right arm\"\n}\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the most probable development after grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42005 0", "demos25 42005 16", "demos25 42354 0"], "conversations": [{"from": "human", "value": "To successfully execute \"place the grasped sunflower seeds into red canvas tote bag\", define the robotic arm's movement. Output trajectory points in xml format."}, {"from": "gpt", "value": "```xml\nplace the grasped sunflower seeds into red canvas tote bag\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped sunflower seeds into red canvas tote bag\n``` physically?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42354 0", "demos25 42354 16"], "conversations": [{"from": "human", "value": "Detail the robotic arm's motion to achieve \"place the grasped sweet bean sauce into red canvas tote bag with the right arm\". Present the solution as trajectory points."}, {"from": "gpt", "value": "```xml\nplace the grasped sweet bean sauce into red canvas tote bag with the right arm\n```"}, {"from": "human", "value": "How should the robot execute the trajectory ```xml\nplace the grasped sweet bean sauce into red canvas tote bag with the right arm\n``` physically?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "The robotic arm holds a green snack bag above a red shopping bag. Various items, including a yellow box and a white tube, are arranged on the table nearby."}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 298", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with red canvas tote bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is grasp the left arm with red canvas tote bag now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "Can grasp the hair conditioner on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is it appropriate to initiate place the grasped packaged ham sausage into red canvas tote bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you commence retrieve the sunflower seeds from the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "If the sausage is hot, what change in approach does the robot need?"}, {"from": "gpt", "value": "The robot needs to handle the sausage more delicately and possibly use a heat shield or cooler grip."}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "How does bright lighting enhance the robot's ability to grasp accurately?"}, {"from": "gpt", "value": "Bright lighting improves visual clarity, aiding the robot in accurately identifying and grasping the object."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 74 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is grasp the left arm with red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it appropriate to initiate grab the packaged ham sausage on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the sweet bean sauce on the table with the right arm officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "The robotic arm hovers above a red shopping bag on the left. A tube, yellow box, and packet lie on the table, ready for packing. The scene is set in a bright supermarket environment."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "How does the robot detect unexpected heat from the bag?"}, {"from": "gpt", "value": "Robot uses thermographic sensors to detect and respond to heat variations."}, {"from": "human", "value": "After grasp the left arm with red canvas tote bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Upon the conclusion of place the grasped packaged ham sausage into red canvas tote bag, what action is anticipated next?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "Is the execution of grasp the left arm with red canvas tote bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Has the performance of grab the packaged ham sausage on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 299 0", "demos25 414 0", "demos25 465 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "Robotic arms flank a table with snacks. A red bag is on the left, a white box on the right. A cylindrical snack stands upright between two yellow packages."}, {"from": "human", "value": "Can grasp the left arm with red canvas tote bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped packaged ham sausage into red canvas tote bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has place the grasped packaged ham sausage into red canvas tote bag been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 298 0", "demos25 299 0", "demos25 414 0", "demos25 619 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm hovers over a table filled with various grocery items. A red shopping bag is open on the left, ready for packing. The items include snacks, a bottle, and a box."}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, what comes next?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1219 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "How might dim lighting alter the robot's precision in placing the bottle?"}, {"from": "gpt", "value": "Dim lighting reduces visibility, increasing the chance of misplacement due to poor depth perception."}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped sunflower seeds into red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What precaution should the robot take if the sauce container is unexpectedly warm?"}, {"from": "gpt", "value": "The robot should use heat-resistant materials and limit contact time to avoid damage to components."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 211", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "To complete Place the grasped hair conditioner into red canvas tote bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically follows place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Are conditions suitable to proceed with place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the performance of grasp the hair conditioner on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1075 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the sunflower seeds from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grasp the hair conditioner on the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 129", "demos25 1076 0", "demos25 1111 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Was retrieve the sunflower seeds from the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the sunflower seeds from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped sunflower seeds into red canvas tote bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 945 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 110"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped hair conditioner into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grab the sweet bean sauce on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Can place the grasped sweet bean sauce into red canvas tote bag with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have we finished with grasp the hair conditioner on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 143", "demos25 1220 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Is the immediate performance of grab the packaged ham sausage on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped sunflower seeds into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag has been accomplished?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a yellow object above a red shopping bag. Several packaged items lie on the table nearby, ready for packing."}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the common progression after retrieve the sunflower seeds from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 786 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has place the grasped hair conditioner into red canvas tote bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, what are the immediate next tasks?"}, {"from": "gpt", "value": "6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a box above a red shopping bag filled with items, positioned on a wooden table near a blue background."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0", "demos25 1330 0"], "conversations": [{"from": "human", "value": "Has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is retrieve the sunflower seeds from the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grab the packaged ham sausage on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the sweet bean sauce on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Upon the conclusion of grasp the hair conditioner on the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "In low light, what feature helps the robot identify the correct item?"}, {"from": "gpt", "value": "The robot relies on pre-programmed shapes and sizes, compensating for reduced visual clarity with tactile feedback."}, {"from": "human", "value": "How does heat from seeds affect the robot's material handling strategy?"}, {"from": "gpt", "value": "Heat may soften materials; the robot adjusts grip strength to prevent damage."}, {"from": "human", "value": "Is the immediate performance of place the grasped hair conditioner into red canvas tote bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped hair conditioner into red canvas tote bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a snack pack above a red shopping bag. The bag is partially filled with items, positioned on the left side of the frame."}, {"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag is behind you?"}, {"from": "gpt", "value": "7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 978 0", "demos25 1076 0", "demos25 1220 0", "demos25 1247 0"], "conversations": [{"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}, {"from": "human", "value": "From the starting observation of this action and the current view, is retrieve the sunflower seeds from the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a red bag open on the left. A box of snacks is positioned above the bag, ready to be placed inside. The scene is set on a wooden surface."}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has grab the sweet bean sauce on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 734 0", "demos25 946 0", "demos25 1076 0", "demos25 1220 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Is the current time opportune for grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "What changes if the sauce bottle emits unexpected heat?"}, {"from": "gpt", "value": "The robot must adjust its handling strategy to prevent overheating of its components."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 359"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped sweet bean sauce into red canvas tote bag with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 1331 0", "demos25 1420 0"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped sweet bean sauce into red canvas tote bag with the right arm reached completion?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a yellow item above a red shopping bag filled with goods, positioned on a wooden surface near a pink backdrop."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0", "demos25 1690 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped sweet bean sauce into red canvas tote bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Can place the grasped sweet bean sauce into red canvas tote bag with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Can grab the packaged ham sausage on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "What is the common progression after grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Have all aspects of place the grasped sweet bean sauce into red canvas tote bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1331 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2083 0", "demos25 2084 139", "demos25 2224 0"], "conversations": [{"from": "human", "value": "Can we undertake grasp the left arm with red canvas tote bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the large bag of potato chips on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that Place the grasped large bag of potato chips into red canvas tote bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1794 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grasp the left arm with red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically unfolds after grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag filled with items. A bottle lies on the floor nearby, while a box and a pink package rest on the table to the right."}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0", "demos25 2260 0"], "conversations": [{"from": "human", "value": "Wanting an organized checkout experience at the market, what comes up right away?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is it within our capacity to execute grasp the left arm with red canvas tote bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have the necessary steps for place the grasped bottled ad calcium milk into red canvas tote bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grasp the pink soap on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag above a table. Below, snacks and a can are neatly arranged, ready for packing. The scene captures a moment of automation in a supermarket setting."}, {"from": "human", "value": "How might dim lighting alter chip bag visibility?"}, {"from": "gpt", "value": "Dim lighting reduces contrast, making edges less distinct; robot needs enhanced vision processing."}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm?"}, {"from": "gpt", "value": "2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1947 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the left arm with red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Was the action of grasp the left arm with red canvas tote bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Is the immediate performance of place the grasped pink soap into red canvas tote bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "What if the bag suddenly became hot during packing?"}, {"from": "gpt", "value": "Robot's thermal sensors trigger, adjusting grip strength to prevent damage and ensuring safety."}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "What is the next event that typically follows grab the large bag of potato chips on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 203", "demos25 1948 0", "demos25 2084 0", "demos25 2224 146"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with red canvas tote bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it appropriate to initiate grab the large bag of potato chips on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grasp the pink soap on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped doubanjiang into red canvas tote bag with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Is the immediate performance of place the grasped doubanjiang into red canvas tote bag with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Following the completion of place the grasped large bag of potato chips into red canvas tote bag, what is projected to happen?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "How does heat from the chips affect the robot's tactile sensors?"}, {"from": "gpt", "value": "Increased heat could trigger the robot's sensors to apply less pressure to avoid damage."}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1744 0", "demos25 1948 0", "demos25 2084 0", "demos25 2224 0", "demos25 2370 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag on the left. Another arm grips a blue bag on the right. A carton and a bottle rest on the table between them."}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Comparing the initial image with the current one, is grasp the pink soap on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}, {"from": "human", "value": "What is the expected outcome immediately following place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What if the milk bottle emits unexpected heat? How does the robot react?"}, {"from": "gpt", "value": "The robot activates cooling mechanisms and adjusts grip strength to handle the heated bottle safely."}, {"from": "human", "value": "What is the common progression after place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2786 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Have the necessary steps for place the grasped doubanjiang into red canvas tote bag with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped bottled ad calcium milk into red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0", "demos25 2817 0"], "conversations": [{"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What precaution should the robot take if the milk bottle is unexpectedly warm?"}, {"from": "gpt", "value": "The robot should slow down its movements to avoid splashing and ensure a secure grip to prevent dropping."}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A red shopping bag filled with items sits on a surface. A robotic arm is positioned above, holding a black object near the bag's opening."}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the doubanjiang on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 146", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Place the grasped pink soap into red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Has place the grasped bottled ad calcium milk into red canvas tote bag been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, what comes next?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A red shopping bag stands upright on a tiled floor, partially filled with items. A robotic arm hovers above, ready to place a can into the bag."}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Is it permissible to start place the grasped bottled ad calcium milk into red canvas tote bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it within our capacity to execute retrieve the bottled ad calcium milk from the table with the right arm now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 120"], "conversations": [{"from": "human", "value": "Is grasp the pink soap on the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Having arranged part of our goods at the supermarket and 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag done, what action flows naturally?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Have we finished with grasp the pink soap on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grab the doubanjiang on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 148", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "How does heat from the environment affect the robot's components?"}, {"from": "gpt", "value": "Environmental heat could cause mechanical parts to expand, potentially affecting the robot's precision and performance."}, {"from": "human", "value": "What is the exact robotic arm action required for Retrieve the bottled ad calcium milk from the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's grippers, positioned above a conveyor belt. The background features a colorful supermarket setting."}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the bottled ad calcium milk from the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped bottled ad calcium milk into red canvas tote bag officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "What is the next event that typically unfolds after grasp the pink soap on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Is the immediate performance of grab the doubanjiang on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2517 0", "demos25 2518 0", "demos25 2555 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped pink soap into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has retrieve the bottled ad calcium milk from the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 119", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag upright on a table. A can lies outside the bag near the bottom right corner. The background features a colorful display."}, {"from": "human", "value": "What is the exact robotic arm action required for Place the grasped bottled ad calcium milk into red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Was place the grasped doubanjiang into red canvas tote bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2407 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped pink soap into red canvas tote bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has the performance of grasp the left arm with red canvas tote bag concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm?"}, {"from": "gpt", "value": "6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "How might dim lighting alter the robot's detection of doubanjiang?"}, {"from": "gpt", "value": "Dim lighting may reduce contrast, making color and texture less distinct for vision-based detection."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2666 0", "demos25 2667 0", "demos25 2696 0", "demos25 2787 0", "demos25 2907 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the bottled ad calcium milk from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped bottled ad calcium milk into red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the doubanjiang on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2667 0", "demos25 2787 0"], "conversations": [{"from": "human", "value": "Is place the grasped large bag of potato chips into red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What is the next event that typically follows retrieve the bottled ad calcium milk from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Has grab the large bag of potato chips on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Was the action of grab the doubanjiang on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped doubanjiang into red canvas tote bag with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Is retrieve the bottled ad calcium milk from the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 442"], "conversations": [{"from": "human", "value": "To complete Place the grasped doubanjiang into red canvas tote bag with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Following the completion of grab the doubanjiang on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "If the bag heats up, what change in grip strength is advisable?"}, {"from": "gpt", "value": "The robot should increase grip strength slightly to compensate for potential material softening."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A red shopping bag sits on a table, partially filled with items. A robotic arm hovers above, holding a yellow object near the bag's opening."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0", "demos25 3018 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped doubanjiang into red canvas tote bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 2908 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 2908 0", "demos25 3350 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped doubanjiang into red canvas tote bag with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3593 0", "demos25 3717 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the large bag of potato chips on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3593 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "Can place the grasped bottled ad calcium milk into red canvas tote bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, what action should you take next?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, what are the next steps to take?"}, {"from": "gpt", "value": "3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3593 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is grasp the left arm with red canvas tote bag now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Since the previous 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm is done, how can we proceed to tidy things up at the store?"}, {"from": "gpt", "value": "2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped large bag of potato chips into red canvas tote bag?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3592 0", "demos25 3593 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Have the necessary steps for place the grasped bottled ad calcium milk into red canvas tote bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grab the doubanjiang on the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3593 0", "demos25 3624 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's claw. Various items, including Lay's chips and a pink box, are scattered on the table below."}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has grab the large bag of potato chips on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag upright on the table. Inside, various items are packed, with a white bottle and a pink packet visible outside the bag."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3593 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Are conditions suitable to proceed with place the grasped large bag of potato chips into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has retrieve the bottled ad calcium milk from the table with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 137", "demos25 3593 0", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with red canvas tote bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is it practical to begin grab the large bag of potato chips on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3455 0", "demos25 3489 0", "demos25 3593 124", "demos25 3718 0", "demos25 3883 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete Grab the large bag of potato chips on the table with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Was the action of grasp the left arm with red canvas tote bag carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4416 0", "demos25 4417 0", "demos25 4452 0", "demos25 4558 126"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped pink soap into red canvas tote bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has retrieve the bottled ad calcium milk from the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped bottled ad calcium milk into red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grab the doubanjiang on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0", "demos25 4589 0"], "conversations": [{"from": "human", "value": "Has grab the doubanjiang on the table with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it permissible to start retrieve the bottled ad calcium milk from the table with the right arm now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the doubanjiang on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 140", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Has grasp the pink soap on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped bottled ad calcium milk into red canvas tote bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is grab the doubanjiang on the table with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 176", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped pink soap into red canvas tote bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Have we finished with grab the large bag of potato chips on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Upon the conclusion of retrieve the bottled ad calcium milk from the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 157", "demos25 4417 0", "demos25 4557 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "What is the next event that typically unfolds after grasp the pink soap on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the bottled ad calcium milk from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped bottled ad calcium milk into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4126 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped pink soap into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grab the doubanjiang on the table with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's gripper. The bag is partially filled, with its handles looped around the arm. The background features a colorful display."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag open over a table. Various items are inside the bag, ready for packing. The arm's grippers secure the bag edges."}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is the execution of grab the doubanjiang on the table with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0", "demos25 4684 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}, {"from": "human", "value": "Given that place the grasped pink soap into red canvas tote bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Comparing the initial image with the current one, is grab the doubanjiang on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4298 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Are we able to begin grasp the left arm with red canvas tote bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the bottled ad calcium milk from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's grippers. A white bottle is positioned near the bag, ready for packing. The scene is set on a flat surface with a colorful background."}, {"from": "human", "value": "Is it accurate to state that place the grasped large bag of potato chips into red canvas tote bag is done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Has place the grasped large bag of potato chips into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is the current time opportune for place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next event that typically follows place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm?"}, {"from": "gpt", "value": "4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4258 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped pink soap into red canvas tote bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can retrieve the bottled ad calcium milk from the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4082 0", "demos25 4259 0", "demos25 4417 0", "demos25 4558 0"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. Inside, various items are packed. A pink package rests on the table beside the bag."}, {"from": "human", "value": "Is it practical to begin grab the large bag of potato chips on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0", "demos25 5208 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped doubanjiang into red canvas tote bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Upon the execution of grab the doubanjiang on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped doubanjiang into red canvas tote bag with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0", "demos25 4815 0"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has place the grasped doubanjiang into red canvas tote bag with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's left side. A yellow item is held by the arm's right side, positioned above the bag, ready for packing."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 0"], "conversations": [{"from": "human", "value": "Is it accurate to state that place the grasped bottled ad calcium milk into red canvas tote bag is done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 4685 523"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped doubanjiang into red canvas tote bag with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5236 0", "demos25 5284 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the left arm with red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Is it possible to begin grasp the pink soap on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0", "demos25 5795 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grab the doubanjiang on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the pink soap on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 192", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Grasp the left arm with red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon the conclusion of grasp the left arm with red canvas tote bag, what action is anticipated next?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A red shopping bag hangs from a table edge. A robotic arm holds a blue item near scattered products, including a yellow box and white package, on a wooden floor."}, {"from": "human", "value": "Was the action of grab the large bag of potato chips on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag open. Various items, including a bottle, cookies, and cleaning supplies, are arranged neatly on the table beside it."}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is place the grasped large bag of potato chips into red canvas tote bag officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped large bag of potato chips into red canvas tote bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5428 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0", "demos25 5879 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is the current time opportune for grab the large bag of potato chips on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that grab the large bag of potato chips on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the pink soap on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5615 0", "demos25 5616 0", "demos25 5766 0", "demos25 5767 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the large bag of potato chips on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped large bag of potato chips into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, what is the next task in line?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Are we able to begin grab the doubanjiang on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5616 0", "demos25 5767 112"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Can grab the large bag of potato chips on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grasp the pink soap on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5880 0", "demos25 5999 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped pink soap into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the bottled ad calcium milk from the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next event that typically unfolds after retrieve the bottled ad calcium milk from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6028 0", "demos25 6116 0", "demos25 6156 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has retrieve the bottled ad calcium milk from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped bottled ad calcium milk into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Can place the grasped pink soap into red canvas tote bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you commence place the grasped pink soap into red canvas tote bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "What is the expected outcome immediately following grasp the pink soap on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's gripper, positioned above a checkout counter. The arm is ready to pack items into the bag."}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A red shopping bag stands upright on the table, partially filled with items. A robotic arm is positioned near the bag, ready to pack more goods."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0", "demos25 6400 0"], "conversations": [{"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Was grab the doubanjiang on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the doubanjiang on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6115 0", "demos25 6116 0", "demos25 6278 0", "demos25 6279 121"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the bottled ad calcium milk from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped bottled ad calcium milk into red canvas tote bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with Grab the doubanjiang on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A red shopping bag filled with items sits on the counter. A robotic arm is positioned above, ready to place a white box into the bag."}, {"from": "human", "value": "What is the expected outcome immediately following place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, what is the next task in line?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0", "demos25 6309 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Can place the grasped bottled ad calcium milk into red canvas tote bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the way this action began and its current status, has grab the doubanjiang on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 119", "demos25 6000 0", "demos25 6116 162", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Place the grasped pink soap into red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Has grab the large bag of potato chips on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped bottled ad calcium milk into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. A bottle of iced tea is partially inside the bag, positioned near the top-left corner of the frame."}, {"from": "human", "value": "What is the most probable development after place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 5909 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped pink soap into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we confirm the completion of place the grasped doubanjiang into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has place the grasped pink soap into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the doubanjiang on the table with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Is the immediate performance of grasp the pink soap on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grab the large bag of potato chips on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 115", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Has grab the large bag of potato chips on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with Retrieve the bottled ad calcium milk from the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Can grab the large bag of potato chips on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5880 0", "demos25 6000 0", "demos25 6116 0", "demos25 6279 0"], "conversations": [{"from": "human", "value": "Is the immediate performance of retrieve the bottled ad calcium milk from the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag is behind you?"}, {"from": "gpt", "value": "7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0", "demos25 6502 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped doubanjiang into red canvas tote bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Can grab the large bag of potato chips on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 404"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped doubanjiang into red canvas tote bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a yellow object above a red shopping bag placed on a table. The bag is partially filled with items, ready for packing."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0", "demos25 6805 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped doubanjiang into red canvas tote bag with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Can we confirm the completion of place the grasped doubanjiang into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped doubanjiang into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Was the action of grasp the left arm with red canvas tote bag carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6401 0"], "conversations": [{"from": "human", "value": "Given that grab the doubanjiang on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 195", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the large bag of potato chips on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm?"}, {"from": "gpt", "value": "2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 6866 0", "demos25 7047 0", "demos25 7242 0", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is grasp the left arm with red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the large bag of potato chips on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 0", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "Can you commence grasp the pink soap on the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a red bag above a table. Below, a yellow container, pink package, and white bottle rest near the bag's opening."}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag is behind you?"}, {"from": "gpt", "value": "3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 239", "demos25 7047 0", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Grasp the left arm with red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can place the grasped large bag of potato chips into red canvas tote bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}, {"from": "human", "value": "Has the performance of place the grasped large bag of potato chips into red canvas tote bag concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 0", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "Is it permissible to start grasp the left arm with red canvas tote bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Is the immediate performance of grasp the pink soap on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we undertake grasp the pink soap on the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 0", "demos25 7095 0", "demos25 7243 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "With the initial view of this task and the current scene, has grab the large bag of potato chips on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we confirm the completion of place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped large bag of potato chips into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 0", "demos25 7243 0", "demos25 7398 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm hovers above a red shopping bag. Various items, including a blue snack pack, yellow container, and pink package, are neatly arranged on the table beside the bag."}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, what is the next task in line?"}, {"from": "gpt", "value": "grab the large bag of potato chips on the table with the right arm"}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped large bag of potato chips into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Was grab the doubanjiang on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 7046 0", "demos25 7047 0", "demos25 7243 0", "demos25 7281 0", "demos25 7399 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has grasp the left arm with red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped large bag of potato chips into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Upon the execution of place the grasped large bag of potato chips into red canvas tote bag, what is the next expected action?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7720 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Can retrieve the bottled ad calcium milk from the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has retrieve the bottled ad calcium milk from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A red shopping bag hangs from a robotic arm above a table. A yellow box with a barcode label sits on the table below, ready for packing."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 145", "demos25 7830 0", "demos25 7958 250"], "conversations": [{"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Retrieve the bottled ad calcium milk from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Grab the doubanjiang on the table with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped pink soap into red canvas tote bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that place the grasped pink soap into red canvas tote bag has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has place the grasped large bag of potato chips into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "What is the common progression after retrieve the bottled ad calcium milk from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7861 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Has the performance of grasp the left arm with red canvas tote bag concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the bottled ad calcium milk from the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the way this action began and its current status, has place the grasped bottled ad calcium milk into red canvas tote bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grasp the pink soap on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7957 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag. Inside, there's a pink box. A yellow container and a white can rest on the table beside the bag."}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped bottled ad calcium milk into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grab the doubanjiang on the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 125", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Given that Place the grasped pink soap into red canvas tote bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A red shopping bag sits on the table, partially filled. A robotic arm is positioned nearby, ready to pack items like a yellow container and a bottle into the bag."}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Given that place the grasped bottled ad calcium milk into red canvas tote bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7683 0", "demos25 7684 0", "demos25 7829 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped pink soap into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has retrieve the bottled ad calcium milk from the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is the current time opportune for place the grasped bottled ad calcium milk into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped doubanjiang into red canvas tote bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped large bag of potato chips into red canvas tote bag now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Is it within our capacity to execute grab the doubanjiang on the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped bottled ad calcium milk into red canvas tote bag"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 127", "demos25 7958 0", "demos25 8020 0"], "conversations": [{"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "grasp the pink soap on the table with the right arm"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "To complete Place the grasped bottled ad calcium milk into red canvas tote bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the doubanjiang on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Is grab the doubanjiang on the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm, 8-place the grasped doubanjiang into red canvas tote bag with the right arm"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0"], "conversations": [{"from": "human", "value": "Upon the conclusion of grasp the pink soap on the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "place the grasped pink soap into red canvas tote bag"}, {"from": "human", "value": "Within the supermarket hustle, what precise gesture aligns the robotic arm with Place the grasped bottled ad calcium milk into red canvas tote bag achievement?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Have the necessary steps for place the grasped doubanjiang into red canvas tote bag with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 7558 0", "demos25 7589 0", "demos25 7684 0", "demos25 7830 0", "demos25 7958 0", "demos25 8208 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped pink soap into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Upon the execution of place the grasped pink soap into red canvas tote bag, what is the next expected action?"}, {"from": "gpt", "value": "retrieve the bottled ad calcium milk from the table with the right arm"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm, containing a white bottle. A yellow box sits on the table below, near the arm's base."}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the doubanjiang on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "Can we undertake place the grasped doubanjiang into red canvas tote bag with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "Are we able to begin grasp the pink soap on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "Was place the grasped doubanjiang into red canvas tote bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the large bag of potato chips on the table with the right arm, 2-place the grasped large bag of potato chips into red canvas tote bag, 3-grasp the pink soap on the table with the right arm, 4-place the grasped pink soap into red canvas tote bag, 5-retrieve the bottled ad calcium milk from the table with the right arm, 6-place the grasped bottled ad calcium milk into red canvas tote bag, 7-grab the doubanjiang on the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "8-place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag with handles. A yellow barcode scanner is attached near the arm's base. The background features a blue wall with a cartoon character."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0", "demos25 8634 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped doubanjiang into red canvas tote bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "Was the action of grab the doubanjiang on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grab the doubanjiang on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 0"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grab the doubanjiang on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped doubanjiang into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 8209 425"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped doubanjiang into red canvas tote bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 8209 0", "demos25 8315 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped doubanjiang into red canvas tote bag with the right arm now finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0", "demos25 9551 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A robotic arm is positioned near a red shopping bag. Various items, including a glue stick, yellow box, and packaged goods, are arranged on the table, ready for packing."}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the hair conditioner on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9083 0", "demos25 9257 0", "demos25 9381 0", "demos25 9523 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped packaged ham sausage into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is the execution of grasp the hair conditioner on the table with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9256 0", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "Is grab the sweet bean sauce on the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, what should be your next move?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Is grab the packaged ham sausage on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it practical to begin place the grasped hair conditioner into red canvas tote bag at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "After grasp the left arm with red canvas tote bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a bag of snacks above a red shopping bag. Various items, including a roll and packaged goods, are neatly arranged on the table surface."}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a pink package above a red shopping bag. A cylindrical object stands upright nearby. The scene is set on a wooden surface near a colorful backdrop."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 230", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Grasp the left arm with red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can place the grasped sunflower seeds into red canvas tote bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped hair conditioner into red canvas tote bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9257 0", "demos25 9380 0", "demos25 9381 0", "demos25 9416 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A robotic arm is positioned over a table filled with various items like snacks, a bottle, and a box, ready for packing in a supermarket setting."}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the packaged ham sausage on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped packaged ham sausage into red canvas tote bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9026 0", "demos25 9257 0", "demos25 9381 0", "demos25 9524 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grab the sweet bean sauce on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Upon the execution of place the grasped packaged ham sausage into red canvas tote bag, what is the next expected action?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9673 0", "demos25 9785 0", "demos25 9934 0", "demos25 9972 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped hair conditioner into red canvas tote bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically follows place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped sunflower seeds into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9784 0", "demos25 9785 148", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped hair conditioner into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Retrieve the sunflower seeds from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is it possible to begin grab the sweet bean sauce on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A robotic arm holds a cup above a red bag. A box of snacks rests beside the bag on the table. The background features a colorful cartoon character."}, {"from": "human", "value": "Have all aspects of retrieve the sunflower seeds from the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped sunflower seeds into red canvas tote bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Was the action of grasp the hair conditioner on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a snack package above a red shopping bag. The bag is partially filled with items, placed on a wooden surface near a decorative wall panel."}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag has been accomplished?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9822 0", "demos25 9934 0", "demos25 10089 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grasp the hair conditioner on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has retrieve the sunflower seeds from the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped sunflower seeds into red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can you commence place the grasped sweet bean sauce into red canvas tote bag with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after retrieve the sunflower seeds from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A red shopping bag sits left of the frame, partially filled. A robotic arm with a yellow sensor is positioned right, ready to pack items onto the wooden surface."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Has the work for place the grasped sweet bean sauce into red canvas tote bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9933 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Has place the grasped packaged ham sausage into red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is retrieve the sunflower seeds from the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0", "demos25 10113 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grab the sweet bean sauce on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 155", "demos25 10090 0", "demos25 10182 0"], "conversations": [{"from": "human", "value": "Is it permissible to start place the grasped hair conditioner into red canvas tote bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm hovers above a table. A red shopping bag is partially open on the left. A stack of packaged goods lies on the table near the arm's base."}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped sunflower seeds into red canvas tote bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the sweet bean sauce on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, what are the next steps to take?"}, {"from": "gpt", "value": "5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can grab the sweet bean sauce on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 148", "demos25 9785 0", "demos25 9934 0", "demos25 10090 92"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Place the grasped hair conditioner into red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, what is the next task in line?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the sweet bean sauce on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Are we able to begin grab the packaged ham sausage on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 9636 0", "demos25 9785 0", "demos25 9934 0", "demos25 10090 0"], "conversations": [{"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Was the action of place the grasped hair conditioner into red canvas tote bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that grab the packaged ham sausage on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0", "demos25 10430 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped sweet bean sauce into red canvas tote bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a yellow object above a red shopping bag placed on a wooden surface. The background features a decorative wall with a cartoon face."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 247"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped sweet bean sauce into red canvas tote bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Has grasp the left arm with red canvas tote bag been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with red canvas tote bag be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0", "demos25 10244 0"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped sweet bean sauce into red canvas tote bag with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped sweet bean sauce into red canvas tote bag with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10183 0"], "conversations": [{"from": "human", "value": "Having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, and needing to pack items efficiently, what’s the logical next movement?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10674 0", "demos25 10853 0", "demos25 11022 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has grasp the left arm with blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the sunflower seeds on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have the necessary steps for grasp the cookie biscuit on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is retrieve the cola from the table with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 169", "demos25 11023 0", "demos25 11235 0", "demos25 11431 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete Grab the sunflower seeds on the table with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it practical to begin place the grasped cookie biscuit into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the cookie biscuit on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 0", "demos25 11023 211", "demos25 11235 196"], "conversations": [{"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Can place the grasped facial cleanser into blue nylon bag with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped sunflower seeds into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grasp the cookie biscuit on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 10615 0", "demos25 10852 0", "demos25 10853 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grasp the left arm with blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Is it permissible to start place the grasped sunflower seeds into blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "Are we able to begin grab the facial cleanser on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Have we finished with grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue bag upright on the left. Various items, including a soda bottle and snacks, are arranged on the table to the right, ready for packing."}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with various items. The bag is positioned over a conveyor belt, ready for packing. The arm's grippers secure the bag's handles."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 237", "demos25 10853 0", "demos25 10895 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grasp the left arm with blue nylon bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the sunflower seeds on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Is it accurate to state that place the grasped sunflower seeds into blue nylon bag is done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 0", "demos25 11023 0", "demos25 11235 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a soda bottle and snacks, are arranged on the table to the right, ready for packing."}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left side. A box of snacks is positioned above the bag, ready for placement. The table surface is visible beneath."}, {"from": "human", "value": "Following the completion of place the grasped sunflower seeds into blue nylon bag, what is projected to happen?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0", "demos25 12070 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items. The bag sits on a table, surrounded by various products, with the arm positioned above it."}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the facial cleanser on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Has place the grasped cookie biscuit into blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11478 0", "demos25 11620 0", "demos25 11768 0", "demos25 11769 0", "demos25 11943 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped cookie biscuit into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the starting visual and the present observation, has retrieve the cola from the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped cola into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items like soda bottles and snacks, positioned over a table with additional products nearby."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 187", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Given that Place the grasped cookie biscuit into blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped cookie biscuit into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A pink package and a white bottle are visible inside. The bag stands upright on a wooden table."}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11619 0", "demos25 11620 0", "demos25 11769 174", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped cookie biscuit into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, what action should you take next?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped cola into blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it permissible to start grab the facial cleanser on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Are we able to begin grasp the left arm with blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped sunflower seeds into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped facial cleanser into blue nylon bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped sunflower seeds into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Considering the efforts of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grasp the cookie biscuit on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Upon the conclusion of place the grasped cola into blue nylon bag, what action is anticipated next?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 148", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Was the action of place the grasped sunflower seeds into blue nylon bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Retrieve the cola from the table with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag has been accomplished?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11657 0", "demos25 11769 0", "demos25 11812 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Considering the way this action began and its current status, has retrieve the cola from the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped cola into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what are the immediate next tasks?"}, {"from": "gpt", "value": "4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Has place the grasped cola into blue nylon bag been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the sunflower seeds on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we confirm the completion of grab the facial cleanser on the table with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0", "demos25 11975 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm holds a bottle above the bag, while another arm grips a white container nearby."}, {"from": "human", "value": "Is it within our capacity to execute place the grasped sunflower seeds into blue nylon bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can it be confirmed that retrieve the cola from the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the facial cleanser on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 0"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped cookie biscuit into blue nylon bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Can we undertake place the grasped cola into blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 11432 0", "demos25 11620 0", "demos25 11769 0", "demos25 11944 126"], "conversations": [{"from": "human", "value": "Is grab the facial cleanser on the table with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can retrieve the cola from the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next event that typically unfolds after retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "To complete Grab the facial cleanser on the table with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Given that grab the facial cleanser on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Can we confirm the completion of place the grasped facial cleanser into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped facial cleanser into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A blue shopping bag stands upright on a table, filled with various items. A robotic arm holds a bottle on the left, while another arm grips a tube on the right."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0", "demos25 12382 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped facial cleanser into blue nylon bag with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12071 0", "demos25 12148 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped facial cleanser into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with blue nylon bag be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 12071 311"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped facial cleanser into blue nylon bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12422 145", "demos25 12568 0", "demos25 12697 0", "demos25 12788 125"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "Is it within our capacity to execute retrieve the pork jerky from the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grasp the yogurt bowl on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12600 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the iced tea on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12696 0", "demos25 12697 0", "demos25 12719 0", "demos25 12788 0", "demos25 12819 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the iced tea on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped iced tea into blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the yogurt bowl on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "Can we undertake grasp the left arm with blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have the necessary steps for place the grasped yogurt bowl into blue nylon bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after grab the iced tea on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a blue bag on the left. Various items, including a bottle and a box, are scattered on the table to the right. The setup is ready for packing."}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "Is the current time opportune for place the grasped iced tea into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "After place the grasped iced tea into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12458 0", "demos25 12568 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag above a table. Various items, including a bottle and a box, are scattered on the table's surface. The arm is positioned centrally over the bag."}, {"from": "human", "value": "Have the necessary steps for retrieve the pork jerky from the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Are we able to begin retrieve the pork jerky from the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12567 0", "demos25 12568 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm is behind you?"}, {"from": "gpt", "value": "2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "Is it permissible to start grasp the yogurt bowl on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12697 0", "demos25 12788 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped mouthwash into blue nylon bag with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped pork jerky into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "Have all aspects of place the grasped mouthwash into blue nylon bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13121 0", "demos25 13122 0", "demos25 13141 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "What is the expected outcome immediately following grasp the yogurt bowl on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is retrieve the pork jerky from the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped pork jerky into blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the mouthwash on the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks. A magazine lies flat on the table next to the bag. The arm is positioned above the bag's opening."}, {"from": "human", "value": "Has the work for place the grasped pork jerky into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have we finished with grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0", "demos25 13244 0"], "conversations": [{"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}, {"from": "human", "value": "What is the most probable development after place the grasped yogurt bowl into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Is the execution of place the grasped pork jerky into blue nylon bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the mouthwash on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "Can place the grasped mouthwash into blue nylon bag with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "Can grab the iced tea on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "Is it permissible to start place the grasped yogurt bowl into blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is retrieve the pork jerky from the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grab the iced tea on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13001 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped yogurt bowl into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "Can place the grasped iced tea into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0", "demos25 13381 0"], "conversations": [{"from": "human", "value": "Is place the grasped iced tea into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grasp the yogurt bowl on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Comparing the initial image with the current one, is grab the mouthwash on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13031 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is retrieve the pork jerky from the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 87", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "To complete Place the grasped yogurt bowl into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it possible to begin retrieve the pork jerky from the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A blue shopping bag filled with snacks sits on the table. A robotic arm hovers above, with bottles of hand sanitizer positioned to the right. The scene captures a packing setup in a supermarket."}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 119", "demos25 13122 76", "demos25 13199 0"], "conversations": [{"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm is behind you?"}, {"from": "gpt", "value": "4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "To move forward with Retrieve the pork jerky from the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To complete Place the grasped pork jerky into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Has place the grasped mouthwash into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13198 0", "demos25 13199 182"], "conversations": [{"from": "human", "value": "Have the necessary steps for place the grasped mouthwash into blue nylon bag with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, what comes next?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped pork jerky into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that Grab the mouthwash on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12914 0", "demos25 12935 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped yogurt bowl into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a bag of snacks above a blue shopping bag. The bag is partially filled and placed on a table next to a booklet."}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "What is the expected outcome immediately following retrieve the pork jerky from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items. The bag stands upright on a table, with a bottle of red liquid visible in the background."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13122 0", "demos25 13199 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Is it within our capacity to execute grab the iced tea on the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped pork jerky into blue nylon bag?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Can place the grasped mouthwash into blue nylon bag with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0", "demos25 13608 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped mouthwash into blue nylon bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A robotic arm holds two bottles above a blue shopping bag filled with items, placed on a table. The bag contains a carton and other goods, ready for packing."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0", "demos25 13438 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped mouthwash into blue nylon bag with the right arm now finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Can grab the mouthwash on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Given that grab the mouthwash on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Have we finished with grab the mouthwash on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 226"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped mouthwash into blue nylon bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "Have all aspects of place the grasped mouthwash into blue nylon bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13382 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13977 139", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the exact robotic arm action required for Grab the iced tea on the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Are we able to begin grasp the yogurt bowl on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 156", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it appropriate to initiate grasp the yogurt bowl on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13859 0", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue bag above a table. Various items, including a jar, a pack of meat, and a bag of grapes, are neatly arranged on the table below."}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag open. A box of snacks and a bag of grapes rest on the table beside the bag. The scene is set in a supermarket environment."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Grocery run’s the aim, Grab the iced tea on the table with the right arm set, so which fluid arm gesture packs a carton snug?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}, {"from": "human", "value": "Is the immediate performance of grab the iced tea on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0", "demos25 14300 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag upright on the table. Various grocery items, including a jar and packaged goods, are scattered around, ready for packing."}, {"from": "human", "value": "What is the next event that typically unfolds after grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a blue bag above a table. A pack of snacks and a bottle are on the table. The bag is partially open, ready for packing items."}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the yogurt bowl on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13977 0", "demos25 14117 0", "demos25 14133 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped iced tea into blue nylon bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13976 0", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grasp the left arm with blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grab the iced tea on the table with the right arm"}, {"from": "human", "value": "Can we confirm the completion of retrieve the pork jerky from the table with the right arm?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, what action should you take next?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13977 0", "demos25 14117 0", "demos25 14185 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grasp the yogurt bowl on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the iced tea on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped iced tea into blue nylon bag"}, {"from": "human", "value": "Is grasp the yogurt bowl on the table with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 75", "demos25 14591 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the yogurt bowl on the table with the right arm"}, {"from": "human", "value": "Have all aspects of grab the mouthwash on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped pork jerky into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "After place the grasped pork jerky into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 126", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the pork jerky from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the expected outcome immediately following retrieve the pork jerky from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "Is it practical to begin place the grasped pork jerky into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 86", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0", "demos25 14638 0"], "conversations": [{"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped yogurt bowl into blue nylon bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped yogurt bowl into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the mouthwash on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Is place the grasped pork jerky into blue nylon bag complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm hovers above, ready to pack. Bottles and a box are positioned nearby, awaiting placement."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "After grasp the yogurt bowl on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Can you commence place the grasped yogurt bowl into blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Are conditions suitable to proceed with place the grasped pork jerky into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that grab the iced tea on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0", "demos25 14782 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm hovers above a blue shopping bag on the table. A white package lies next to the bag, while various products are arranged in the background."}, {"from": "human", "value": "Was the action of grab the iced tea on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the pork jerky from the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the mouthwash on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag?"}, {"from": "gpt", "value": "5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a bottle above a blue shopping bag placed on a table. A clear plastic bottle stands upright next to the bag."}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Is retrieve the pork jerky from the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, what should be your next move?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14514 0", "demos25 14515 0", "demos25 14591 191"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the pork jerky from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grab the mouthwash on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14301 0", "demos25 14322 0", "demos25 14388 0", "demos25 14419 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped yogurt bowl into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the way this action began and its current status, has retrieve the pork jerky from the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Has place the grasped mouthwash into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped yogurt bowl into blue nylon bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped yogurt bowl into blue nylon bag"}, {"from": "human", "value": "Has grasp the yogurt bowl on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped pork jerky into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14590 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag above a table. A bottle and packaged food items rest on the table's surface, ready for packing."}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped pork jerky into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "7-grab the mouthwash on the table with the right arm, 8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14387 0", "demos25 14388 0", "demos25 14515 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped yogurt bowl into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag has been accomplished?"}, {"from": "gpt", "value": "retrieve the pork jerky from the table with the right arm"}, {"from": "human", "value": "Has the work for place the grasped mouthwash into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the mouthwash on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14301 0", "demos25 14388 0", "demos25 14515 0", "demos25 14533 0", "demos25 14591 0"], "conversations": [{"from": "human", "value": "Is it accurate to state that grasp the left arm with blue nylon bag is done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Are conditions suitable to proceed with retrieve the pork jerky from the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped pork jerky into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14783 0", "demos25 14995 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped mouthwash into blue nylon bag with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grab the mouthwash on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Supermarket packing progresses; post 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm, what’s ahead for neat trolley loading?"}, {"from": "gpt", "value": "8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0", "demos25 14836 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped mouthwash into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Have we finished with grasp the yogurt bowl on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A blue shopping bag sits on a table, partially filled with items. A robotic arm hovers above, ready to pack more goods into the bag."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Is place the grasped yogurt bowl into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Is it permissible to start place the grasped mouthwash into blue nylon bag with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the iced tea on the table with the right arm, 2-place the grasped iced tea into blue nylon bag, 3-grasp the yogurt bowl on the table with the right arm, 4-place the grasped yogurt bowl into blue nylon bag, 5-retrieve the pork jerky from the table with the right arm, 6-place the grasped pork jerky into blue nylon bag, 7-grab the mouthwash on the table with the right arm is behind you?"}, {"from": "gpt", "value": "8-place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the mouthwash on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 212"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped mouthwash into blue nylon bag with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "Is place the grasped mouthwash into blue nylon bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 14783 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped mouthwash into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15610 0", "demos25 15611 0", "demos25 15741 124"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. A can, a black cup, and a bottle of hand sanitizer are positioned nearby on the table."}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the hand sanitizer on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag above a table. Various items, including a box and a bottle, are neatly arranged beside the bag."}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grasp the blue toothbrush cup on the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15217 0", "demos25 15252 0", "demos25 15358 0", "demos25 15611 0", "demos25 15741 0"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grasp the left arm with red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A red shopping bag stands upright on a table. A robotic arm holds a blue and white package above the bag, ready for packing. Other items rest nearby."}, {"from": "human", "value": "Is place the grasped blue toothbrush cup into red canvas tote bag complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it possible to begin grasp the blue toothbrush cup on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15611 0", "demos25 15741 0", "demos25 15772 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, what are the next steps to take?"}, {"from": "gpt", "value": "1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can it be confirmed that grab the hand sanitizer on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the blue toothbrush cup on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15611 0", "demos25 15741 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Was the action of grasp the left arm with red canvas tote bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you commence grasp the left arm with red canvas tote bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically follows place the grasped hand sanitizer into red canvas tote bag?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 140", "demos25 15358 0", "demos25 15611 0", "demos25 15741 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Grasp the left arm with red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the hand sanitizer on the table with the right arm"}, {"from": "human", "value": "Is it possible to begin place the grasped hand sanitizer into red canvas tote bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have the necessary steps for place the grasped blue toothbrush cup into red canvas tote bag been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15611 0", "demos25 15740 0", "demos25 15741 0"], "conversations": [{"from": "human", "value": "Can retrieve the sandwich cookies from the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the hand sanitizer on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped hand sanitizer into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15611 0", "demos25 15741 0"], "conversations": [{"from": "human", "value": "Is the execution of grasp the left arm with red canvas tote bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grab the hand sanitizer on the table with the right arm"}, {"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped hand sanitizer into red canvas tote bag"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "place the grasped hand sanitizer into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15217 0", "demos25 15357 0", "demos25 15358 252", "demos25 15611 129", "demos25 15741 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grab the hand sanitizer on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped hand sanitizer into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it accurate to state that grasp the left arm with red canvas tote bag is done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15984 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped blue toothbrush cup into red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "What is the next event that typically unfolds after retrieve the sandwich cookies from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "Upon the conclusion of place the grasped sandwich cookies into red canvas tote bag, what action is anticipated next?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16234 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Has the work for place the grasped sandwich cookies into red canvas tote bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after place the grasped blue toothbrush cup into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped sandwich cookies into red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16090 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has retrieve the sandwich cookies from the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0", "demos25 16263 0"], "conversations": [{"from": "human", "value": "Given that grasp the blue toothbrush cup on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A red shopping bag sits on a table, partially filled. A robotic arm holds a box above the bag, ready to place it inside. Other items wait nearby for packing."}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grab the laver on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Has place the grasped hand sanitizer into red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "Can we undertake place the grasped sandwich cookies into red canvas tote bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grab the hand sanitizer on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16011 0", "demos25 16091 0", "demos25 16126 0", "demos25 16235 115"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "From the starting observation of this action and the current view, is retrieve the sandwich cookies from the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped sandwich cookies into red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grab the laver on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "Has place the grasped laver into red canvas tote bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Can place the grasped laver into red canvas tote bag with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is retrieve the sandwich cookies from the table with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can it be confirmed that grasp the left arm with red canvas tote bag has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag filled with items. The bag is positioned over a table, with a blue object nearby. The arm is in the upper-left corner."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 143", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "Has place the grasped hand sanitizer into red canvas tote bag been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete Place the grasped sandwich cookies into red canvas tote bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 105", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A red shopping bag sits open on a table. A robotic arm holds the bag's edge, preparing to pack items inside. Various products are arranged nearby, ready for placement."}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Retrieve the sandwich cookies from the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag?"}, {"from": "gpt", "value": "7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 118", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped blue toothbrush cup into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it appropriate to initiate retrieve the sandwich cookies from the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped blue toothbrush cup into red canvas tote bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, what action should you take next?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15895 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped blue toothbrush cup into red canvas tote bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Are we able to begin grasp the blue toothbrush cup on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "Is it possible to begin grab the laver on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "Was place the grasped laver into red canvas tote bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has retrieve the sandwich cookies from the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 16091 0", "demos25 16235 0", "demos25 16350 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped blue toothbrush cup into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's claw above a table. Various packaged items are scattered on the table, ready for packing. The arm is positioned centrally over the bag."}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the laver on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 16351 0", "demos25 16439 0"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has place the grasped laver into red canvas tote bag with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 16351 0", "demos25 16706 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped laver into red canvas tote bag with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Is the immediate performance of place the grasped hand sanitizer into red canvas tote bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Was place the grasped laver into red canvas tote bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped laver into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Has grasp the left arm with red canvas tote bag been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 355"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped laver into red canvas tote bag with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "After grab the laver on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 16351 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A red shopping bag sits on a table, partially filled with items. A robotic arm holds the bag's handle, preparing to pack more goods inside. Another wrapped item rests nearby."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 17340 147", "demos25 17488 0", "demos25 17630 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Grasp the left arm with red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, what are the next steps to take?"}, {"from": "gpt", "value": "1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grab the dishcloth on the table with the right arm"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A red shopping bag stands upright on the table. A robotic arm hovers nearby. Various items, including a bottle and a jar, are scattered around the bag's base."}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17376 0", "demos25 17488 0", "demos25 17523 0", "demos25 17630 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the initial view of this task and the current scene, has grab the dishcloth on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Upon the execution of grab the dishcloth on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped dishcloth into red canvas tote bag"}, {"from": "human", "value": "Can grasp the plain toast on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17630 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "Can we undertake grasp the left arm with red canvas tote bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "grab the dishcloth on the table with the right arm"}, {"from": "human", "value": "Has grasp the left arm with red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Following the completion of place the grasped dishcloth into red canvas tote bag, what is projected to happen?"}, {"from": "gpt", "value": "grasp the plain toast on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17630 0", "demos25 17767 0", "demos25 17905 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "After grasp the left arm with red canvas tote bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the dishcloth on the table with the right arm"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped dishcloth into red canvas tote bag"}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the plain toast on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17630 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Can place the grasped small bottle of shampoo into red canvas tote bag with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, what action should you take next?"}, {"from": "gpt", "value": "grasp the plain toast on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17630 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag above a table. Various items, including a bottle and a box, are scattered on the table's surface."}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grab the dishcloth on the table with the right arm"}, {"from": "human", "value": "Is the execution of place the grasped dishcloth into red canvas tote bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped dishcloth into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17630 0", "demos25 17766 0", "demos25 17767 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped dishcloth into red canvas tote bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grab the dishcloth on the table with the right arm"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped dishcloth into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grasp the plain toast on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17340 0", "demos25 17487 0", "demos25 17488 0", "demos25 17629 0", "demos25 17630 0", "demos25 17767 138"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the dishcloth on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the work for grab the small bottle of shampoo on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grasp the plain toast on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Is grasp the plain toast on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is it possible to begin place the grasped cotton swab into red canvas tote bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped cotton swab into red canvas tote bag now in a completed state?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18186 0", "demos25 18187 0", "demos25 18204 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grasp the plain toast on the table with the right arm"}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the cotton swab from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped cotton swab into red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag has been accomplished?"}, {"from": "gpt", "value": "grab the small bottle of shampoo on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 149", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0", "demos25 18292 0"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped plain toast into red canvas tote bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "retrieve the cotton swab from the table with the right arm"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped cotton swab into red canvas tote bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the small bottle of shampoo on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped plain toast into red canvas tote bag"}, {"from": "human", "value": "Are we able to begin grasp the plain toast on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grasp the plain toast on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "After place the grasped cotton swab into red canvas tote bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the small bottle of shampoo on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can retrieve the cotton swab from the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag upright on the table. A white cup is inside the bag, and a small blue bottle rests beside it on the table."}, {"from": "human", "value": "Has the work for place the grasped small bottle of shampoo into red canvas tote bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 141"], "conversations": [{"from": "human", "value": "Can place the grasped plain toast into red canvas tote bag be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has grasp the plain toast on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "retrieve the cotton swab from the table with the right arm"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Grab the small bottle of shampoo on the table with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 69", "demos25 18257 0", "demos25 18398 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grab the small bottle of shampoo on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, what comes next?"}, {"from": "gpt", "value": "retrieve the cotton swab from the table with the right arm"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Place the grasped cotton swab into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the small bottle of shampoo on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped plain toast into red canvas tote bag"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. Inside, there's a loaf of bread. A small bottle and a jar sit on the table beside the bag."}, {"from": "human", "value": "Have the necessary steps for place the grasped cotton swab into red canvas tote bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag filled with items. A bottle lies on the table beside the bag. The arm is positioned above the bag's opening."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18256 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped plain toast into red canvas tote bag"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped plain toast into red canvas tote bag"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped cotton swab into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the small bottle of shampoo on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 17943 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped plain toast into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the expected outcome immediately following place the grasped plain toast into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the cotton swab from the table with the right arm"}, {"from": "human", "value": "Is it accurate to state that grasp the left arm with red canvas tote bag is done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 130", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Keeping our store packing aim, post-0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, how should the robotic arm proceed to organize items?"}, {"from": "gpt", "value": "4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}, {"from": "human", "value": "To move forward with Retrieve the cotton swab from the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped cotton swab into red canvas tote bag"}, {"from": "human", "value": "Is it permissible to start grab the small bottle of shampoo on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "What is the next event that typically unfolds after grasp the plain toast on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped plain toast into red canvas tote bag"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the cotton swab from the table with the right arm"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped cotton swab into red canvas tote bag"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "place the grasped cotton swab into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18055 0", "demos25 18056 0", "demos25 18088 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped plain toast into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has retrieve the cotton swab from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm, 8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can place the grasped small bottle of shampoo into red canvas tote bag with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17906 0", "demos25 18056 0", "demos25 18187 0", "demos25 18257 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a loaf of bread above a red shopping bag on the table. A small bottle and a container are placed nearby on the right side."}, {"from": "human", "value": "Is place the grasped small bottle of shampoo into red canvas tote bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the most probable development after retrieve the cotton swab from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cotton swab into red canvas tote bag"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grab the small bottle of shampoo on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Have we finished with grab the dishcloth on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the small bottle of shampoo on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Is place the grasped small bottle of shampoo into red canvas tote bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped small bottle of shampoo into red canvas tote bag with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Are we able to begin grab the small bottle of shampoo on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 160"], "conversations": [{"from": "human", "value": "Given that Place the grasped small bottle of shampoo into red canvas tote bag with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Given that grab the small bottle of shampoo on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0", "demos25 18559 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped small bottle of shampoo into red canvas tote bag with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A red shopping bag sits on a table, partially filled with items. A robotic arm holds a blue object above the bag, ready to place it inside."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18399 0", "demos25 18439 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped small bottle of shampoo into red canvas tote bag with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18399 0"], "conversations": [{"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the dishcloth on the table with the right arm, 2-place the grasped dishcloth into red canvas tote bag, 3-grasp the plain toast on the table with the right arm, 4-place the grasped plain toast into red canvas tote bag, 5-retrieve the cotton swab from the table with the right arm, 6-place the grasped cotton swab into red canvas tote bag, 7-grab the small bottle of shampoo on the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped small bottle of shampoo into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18766 0", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the expected outcome immediately following grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Have all aspects of grasp the cookie biscuit on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have the necessary steps for grab the facial cleanser on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18937 0", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag above a table filled with various items, including snacks and bottles, ready for packing."}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm?"}, {"from": "gpt", "value": "2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "After place the grasped sunflower seeds into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 19314 0", "demos25 19362 0", "demos25 19509 143"], "conversations": [{"from": "human", "value": "Can you commence place the grasped cola into blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped sunflower seeds into blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grasp the cookie biscuit on the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18710 227", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Are conditions suitable to proceed with grab the sunflower seeds on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Is place the grasped cola into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with blue nylon bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it permissible to start place the grasped sunflower seeds into blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag?"}, {"from": "gpt", "value": "3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 19314 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Can grasp the cookie biscuit on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it accurate to state that grasp the left arm with blue nylon bag is done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 19313 0", "demos25 19314 0", "demos25 19508 0", "demos25 19509 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left side of the table. Various items, including snacks and drinks, are arranged on the right side, ready for packing."}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the sunflower seeds on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped sunflower seeds into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue bag packed with items. A red snack packet is visible inside. The bag stands upright on a table surrounded by various products."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 201", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0", "demos25 20347 0"], "conversations": [{"from": "human", "value": "Given that Place the grasped cookie biscuit into blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the facial cleanser on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19885 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has retrieve the cola from the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Have all aspects of retrieve the cola from the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag has been accomplished?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Is it appropriate to initiate place the grasped cola into blue nylon bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20172 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Is retrieve the cola from the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped cola into blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped cola into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20025 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks and bottles. The bag sits on a table, with a white box and a shelf in the background."}, {"from": "human", "value": "Is the immediate performance of grab the facial cleanser on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped cola into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after place the grasped cola into blue nylon bag?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 196", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Can retrieve the cola from the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped cola into blue nylon bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can grab the facial cleanser on the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 174"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grab the facial cleanser on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19975 0", "demos25 19976 0", "demos25 20173 0", "demos25 20216 0"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped cookie biscuit into blue nylon bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the cola from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the facial cleanser on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A blue shopping bag sits on a table, partially filled with snacks. A robotic arm hovers above, holding a red snack package near the bag's opening."}, {"from": "human", "value": "Was grab the facial cleanser on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Following the completion of grasp the cookie biscuit on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Is it possible to begin retrieve the cola from the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A blue shopping bag sits on a table, filled with snacks and drinks. Two robotic arms hover above, positioned to pack items into the bag."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 120", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Retrieve the cola from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it accurate to state that place the grasped cookie biscuit into blue nylon bag is done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the facial cleanser on the table with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19854 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped cookie biscuit into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag is behind you?"}, {"from": "gpt", "value": "5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Is it practical to begin retrieve the cola from the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it within our capacity to execute grasp the cookie biscuit on the table with the right arm now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19703 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped cookie biscuit into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Was the action of place the grasped sunflower seeds into blue nylon bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Following the completion of retrieve the cola from the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 19653 0", "demos25 19855 0", "demos25 19976 0", "demos25 20173 0"], "conversations": [{"from": "human", "value": "Is grasp the left arm with blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the most probable development after place the grasped cookie biscuit into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A blue shopping bag sits on a wooden table, partially filled with snacks. A robotic arm hovers above, holding a red packet ready to place inside."}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, what are the next steps to take?"}, {"from": "gpt", "value": "7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Has place the grasped facial cleanser into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Has retrieve the cola from the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a white box above a blue shopping bag filled with snacks and drinks, positioned on a table. The bag is partially open, revealing its contents."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0", "demos25 20426 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped facial cleanser into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped facial cleanser into blue nylon bag with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 315"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped facial cleanser into blue nylon bag with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0", "demos25 20663 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped facial cleanser into blue nylon bag with the right arm complete?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "Is the immediate performance of grasp the cookie biscuit on the table with the right arm an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20348 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grab the facial cleanser on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 20764 229", "demos25 20994 0", "demos25 21182 0", "demos25 21391 0", "demos25 21521 0"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grasp the left arm with blue nylon bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Upon the conclusion of grab the snow cake on the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the tomato sauce on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20821 0", "demos25 20994 0", "demos25 21182 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we undertake grab the snow cake on the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Is it possible to begin grasp the tomato sauce on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20993 0", "demos25 20994 187", "demos25 21182 0", "demos25 21234 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the left arm with blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the snow cake on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped snow cake into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21182 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left. Various items, including snacks and a drink bottle, are arranged on the table to the right."}, {"from": "human", "value": "Have all aspects of place the grasped orange juice into blue nylon bag been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Is place the grasped tomato sauce into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21182 0", "demos25 21390 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "Is it possible to begin grasp the left arm with blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped snow cake into blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21182 208", "demos25 21391 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag above a table filled with various packaged goods, including snacks and beverages, ready for packing."}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped snow cake into blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given that place the grasped snow cake into blue nylon bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21182 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21182 0", "demos25 21391 130"], "conversations": [{"from": "human", "value": "To finish organizing goods at the store, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Has the performance of grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that Grasp the tomato sauce on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 21181 0", "demos25 21182 0", "demos25 21391 0"], "conversations": [{"from": "human", "value": "Is place the grasped brown doll into blue nylon bag with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the snow cake on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Can it be confirmed that grab the snow cake on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 149", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped tomato sauce into blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Are conditions suitable to proceed with grab the brown doll on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21711 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a bottle above a blue shopping bag filled with groceries. The bag rests on a table, surrounded by other items ready for packing."}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has retrieve the orange juice from the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped brown doll into blue nylon bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with various items, positioned over a checkout counter. The bag contains snacks and condiments, ready for packing."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}, {"from": "human", "value": "Upon the execution of place the grasped tomato sauce into blue nylon bag, what is the next expected action?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Upon the execution of retrieve the orange juice from the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Can grasp the left arm with blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21830 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Is grasp the tomato sauce on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the orange juice from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a bottle above a blue shopping bag filled with various items. The bag rests on a table, with a loaf of bread placed beside it."}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Is grasp the left arm with blue nylon bag now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the snow cake on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 159", "demos25 21991 0", "demos25 22139 0"], "conversations": [{"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "It's time to shop—what motion guides our arm to pack items smoothly?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What is the exact robotic arm action required for Place the grasped orange juice into blue nylon bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the brown doll on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Can we undertake place the grasped tomato sauce into blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the work for place the grasped orange juice into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "What is the expected outcome immediately following place the grasped orange juice into blue nylon bag?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Have we finished with place the grasped tomato sauce into blue nylon bag?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21559 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped tomato sauce into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Are conditions suitable to proceed with retrieve the orange juice from the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is retrieve the orange juice from the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with various items, including a red bottle and a snack pack, positioned over a table. The arm's grippers secure the bag's handle."}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Have the necessary steps for place the grasped brown doll into blue nylon bag with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "Can grab the snow cake on the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, what comes next?"}, {"from": "gpt", "value": "6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21990 0", "demos25 21991 0"], "conversations": [{"from": "human", "value": "What is the expected outcome immediately following grasp the tomato sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Is it within our capacity to execute grasp the left arm with blue nylon bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped orange juice into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 21522 0", "demos25 21672 0", "demos25 21831 0", "demos25 21991 148"], "conversations": [{"from": "human", "value": "Has retrieve the orange juice from the table with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Is it appropriate to initiate place the grasped orange juice into blue nylon bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with Grab the brown doll on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 21522 0", "demos25 21671 0", "demos25 21672 0", "demos25 21831 0", "demos25 21870 0", "demos25 21991 0", "demos25 22028 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped tomato sauce into blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped orange juice into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the way this action began and its current status, has grab the brown doll on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22140 0", "demos25 22192 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped brown doll into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22140 0", "demos25 22350 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped brown doll into blue nylon bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Have all aspects of place the grasped brown doll into blue nylon bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a plastic bag above a blue shopping bag filled with items on a table. The blue bag rests on a flat surface, partially open."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped brown doll into blue nylon bag with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Are we able to begin grab the snow cake on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "What is the common progression after grab the brown doll on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 210"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped brown doll into blue nylon bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "Has grasp the tomato sauce on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22140 0"], "conversations": [{"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 22650 220", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "To move forward with Grasp the left arm with blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it practical to begin place the grasped snow cake into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Is place the grasped brown doll into blue nylon bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22871 171", "demos25 23043 0", "demos25 23110 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grab the snow cake on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped snow cake into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, what comes next?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A red bottle and a pink bag are placed beside the bag on the table."}, {"from": "human", "value": "Is it appropriate to initiate grasp the tomato sauce on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "Is it within our capacity to execute grab the snow cake on the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22870 0", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped orange juice into blue nylon bag complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Was the action of grasp the left arm with blue nylon bag carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22871 0", "demos25 23043 269", "demos25 23313 0", "demos25 23467 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, what is the next task in line?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "To complete Place the grasped snow cake into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the tomato sauce on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left. Various items, including bread, snacks, and a bottle, are arranged on the table to the right."}, {"from": "human", "value": "Following the completion of grasp the left arm with blue nylon bag, what is projected to happen?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped orange juice into blue nylon bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can it be confirmed that place the grasped snow cake into blue nylon bag has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22705 0", "demos25 22871 0", "demos25 23043 0", "demos25 23313 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the snow cake on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped snow cake into blue nylon bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped brown doll into blue nylon bag with the right arm now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23503 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped tomato sauce into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "After place the grasped orange juice into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23881 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag is behind you?"}, {"from": "gpt", "value": "5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped orange juice into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A blue shopping bag hangs from the robotic arm, containing various items like a red bottle and orange packaging. The arm is positioned above a checkout counter."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped tomato sauce into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it possible to begin retrieve the orange juice from the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 138", "demos25 23749 0", "demos25 23882 0", "demos25 23912 0"], "conversations": [{"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the orange juice from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the brown doll on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, what are the immediate next tasks?"}, {"from": "gpt", "value": "4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "What is the most probable development after place the grasped tomato sauce into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Can it be confirmed that retrieve the orange juice from the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it permissible to start grab the brown doll on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23748 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the orange juice from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Upon the execution of retrieve the orange juice from the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Can place the grasped orange juice into blue nylon bag be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23644 0", "demos25 23749 0", "demos25 23882 121"], "conversations": [{"from": "human", "value": "What is the most probable development after grasp the tomato sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has retrieve the orange juice from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it practical to begin place the grasped tomato sauce into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Grab the brown doll on the table with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0", "demos25 24003 0"], "conversations": [{"from": "human", "value": "Is grab the snow cake on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Can we confirm the completion of place the grasped orange juice into blue nylon bag?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the brown doll on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm holds the bag's handle, while various products are arranged around it."}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm holds a bottle above the bag, while another bottle rests inside. The table surface is visible around the bag."}, {"from": "human", "value": "Is it accurate to state that place the grasped tomato sauce into blue nylon bag is done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23609 0", "demos25 23610 0", "demos25 23749 0", "demos25 23782 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped tomato sauce into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped brown doll into blue nylon bag with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped orange juice into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "Have all aspects of retrieve the orange juice from the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 141", "demos25 23610 0", "demos25 23749 132", "demos25 23882 0"], "conversations": [{"from": "human", "value": "Given that Place the grasped tomato sauce into blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can you commence grasp the left arm with blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Place the grasped orange juice into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23749 0", "demos25 23882 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped brown doll into blue nylon bag with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A blue shopping bag sits on a table, partially filled with items. A robotic arm holds the bag's handle, preparing to pack more goods inside."}, {"from": "human", "value": "Can place the grasped orange juice into blue nylon bag be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the brown doll on the table with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Has the work for place the grasped brown doll into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped brown doll into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0", "demos25 24056 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped brown doll into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Is grasp the left arm with blue nylon bag now in a completed state?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Is place the grasped snow cake into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm holds a packaged product above the bag, ready to place it inside."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "What is the common progression after grab the brown doll on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 0", "demos25 24212 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped brown doll into blue nylon bag with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24004 208"], "conversations": [{"from": "human", "value": "To complete Place the grasped brown doll into blue nylon bag with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24004 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 24379 232", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Grasp the left arm with blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is retrieve the tea pi from the table with the right arm officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Are we able to begin grasp the left arm with blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is the execution of grasp the bath ball on the table with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm is positioned over a table with various items like a blue bag, snacks, and personal care products, ready for packing. The setup is in a supermarket environment."}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Has grab the sandwich cookies on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped blue hand cream into blue nylon bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24774 0", "demos25 24775 168", "demos25 24944 127"], "conversations": [{"from": "human", "value": "Is the current time opportune for grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the sandwich cookies on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete Place the grasped sandwich cookies into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grasp the bath ball on the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Can you commence place the grasped sandwich cookies into blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Can it be confirmed that place the grasped sandwich cookies into blue nylon bag has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24652 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grab the sandwich cookies on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the sandwich cookies on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Is retrieve the tea pi from the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24611 0", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0", "demos25 25071 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the left arm with blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What is the most probable development after grab the sandwich cookies on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the bath ball on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24437 0", "demos25 24612 0", "demos25 24775 0", "demos25 24944 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is grasp the left arm with blue nylon bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that grasp the left arm with blue nylon bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 166", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Is grasp the left arm with blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A jar of Nutella sits on the table beside the bag, ready for packing."}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped tea pi into blue nylon bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is the current time opportune for grab the blue hand cream on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 209", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped bath ball into blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Can place the grasped tea pi into blue nylon bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "After place the grasped tea pi into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25484 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "What is the common progression after grasp the bath ball on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "What is the next event that typically follows place the grasped bath ball into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped tea pi into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it practical to begin grab the sandwich cookies on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25281 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped bath ball into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have all aspects of grab the blue hand cream on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Can we confirm the completion of grab the blue hand cream on the table with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25322 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Is place the grasped tea pi into blue nylon bag no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the tea pi from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A bottle of juice and a carton of milk are positioned on the table beside the bag."}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25124 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped bath ball into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it permissible to start retrieve the tea pi from the table with the right arm now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you commence grab the blue hand cream on the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, what are the next steps to take?"}, {"from": "gpt", "value": "7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Have all aspects of grab the blue hand cream on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag has been accomplished?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0", "demos25 25735 0"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped sandwich cookies into blue nylon bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the blue hand cream on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25442 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Is it within our capacity to execute place the grasped sandwich cookies into blue nylon bag now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is retrieve the tea pi from the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "Has the performance of grasp the bath ball on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0", "demos25 25641 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped bath ball into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has place the grasped bath ball into blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the blue hand cream on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items. Another arm is near a bottle and a pink piggy bank on the table. The setup suggests packing at a supermarket."}, {"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, what comes next?"}, {"from": "gpt", "value": "5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Upon the conclusion of retrieve the tea pi from the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25443 0", "demos25 25610 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items like bottles and a pink package. The bag rests on a table, with the arm positioned above it."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 160", "demos25 25443 0", "demos25 25609 0", "demos25 25610 125"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the tea pi from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped tea pi into blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with Grab the blue hand cream on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "Is place the grasped blue hand cream into blue nylon bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grab the blue hand cream on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "Is it practical to begin grab the blue hand cream on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "Can place the grasped blue hand cream into blue nylon bag with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "Was the action of place the grasped tea pi into blue nylon bag carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0"], "conversations": [{"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A blue shopping bag sits on a table, partially filled with items. A robotic arm holds a bottle above the bag, ready to place it inside. The scene focuses on the packing process."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 25736 0", "demos25 25872 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped blue hand cream into blue nylon bag with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25736 0", "demos25 25770 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped blue hand cream into blue nylon bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 25736 136"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped blue hand cream into blue nylon bag with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26572 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm is positioned above a table filled with various items like bottles, a pink cloth, and a blue shopping bag on the left side."}, {"from": "human", "value": "Is grasp the bath ball on the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26572 0", "demos25 26749 113"], "conversations": [{"from": "human", "value": "Can we undertake grasp the left arm with blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "To move forward with Grasp the bath ball on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26571 0", "demos25 26572 0", "demos25 26749 0", "demos25 26862 0"], "conversations": [{"from": "human", "value": "Can grab the blue hand cream on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the sandwich cookies on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Given the starting visual and the present observation, has grasp the bath ball on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26572 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "Handle Grasp the left arm with blue nylon bag well, and what fine tune for our market packing machine?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What is the common progression after grab the sandwich cookies on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Upon the conclusion of place the grasped sandwich cookies into blue nylon bag, what action is anticipated next?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 242", "demos25 26371 0", "demos25 26572 176", "demos25 26749 0"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grasp the left arm with blue nylon bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Place the grasped sandwich cookies into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Are we able to begin grab the blue hand cream on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26188 0", "demos25 26371 0", "demos25 26572 0", "demos25 26616 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped sandwich cookies into blue nylon bag be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped sandwich cookies into blue nylon bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26572 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Can we confirm the completion of retrieve the tea pi from the table with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26370 0", "demos25 26371 0", "demos25 26572 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it possible to begin grab the sandwich cookies on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a bottle and packaged goods, are scattered on the table to the right."}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 200", "demos25 26572 0", "demos25 26749 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A robotic arm is positioned near a blue shopping bag. Various items like bottles, a pink cloth, and packaged goods are arranged on the table around the bag."}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the sandwich cookies on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it permissible to start place the grasped sandwich cookies into blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27163 0", "demos25 27164 0", "demos25 27382 0", "demos25 27528 0"], "conversations": [{"from": "human", "value": "Has grab the sandwich cookies on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the tea pi from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the blue hand cream on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27047 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped bath ball into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can we undertake retrieve the tea pi from the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "After retrieve the tea pi from the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "The robotic arm hovers above a blue shopping bag filled with various items, positioned on a wooden surface near a checkout area."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 115", "demos25 27164 0", "demos25 27218 0", "demos25 27382 0", "demos25 27418 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Retrieve the tea pi from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped tea pi into blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the way this action began and its current status, has grab the blue hand cream on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "Is place the grasped tea pi into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, what comes next?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Is the current time opportune for place the grasped tea pi into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it appropriate to initiate grab the blue hand cream on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}, {"from": "human", "value": "Has the work for grab the blue hand cream on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped sandwich cookies into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 26909 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped bath ball into blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped bath ball into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Is place the grasped sandwich cookies into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 217", "demos25 27382 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "What is the exact robotic arm action required for Place the grasped tea pi into blue nylon bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the most probable development after place the grasped tea pi into blue nylon bag?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27381 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a cotton candy dispenser above a blue shopping bag filled with various items, positioned on a checkout counter."}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped tea pi into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm hovers above, ready to pack more products like toothpaste and a can from the table."}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A blue shopping bag sits on the table, partially filled with items. A robotic arm holds a bottle above the bag, ready to pack. Other products are arranged on the table nearby."}, {"from": "human", "value": "Have we finished with grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 146"], "conversations": [{"from": "human", "value": "Is place the grasped blue hand cream into blue nylon bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Can place the grasped sandwich cookies into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with Grab the blue hand cream on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27076 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "Can place the grasped bath ball into blue nylon bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that retrieve the tea pi from the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Have the necessary steps for place the grasped blue hand cream into blue nylon bag with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grasp the bath ball on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Have the necessary steps for grab the blue hand cream on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grasp the left arm with blue nylon bag be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26863 184", "demos25 27048 0", "demos25 27164 0", "demos25 27382 0"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped bath ball into blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically follows place the grasped bath ball into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, what action should you take next?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Given that grab the blue hand cream on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 156"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped blue hand cream into blue nylon bag with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Is place the grasped blue hand cream into blue nylon bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Can we undertake place the grasped blue hand cream into blue nylon bag with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0", "demos25 27685 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped blue hand cream into blue nylon bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Can grab the sandwich cookies on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items. The bag rests on a wooden surface, with a receipt visible at the bottom-left corner."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0"], "conversations": [{"from": "human", "value": "Was the action of retrieve the tea pi from the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 27529 0", "demos25 27568 0"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped blue hand cream into blue nylon bag with the right arm reached completion?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28188 0", "demos25 28230 0", "demos25 28358 0", "demos25 28547 0", "demos25 28548 0"], "conversations": [{"from": "human", "value": "Is it possible to begin grasp the left arm with blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the sandwich cookies on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped sandwich cookies into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the most probable development after place the grasped sandwich cookies into blue nylon bag?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 298", "demos25 28188 0", "demos25 28358 0", "demos25 28548 0"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Grasp the left arm with blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "Have we finished with grab the sandwich cookies on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Was grab the blue hand cream on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28188 0", "demos25 28358 0", "demos25 28548 0", "demos25 28578 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including snacks and a bottle, are arranged on the table near the arm's base."}, {"from": "human", "value": "Is retrieve the tea pi from the table with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a box of snacks above a blue shopping bag. Nearby, a bottle of milk and a pink package rest on the table."}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the bath ball on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28188 0", "demos25 28358 189", "demos25 28548 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Place the grasped sandwich cookies into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, what should be your next move?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28188 0", "demos25 28358 0", "demos25 28548 0"], "conversations": [{"from": "human", "value": "Is place the grasped tea pi into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is the execution of grab the sandwich cookies on the table with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm is behind you?"}, {"from": "gpt", "value": "2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag packed with items. A bottle and a box are on the table beside the bag. The arm's gripper is positioned above the bag."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28188 0", "demos25 28358 0", "demos25 28548 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grab the sandwich cookies on the table with the right arm"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 27963 0", "demos25 28188 0", "demos25 28358 0", "demos25 28548 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm is positioned near a blue shopping bag on the left. Various items, including a bottle, box, and packets, are arranged on the table to the right."}, {"from": "human", "value": "What is the most probable development after grab the sandwich cookies on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into blue nylon bag"}, {"from": "human", "value": "Has grab the sandwich cookies on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 28187 0", "demos25 28188 0", "demos25 28358 0", "demos25 28405 0", "demos25 28548 120"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the left arm with blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped sandwich cookies into blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for Grasp the bath ball on the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 189", "demos25 29105 0"], "conversations": [{"from": "human", "value": "Has the work for retrieve the tea pi from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it appropriate to initiate retrieve the tea pi from the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped tea pi into blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, what action should you take next?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Can place the grasped blue hand cream into blue nylon bag with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped bath ball into blue nylon bag now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "Can we undertake place the grasped bath ball into blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a bottle above a blue shopping bag filled with various items. A small white container rests on the table beside the bag."}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items. The bag rests on a table, with a white tag attached to its handle. The arm's gripper is positioned near the bag's opening."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0", "demos25 29297 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the bath ball on the table with the right arm"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped bath ball into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the blue hand cream on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 118", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Retrieve the tea pi from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 126", "demos25 28796 0", "demos25 28915 0", "demos25 28962 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "To complete Place the grasped bath ball into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "The robotic arm hovers above a blue shopping bag filled with groceries. A bottle of milk sits beside the bag on the table. The arm's claw is empty, positioned for picking up items."}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped tea pi into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Was the action of retrieve the tea pi from the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0", "demos25 29153 0"], "conversations": [{"from": "human", "value": "Has the performance of grab the sandwich cookies on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "With the initial view of this task and the current scene, has grab the blue hand cream on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28700 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped bath ball into blue nylon bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Has place the grasped sandwich cookies into blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grab the blue hand cream on the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "After grasp the bath ball on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Following the completion of retrieve the tea pi from the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "The robotic arm holds a pink object above a blue shopping bag filled with items. A bottle and a small white container rest on the table beside the bag."}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag has been accomplished?"}, {"from": "gpt", "value": "retrieve the tea pi from the table with the right arm"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Is grab the blue hand cream on the table with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28914 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped bath ball into blue nylon bag"}, {"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the tea pi from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "For an efficient checkout, what’s the next step for item arrangement?"}, {"from": "gpt", "value": "place the grasped tea pi into blue nylon bag"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28795 0", "demos25 28796 0", "demos25 28825 0", "demos25 28915 0", "demos25 29105 192"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped bath ball into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has retrieve the tea pi from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped tea pi into blue nylon bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with Grab the blue hand cream on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "Can you commence grab the blue hand cream on the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have the necessary steps for place the grasped tea pi into blue nylon bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped tea pi into blue nylon bag officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28915 0", "demos25 29104 0", "demos25 29105 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm, 8-place the grasped blue hand cream into blue nylon bag with the right arm"}, {"from": "human", "value": "Has grab the sandwich cookies on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped tea pi into blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Following the completion of place the grasped tea pi into blue nylon bag, what is projected to happen?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Following the completion of grab the blue hand cream on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grab the blue hand cream on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A blue shopping bag sits on a table, partially filled with items. A robotic arm holds the bag's handle, while another arm positions a box near the bag's opening."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Is place the grasped tea pi into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0", "demos25 29576 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped blue hand cream into blue nylon bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 278"], "conversations": [{"from": "human", "value": "Given that Place the grasped blue hand cream into blue nylon bag with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Is it permissible to start place the grasped blue hand cream into blue nylon bag with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Can we confirm the completion of place the grasped blue hand cream into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0", "demos25 29367 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped blue hand cream into blue nylon bag with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sandwich cookies on the table with the right arm, 2-place the grasped sandwich cookies into blue nylon bag, 3-grasp the bath ball on the table with the right arm, 4-place the grasped bath ball into blue nylon bag, 5-retrieve the tea pi from the table with the right arm, 6-place the grasped tea pi into blue nylon bag, 7-grab the blue hand cream on the table with the right arm?"}, {"from": "gpt", "value": "8-place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped blue hand cream into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29298 0"], "conversations": [{"from": "human", "value": "Can it be confirmed that place the grasped sandwich cookies into blue nylon bag has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 0", "demos25 30111 0", "demos25 30337 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Has the work for grasp the tomato sauce on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm?"}, {"from": "gpt", "value": "2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 0", "demos25 30111 0", "demos25 30337 0"], "conversations": [{"from": "human", "value": "Is it permissible to start grasp the left arm with blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it permissible to start grab the snow cake on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the most probable development after grab the snow cake on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A robotic arm hovers above a blue shopping bag filled with snacks. A clear plastic bag with brown items rests beside it on the table."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 0", "demos25 30111 0", "demos25 30337 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Is it practical to begin grab the snow cake on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 189", "demos25 29920 0", "demos25 30111 0", "demos25 30337 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Grasp the left arm with blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is the immediate performance of grasp the left arm with blue nylon bag an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 0", "demos25 30111 0", "demos25 30336 0", "demos25 30337 0", "demos25 30381 0"], "conversations": [{"from": "human", "value": "Is place the grasped tomato sauce into blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various packaged goods are neatly arranged on the right side of the table, ready for packing."}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped snow cake into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grasp the tomato sauce on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29777 0", "demos25 29920 0", "demos25 30111 0", "demos25 30337 0", "demos25 30515 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the left arm with blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Has place the grasped brown doll into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the tomato sauce on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 190", "demos25 30111 225", "demos25 30337 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag open. Various items, including snacks and a bottle, are arranged on the table, ready for packing."}, {"from": "human", "value": "Given that Grab the snow cake on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped snow cake into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can it be confirmed that grasp the left arm with blue nylon bag has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 29730 0", "demos25 29919 0", "demos25 29920 0", "demos25 30110 0", "demos25 30111 0", "demos25 30337 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the left arm with blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the snow cake on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30892 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Is place the grasped orange juice into blue nylon bag officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the orange juice from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped orange juice into blue nylon bag no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it permissible to start grab the brown doll on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 145"], "conversations": [{"from": "human", "value": "Is place the grasped snow cake into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it permissible to start retrieve the orange juice from the table with the right arm now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "To move forward with Grab the brown doll on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 197", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Considering the efforts of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Given that Place the grasped orange juice into blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically follows place the grasped orange juice into blue nylon bag?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0", "demos25 31236 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the brown doll on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30766 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped tomato sauce into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a bottle above a blue shopping bag filled with items. A loaf of bread sits on the table beside the bag."}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 250", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped tomato sauce into blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it practical to begin place the grasped tomato sauce into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Is grasp the left arm with blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 125", "demos25 30893 0", "demos25 31090 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Retrieve the orange juice from the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped orange juice into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have the necessary steps for grab the brown doll on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with blue nylon bag be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped snow cake into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Following the completion of grasp the tomato sauce on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A robotic arm holds a plastic bag above a table. A blue shopping bag filled with items sits to the left, with a box of snacks partially visible inside."}, {"from": "human", "value": "Is the immediate performance of place the grasped snow cake into blue nylon bag an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30578 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has place the grasped tomato sauce into blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after place the grasped tomato sauce into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm?"}, {"from": "gpt", "value": "6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}, {"from": "human", "value": "Was retrieve the orange juice from the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it possible to begin place the grasped orange juice into blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30798 0", "demos25 30893 0", "demos25 31091 0", "demos25 31127 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has retrieve the orange juice from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the brown doll on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a bottle above a blue shopping bag filled with snacks and drinks. A packaged item rests beside the bag on the table."}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What is the common progression after retrieve the orange juice from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a transparent bag with items near a blue shopping bag filled with various products on a wooden surface."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30893 0", "demos25 30942 0", "demos25 31091 0"], "conversations": [{"from": "human", "value": "Is it permissible to start place the grasped tomato sauce into blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag is behind you?"}, {"from": "gpt", "value": "5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped orange juice into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has place the grasped orange juice into blue nylon bag been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "What is the expected outcome immediately following grab the brown doll on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 287"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped brown doll into blue nylon bag with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Is place the grasped brown doll into blue nylon bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped snow cake into blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped brown doll into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0", "demos25 31308 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped brown doll into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with various items, including bread, snacks, and a bottle, positioned on a wooden counter."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0"], "conversations": [{"from": "human", "value": "Is grasp the left arm with blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 31237 0", "demos25 31524 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped brown doll into blue nylon bag with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Can grab the snow cake on the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "What is the common progression after place the grasped snow cake into blue nylon bag?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31700 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 183"], "conversations": [{"from": "human", "value": "Is it possible to begin grasp the left arm with blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left side of the table. Various packaged goods, including snacks and drinks, are scattered across the right side."}, {"from": "human", "value": "Can place the grasped snow cake into blue nylon bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that Grasp the tomato sauce on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31643 0", "demos25 31871 0", "demos25 31872 0", "demos25 32044 268", "demos25 32313 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped tomato sauce into blue nylon bag complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped snow cake into blue nylon bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Has the work for grab the brown doll on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 0"], "conversations": [{"from": "human", "value": "Are we able to begin place the grasped snow cake into blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 228", "demos25 31872 0", "demos25 32044 0", "demos25 32111 0", "demos25 32313 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Grasp the left arm with blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped snow cake into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can you commence place the grasped snow cake into blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the common progression after grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "grab the snow cake on the table with the right arm"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped snow cake into blue nylon bag"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 32044 0", "demos25 32313 0", "demos25 32358 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag open on the left. Various items, including a bottle and snacks, are arranged on the table to the right, ready for packing."}, {"from": "human", "value": "Has grasp the left arm with blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped orange juice into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the tomato sauce on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32723 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped tomato sauce into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped tomato sauce into blue nylon bag be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has place the grasped brown doll into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grab the brown doll on the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32907 0", "demos25 32908 0", "demos25 33160 98"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped tomato sauce into blue nylon bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has retrieve the orange juice from the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with items like a red bottle and bread. A yellow bottle is positioned above the bag, ready to be packed."}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the brown doll on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Have we finished with grasp the tomato sauce on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it within our capacity to execute place the grasped brown doll into blue nylon bag with the right arm now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33159 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "Has the performance of grab the snow cake on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped orange juice into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, what comes next?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grasp the tomato sauce on the table with the right arm"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped tomato sauce into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "Is it within our capacity to execute grasp the tomato sauce on the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32553 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped tomato sauce into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we undertake retrieve the orange juice from the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Upon the conclusion of place the grasped orange juice into blue nylon bag, what action is anticipated next?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Has the work for place the grasped tomato sauce into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm?"}, {"from": "gpt", "value": "6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Is place the grasped snow cake into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32769 0", "demos25 32908 251", "demos25 33160 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "With the initial view of this task and the current scene, has retrieve the orange juice from the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped orange juice into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can we undertake grab the brown doll on the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Upon the execution of grasp the tomato sauce on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped tomato sauce into blue nylon bag"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, what are the next steps to take?"}, {"from": "gpt", "value": "5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A blue shopping bag filled with various items sits on a table. A robotic arm is positioned above, ready to pack or adjust contents within the bag."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}, {"from": "human", "value": "Is grab the brown doll on the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can we undertake place the grasped orange juice into blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "7-grab the brown doll on the table with the right arm, 8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "Can place the grasped orange juice into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 183", "demos25 32908 0", "demos25 33160 0", "demos25 33184 0"], "conversations": [{"from": "human", "value": "Has place the grasped snow cake into blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Retrieve the orange juice from the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the brown doll on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 0", "demos25 32724 0", "demos25 32908 0", "demos25 32970 0", "demos25 33160 0", "demos25 33258 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks and a bottle. Another arm is positioned near a loaf of bread and a soda bottle on the table."}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "retrieve the orange juice from the table with the right arm"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped orange juice into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the brown doll on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 32497 226", "demos25 32724 0", "demos25 32908 0", "demos25 33160 0"], "conversations": [{"from": "human", "value": "To complete Place the grasped tomato sauce into blue nylon bag within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items like snacks and a bottle. A packaged item rests on the table beside the bag."}, {"from": "human", "value": "Upon the execution of retrieve the orange juice from the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped orange juice into blue nylon bag"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 208"], "conversations": [{"from": "human", "value": "Given that Place the grasped brown doll into blue nylon bag with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grab the brown doll on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0", "demos25 33311 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped brown doll into blue nylon bag with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks and a bottle. Another arm places a packaged item into the bag on a wooden surface."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0", "demos25 33467 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped brown doll into blue nylon bag with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "Can we undertake place the grasped brown doll into blue nylon bag with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "Has the performance of grasp the tomato sauce on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the snow cake on the table with the right arm, 2-place the grasped snow cake into blue nylon bag, 3-grasp the tomato sauce on the table with the right arm, 4-place the grasped tomato sauce into blue nylon bag, 5-retrieve the orange juice from the table with the right arm, 6-place the grasped orange juice into blue nylon bag, 7-grab the brown doll on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "8-place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "Has place the grasped brown doll into blue nylon bag with the right arm been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "Can grasp the tomato sauce on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33259 0"], "conversations": [{"from": "human", "value": "What is the next event that typically unfolds after grab the brown doll on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped brown doll into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 34077 153", "demos25 34231 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the next event that typically follows grasp the left arm with blue nylon bag?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "To move forward with Place the grasped sunflower seeds into blue nylon bag, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 33904 0", "demos25 34077 0", "demos25 34115 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "The checkout line beckons; all items must be arranged soon. What follows now?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grab the sunflower seeds on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped sunflower seeds into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 216", "demos25 33847 229", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To complete Grab the sunflower seeds on the table with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Has grab the sunflower seeds on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33684 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the left arm with blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Are we able to begin place the grasped cookie biscuit into blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks and drinks. A bottle of water and a box of cookies are positioned near the arm on the table."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 122"], "conversations": [{"from": "human", "value": "Can place the grasped cookie biscuit into blue nylon bag be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the facial cleanser on the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "A robotic arm holds a pink package above a blue shopping bag. The bag rests on a table, partially filled with items. The arm's grippers secure the package for placement."}, {"from": "human", "value": "What is the exact robotic arm action required for Grasp the cookie biscuit on the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a box near a blue shopping bag filled with items. Various products, including snacks and beverages, are arranged on the table around the arm."}, {"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Is retrieve the cola from the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33846 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, what should be your next move?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Is it possible to begin place the grasped sunflower seeds into blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has place the grasped cola into blue nylon bag been completed as intended?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Can grab the sunflower seeds on the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "With shopping finished and 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag done, what must the robot do next for order?"}, {"from": "gpt", "value": "3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 34077 0", "demos25 34231 0"], "conversations": [{"from": "human", "value": "Is it possible to begin grasp the left arm with blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag upright on the left. Various items, including snacks and a bottle, are arranged on the table to the right, ready for packing."}, {"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Is the immediate performance of place the grasped cookie biscuit into blue nylon bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 180"], "conversations": [{"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what should be your next move?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Are we able to begin place the grasped cola into blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grab the facial cleanser on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34354 174", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped cookie biscuit into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can retrieve the cola from the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks. A white tube of lotion sits beside the bag on the table. The arm is positioned above the bag."}, {"from": "human", "value": "Is place the grasped facial cleanser into blue nylon bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34684 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks. A tube of toothpaste lies beside it on the table. The arm's grippers are positioned near the bag's handle."}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped cola into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "Was grab the facial cleanser on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has the work for grab the facial cleanser on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can place the grasped cola into blue nylon bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Have all aspects of place the grasped facial cleanser into blue nylon bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "What comes next on the path to reaching packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Is retrieve the cola from the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped sunflower seeds into blue nylon bag be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 108", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "Is grasp the left arm with blue nylon bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Retrieve the cola from the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next event that typically unfolds after retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Given that place the grasped cola into blue nylon bag has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 187", "demos25 34826 0"], "conversations": [{"from": "human", "value": "Was the action of grab the sunflower seeds on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped cola into blue nylon bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grasp the cookie biscuit on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks. A tube of toothpaste rests on the table nearby, ready for packing. The scene is set in a supermarket environment."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34528 0", "demos25 34529 0", "demos25 34556 0", "demos25 34638 0", "demos25 34826 0", "demos25 35006 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped cookie biscuit into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has retrieve the cola from the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the facial cleanser on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0", "demos25 34871 0"], "conversations": [{"from": "human", "value": "Is the current time opportune for place the grasped cookie biscuit into blue nylon bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the performance of grasp the cookie biscuit on the table with the right arm concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you commence place the grasped cookie biscuit into blue nylon bag without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the facial cleanser on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34397 0", "demos25 34529 0", "demos25 34638 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped cookie biscuit into blue nylon bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically follows place the grasped cookie biscuit into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Can we undertake grab the facial cleanser on the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 34354 0", "demos25 34529 0", "demos25 34637 0", "demos25 34638 0", "demos25 34825 0", "demos25 34826 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A bottle is partially inserted into the bag. A small white container sits on the table nearby."}, {"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the cola from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped cola into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Was the action of grasp the cookie biscuit on the table with the right arm carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Given that grab the facial cleanser on the table with the right arm has occurred, what is the most likely subsequent event?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0", "demos25 35063 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped facial cleanser into blue nylon bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35007 0", "demos25 35232 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped facial cleanser into blue nylon bag with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped facial cleanser into blue nylon bag with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Can grab the facial cleanser on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a white receipt above a blue shopping bag filled with various packaged items, positioned on a flat surface."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 225"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped facial cleanser into blue nylon bag with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Was the action of grasp the left arm with blue nylon bag carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Given your current status of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "Was place the grasped facial cleanser into blue nylon bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35007 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 35326 214", "demos25 35541 0", "demos25 35701 0", "demos25 35739 0", "demos25 35856 153"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped sunflower seeds into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grasp the cookie biscuit on the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Can place the grasped sunflower seeds into blue nylon bag be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm holds a blue bag with snacks inside. Another arm reaches toward a soda bottle and cookies on the table. Items are neatly arranged for packing."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0"], "conversations": [{"from": "human", "value": "Are we able to begin retrieve the cola from the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Is retrieve the cola from the table with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have the necessary steps for grab the facial cleanser on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35700 0", "demos25 35701 0", "demos25 35856 0", "demos25 36009 0"], "conversations": [{"from": "human", "value": "Is it possible to begin grasp the left arm with blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Comparing the initial image with the current one, is grab the sunflower seeds on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the cookie biscuit on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35379 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0", "demos25 35894 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the cookie biscuit on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35540 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the left arm with blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grab the sunflower seeds on the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a pink snack package above a blue shopping bag. Various items, including a bottle and cookies, are already inside the bag on the table."}, {"from": "human", "value": "Can place the grasped cola into blue nylon bag be put into action presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0"], "conversations": [{"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Have all aspects of place the grasped cola into blue nylon bag been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Upon the execution of place the grasped sunflower seeds into blue nylon bag, what is the next expected action?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35701 0", "demos25 35856 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a snack package near a blue shopping bag. A bottle of soda and other items rest on the table beside the arm."}, {"from": "human", "value": "Is grasp the left arm with blue nylon bag now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped cola into blue nylon bag be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0", "demos25 36498 0"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped cookie biscuit into blue nylon bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grasp the left arm with blue nylon bag be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the facial cleanser on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 134", "demos25 36145 0", "demos25 36259 0", "demos25 36453 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped cookie biscuit into blue nylon bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Following the completion of place the grasped cookie biscuit into blue nylon bag, what is projected to happen?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped cola into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36144 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped cookie biscuit into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Have all aspects of place the grasped cola into blue nylon bag been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Please give an account of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a bottle near a blue shopping bag filled with various items, positioned on a table. Another robotic arm assists in the packing process."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36307 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, what are the next steps to take?"}, {"from": "gpt", "value": "5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped cola into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 113", "demos25 36259 194", "demos25 36454 178"], "conversations": [{"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag containing snacks. A soda bottle and a small white box are placed on the table beside the bag."}, {"from": "human", "value": "What is the exact robotic arm action required for Retrieve the cola from the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped cola into blue nylon bag, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grab the facial cleanser on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm?"}, {"from": "gpt", "value": "4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm is behind you?"}, {"from": "gpt", "value": "6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Has the performance of grasp the left arm with blue nylon bag concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped cookie biscuit into blue nylon bag now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped sunflower seeds into blue nylon bag be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped facial cleanser into blue nylon bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36173 0", "demos25 36259 0", "demos25 36454 0", "demos25 36632 0"], "conversations": [{"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the cola from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grasp the cookie biscuit on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the facial cleanser on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Have all aspects of place the grasped cookie biscuit into blue nylon bag been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Can you commence retrieve the cola from the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can retrieve the cola from the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "After retrieve the cola from the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Is grab the facial cleanser on the table with the right arm no longer in progress?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically unfolds after place the grasped cola into blue nylon bag?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36043 0", "demos25 36145 0", "demos25 36258 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped cookie biscuit into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the cola from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can we undertake place the grasped cola into blue nylon bag at this juncture?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it appropriate to initiate grab the facial cleanser on the table with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Upon the conclusion of grasp the cookie biscuit on the table with the right arm, what action is anticipated next?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm already done, what is the subsequent step?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Is the immediate performance of place the grasped facial cleanser into blue nylon bag with the right arm an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36010 0", "demos25 36145 0", "demos25 36259 0", "demos25 36454 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks. A bottle of Coca-Cola stands upright on the table beside the bag. The arm is positioned above the bottle."}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with items. A small white box sits on the table nearby, ready for packing. The scene is set in a supermarket environment."}, {"from": "human", "value": "Has place the grasped cookie biscuit into blue nylon bag been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Upon the execution of grab the facial cleanser on the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0", "demos25 36782 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped facial cleanser into blue nylon bag with the right arm complete?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with various items like bottles and snacks. Another arm assists by placing a pink object into the bag."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Can you commence retrieve the cola from the table with the right arm without any further waiting?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0", "demos25 36670 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped facial cleanser into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Has place the grasped sunflower seeds into blue nylon bag been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Has the work for place the grasped facial cleanser into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped facial cleanser into blue nylon bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 149"], "conversations": [{"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped facial cleanser into blue nylon bag with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36633 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 0", "demos25 37176 0", "demos25 37302 0", "demos25 37332 0", "demos25 37424 0", "demos25 37460 0"], "conversations": [{"from": "human", "value": "Is it permissible to start grasp the left arm with blue nylon bag now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With the initial view of this task and the current scene, has grab the sunflower seeds on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped sunflower seeds into blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the way this action began and its current status, has grasp the cookie biscuit on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37134 0", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the left arm with blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that grasp the left arm with blue nylon bag has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can it be confirmed that grab the sunflower seeds on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "Are we able to begin grab the sunflower seeds on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grasp the cookie biscuit on the table with the right arm be done now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Has place the grasped sunflower seeds into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 36993 0", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the left arm with blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a soda bottle and snacks, are arranged on the table to the right, ready for packing."}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag filled with snacks and drinks. Items like chips and soda bottles are neatly packed inside, ready for checkout."}, {"from": "human", "value": "Is the immediate performance of place the grasped cola into blue nylon bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 188", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "Given that Grasp the left arm with blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "Have all aspects of grasp the cookie biscuit on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "After place the grasped sunflower seeds into blue nylon bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 166", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "What specific action is presently feasible to execute?"}, {"from": "gpt", "value": "grasp the left arm with blue nylon bag"}, {"from": "human", "value": "What is the exact robotic arm action required for Grab the sunflower seeds on the table with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it possible to begin place the grasped sunflower seeds into blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 0", "demos25 37302 0", "demos25 37424 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a soda bottle and snacks, are arranged on the table, ready for packing."}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the sunflower seeds on the table with the right arm"}, {"from": "human", "value": "After grab the sunflower seeds on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into blue nylon bag"}, {"from": "human", "value": "Can we confirm the completion of grab the facial cleanser on the table with the right arm?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37767 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, and your target being packing in the supermarket, what are the subsequent activities to complete the task?"}, {"from": "gpt", "value": "4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the cola from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a red item above a blue shopping bag filled with snacks. A tube of toothpaste sits on the table beside the bag."}, {"from": "human", "value": "Are we able to begin place the grasped cola into blue nylon bag at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0", "demos25 38135 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grasp the cookie biscuit on the table with the right arm"}, {"from": "human", "value": "Has the work for retrieve the cola from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": "6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the facial cleanser on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks. Another arm positions a box of cookies above the bag, ready for packing. The scene focuses on the interaction between the arms and the bag."}, {"from": "human", "value": "Is place the grasped cookie biscuit into blue nylon bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the work for place the grasped facial cleanser into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38026 0", "demos25 38027 108"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "To make progress toward packing in the supermarket, what is the next action to perform?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped cola into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Grab the facial cleanser on the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Is the immediate performance of place the grasped facial cleanser into blue nylon bag with the right arm an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it possible to begin retrieve the cola from the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it possible to begin place the grasped cola into blue nylon bag right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the common progression after place the grasped cola into blue nylon bag?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Was place the grasped cola into blue nylon bag brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "You are pursuing packing in the supermarket. After completing 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, what are the immediate next tasks?"}, {"from": "gpt", "value": "5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}, {"from": "human", "value": "Following the completion of retrieve the cola from the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Have we finished with grab the sunflower seeds on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37726 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped cookie biscuit into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "A blue shopping bag filled with snacks sits on the table. A robotic arm with a gripper is positioned above, ready to pack items into the bag."}, {"from": "human", "value": "Has grasp the cookie biscuit on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37609 0", "demos25 37727 0", "demos25 37888 0", "demos25 37922 0", "demos25 38027 0", "demos25 38054 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped cookie biscuit into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped cola into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering the way this action began and its current status, has grab the facial cleanser on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 156", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped cookie biscuit into blue nylon bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it practical to begin grasp the left arm with blue nylon bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks. Another arm positions a white box nearby, ready for packing. The table surface is visible beneath the items."}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 160", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grasp the cookie biscuit on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Retrieve the cola from the table with the right arm, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "7-grab the facial cleanser on the table with the right arm, 8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 138", "demos25 38027 0"], "conversations": [{"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "Given that Place the grasped cola into blue nylon bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is place the grasped facial cleanser into blue nylon bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Is grab the sunflower seeds on the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "While getting the cart ready, what precise gesture does the robotic arm execute for Retrieve the cola from the table with the right arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped cola into blue nylon bag"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, what action should you take next?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Is the execution of place the grasped cookie biscuit into blue nylon bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next event that typically follows place the grasped cookie biscuit into blue nylon bag?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "retrieve the cola from the table with the right arm"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37887 0", "demos25 37888 0", "demos25 38027 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped cookie biscuit into blue nylon bag"}, {"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the cola from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can place the grasped facial cleanser into blue nylon bag with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it possible to begin grab the facial cleanser on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped facial cleanser into blue nylon bag with the right arm immediately?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "After grab the facial cleanser on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag filled with snacks. The bag is positioned on a table, with the arm's grippers securing its edges."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0", "demos25 38190 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped facial cleanser into blue nylon bag with the right arm now finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Was place the grasped facial cleanser into blue nylon bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Is retrieve the cola from the table with the right arm now in a completed state?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Can place the grasped cola into blue nylon bag be done now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0", "demos25 38352 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped facial cleanser into blue nylon bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the facial cleanser on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 216"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Place the grasped facial cleanser into blue nylon bag with the right arm, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38136 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with blue nylon bag, 1-grab the sunflower seeds on the table with the right arm, 2-place the grasped sunflower seeds into blue nylon bag, 3-grasp the cookie biscuit on the table with the right arm, 4-place the grasped cookie biscuit into blue nylon bag, 5-retrieve the cola from the table with the right arm, 6-place the grasped cola into blue nylon bag, 7-grab the facial cleanser on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped facial cleanser into blue nylon bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 38469 312", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "What is the exact robotic arm action required for Grasp the left arm with red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is the current time opportune for place the grasped hand sanitizer into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the sandwich cookies from the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38781 0", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0", "demos25 39325 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the left arm with red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the hand sanitizer on the table with the right arm"}, {"from": "human", "value": "Was grab the laver on the table with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering how this action began and what is seen now, has grasp the blue toothbrush cup on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38916 0", "demos25 39171 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "It’s shopping time! To ensure all items find their way into bags smoothly, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is it possible to begin grab the hand sanitizer on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped hand sanitizer into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has grasp the left arm with red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "After grab the hand sanitizer on the table with the right arm, what action is generally next in sequence?"}, {"from": "gpt", "value": "place the grasped hand sanitizer into red canvas tote bag"}, {"from": "human", "value": "What is the next event that typically follows place the grasped hand sanitizer into red canvas tote bag?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "A robotic arm holds a blue cup above a red shopping bag. A bottle of hand sanitizer sits beside the bag. The background features a colorful supermarket display."}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the hand sanitizer on the table with the right arm"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped hand sanitizer into red canvas tote bag"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag on the left, while another arm grips a blue item near a cardboard box on the right, set against a colorful backdrop."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 133", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "Are we able to begin place the grasped sandwich cookies into red canvas tote bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with Grab the hand sanitizer on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a red bag above a table with a cup, box, and roll of tape. Another arm is positioned near the bag, ready to assist in packing items."}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped hand sanitizer into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Has place the grasped hand sanitizer into red canvas tote bag been completed as intended?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it practical to begin grab the hand sanitizer on the table with the right arm at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is place the grasped sandwich cookies into red canvas tote bag no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38916 0", "demos25 39172 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with red canvas tote bag be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the most probable development after grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "grab the hand sanitizer on the table with the right arm"}, {"from": "human", "value": "Has grab the hand sanitizer on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38547 0", "demos25 38782 0", "demos25 38916 0", "demos25 38979 0", "demos25 39172 153"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grasp the left arm with red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped hand sanitizer into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that Grasp the blue toothbrush cup on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39558 0", "demos25 39655 0", "demos25 39712 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "Can place the grasped blue toothbrush cup into red canvas tote bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the way this action began and its current status, has retrieve the sandwich cookies from the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped sandwich cookies into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "The robotic arm holds a blue item above a red shopping bag. The bag is partially filled with goods, placed on a table near a display window."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What is the immediate next step in reaching packing in the supermarket, considering 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm has been accomplished?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag on the left. Various items, including a white bottle, are arranged on the table near the center. Another robotic arm is positioned on the right."}, {"from": "human", "value": "Are conditions suitable to proceed with place the grasped sandwich cookies into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped blue toothbrush cup into red canvas tote bag capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 199", "demos25 39526 0", "demos25 39654 0", "demos25 39655 0", "demos25 39887 0", "demos25 39927 0"], "conversations": [{"from": "human", "value": "With Packing in the supermarket as the main task, and Place the grasped blue toothbrush cup into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has retrieve the sandwich cookies from the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grab the laver on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "grasp the blue toothbrush cup on the table with the right arm"}, {"from": "human", "value": "What are the tasks to undertake to achieve packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag?"}, {"from": "gpt", "value": "5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "Have the necessary steps for place the grasped sandwich cookies into red canvas tote bag been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39886 0", "demos25 39887 0", "demos25 40048 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "Can grasp the blue toothbrush cup on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped sandwich cookies into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the laver on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 231", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "Is place the grasped sandwich cookies into red canvas tote bag fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for Place the grasped sandwich cookies into red canvas tote bag, as part of working towards Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the objective of packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag already done, what is the subsequent step?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "Have we finished with grab the hand sanitizer on the table with the right arm?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "What occurred immediately prior to this?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a blue cup near a red shopping bag. Another arm grips a packaged item above the bag. Both items are positioned over a wooden surface."}, {"from": "human", "value": "Can retrieve the sandwich cookies from the table with the right arm be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open while another arm places a packaged item inside. The items and bag rest on a flat surface."}, {"from": "human", "value": "Is the execution of grab the laver on the table with the right arm feasible at present?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "Is retrieve the sandwich cookies from the table with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically follows place the grasped blue toothbrush cup into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39525 0", "demos25 39526 128", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped blue toothbrush cup into red canvas tote bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Retrieve the sandwich cookies from the table with the right arm, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What is the most probable development after retrieve the sandwich cookies from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "After place the grasped sandwich cookies into red canvas tote bag, what action is generally next in sequence?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39375 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped blue toothbrush cup into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "Is the immediate performance of place the grasped blue toothbrush cup into red canvas tote bag an option?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is grab the laver on the table with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 161"], "conversations": [{"from": "human", "value": "Are we able to begin place the grasped laver into red canvas tote bag with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, what action should you take next?"}, {"from": "gpt", "value": "retrieve the sandwich cookies from the table with the right arm"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the laver on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm is behind you?"}, {"from": "gpt", "value": "4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, 8-place the grasped laver into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped sandwich cookies into red canvas tote bag"}, {"from": "human", "value": "Is place the grasped hand sanitizer into red canvas tote bag now in a completed state?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 39326 0", "demos25 39526 0", "demos25 39655 0", "demos25 39887 0"], "conversations": [{"from": "human", "value": "What is the expected outcome immediately following grasp the blue toothbrush cup on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped blue toothbrush cup into red canvas tote bag"}, {"from": "human", "value": "Can it be confirmed that place the grasped hand sanitizer into red canvas tote bag has occurred?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grab the hand sanitizer on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, what are the next steps to take?"}, {"from": "gpt", "value": "8-place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grab the laver on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0", "demos25 40136 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped laver into red canvas tote bag with the right arm concluded?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40049 0", "demos25 40398 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped laver into red canvas tote bag with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "Has grab the hand sanitizer on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "grab the laver on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the hand sanitizer on the table with the right arm, 2-place the grasped hand sanitizer into red canvas tote bag, 3-grasp the blue toothbrush cup on the table with the right arm, 4-place the grasped blue toothbrush cup into red canvas tote bag, 5-retrieve the sandwich cookies from the table with the right arm, 6-place the grasped sandwich cookies into red canvas tote bag, 7-grab the laver on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag on the left. A black box with a white label is positioned above the bag, ready for packing. The scene is set on a wooden surface."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "Have all aspects of place the grasped laver into red canvas tote bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 349"], "conversations": [{"from": "human", "value": "To move forward with Place the grasped laver into red canvas tote bag with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "Are we able to begin grasp the left arm with red canvas tote bag at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped laver into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40049 0"], "conversations": [{"from": "human", "value": "What action is within reach to begin now?"}, {"from": "gpt", "value": "place the grasped laver into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Can we confirm the completion of place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grasp the hair conditioner on the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 305", "demos25 40878 0", "demos25 41080 0", "demos25 41384 181"], "conversations": [{"from": "human", "value": "Given the objective Packing in the supermarket, and the active subtask Grasp the left arm with red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is it practical to begin place the grasped packaged ham sausage into red canvas tote bag at this moment?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "To move forward with Grasp the hair conditioner on the table with the right arm, as part of Packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "Are we able to begin place the grasped hair conditioner into red canvas tote bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, what comes next?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "What can you proceed with right now?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with red canvas tote bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm. Various items, including a stack of cups and packaged snacks, are neatly arranged on the table beside it."}, {"from": "human", "value": "Is place the grasped hair conditioner into red canvas tote bag no longer in progress?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm holds a red bag on the left. Various items, including a bottle and a box, are neatly arranged on the table to the right."}, {"from": "human", "value": "Within the supermarket setup for Grab the packaged ham sausage on the table with the right arm, illustrate the precise arm motions needed for success."}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, what is the next task in line?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Is it permissible to start grab the packaged ham sausage on the table with the right arm now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the expected outcome immediately following grab the packaged ham sausage on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "Can it be confirmed that grab the packaged ham sausage on the table with the right arm has occurred?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40877 0", "demos25 40878 0", "demos25 41080 0", "demos25 41383 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped packaged ham sausage into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is retrieve the sunflower seeds from the table with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40572 0", "demos25 40648 0", "demos25 40878 0", "demos25 41079 0", "demos25 41080 0", "demos25 41384 0"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the packaged ham sausage on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "With the objective of packing in the supermarket in mind, and having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, what are the next steps to take?"}, {"from": "gpt", "value": "3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Is it within our capacity to execute grab the sweet bean sauce on the table with the right arm now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given packing in the supermarket as your goal, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag completed, what are the subsequent actions you should undertake?"}, {"from": "gpt", "value": "5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can grab the sweet bean sauce on the table with the right arm be put into effect presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the common progression after place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41831 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0", "demos25 42353 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped hair conditioner into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Could you describe the scene that is currently visible?"}, {"from": "gpt", "value": "A robotic arm holds a red bag on the left, while a box of snacks rests on the table's right side. The background features a colorful display."}, {"from": "human", "value": "Have all aspects of place the grasped sweet bean sauce into red canvas tote bag with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the sweet bean sauce on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42050 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Have all aspects of grab the sweet bean sauce on the table with the right arm been addressed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Can grab the packaged ham sausage on the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped sunflower seeds into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42004 0", "demos25 42005 181", "demos25 42187 166"], "conversations": [{"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the sunflower seeds from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for Place the grasped sunflower seeds into red canvas tote bag, while working on Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With Packing in the supermarket as the main task, and Grab the sweet bean sauce on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "What activity did you just finalize?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Has grab the packaged ham sausage on the table with the right arm been concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 265", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Given that Place the grasped hair conditioner into red canvas tote bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "To successfully achieve packing in the supermarket, what should be done now?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What event just took place?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, what comes next?"}, {"from": "gpt", "value": "7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Which action was just brought to a close?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Upon the execution of retrieve the sunflower seeds from the table with the right arm, what is the next expected action?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Is the current time opportune for grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "What is the visual information presented now?"}, {"from": "gpt", "value": "The robotic arm holds a red bag upright. A cup is inside the bag. A packet lies on the table next to the bag. The arm is positioned above the bag."}, {"from": "human", "value": "Is grab the packaged ham sausage on the table with the right arm now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, what comes next?"}, {"from": "gpt", "value": "6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "The robotic arm holds a yellow item above a red shopping bag placed on the table. The bag contains various items, and the arm is positioned over it."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "What action can be put into motion without delay?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Can we confirm the completion of place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Are we able to begin retrieve the sunflower seeds from the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Following the completion of grasp the hair conditioner on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "You are working towards packing in the supermarket. After completing the steps of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, what should be your next move?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm fully accomplished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42186 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Is place the grasped packaged ham sausage into red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped sunflower seeds into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41632 0", "demos25 41832 172", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped hair conditioner into red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete Retrieve the sunflower seeds from the table with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "What are the key features of the current visual setting?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag on the left. A yellow barcode scanner rests on the table beside it. The background features a colorful display board."}, {"from": "human", "value": "Has grab the packaged ham sausage on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 41875 0", "demos25 42005 0", "demos25 42187 0", "demos25 42228 0"], "conversations": [{"from": "human", "value": "Considering you want to accomplish packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "From the starting observation of this action and the current view, is retrieve the sunflower seeds from the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it appropriate to initiate place the grasped sunflower seeds into red canvas tote bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the sweet bean sauce on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 41566 0", "demos25 41832 0", "demos25 42005 0", "demos25 42187 0"], "conversations": [{"from": "human", "value": "Is it appropriate to initiate place the grasped hair conditioner into red canvas tote bag immediately?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Upon the execution of place the grasped hair conditioner into red canvas tote bag, what is the next expected action?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What step comes next in order to achieve packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What action was just carried out?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Can grab the sweet bean sauce on the table with the right arm be started at the current time?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0", "demos25 42619 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped sweet bean sauce into red canvas tote bag with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Is place the grasped packaged ham sausage into red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Given the current situation, what action can be taken?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0", "demos25 42420 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped sweet bean sauce into red canvas tote bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "What is the most probable development after grab the sweet bean sauce on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Is it possible to begin place the grasped sweet bean sauce into red canvas tote bag with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a yellow object near a red shopping bag on a table. The background features a colorful display with cartoon characters."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 265"], "conversations": [{"from": "human", "value": "To complete Place the grasped sweet bean sauce into red canvas tote bag with the right arm within the greater task of Packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42354 0"], "conversations": [{"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "Are we able to begin grab the sweet bean sauce on the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically unfolds after grasp the left arm with red canvas tote bag?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "Was place the grasped sweet bean sauce into red canvas tote bag with the right arm brought to an end?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42729 0", "demos25 42853 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "Please provide a description of the current scene."}, {"from": "gpt", "value": "The robotic arm holds a red bag on the left. A box is placed inside the bag. Another box rests on the table near the arm's base."}, {"from": "human", "value": "Is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42993 0", "demos25 43200 0", "demos25 43201 0", "demos25 43403 0"], "conversations": [{"from": "human", "value": "Can grasp the left arm with red canvas tote bag be performed at this moment?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Has the performance of grasp the left arm with red canvas tote bag concluded?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped packaged ham sausage into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the hair conditioner on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42992 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "What action is available to undertake at this moment?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the packaged ham sausage on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Are we able to begin place the grasped sunflower seeds into red canvas tote bag at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42887 0", "demos25 42993 0", "demos25 43201 202"], "conversations": [{"from": "human", "value": "Feeling the hustle at the store, what’s the next move for wrapping goods?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the packaged ham sausage on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Has grab the packaged ham sausage on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that Grasp the hair conditioner on the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view."}, {"from": "gpt", "value": "A robotic arm hovers over a table filled with items like a red shirt, snacks, and a roll of paper towels, ready for packing."}, {"from": "human", "value": "What should be done next to help reach packing in the supermarket?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grab the packaged ham sausage on the table with the right arm"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grasp the left arm with red canvas tote bag"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "The robotic arm is positioned above a table filled with various items. A red shopping bag is open on the left, ready for packing. Items like snacks and a roll are scattered across the table."}, {"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, and your target being packing in the supermarket, what is the subsequent activity?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 164", "demos25 42853 0", "demos25 42993 207", "demos25 43201 0"], "conversations": [{"from": "human", "value": "Given that we are working on Packing in the supermarket, and currently handling Grasp the left arm with red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm complete at this time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that Place the grasped packaged ham sausage into red canvas tote bag is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Can we undertake grasp the hair conditioner on the table with the right arm at this juncture?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 42688 0", "demos25 42852 0", "demos25 42853 0", "demos25 42993 0", "demos25 43201 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can grab the packaged ham sausage on the table with the right arm be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Considering the efforts of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm for the purpose of packing in the supermarket, what is the next course of action?"}, {"from": "gpt", "value": "place the grasped packaged ham sausage into red canvas tote bag"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm hovers above a red shopping bag. A box of snacks rests on the table beside the bag. The checkout counter is visible in the background."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm capable of being carried out now?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the next event that typically follows place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Are we able to begin retrieve the sunflower seeds from the table with the right arm at this point in time?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Is it possible to begin grab the sweet bean sauce on the table with the right arm right away?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "What is the latest action that has been performed?"}, {"from": "gpt", "value": "grasp the hair conditioner on the table with the right arm"}, {"from": "human", "value": "Given your current status of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag on the path to packing in the supermarket, what are the next actions that need to be addressed?"}, {"from": "gpt", "value": "5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "What is the expected outcome immediately following retrieve the sunflower seeds from the table with the right arm?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What was the last step that was completed?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "To achieve packing in the supermarket, and with 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm already done, what are the remaining actions needed?"}, {"from": "gpt", "value": "4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Can you outline the contents of the current scene?"}, {"from": "gpt", "value": "The robotic arm holds a yellow item above a red shopping bag. A box of snacks rests on the table below. The scene is set in a brightly lit supermarket environment."}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "What are the following steps you should take to complete packing in the supermarket, now that 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag is behind you?"}, {"from": "gpt", "value": "7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43833 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag. A yellow container and a pink box are on the table beside it. The arm is positioned above the table."}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has retrieve the sunflower seeds from the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is it accurate to state that place the grasped hair conditioner into red canvas tote bag is done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What elements are present in the current visual?"}, {"from": "gpt", "value": "The robotic arm holds a yellow box near its grippers. A red shopping bag hangs from the arm's left side. The background features a colorful display with a cartoon character."}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "You are aiming for packing in the supermarket. After completing 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, what is the next task in line?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "What is the next available action?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What is the current scene's appearance?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag open. Various items are inside the bag, positioned for packing. The arm's grippers secure the bag's edges."}, {"from": "human", "value": "Has place the grasped sunflower seeds into red canvas tote bag been concluded?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43471 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped hair conditioner into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What should be the next step to move toward packing in the supermarket?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "Considering the steps 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm have been completed, what are the actions required to realize packing in the supermarket?"}, {"from": "gpt", "value": "6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "Can place the grasped hair conditioner into red canvas tote bag be carried out now?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the next logical step to fulfill packing in the supermarket, having finished 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag?"}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What should be your next priority in order to fulfill packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Considering packing in the supermarket as the objective, what step comes next?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 271", "demos25 43676 157", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped hair conditioner into red canvas tote bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given that Retrieve the sunflower seeds from the table with the right arm is the current focus for Packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "place the grasped sunflower seeds into red canvas tote bag"}, {"from": "human", "value": "Is the immediate performance of place the grasped hair conditioner into red canvas tote bag an option?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 175", "demos25 44010 0"], "conversations": [{"from": "human", "value": "Has the work for retrieve the sunflower seeds from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What was the preceding action that concluded?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out Place the grasped sunflower seeds into red canvas tote bag, within the context of Packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given your progress of 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag towards packing in the supermarket, what is the next part of the process?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 118"], "conversations": [{"from": "human", "value": "What is the next event that typically follows grasp the hair conditioner on the table with the right arm?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Has grasp the left arm with red canvas tote bag been successfully executed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is the execution of place the grasped sunflower seeds into red canvas tote bag feasible at present?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Grab the sweet bean sauce on the table with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43715 0", "demos25 43834 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "What can be undertaken at this very moment?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has retrieve the sunflower seeds from the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Please specify the last action that was undertaken."}, {"from": "gpt", "value": "retrieve the sunflower seeds from the table with the right arm"}, {"from": "human", "value": "What is the most probable development after place the grasped sunflower seeds into red canvas tote bag?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43675 0", "demos25 43676 0", "demos25 43834 0", "demos25 44009 0", "demos25 44010 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped hair conditioner into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Can retrieve the sunflower seeds from the table with the right arm be commenced without delay?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped sunflower seeds into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What can be done at this point in time?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 43877 0", "demos25 44010 0", "demos25 44128 0"], "conversations": [{"from": "human", "value": "What is the necessary next step in realizing packing in the supermarket?"}, {"from": "gpt", "value": "place the grasped hair conditioner into red canvas tote bag"}, {"from": "human", "value": "Can grab the sweet bean sauce on the table with the right arm be put into action presently?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped sunflower seeds into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the sweet bean sauce on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 43404 0", "demos25 43676 0", "demos25 43834 0", "demos25 44010 0", "demos25 44039 0"], "conversations": [{"from": "human", "value": "Have we finished with place the grasped packaged ham sausage into red canvas tote bag?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Have the necessary steps for grab the sweet bean sauce on the table with the right arm been taken?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the sweet bean sauce on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 44129 0", "demos25 44205 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped sweet bean sauce into red canvas tote bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Is it practical to begin place the grasped hair conditioner into red canvas tote bag at this moment?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "With the aim of packing in the supermarket, what should you focus on next?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving."}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. A conveyor belt with items is positioned above, ready for packing. The scene is set in a brightly lit supermarket checkout area."}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Is place the grasped sweet bean sauce into red canvas tote bag with the right arm officially finished?"}, {"from": "gpt", "value": "no"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Considering you are working toward packing in the supermarket, and these steps are complete: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, what comes next?"}, {"from": "gpt", "value": "8-place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "What action is currently permissible to commence?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "To reach your goal of packing in the supermarket, and having finished these steps: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, 3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, what action should you take next?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Following the completion of grab the sweet bean sauce on the table with the right arm, what is projected to happen?"}, {"from": "gpt", "value": "place the grasped sweet bean sauce into red canvas tote bag with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 305"], "conversations": [{"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with Place the grasped sweet bean sauce into red canvas tote bag with the right arm, as part of Packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Has grab the packaged ham sausage on the table with the right arm been successfully executed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "Are conditions suitable to proceed with place the grasped sweet bean sauce into red canvas tote bag with the right arm?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0"], "conversations": [{"from": "human", "value": "What was the most recent step that was taken?"}, {"from": "gpt", "value": "grab the sweet bean sauce on the table with the right arm"}], "view": [["observation.images.head"]]} -{"lerobot": ["demos25 44129 0", "demos25 44434 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped sweet bean sauce into red canvas tote bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 0 0", "demos25 0 299", "demos25 0 0", "demos25 299 0", "demos25 299 28"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene? What are the necessary subtasks for realizing packing in the supermarket?"}, {"from": "gpt", "value": "Robotic arms flank a table with snacks. A red bag is on the left, a white box on the right. A cylindrical snack stands upright between two yellow packages. 0. grasp the left arm with red canvas tote bag 1. grab the packaged ham sausage on the table with the right arm 2. place the grasped packaged ham sausage into red canvas tote bag 3. grasp the hair conditioner on the table with the right arm 4. place the grasped hair conditioner into red canvas tote bag 5. retrieve the sunflower seeds from the table with the right arm 6. place the grasped sunflower seeds into red canvas tote bag 7. grab the sweet bean sauce on the table with the right arm 8. place the grasped sweet bean sauce into red canvas tote bag with the right arm "}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the left arm with red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the packaged ham sausage on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 299 0", "demos25 327 0", "demos25 327 87", "demos25 299 0", "demos25 414 0", "demos25 414 205"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has grab the packaged ham sausage on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the packaged ham sausage on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the packaged ham sausage on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped packaged ham sausage into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 414 0", "demos25 619 0", "demos25 619 28", "demos25 619 0", "demos25 647 0", "demos25 647 87"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped packaged ham sausage into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the hair conditioner on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the hair conditioner on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that grasp the hair conditioner on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 619 0", "demos25 734 0", "demos25 734 212", "demos25 734 0", "demos25 946 0", "demos25 946 32"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the hair conditioner on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped hair conditioner into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped hair conditioner into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with retrieve the sunflower seeds from the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 946 0", "demos25 978 0", "demos25 978 98", "demos25 946 0", "demos25 1076 0", "demos25 1076 144"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has retrieve the sunflower seeds from the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and retrieve the sunflower seeds from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has retrieve the sunflower seeds from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped sunflower seeds into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 1076 0", "demos25 1220 0", "demos25 1220 27", "demos25 1220 0", "demos25 1247 0", "demos25 1247 84"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped sunflower seeds into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the sweet bean sauce on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the sweet bean sauce on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the sweet bean sauce on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 1220 0", "demos25 1331 0", "demos25 1331 359", "demos25 1331 0", "demos25 1690 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grab the sweet bean sauce on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped sweet bean sauce into red canvas tote bag with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped sweet bean sauce into red canvas tote bag with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 1719 0", "demos25 1744 204", "demos25 1744 0", "demos25 1948 0", "demos25 1948 136"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene? How would you segment packing in the supermarket into actionable subtasks?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag above a table. Below, snacks and a can are neatly arranged, ready for packing. The scene captures a moment of automation in a supermarket setting. 0. grasp the left arm with red canvas tote bag 1. grab the large bag of potato chips on the table with the right arm 2. place the grasped large bag of potato chips into red canvas tote bag 3. grasp the pink soap on the table with the right arm 4. place the grasped pink soap into red canvas tote bag 5. retrieve the bottled ad calcium milk from the table with the right arm 6. place the grasped bottled ad calcium milk into red canvas tote bag 7. grab the doubanjiang on the table with the right arm 8. place the grasped doubanjiang into red canvas tote bag with the right arm "}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the left arm with red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the left arm with red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the large bag of potato chips on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 1948 0", "demos25 2084 0", "demos25 2084 35", "demos25 2084 0", "demos25 2119 0", "demos25 2119 105"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the large bag of potato chips on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped large bag of potato chips into red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped large bag of potato chips into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped large bag of potato chips into red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2084 0", "demos25 2224 0", "demos25 2224 147", "demos25 2224 0", "demos25 2371 0", "demos25 2371 147"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped large bag of potato chips into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the pink soap on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the pink soap on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped pink soap into red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2371 0", "demos25 2518 0", "demos25 2518 149", "demos25 2518 0", "demos25 2667 0", "demos25 2667 30"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped pink soap into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for retrieve the bottled ad calcium milk from the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the bottled ad calcium milk from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped bottled ad calcium milk into red canvas tote bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2667 0", "demos25 2697 0", "demos25 2697 90", "demos25 2667 0", "demos25 2787 0", "demos25 2787 121"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped bottled ad calcium milk into red canvas tote bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped bottled ad calcium milk into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped bottled ad calcium milk into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grab the doubanjiang on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 2787 0", "demos25 2908 0", "demos25 2908 442", "demos25 2908 0", "demos25 3350 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the doubanjiang on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped doubanjiang into red canvas tote bag with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped doubanjiang into red canvas tote bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 3430 0", "demos25 3455 138", "demos25 3455 0", "demos25 3593 0", "demos25 3593 31"], "conversations": [{"from": "human", "value": "Please provide a description of the current scene. To achieve packing in the supermarket, what are the foundational subtasks?"}, {"from": "gpt", "value": "A red shopping bag hangs from the robotic arm's claw. Various items, including Lay's chips and a pink box, are scattered on the table below. 0. grasp the left arm with red canvas tote bag 1. grab the large bag of potato chips on the table with the right arm 2. place the grasped large bag of potato chips into red canvas tote bag 3. grasp the pink soap on the table with the right arm 4. place the grasped pink soap into red canvas tote bag 5. retrieve the bottled ad calcium milk from the table with the right arm 6. place the grasped bottled ad calcium milk into red canvas tote bag 7. grab the doubanjiang on the table with the right arm 8. place the grasped doubanjiang into red canvas tote bag with the right arm "}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with red canvas tote bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the large bag of potato chips on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 3593 0", "demos25 3624 0", "demos25 3624 94", "demos25 3593 0", "demos25 3718 0", "demos25 3718 41"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the large bag of potato chips on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the large bag of potato chips on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the large bag of potato chips on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped large bag of potato chips into red canvas tote bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 3718 0", "demos25 3759 0", "demos25 3759 124", "demos25 3718 0", "demos25 3883 0", "demos25 3883 49"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped large bag of potato chips into red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped large bag of potato chips into red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped large bag of potato chips into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grasp the pink soap on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 3883 0", "demos25 3932 0", "demos25 3932 150", "demos25 3883 0", "demos25 4082 0", "demos25 4082 44"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the pink soap on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grasp the pink soap on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is grasp the pink soap on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped pink soap into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 4082 0", "demos25 4126 0", "demos25 4126 133", "demos25 4082 0", "demos25 4259 0", "demos25 4259 158"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped pink soap into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped pink soap into red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped pink soap into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that retrieve the bottled ad calcium milk from the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 4259 0", "demos25 4417 0", "demos25 4417 141", "demos25 4417 0", "demos25 4558 0", "demos25 4558 127"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the bottled ad calcium milk from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped bottled ad calcium milk into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped bottled ad calcium milk into red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grab the doubanjiang on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 4558 0", "demos25 4685 0", "demos25 4685 130", "demos25 4685 0", "demos25 4815 0", "demos25 4815 393"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grab the doubanjiang on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped doubanjiang into red canvas tote bag with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped doubanjiang into red canvas tote bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped doubanjiang into red canvas tote bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 4685 0", "demos25 5208 0"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped doubanjiang into red canvas tote bag with the right arm complete?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 5210 0", "demos25 5236 48", "demos25 5236 0", "demos25 5284 0", "demos25 5284 145"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. To complete packing in the supermarket, what are the essential sub-components of work?"}, {"from": "gpt", "value": "A robotic arm holds a red shopping bag open. Various items, including a bottle, cookies, and cleaning supplies, are arranged neatly on the table beside it. 0. grasp the left arm with red canvas tote bag 1. grab the large bag of potato chips on the table with the right arm 2. place the grasped large bag of potato chips into red canvas tote bag 3. grasp the pink soap on the table with the right arm 4. place the grasped pink soap into red canvas tote bag 5. retrieve the bottled ad calcium milk from the table with the right arm 6. place the grasped bottled ad calcium milk into red canvas tote bag 7. grab the doubanjiang on the table with the right arm 8. place the grasped doubanjiang into red canvas tote bag with the right arm "}, {"from": "human", "value": "To complete grasp the left arm with red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the left arm with red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5236 0", "demos25 5429 0", "demos25 5429 187", "demos25 5429 0", "demos25 5616 0", "demos25 5616 151"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the large bag of potato chips on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is grab the large bag of potato chips on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped large bag of potato chips into red canvas tote bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5616 0", "demos25 5767 0", "demos25 5767 28", "demos25 5767 0", "demos25 5795 0", "demos25 5795 85"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped large bag of potato chips into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the pink soap on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grasp the pink soap on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the pink soap on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 5767 0", "demos25 5880 0", "demos25 5880 120", "demos25 5880 0", "demos25 6000 0", "demos25 6000 116"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the pink soap on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped pink soap into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped pink soap into red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out retrieve the bottled ad calcium milk from the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 6000 0", "demos25 6116 0", "demos25 6116 163", "demos25 6116 0", "demos25 6279 0", "demos25 6279 30"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the bottled ad calcium milk from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped bottled ad calcium milk into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped bottled ad calcium milk into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the doubanjiang on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 6279 0", "demos25 6309 0", "demos25 6309 92", "demos25 6279 0", "demos25 6401 0", "demos25 6401 404"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the doubanjiang on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete grab the doubanjiang on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the doubanjiang on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped doubanjiang into red canvas tote bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 6401 0", "demos25 6805 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped doubanjiang into red canvas tote bag with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 6807 0", "demos25 6807 60", "demos25 6807 0", "demos25 6867 0", "demos25 6867 180"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. To achieve packing in the supermarket, what are the foundational subtasks?"}, {"from": "gpt", "value": "The robotic arm hovers above a red shopping bag. Various items, including a blue snack pack, yellow container, and pink package, are neatly arranged on the table beside the bag. 0. grasp the left arm with red canvas tote bag 1. grab the large bag of potato chips on the table with the right arm 2. place the grasped large bag of potato chips into red canvas tote bag 3. grasp the pink soap on the table with the right arm 4. place the grasped pink soap into red canvas tote bag 5. retrieve the bottled ad calcium milk from the table with the right arm 6. place the grasped bottled ad calcium milk into red canvas tote bag 7. grab the doubanjiang on the table with the right arm 8. place the grasped doubanjiang into red canvas tote bag with the right arm "}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the left arm with red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grasp the left arm with red canvas tote bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the left arm with red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 6807 0", "demos25 7047 0", "demos25 7047 49", "demos25 7047 0", "demos25 7096 0", "demos25 7096 147"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the left arm with red canvas tote bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the large bag of potato chips on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the large bag of potato chips on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the large bag of potato chips on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7047 0", "demos25 7243 0", "demos25 7243 39", "demos25 7243 0", "demos25 7282 0", "demos25 7282 117"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the large bag of potato chips on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped large bag of potato chips into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has place the grasped large bag of potato chips into red canvas tote bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped large bag of potato chips into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7243 0", "demos25 7399 0", "demos25 7399 39", "demos25 7399 0", "demos25 7438 0", "demos25 7438 120"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped large bag of potato chips into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grasp the pink soap on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the pink soap on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the pink soap on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7399 0", "demos25 7558 0", "demos25 7558 31", "demos25 7558 0", "demos25 7589 0", "demos25 7589 95"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the pink soap on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped pink soap into red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped pink soap into red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped pink soap into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7558 0", "demos25 7684 0", "demos25 7684 36", "demos25 7684 0", "demos25 7720 0", "demos25 7720 110"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped pink soap into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with retrieve the bottled ad calcium milk from the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "From the starting observation of this action and the current view, is retrieve the bottled ad calcium milk from the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and retrieve the bottled ad calcium milk from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7684 0", "demos25 7830 0", "demos25 7830 32", "demos25 7830 0", "demos25 7862 0", "demos25 7862 96"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is retrieve the bottled ad calcium milk from the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped bottled ad calcium milk into red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped bottled ad calcium milk into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete place the grasped bottled ad calcium milk into red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7830 0", "demos25 7958 0", "demos25 7958 62", "demos25 7958 0", "demos25 8020 0", "demos25 8020 189"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped bottled ad calcium milk into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the doubanjiang on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has grab the doubanjiang on the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the doubanjiang on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 7958 0", "demos25 8209 0", "demos25 8209 106", "demos25 8209 0", "demos25 8315 0", "demos25 8315 319"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grab the doubanjiang on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped doubanjiang into red canvas tote bag with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped doubanjiang into red canvas tote bag with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped doubanjiang into red canvas tote bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 8209 0", "demos25 8634 0"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped doubanjiang into red canvas tote bag with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 8969 0", "demos25 9026 231", "demos25 9026 0", "demos25 9257 0", "demos25 9257 31"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. To complete packing in the supermarket, what are the essential sub-components of work?"}, {"from": "gpt", "value": "A robotic arm is positioned over a table filled with various items like snacks, a bottle, and a box, ready for packing in a supermarket setting. 0. grasp the left arm with red canvas tote bag 1. grab the packaged ham sausage on the table with the right arm 2. place the grasped packaged ham sausage into red canvas tote bag 3. grasp the hair conditioner on the table with the right arm 4. place the grasped hair conditioner into red canvas tote bag 5. retrieve the sunflower seeds from the table with the right arm 6. place the grasped sunflower seeds into red canvas tote bag 7. grab the sweet bean sauce on the table with the right arm 8. place the grasped sweet bean sauce into red canvas tote bag with the right arm "}, {"from": "human", "value": "To complete grasp the left arm with red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the packaged ham sausage on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9257 0", "demos25 9288 0", "demos25 9288 93", "demos25 9257 0", "demos25 9381 0", "demos25 9381 35"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has grab the packaged ham sausage on the table with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the packaged ham sausage on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the packaged ham sausage on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped packaged ham sausage into red canvas tote bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9381 0", "demos25 9416 0", "demos25 9416 108", "demos25 9381 0", "demos25 9524 0", "demos25 9524 28"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped packaged ham sausage into red canvas tote bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped packaged ham sausage into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped packaged ham sausage into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grasp the hair conditioner on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9524 0", "demos25 9552 0", "demos25 9552 84", "demos25 9524 0", "demos25 9636 0", "demos25 9636 37"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the hair conditioner on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the hair conditioner on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the hair conditioner on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped hair conditioner into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9636 0", "demos25 9673 0", "demos25 9673 112", "demos25 9636 0", "demos25 9785 0", "demos25 9785 37"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped hair conditioner into red canvas tote bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped hair conditioner into red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped hair conditioner into red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with retrieve the sunflower seeds from the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9785 0", "demos25 9822 0", "demos25 9822 112", "demos25 9785 0", "demos25 9934 0", "demos25 9934 156"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has retrieve the sunflower seeds from the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the sunflower seeds from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has retrieve the sunflower seeds from the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped sunflower seeds into red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 9934 0", "demos25 10090 0", "demos25 10090 93", "demos25 10090 0", "demos25 10183 0", "demos25 10183 61"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped sunflower seeds into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grab the sweet bean sauce on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the sweet bean sauce on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped sweet bean sauce into red canvas tote bag with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 10183 0", "demos25 10244 0", "demos25 10244 186", "demos25 10183 0", "demos25 10430 0"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped sweet bean sauce into red canvas tote bag with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped sweet bean sauce into red canvas tote bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped sweet bean sauce into red canvas tote bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 10615 0", "demos25 10615 59", "demos25 10615 0", "demos25 10674 0", "demos25 10674 179"], "conversations": [{"from": "human", "value": "What is the current scene's appearance? What are the individual subtasks necessary to accomplish packing in the supermarket?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a soda bottle and snacks, are arranged on the table to the right, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the sunflower seeds on the table with the right arm 2. place the grasped sunflower seeds into blue nylon bag 3. grasp the cookie biscuit on the table with the right arm 4. place the grasped cookie biscuit into blue nylon bag 5. retrieve the cola from the table with the right arm 6. place the grasped cola into blue nylon bag 7. grab the facial cleanser on the table with the right arm 8. place the grasped facial cleanser into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has grasp the left arm with blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the left arm with blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 10615 0", "demos25 10853 0", "demos25 10853 170", "demos25 10853 0", "demos25 11023 0", "demos25 11023 53"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the sunflower seeds on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the sunflower seeds on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped sunflower seeds into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 11023 0", "demos25 11076 0", "demos25 11076 159", "demos25 11023 0", "demos25 11235 0", "demos25 11235 197"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped sunflower seeds into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped sunflower seeds into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped sunflower seeds into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the cookie biscuit on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 11235 0", "demos25 11432 0", "demos25 11432 188", "demos25 11432 0", "demos25 11620 0", "demos25 11620 37"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grasp the cookie biscuit on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped cookie biscuit into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped cookie biscuit into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and retrieve the cola from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 11620 0", "demos25 11657 0", "demos25 11657 112", "demos25 11620 0", "demos25 11769 0", "demos25 11769 175"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the cola from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the cola from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the cola from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped cola into blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 11769 0", "demos25 11944 0", "demos25 11944 127", "demos25 11944 0", "demos25 12071 0", "demos25 12071 77"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped cola into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the facial cleanser on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the facial cleanser on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped facial cleanser into blue nylon bag with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12071 0", "demos25 12148 0", "demos25 12148 234", "demos25 12071 0", "demos25 12382 0"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped facial cleanser into blue nylon bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped facial cleanser into blue nylon bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped facial cleanser into blue nylon bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 12422 0", "demos25 12422 36", "demos25 12422 0", "demos25 12458 0", "demos25 12458 110"], "conversations": [{"from": "human", "value": "Please give an account of the current scene. Please outline the constituent subtasks involved in achieving packing in the supermarket."}, {"from": "gpt", "value": "The robotic arm holds a blue bag on the left. Various items, including a bottle and a box, are scattered on the table to the right. The setup is ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the iced tea on the table with the right arm 2. place the grasped iced tea into blue nylon bag 3. grasp the yogurt bowl on the table with the right arm 4. place the grasped yogurt bowl into blue nylon bag 5. retrieve the pork jerky from the table with the right arm 6. place the grasped pork jerky into blue nylon bag 7. grab the mouthwash on the table with the right arm 8. place the grasped mouthwash into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the left arm with blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12422 0", "demos25 12568 0", "demos25 12568 129", "demos25 12568 0", "demos25 12697 0", "demos25 12697 22"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the iced tea on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the iced tea on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped iced tea into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12697 0", "demos25 12719 0", "demos25 12719 69", "demos25 12697 0", "demos25 12788 0", "demos25 12788 31"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped iced tea into blue nylon bag finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped iced tea into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped iced tea into blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the yogurt bowl on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12788 0", "demos25 12819 0", "demos25 12819 95", "demos25 12788 0", "demos25 12914 0", "demos25 12914 88"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has grasp the yogurt bowl on the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete grasp the yogurt bowl on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grasp the yogurt bowl on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped yogurt bowl into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 12914 0", "demos25 13002 0", "demos25 13002 30", "demos25 13002 0", "demos25 13032 0", "demos25 13032 90"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped yogurt bowl into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the pork jerky from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has retrieve the pork jerky from the table with the right arm completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling retrieve the pork jerky from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 13002 0", "demos25 13122 0", "demos25 13122 77", "demos25 13122 0", "demos25 13199 0", "demos25 13199 45"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is retrieve the pork jerky from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped pork jerky into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped pork jerky into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the mouthwash on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 13199 0", "demos25 13244 0", "demos25 13244 138", "demos25 13199 0", "demos25 13382 0", "demos25 13382 56"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has grab the mouthwash on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the mouthwash on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the mouthwash on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped mouthwash into blue nylon bag with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 13382 0", "demos25 13438 0", "demos25 13438 170", "demos25 13382 0", "demos25 13608 0"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped mouthwash into blue nylon bag with the right arm been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped mouthwash into blue nylon bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped mouthwash into blue nylon bag with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 13820 0", "demos25 13820 157", "demos25 13820 0", "demos25 13977 0", "demos25 13977 35"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene? What are the necessary subtasks for realizing packing in the supermarket?"}, {"from": "gpt", "value": "The robotic arm holds a blue bag upright on the table. Various grocery items, including a jar and packaged goods, are scattered around, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the iced tea on the table with the right arm 2. place the grasped iced tea into blue nylon bag 3. grasp the yogurt bowl on the table with the right arm 4. place the grasped yogurt bowl into blue nylon bag 5. retrieve the pork jerky from the table with the right arm 6. place the grasped pork jerky into blue nylon bag 7. grab the mouthwash on the table with the right arm 8. place the grasped mouthwash into blue nylon bag with the right arm "}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the left arm with blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the iced tea on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 13977 0", "demos25 14012 0", "demos25 14012 105", "demos25 13977 0", "demos25 14117 0", "demos25 14117 17"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the iced tea on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the iced tea on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the iced tea on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped iced tea into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14117 0", "demos25 14134 0", "demos25 14134 51", "demos25 14117 0", "demos25 14185 0", "demos25 14185 29"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped iced tea into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with place the grasped iced tea into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped iced tea into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grasp the yogurt bowl on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14185 0", "demos25 14214 0", "demos25 14214 87", "demos25 14185 0", "demos25 14301 0", "demos25 14301 21"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the yogurt bowl on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grasp the yogurt bowl on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grasp the yogurt bowl on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped yogurt bowl into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14301 0", "demos25 14322 0", "demos25 14322 66", "demos25 14301 0", "demos25 14388 0", "demos25 14388 31"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped yogurt bowl into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete place the grasped yogurt bowl into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped yogurt bowl into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the pork jerky from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14388 0", "demos25 14419 0", "demos25 14419 96", "demos25 14388 0", "demos25 14515 0", "demos25 14515 76"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has retrieve the pork jerky from the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the pork jerky from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has retrieve the pork jerky from the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped pork jerky into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14515 0", "demos25 14591 0", "demos25 14591 48", "demos25 14591 0", "demos25 14639 0", "demos25 14639 144"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped pork jerky into blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the mouthwash on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the mouthwash on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the mouthwash on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14591 0", "demos25 14783 0", "demos25 14783 53", "demos25 14783 0", "demos25 14836 0", "demos25 14836 159"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the mouthwash on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped mouthwash into blue nylon bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped mouthwash into blue nylon bag with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped mouthwash into blue nylon bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 14783 0", "demos25 14995 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped mouthwash into blue nylon bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 15182 0", "demos25 15217 35", "demos25 15217 0", "demos25 15252 0", "demos25 15252 106"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible? Please outline the constituent subtasks involved in achieving packing in the supermarket."}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag open. A can, a black cup, and a bottle of hand sanitizer are positioned nearby on the table. 0. grasp the left arm with red canvas tote bag 1. grab the hand sanitizer on the table with the right arm 2. place the grasped hand sanitizer into red canvas tote bag 3. grasp the blue toothbrush cup on the table with the right arm 4. place the grasped blue toothbrush cup into red canvas tote bag 5. retrieve the sandwich cookies from the table with the right arm 6. place the grasped sandwich cookies into red canvas tote bag 7. grab the laver on the table with the right arm 8. place the grasped laver into red canvas tote bag with the right arm "}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grasp the left arm with red canvas tote bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15217 0", "demos25 15358 0", "demos25 15358 253", "demos25 15358 0", "demos25 15611 0", "demos25 15611 32"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the hand sanitizer on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the hand sanitizer on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped hand sanitizer into red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15611 0", "demos25 15643 0", "demos25 15643 98", "demos25 15611 0", "demos25 15741 0", "demos25 15741 31"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped hand sanitizer into red canvas tote bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped hand sanitizer into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped hand sanitizer into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grasp the blue toothbrush cup on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15741 0", "demos25 15772 0", "demos25 15772 94", "demos25 15741 0", "demos25 15866 0", "demos25 15866 119"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the blue toothbrush cup on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete grasp the blue toothbrush cup on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the blue toothbrush cup on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped blue toothbrush cup into red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15866 0", "demos25 15985 0", "demos25 15985 26", "demos25 15985 0", "demos25 16011 0", "demos25 16011 80"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped blue toothbrush cup into red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for retrieve the sandwich cookies from the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has retrieve the sandwich cookies from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out retrieve the sandwich cookies from the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 15985 0", "demos25 16091 0", "demos25 16091 144", "demos25 16091 0", "demos25 16235 0", "demos25 16235 116"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is retrieve the sandwich cookies from the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped sandwich cookies into red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped sandwich cookies into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grab the laver on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 16235 0", "demos25 16351 0", "demos25 16351 88", "demos25 16351 0", "demos25 16439 0", "demos25 16439 267"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the laver on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped laver into red canvas tote bag with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped laver into red canvas tote bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped laver into red canvas tote bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 16351 0", "demos25 16706 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped laver into red canvas tote bag with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 17319 0", "demos25 17340 37", "demos25 17340 0", "demos25 17377 0", "demos25 17377 111"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. To complete packing in the supermarket, what are the essential sub-components of work?"}, {"from": "gpt", "value": "The robotic arm holds a red shopping bag above a table. Various items, including a bottle and a box, are scattered on the table's surface. 0. grasp the left arm with red canvas tote bag 1. grab the dishcloth on the table with the right arm 2. place the grasped dishcloth into red canvas tote bag 3. grasp the plain toast on the table with the right arm 4. place the grasped plain toast into red canvas tote bag 5. retrieve the cotton swab from the table with the right arm 6. place the grasped cotton swab into red canvas tote bag 7. grab the small bottle of shampoo on the table with the right arm 8. place the grasped small bottle of shampoo into red canvas tote bag with the right arm "}, {"from": "human", "value": "To complete grasp the left arm with red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the left arm with red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the left arm with red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17340 0", "demos25 17488 0", "demos25 17488 35", "demos25 17488 0", "demos25 17523 0", "demos25 17523 107"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grasp the left arm with red canvas tote bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the dishcloth on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "From the starting observation of this action and the current view, is grab the dishcloth on the table with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete grab the dishcloth on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17488 0", "demos25 17630 0", "demos25 17630 34", "demos25 17630 0", "demos25 17664 0", "demos25 17664 103"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grab the dishcloth on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped dishcloth into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped dishcloth into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped dishcloth into red canvas tote bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17630 0", "demos25 17767 0", "demos25 17767 34", "demos25 17767 0", "demos25 17801 0", "demos25 17801 105"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped dishcloth into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grasp the plain toast on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the plain toast on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the plain toast on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 17767 0", "demos25 17906 0", "demos25 17906 150", "demos25 17906 0", "demos25 18056 0", "demos25 18056 32"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the plain toast on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped plain toast into red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped plain toast into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and retrieve the cotton swab from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18056 0", "demos25 18088 0", "demos25 18088 99", "demos25 18056 0", "demos25 18187 0", "demos25 18187 17"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that retrieve the cotton swab from the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling retrieve the cotton swab from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has retrieve the cotton swab from the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped cotton swab into red canvas tote bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18187 0", "demos25 18204 0", "demos25 18204 53", "demos25 18187 0", "demos25 18257 0", "demos25 18257 35"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped cotton swab into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with place the grasped cotton swab into red canvas tote bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped cotton swab into red canvas tote bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grab the small bottle of shampoo on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18257 0", "demos25 18292 0", "demos25 18292 107", "demos25 18257 0", "demos25 18399 0", "demos25 18399 160"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grab the small bottle of shampoo on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete grab the small bottle of shampoo on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the small bottle of shampoo on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped small bottle of shampoo into red canvas tote bag with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18399 0", "demos25 18559 0"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped small bottle of shampoo into red canvas tote bag with the right arm now done?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 18710 0", "demos25 18710 57", "demos25 18710 0", "demos25 18767 0", "demos25 18767 171"], "conversations": [{"from": "human", "value": "Can you outline the contents of the current scene? Detail the sequence of subtasks required for packing in the supermarket."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left side of the table. Various items, including snacks and drinks, are arranged on the right side, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the sunflower seeds on the table with the right arm 2. place the grasped sunflower seeds into blue nylon bag 3. grasp the cookie biscuit on the table with the right arm 4. place the grasped cookie biscuit into blue nylon bag 5. retrieve the cola from the table with the right arm 6. place the grasped cola into blue nylon bag 7. grab the facial cleanser on the table with the right arm 8. place the grasped facial cleanser into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the left arm with blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 18710 0", "demos25 18938 0", "demos25 18938 376", "demos25 18938 0", "demos25 19314 0", "demos25 19314 48"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the sunflower seeds on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the sunflower seeds on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped sunflower seeds into blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 19314 0", "demos25 19362 0", "demos25 19362 147", "demos25 19314 0", "demos25 19509 0", "demos25 19509 144"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped sunflower seeds into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped sunflower seeds into blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped sunflower seeds into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the cookie biscuit on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 19509 0", "demos25 19653 0", "demos25 19653 202", "demos25 19653 0", "demos25 19855 0", "demos25 19855 30"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the cookie biscuit on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped cookie biscuit into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped cookie biscuit into blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that retrieve the cola from the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 19855 0", "demos25 19885 0", "demos25 19885 91", "demos25 19855 0", "demos25 19976 0", "demos25 19976 197"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has retrieve the cola from the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and retrieve the cola from the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has retrieve the cola from the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped cola into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 19976 0", "demos25 20173 0", "demos25 20173 175", "demos25 20173 0", "demos25 20348 0", "demos25 20348 315"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped cola into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the facial cleanser on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grab the facial cleanser on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped facial cleanser into blue nylon bag with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 20348 0", "demos25 20663 0"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped facial cleanser into blue nylon bag with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 20764 0", "demos25 20764 57", "demos25 20764 0", "demos25 20821 0", "demos25 20821 173"], "conversations": [{"from": "human", "value": "What are the key features of the current visual setting? Detail the sequence of subtasks required for packing in the supermarket."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left. Various items, including snacks and a drink bottle, are arranged on the table to the right. 0. grasp the left arm with blue nylon bag 1. grab the snow cake on the table with the right arm 2. place the grasped snow cake into blue nylon bag 3. grasp the tomato sauce on the table with the right arm 4. place the grasped tomato sauce into blue nylon bag 5. retrieve the orange juice from the table with the right arm 6. place the grasped orange juice into blue nylon bag 7. grab the brown doll on the table with the right arm 8. place the grasped brown doll into blue nylon bag with the right arm "}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has grasp the left arm with blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 20764 0", "demos25 20994 0", "demos25 20994 188", "demos25 20994 0", "demos25 21182 0", "demos25 21182 52"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grasp the left arm with blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the snow cake on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the snow cake on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped snow cake into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 21182 0", "demos25 21234 0", "demos25 21234 157", "demos25 21182 0", "demos25 21391 0", "demos25 21391 131"], "conversations": [{"from": "human", "value": "Considering the way this action began and its current status, has place the grasped snow cake into blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped snow cake into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped snow cake into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the tomato sauce on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 21391 0", "demos25 21522 0", "demos25 21522 150", "demos25 21522 0", "demos25 21672 0", "demos25 21672 39"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the tomato sauce on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped tomato sauce into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped tomato sauce into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out retrieve the orange juice from the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 21672 0", "demos25 21711 0", "demos25 21711 120", "demos25 21672 0", "demos25 21831 0", "demos25 21831 160"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has retrieve the orange juice from the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete retrieve the orange juice from the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the orange juice from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped orange juice into blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 21831 0", "demos25 21991 0", "demos25 21991 149", "demos25 21991 0", "demos25 22140 0", "demos25 22140 52"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped orange juice into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the brown doll on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the brown doll on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped brown doll into blue nylon bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 22140 0", "demos25 22192 0", "demos25 22192 158", "demos25 22140 0", "demos25 22350 0"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped brown doll into blue nylon bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped brown doll into blue nylon bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is place the grasped brown doll into blue nylon bag with the right arm finalized?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 22650 0", "demos25 22650 221", "demos25 22650 0", "demos25 22871 0", "demos25 22871 172"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view. Detail the sequence of subtasks required for packing in the supermarket."}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag on the left. Various items, including bread, snacks, and a bottle, are arranged on the table to the right. 0. grasp the left arm with blue nylon bag 1. grab the snow cake on the table with the right arm 2. place the grasped snow cake into blue nylon bag 3. grasp the tomato sauce on the table with the right arm 4. place the grasped tomato sauce into blue nylon bag 5. retrieve the orange juice from the table with the right arm 6. place the grasped orange juice into blue nylon bag 7. grab the brown doll on the table with the right arm 8. place the grasped brown doll into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the left arm with blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for grab the snow cake on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 22871 0", "demos25 23043 0", "demos25 23043 270", "demos25 23043 0", "demos25 23313 0", "demos25 23313 38"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the snow cake on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped snow cake into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped snow cake into blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grasp the tomato sauce on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 23313 0", "demos25 23351 0", "demos25 23351 117", "demos25 23313 0", "demos25 23468 0", "demos25 23468 142"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the tomato sauce on the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the tomato sauce on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the tomato sauce on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped tomato sauce into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 23468 0", "demos25 23610 0", "demos25 23610 139", "demos25 23610 0", "demos25 23749 0", "demos25 23749 33"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped tomato sauce into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling retrieve the orange juice from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has retrieve the orange juice from the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped orange juice into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 23749 0", "demos25 23782 0", "demos25 23782 100", "demos25 23749 0", "demos25 23882 0", "demos25 23882 122"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped orange juice into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped orange juice into blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped orange juice into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the brown doll on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 23882 0", "demos25 24004 0", "demos25 24004 52", "demos25 24004 0", "demos25 24056 0", "demos25 24056 156"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the brown doll on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped brown doll into blue nylon bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped brown doll into blue nylon bag with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped brown doll into blue nylon bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24004 0", "demos25 24212 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped brown doll into blue nylon bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 24379 0", "demos25 24379 58", "demos25 24379 0", "demos25 24437 0", "demos25 24437 175"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. To achieve packing in the supermarket, what are the foundational subtasks?"}, {"from": "gpt", "value": "A robotic arm is positioned over a table with various items like a blue bag, snacks, and personal care products, ready for packing. The setup is in a supermarket environment. 0. grasp the left arm with blue nylon bag 1. grab the sandwich cookies on the table with the right arm 2. place the grasped sandwich cookies into blue nylon bag 3. grasp the bath ball on the table with the right arm 4. place the grasped bath ball into blue nylon bag 5. retrieve the tea pi from the table with the right arm 6. place the grasped tea pi into blue nylon bag 7. grab the blue hand cream on the table with the right arm 8. place the grasped blue hand cream into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has grasp the left arm with blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24379 0", "demos25 24612 0", "demos25 24612 163", "demos25 24612 0", "demos25 24775 0", "demos25 24775 42"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the sandwich cookies on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the sandwich cookies on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped sandwich cookies into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24775 0", "demos25 24817 0", "demos25 24817 127", "demos25 24775 0", "demos25 24944 0", "demos25 24944 128"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has place the grasped sandwich cookies into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped sandwich cookies into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped sandwich cookies into blue nylon bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the bath ball on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 24944 0", "demos25 25072 0", "demos25 25072 52", "demos25 25072 0", "demos25 25124 0", "demos25 25124 158"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has grasp the bath ball on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped bath ball into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped bath ball into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped bath ball into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 25072 0", "demos25 25282 0", "demos25 25282 40", "demos25 25282 0", "demos25 25322 0", "demos25 25322 121"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped bath ball into blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with retrieve the tea pi from the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the tea pi from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the tea pi from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 25282 0", "demos25 25443 0", "demos25 25443 167", "demos25 25443 0", "demos25 25610 0", "demos25 25610 31"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the tea pi from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped tea pi into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped tea pi into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grab the blue hand cream on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 25610 0", "demos25 25641 0", "demos25 25641 95", "demos25 25610 0", "demos25 25736 0", "demos25 25736 34"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the blue hand cream on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the blue hand cream on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the blue hand cream on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped blue hand cream into blue nylon bag with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 25736 0", "demos25 25770 0", "demos25 25770 102", "demos25 25736 0", "demos25 25872 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped blue hand cream into blue nylon bag with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped blue hand cream into blue nylon bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped blue hand cream into blue nylon bag with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 26128 0", "demos25 26128 60", "demos25 26128 0", "demos25 26188 0", "demos25 26188 183"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view. Please provide a breakdown of packing in the supermarket into its logical subtasks."}, {"from": "gpt", "value": "A robotic arm is positioned near a blue shopping bag. Various items like bottles, a pink cloth, and packaged goods are arranged on the table around the bag. 0. grasp the left arm with blue nylon bag 1. grab the sandwich cookies on the table with the right arm 2. place the grasped sandwich cookies into blue nylon bag 3. grasp the bath ball on the table with the right arm 4. place the grasped bath ball into blue nylon bag 5. retrieve the tea pi from the table with the right arm 6. place the grasped tea pi into blue nylon bag 7. grab the blue hand cream on the table with the right arm 8. place the grasped blue hand cream into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grasp the left arm with blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the left arm with blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26128 0", "demos25 26371 0", "demos25 26371 201", "demos25 26371 0", "demos25 26572 0", "demos25 26572 177"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the sandwich cookies on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the sandwich cookies on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped sandwich cookies into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26572 0", "demos25 26749 0", "demos25 26749 28", "demos25 26749 0", "demos25 26777 0", "demos25 26777 86"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped sandwich cookies into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the bath ball on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grasp the bath ball on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the bath ball on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26749 0", "demos25 26863 0", "demos25 26863 46", "demos25 26863 0", "demos25 26909 0", "demos25 26909 139"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the bath ball on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped bath ball into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped bath ball into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped bath ball into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 26863 0", "demos25 27048 0", "demos25 27048 29", "demos25 27048 0", "demos25 27077 0", "demos25 27077 87"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is place the grasped bath ball into blue nylon bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with retrieve the tea pi from the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has retrieve the tea pi from the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with retrieve the tea pi from the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 27048 0", "demos25 27164 0", "demos25 27164 54", "demos25 27164 0", "demos25 27218 0", "demos25 27218 164"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is retrieve the tea pi from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped tea pi into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped tea pi into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped tea pi into blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 27164 0", "demos25 27382 0", "demos25 27382 36", "demos25 27382 0", "demos25 27418 0", "demos25 27418 111"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped tea pi into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grab the blue hand cream on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the blue hand cream on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the blue hand cream on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 27382 0", "demos25 27529 0", "demos25 27529 156", "demos25 27529 0", "demos25 27685 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grab the blue hand cream on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped blue hand cream into blue nylon bag with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped blue hand cream into blue nylon bag with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 27889 0", "demos25 27889 299", "demos25 27889 0", "demos25 28188 0", "demos25 28188 42"], "conversations": [{"from": "human", "value": "What is the visual information presented now? To achieve packing in the supermarket, what are the foundational subtasks?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including snacks and a bottle, are arranged on the table near the arm's base. 0. grasp the left arm with blue nylon bag 1. grab the sandwich cookies on the table with the right arm 2. place the grasped sandwich cookies into blue nylon bag 3. grasp the bath ball on the table with the right arm 4. place the grasped bath ball into blue nylon bag 5. retrieve the tea pi from the table with the right arm 6. place the grasped tea pi into blue nylon bag 7. grab the blue hand cream on the table with the right arm 8. place the grasped blue hand cream into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the left arm with blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grasp the left arm with blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grab the sandwich cookies on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28188 0", "demos25 28230 0", "demos25 28230 128", "demos25 28188 0", "demos25 28358 0", "demos25 28358 190"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the sandwich cookies on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the sandwich cookies on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the sandwich cookies on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped sandwich cookies into blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28358 0", "demos25 28548 0", "demos25 28548 121", "demos25 28548 0", "demos25 28669 0", "demos25 28669 127"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped sandwich cookies into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the bath ball on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the bath ball on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped bath ball into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28669 0", "demos25 28796 0", "demos25 28796 119", "demos25 28796 0", "demos25 28915 0", "demos25 28915 47"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped bath ball into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the tea pi from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the tea pi from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped tea pi into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 28915 0", "demos25 28962 0", "demos25 28962 143", "demos25 28915 0", "demos25 29105 0", "demos25 29105 48"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped tea pi into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with place the grasped tea pi into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped tea pi into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the blue hand cream on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 29105 0", "demos25 29153 0", "demos25 29153 145", "demos25 29105 0", "demos25 29298 0", "demos25 29298 278"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grab the blue hand cream on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the blue hand cream on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the blue hand cream on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped blue hand cream into blue nylon bag with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 29298 0", "demos25 29576 0"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped blue hand cream into blue nylon bag with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 29677 0", "demos25 29730 47", "demos25 29730 0", "demos25 29777 0", "demos25 29777 143"], "conversations": [{"from": "human", "value": "Detail the current scene you are perceiving. Please outline the constituent subtasks involved in achieving packing in the supermarket."}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag open. Various items, including snacks and a bottle, are arranged on the table, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the snow cake on the table with the right arm 2. place the grasped snow cake into blue nylon bag 3. grasp the tomato sauce on the table with the right arm 4. place the grasped tomato sauce into blue nylon bag 5. retrieve the orange juice from the table with the right arm 6. place the grasped orange juice into blue nylon bag 7. grab the brown doll on the table with the right arm 8. place the grasped brown doll into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grasp the left arm with blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the left arm with blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 29730 0", "demos25 29920 0", "demos25 29920 47", "demos25 29920 0", "demos25 29967 0", "demos25 29967 144"], "conversations": [{"from": "human", "value": "Judging from the start of this action and the current visual, has grasp the left arm with blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the snow cake on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the snow cake on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the snow cake on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 29920 0", "demos25 30111 0", "demos25 30111 226", "demos25 30111 0", "demos25 30337 0", "demos25 30337 44"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grab the snow cake on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped snow cake into blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped snow cake into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the tomato sauce on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 30337 0", "demos25 30381 0", "demos25 30381 135", "demos25 30337 0", "demos25 30516 0", "demos25 30516 251"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the tomato sauce on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the tomato sauce on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the tomato sauce on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped tomato sauce into blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 30516 0", "demos25 30767 0", "demos25 30767 31", "demos25 30767 0", "demos25 30798 0", "demos25 30798 95"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped tomato sauce into blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the orange juice from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that retrieve the orange juice from the table with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with retrieve the orange juice from the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 30767 0", "demos25 30893 0", "demos25 30893 198", "demos25 30893 0", "demos25 31091 0", "demos25 31091 146"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has retrieve the orange juice from the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped orange juice into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped orange juice into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the brown doll on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31091 0", "demos25 31237 0", "demos25 31237 71", "demos25 31237 0", "demos25 31308 0", "demos25 31308 216"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grab the brown doll on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped brown doll into blue nylon bag with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped brown doll into blue nylon bag with the right arm is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped brown doll into blue nylon bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31237 0", "demos25 31524 0"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped brown doll into blue nylon bag with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 31643 0", "demos25 31643 57", "demos25 31643 0", "demos25 31700 0", "demos25 31700 172"], "conversations": [{"from": "human", "value": "What elements are present in the current visual? To complete packing in the supermarket, what are the essential sub-components of work?"}, {"from": "gpt", "value": "A robotic arm holds a blue shopping bag open on the left. Various items, including a bottle and snacks, are arranged on the table to the right, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the snow cake on the table with the right arm 2. place the grasped snow cake into blue nylon bag 3. grasp the tomato sauce on the table with the right arm 4. place the grasped tomato sauce into blue nylon bag 5. retrieve the orange juice from the table with the right arm 6. place the grasped orange juice into blue nylon bag 7. grab the brown doll on the table with the right arm 8. place the grasped brown doll into blue nylon bag with the right arm "}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grasp the left arm with blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the left arm with blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the left arm with blue nylon bag, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31643 0", "demos25 31872 0", "demos25 31872 43", "demos25 31872 0", "demos25 31915 0", "demos25 31915 129"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the left arm with blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grab the snow cake on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "In light of the initial observation of this task and the current visual, is grab the snow cake on the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the snow cake on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 31872 0", "demos25 32044 0", "demos25 32044 269", "demos25 32044 0", "demos25 32313 0", "demos25 32313 46"], "conversations": [{"from": "human", "value": "Comparing the initial image with the current one, is grab the snow cake on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped snow cake into blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped snow cake into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the tomato sauce on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 32313 0", "demos25 32359 0", "demos25 32359 138", "demos25 32313 0", "demos25 32497 0", "demos25 32497 56"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grasp the tomato sauce on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the tomato sauce on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the tomato sauce on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped tomato sauce into blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 32497 0", "demos25 32553 0", "demos25 32553 171", "demos25 32497 0", "demos25 32724 0", "demos25 32724 184"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped tomato sauce into blue nylon bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To complete place the grasped tomato sauce into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped tomato sauce into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for retrieve the orange juice from the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 32724 0", "demos25 32908 0", "demos25 32908 63", "demos25 32908 0", "demos25 32971 0", "demos25 32971 189"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has retrieve the orange juice from the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped orange juice into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped orange juice into blue nylon bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped orange juice into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 32908 0", "demos25 33160 0", "demos25 33160 24", "demos25 33160 0", "demos25 33184 0", "demos25 33184 75"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped orange juice into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the brown doll on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grab the brown doll on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the brown doll on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 33160 0", "demos25 33259 0", "demos25 33259 208", "demos25 33259 0", "demos25 33467 0"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grab the brown doll on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped brown doll into blue nylon bag with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped brown doll into blue nylon bag with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 33630 0", "demos25 33630 54", "demos25 33630 0", "demos25 33684 0", "demos25 33684 163"], "conversations": [{"from": "human", "value": "Could you describe the scene that is currently visible? What are the individual subtasks necessary to accomplish packing in the supermarket?"}, {"from": "gpt", "value": "The robotic arm holds a box near a blue shopping bag filled with items. Various products, including snacks and beverages, are arranged on the table around the arm. 0. grasp the left arm with blue nylon bag 1. grab the sunflower seeds on the table with the right arm 2. place the grasped sunflower seeds into blue nylon bag 3. grasp the cookie biscuit on the table with the right arm 4. place the grasped cookie biscuit into blue nylon bag 5. retrieve the cola from the table with the right arm 6. place the grasped cola into blue nylon bag 7. grab the facial cleanser on the table with the right arm 8. place the grasped facial cleanser into blue nylon bag with the right arm "}, {"from": "human", "value": "To move forward with grasp the left arm with blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the left arm with blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 33630 0", "demos25 33847 0", "demos25 33847 57", "demos25 33847 0", "demos25 33904 0", "demos25 33904 173"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with blue nylon bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grab the sunflower seeds on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the sunflower seeds on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grab the sunflower seeds on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 33847 0", "demos25 34077 0", "demos25 34077 38", "demos25 34077 0", "demos25 34115 0", "demos25 34115 116"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has grab the sunflower seeds on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped sunflower seeds into blue nylon bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped sunflower seeds into blue nylon bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped sunflower seeds into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34077 0", "demos25 34231 0", "demos25 34231 123", "demos25 34231 0", "demos25 34354 0", "demos25 34354 43"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped sunflower seeds into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the cookie biscuit on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grasp the cookie biscuit on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped cookie biscuit into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34354 0", "demos25 34397 0", "demos25 34397 132", "demos25 34354 0", "demos25 34529 0", "demos25 34529 27"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has place the grasped cookie biscuit into blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped cookie biscuit into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped cookie biscuit into blue nylon bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for retrieve the cola from the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34529 0", "demos25 34556 0", "demos25 34556 82", "demos25 34529 0", "demos25 34638 0", "demos25 34638 47"], "conversations": [{"from": "human", "value": "With the initial view of this task and the current scene, has retrieve the cola from the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the cola from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the cola from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped cola into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34638 0", "demos25 34685 0", "demos25 34685 141", "demos25 34638 0", "demos25 34826 0", "demos25 34826 181"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped cola into blue nylon bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped cola into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped cola into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the facial cleanser on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 34826 0", "demos25 35007 0", "demos25 35007 225", "demos25 35007 0", "demos25 35232 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has grab the facial cleanser on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped facial cleanser into blue nylon bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped facial cleanser into blue nylon bag with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 35326 0", "demos25 35326 53", "demos25 35326 0", "demos25 35379 0", "demos25 35379 162"], "conversations": [{"from": "human", "value": "What is the visual information presented now? Detail the sequence of subtasks required for packing in the supermarket."}, {"from": "gpt", "value": "The robotic arm holds a snack package near a blue shopping bag. A bottle of soda and other items rest on the table beside the arm. 0. grasp the left arm with blue nylon bag 1. grab the sunflower seeds on the table with the right arm 2. place the grasped sunflower seeds into blue nylon bag 3. grasp the cookie biscuit on the table with the right arm 4. place the grasped cookie biscuit into blue nylon bag 5. retrieve the cola from the table with the right arm 6. place the grasped cola into blue nylon bag 7. grab the facial cleanser on the table with the right arm 8. place the grasped facial cleanser into blue nylon bag with the right arm "}, {"from": "human", "value": "Given that grasp the left arm with blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grasp the left arm with blue nylon bag reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 35326 0", "demos25 35541 0", "demos25 35541 160", "demos25 35541 0", "demos25 35701 0", "demos25 35701 155"], "conversations": [{"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the left arm with blue nylon bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grab the sunflower seeds on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering how this action began and what is seen now, has grab the sunflower seeds on the table with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped sunflower seeds into blue nylon bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 35701 0", "demos25 35856 0", "demos25 35856 38", "demos25 35856 0", "demos25 35894 0", "demos25 35894 116"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped sunflower seeds into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grasp the cookie biscuit on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the cookie biscuit on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grasp the cookie biscuit on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 35856 0", "demos25 36010 0", "demos25 36010 135", "demos25 36010 0", "demos25 36145 0", "demos25 36145 28"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grasp the cookie biscuit on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with place the grasped cookie biscuit into blue nylon bag, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped cookie biscuit into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling retrieve the cola from the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36145 0", "demos25 36173 0", "demos25 36173 86", "demos25 36145 0", "demos25 36259 0", "demos25 36259 48"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is retrieve the cola from the table with the right arm now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out retrieve the cola from the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has retrieve the cola from the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped cola into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36259 0", "demos25 36307 0", "demos25 36307 147", "demos25 36259 0", "demos25 36454 0", "demos25 36454 44"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped cola into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped cola into blue nylon bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped cola into blue nylon bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the facial cleanser on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36454 0", "demos25 36498 0", "demos25 36498 135", "demos25 36454 0", "demos25 36633 0", "demos25 36633 37"], "conversations": [{"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grab the facial cleanser on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the facial cleanser on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grab the facial cleanser on the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling place the grasped facial cleanser into blue nylon bag with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36633 0", "demos25 36670 0", "demos25 36670 112", "demos25 36633 0", "demos25 36782 0"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has place the grasped facial cleanser into blue nylon bag with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped facial cleanser into blue nylon bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on what was observed at the start of this action and the current state, has place the grasped facial cleanser into blue nylon bag with the right arm finished?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 36946 0", "demos25 36946 47", "demos25 36946 0", "demos25 36993 0", "demos25 36993 142"], "conversations": [{"from": "human", "value": "What elements are present in the current visual? How would you divide packing in the supermarket into its operational subtasks?"}, {"from": "gpt", "value": "The robotic arm holds a blue shopping bag on the left. Various items, including a soda bottle and snacks, are arranged on the table, ready for packing. 0. grasp the left arm with blue nylon bag 1. grab the sunflower seeds on the table with the right arm 2. place the grasped sunflower seeds into blue nylon bag 3. grasp the cookie biscuit on the table with the right arm 4. place the grasped cookie biscuit into blue nylon bag 5. retrieve the cola from the table with the right arm 6. place the grasped cola into blue nylon bag 7. grab the facial cleanser on the table with the right arm 8. place the grasped facial cleanser into blue nylon bag with the right arm "}, {"from": "human", "value": "To move forward with grasp the left arm with blue nylon bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the way this action began and its current status, has grasp the left arm with blue nylon bag completed?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with blue nylon bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 36946 0", "demos25 37135 0", "demos25 37135 167", "demos25 37135 0", "demos25 37302 0", "demos25 37302 30"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has grasp the left arm with blue nylon bag finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with grab the sunflower seeds on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grab the sunflower seeds on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped sunflower seeds into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 37302 0", "demos25 37332 0", "demos25 37332 92", "demos25 37302 0", "demos25 37424 0", "demos25 37424 36"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has place the grasped sunflower seeds into blue nylon bag wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped sunflower seeds into blue nylon bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped sunflower seeds into blue nylon bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the cookie biscuit on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 37424 0", "demos25 37460 0", "demos25 37460 110", "demos25 37424 0", "demos25 37570 0", "demos25 37570 157"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the cookie biscuit on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grasp the cookie biscuit on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is grasp the cookie biscuit on the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped cookie biscuit into blue nylon bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 37570 0", "demos25 37727 0", "demos25 37727 161", "demos25 37727 0", "demos25 37888 0", "demos25 37888 139"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped cookie biscuit into blue nylon bag come to an end?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with retrieve the cola from the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is retrieve the cola from the table with the right arm now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped cola into blue nylon bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 37888 0", "demos25 38027 0", "demos25 38027 109", "demos25 38027 0", "demos25 38136 0", "demos25 38136 54"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has place the grasped cola into blue nylon bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grab the facial cleanser on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is grab the facial cleanser on the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped facial cleanser into blue nylon bag with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 38136 0", "demos25 38190 0", "demos25 38190 162", "demos25 38136 0", "demos25 38352 0"], "conversations": [{"from": "human", "value": "From the starting observation of this action and the current view, is place the grasped facial cleanser into blue nylon bag with the right arm finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped facial cleanser into blue nylon bag with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped facial cleanser into blue nylon bag with the right arm been completed?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 38469 0", "demos25 38469 78", "demos25 38469 0", "demos25 38547 0", "demos25 38547 235"], "conversations": [{"from": "human", "value": "Provide a scene description of the current view. Detail the sequence of subtasks required for packing in the supermarket."}, {"from": "gpt", "value": "A robotic arm holds a blue cup above a red shopping bag. A bottle of hand sanitizer sits beside the bag. The background features a colorful supermarket display. 0. grasp the left arm with red canvas tote bag 1. grab the hand sanitizer on the table with the right arm 2. place the grasped hand sanitizer into red canvas tote bag 3. grasp the blue toothbrush cup on the table with the right arm 4. place the grasped blue toothbrush cup into red canvas tote bag 5. retrieve the sandwich cookies from the table with the right arm 6. place the grasped sandwich cookies into red canvas tote bag 7. grab the laver on the table with the right arm 8. place the grasped laver into red canvas tote bag with the right arm "}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Based on the initial and current observations, can it be confirmed that grasp the left arm with red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that grasp the left arm with red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 38469 0", "demos25 38782 0", "demos25 38782 33", "demos25 38782 0", "demos25 38815 0", "demos25 38815 101"], "conversations": [{"from": "human", "value": "Upon reviewing the initial and current observations, is grasp the left arm with red canvas tote bag finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the hand sanitizer on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the initial view of this task and the current scene, has grab the hand sanitizer on the table with the right arm reached completion?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out grab the hand sanitizer on the table with the right arm, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 38782 0", "demos25 38916 0", "demos25 38916 256", "demos25 38916 0", "demos25 39172 0", "demos25 39172 38"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is grab the hand sanitizer on the table with the right arm complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with place the grasped hand sanitizer into red canvas tote bag, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped hand sanitizer into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grasp the blue toothbrush cup on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39172 0", "demos25 39210 0", "demos25 39210 116", "demos25 39172 0", "demos25 39326 0", "demos25 39326 50"], "conversations": [{"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the blue toothbrush cup on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the blue toothbrush cup on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has grasp the blue toothbrush cup on the table with the right arm been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped blue toothbrush cup into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39326 0", "demos25 39376 0", "demos25 39376 150", "demos25 39326 0", "demos25 39526 0", "demos25 39526 32"], "conversations": [{"from": "human", "value": "In light of the initial observation of this task and the current visual, is place the grasped blue toothbrush cup into red canvas tote bag now finished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped blue toothbrush cup into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "With the beginning observation of this step and the current observation, has place the grasped blue toothbrush cup into red canvas tote bag been fully carried out?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To move forward with retrieve the sandwich cookies from the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39526 0", "demos25 39558 0", "demos25 39558 97", "demos25 39526 0", "demos25 39655 0", "demos25 39655 58"], "conversations": [{"from": "human", "value": "Given the visual information from the start of this action and the present, has retrieve the sandwich cookies from the table with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the sandwich cookies from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Upon reviewing the initial and current observations, is retrieve the sandwich cookies from the table with the right arm finalized?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped sandwich cookies into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39655 0", "demos25 39713 0", "demos25 39713 174", "demos25 39655 0", "demos25 39887 0", "demos25 39887 40"], "conversations": [{"from": "human", "value": "With the starting conditions of this action and the current view, has place the grasped sandwich cookies into red canvas tote bag been executed successfully?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for place the grasped sandwich cookies into red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped sandwich cookies into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that grab the laver on the table with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 39887 0", "demos25 39927 0", "demos25 39927 122", "demos25 39887 0", "demos25 40049 0", "demos25 40049 349"], "conversations": [{"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the laver on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grab the laver on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has grab the laver on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped laver into red canvas tote bag with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 40049 0", "demos25 40398 0"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has place the grasped laver into red canvas tote bag with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 40541 0", "demos25 40572 76", "demos25 40572 0", "demos25 40648 0", "demos25 40648 230"], "conversations": [{"from": "human", "value": "Please give an account of the current scene. Please outline the constituent subtasks involved in achieving packing in the supermarket."}, {"from": "gpt", "value": "The robotic arm holds a red bag on the left. Various items, including a bottle and a box, are neatly arranged on the table to the right. 0. grasp the left arm with red canvas tote bag 1. grab the packaged ham sausage on the table with the right arm 2. place the grasped packaged ham sausage into red canvas tote bag 3. grasp the hair conditioner on the table with the right arm 4. place the grasped hair conditioner into red canvas tote bag 5. retrieve the sunflower seeds from the table with the right arm 6. place the grasped sunflower seeds into red canvas tote bag 7. grab the sweet bean sauce on the table with the right arm 8. place the grasped sweet bean sauce into red canvas tote bag with the right arm "}, {"from": "human", "value": "Given that grasp the left arm with red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "By comparing the start observation of this action step with the current observation, has grasp the left arm with red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grasp the left arm with red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 40572 0", "demos25 40878 0", "demos25 40878 50", "demos25 40878 0", "demos25 40928 0", "demos25 40928 152"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the left arm with red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that we are working on packing in the supermarket, and currently handling grab the packaged ham sausage on the table with the right arm, what is the precise action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grab the packaged ham sausage on the table with the right arm concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with grab the packaged ham sausage on the table with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 40878 0", "demos25 41080 0", "demos25 41080 76", "demos25 41080 0", "demos25 41156 0", "demos25 41156 228"], "conversations": [{"from": "human", "value": "Given the starting visual and the present observation, has grab the packaged ham sausage on the table with the right arm reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "With packing in the supermarket as the main task, and place the grasped packaged ham sausage into red canvas tote bag being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped packaged ham sausage into red canvas tote bag been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "Given that place the grasped packaged ham sausage into red canvas tote bag is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 41080 0", "demos25 41384 0", "demos25 41384 182", "demos25 41384 0", "demos25 41566 0", "demos25 41566 66"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has place the grasped packaged ham sausage into red canvas tote bag been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the hair conditioner on the table with the right arm, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the hair conditioner on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped hair conditioner into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 41566 0", "demos25 41632 0", "demos25 41632 200", "demos25 41566 0", "demos25 41832 0", "demos25 41832 173"], "conversations": [{"from": "human", "value": "Based on the initial and current observations, can it be confirmed that place the grasped hair conditioner into red canvas tote bag is complete?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for place the grasped hair conditioner into red canvas tote bag, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped hair conditioner into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask retrieve the sunflower seeds from the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 41832 0", "demos25 42005 0", "demos25 42005 182", "demos25 42005 0", "demos25 42187 0", "demos25 42187 167"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the sunflower seeds from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask place the grasped sunflower seeds into red canvas tote bag, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has place the grasped sunflower seeds into red canvas tote bag been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given the objective packing in the supermarket, and the active subtask grab the sweet bean sauce on the table with the right arm, what is the exact action chunk for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42187 0", "demos25 42354 0", "demos25 42354 66", "demos25 42354 0", "demos25 42420 0", "demos25 42420 199"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grab the sweet bean sauce on the table with the right arm reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "Given that place the grasped sweet bean sauce into red canvas tote bag with the right arm is the current focus for packing in the supermarket, what detailed action should the robotic arm carry out?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the visual information from the start of this action and the present, has place the grasped sweet bean sauce into red canvas tote bag with the right arm been finalized?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "To move forward with place the grasped sweet bean sauce into red canvas tote bag with the right arm, as part of packing in the supermarket, what is the next distinct action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42354 0", "demos25 42619 0"], "conversations": [{"from": "human", "value": "Considering how this action began and what is seen now, has place the grasped sweet bean sauce into red canvas tote bag with the right arm come to an end?"}, {"from": "gpt", "value": "yes"}], "view": [["observation.images.head"], ["observation.images.head"]]} -{"lerobot": ["demos25 42661 0", "demos25 42688 41", "demos25 42688 0", "demos25 42729 0", "demos25 42729 124"], "conversations": [{"from": "human", "value": "What elements are present in the current visual? How would you divide packing in the supermarket into its operational subtasks?"}, {"from": "gpt", "value": "A robotic arm hovers over a table filled with items like a red shirt, snacks, and a roll of paper towels, ready for packing. 0. grasp the left arm with red canvas tote bag 1. grab the packaged ham sausage on the table with the right arm 2. place the grasped packaged ham sausage into red canvas tote bag 3. grasp the hair conditioner on the table with the right arm 4. place the grasped hair conditioner into red canvas tote bag 5. retrieve the sunflower seeds from the table with the right arm 6. place the grasped sunflower seeds into red canvas tote bag 7. grab the sweet bean sauce on the table with the right arm 8. place the grasped sweet bean sauce into red canvas tote bag with the right arm "}, {"from": "human", "value": "To complete grasp the left arm with red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Reflecting on the beginning observation and the current state, has grasp the left arm with red canvas tote bag concluded?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "What is the exact robotic arm action required for grasp the left arm with red canvas tote bag, as part of working towards packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42688 0", "demos25 42853 0", "demos25 42853 140", "demos25 42853 0", "demos25 42993 0", "demos25 42993 208"], "conversations": [{"from": "human", "value": "Given the initial visual of this action step and the current scene, has grasp the left arm with red canvas tote bag reached its completion point?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the individual action that the robotic arm needs to perform for grab the packaged ham sausage on the table with the right arm, while working on packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Judging from the start of this action and the current visual, has grab the packaged ham sausage on the table with the right arm been accomplished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped packaged ham sausage into red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 42993 0", "demos25 43201 0", "demos25 43201 50", "demos25 43201 0", "demos25 43251 0", "demos25 43251 153"], "conversations": [{"from": "human", "value": "Based on the observation at the start of this action and the current observation, is place the grasped packaged ham sausage into red canvas tote bag complete?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with grasp the hair conditioner on the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Taking into account the start of this action and what is currently observed, has grasp the hair conditioner on the table with the right arm wrapped up?"}, {"from": "gpt", "value": "no"}, {"from": "human", "value": "With packing in the supermarket as the main task, and grasp the hair conditioner on the table with the right arm being the current subtask, what specific motion is required from the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 43201 0", "demos25 43404 0", "demos25 43404 272", "demos25 43404 0", "demos25 43676 0", "demos25 43676 158"], "conversations": [{"from": "human", "value": "Considering the initial observation of this action and the current view, has grasp the hair conditioner on the table with the right arm been completed?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the specific movement sequence for the robotic arm to carry out place the grasped hair conditioner into red canvas tote bag, within the context of packing in the supermarket?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Comparing the initial image with the current one, is place the grasped hair conditioner into red canvas tote bag now done?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "What is the immediate, detailed action the robotic arm needs to take to proceed with retrieve the sunflower seeds from the table with the right arm, as part of packing in the supermarket?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"], ["observation.images.head"], ["observation.images.head"], ["observation.images.head", "observation.images.hand_left", "observation.images.hand_right"]]} -{"lerobot": ["demos25 43676 0", "demos25 43834 0", "demos25 43834 176", "demos25 43834 0", "demos25 44010 0", "demos25 44010 119"], "conversations": [{"from": "human", "value": "Based on what was observed at the start of this action and the current state, has retrieve the sunflower seeds from the table with the right arm finished?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete place the grasped sunflower seeds into red canvas tote bag within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}, {"from": "human", "value": "Given the starting visual and the present observation, has place the grasped sunflower seeds into red canvas tote bag reached its conclusion?"}, {"from": "gpt", "value": "yes"}, {"from": "human", "value": "To complete grab the sweet bean sauce on the table with the right arm within the greater task of packing in the supermarket, what is the next fine-grained action for the robotic arm?"}, {"from": "gpt", "value": ""}], "view": [["observation.images.head"], 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plaintext."}, {"from": "gpt", "value": "```plaintext\n86,82,345,249 front bowl w/carrots in it\n```"}], "seq_length": 183} {"question_id": "710706", "image": "images/COCO_train2014_000000580957_2.jpg", "conversations": [{"from": "human", "value": "Locate bowl of carrots and annotate its bounding box, providing the output in json format."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [86, 82, 345, 249], \"label\": \"bowl of carrots\"}\n]\n```"}], "seq_length": 198} -{"question_id": "710706", "image": "images/COCO_train2014_000000580957_2.jpg", "conversations": [{"from": "human", "value": "Output in plaintext format the bounding box for bowl of carrots."}, {"from": "gpt", "value": "```plaintext\n86,82,345,249 bowl of carrots\n```"}], "seq_length": 173} -{"question_id": "710706", "image": "images/COCO_train2014_000000580957_2.jpg", "conversations": [{"from": "human", "value": "Identify and mark the bounding box for carrots, with results in json format."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [86, 82, 345, 249], \"label\": \"carrots\"}\n]\n```"}], "seq_length": 194} {"question_id": "480908", "image": "images/COCO_train2014_000000580905_2.jpg", "conversations": [{"from": "human", "value": "Identify and mark the bounding box for little girl."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [67, 157, 167, 252], \"label\": \"little girl\"}\n]\n```"}], "seq_length": 190} -{"question_id": "285480", "image": "images/COCO_train2014_000000263111_0.jpg", "conversations": [{"from": "human", "value": "Output in plaintext format the bounding box for colorful umbrella."}, {"from": "gpt", "value": "```plaintext\n169,19,299,105 colorful umbrella\n```"}], "seq_length": 172} -{"question_id": "98434", "image": "images/COCO_train2014_000000168643_2.jpg", "conversations": [{"from": "human", "value": "In json format, locate and draw a bounding box around left couch."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [17, 138, 163, 240], \"label\": \"left couch\"}\n]\n```"}], "seq_length": 193} {"question_id": "98434", "image": "images/COCO_train2014_000000168643_2.jpg", "conversations": [{"from": "human", "value": "In json format, please annotate the bounding box for 3 cushion couch."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [17, 138, 163, 240], \"label\": \"3 cushion couch\"}\n]\n```"}], "seq_length": 195} +{"question_id": "1071411", "image": "images/COCO_train2014_000000579299_4.jpg", "conversations": [{"from": "human", "value": "Format the bounding box coordinates for front pizza."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [7, 118, 298, 299], \"label\": \"front pizza\"}\n]\n```"}]} +{"question_id": "2191497", "image": "images/COCO_train2014_000000567396_13.jpg", "conversations": [{"from": "human", "value": "please annotate the bounding box for horse far left."}, {"from": "gpt", "value": "```json\n[\n {\"bbox_2d\": [18, 88, 70, 237], \"label\": \"horse far left\"}\n]\n```"}]} +{"question_id": "710706", "image": "images/COCO_train2014_000000580957_2.jpg", "conversations": [{"from": "human", "value": "Please provide the bounding box for front bowl w/carrots in it, formatted as plaintext."}, {"from": "gpt", "value": "```plaintext\n86,82,345,249 front bowl w/carrots in it\n```"}], "seq_length": 183} +{"question_id": "710706", "image": "images/COCO_train2014_000000580957_2.jpg", "conversations": [{"from": "human", "value": "Output in plaintext format the bounding box for bowl of carrots."}, {"from": "gpt", "value": "```plaintext\n86,82,345,249 bowl of carrots\n```"}], "seq_length": 173} +{"question_id": "285480", "image": "images/COCO_train2014_000000263111_0.jpg", "conversations": [{"from": "human", "value": "Output in plaintext format the bounding box for colorful umbrella."}, {"from": "gpt", "value": "```plaintext\n169,19,299,105 colorful umbrella\n```"}], "seq_length": 172} {"question_id": "98434", "image": "images/COCO_train2014_000000168643_2.jpg", "conversations": [{"from": "human", "value": "Draw a bounding box around left couch, and return the coordinates in plaintext format."}, {"from": "gpt", "value": "```plaintext\n17,138,163,240 left couch\n```"}], "seq_length": 177} diff --git a/eo/data/lerobot_dataset.py b/eo/data/lerobot_dataset.py index e6fb386..d59bb69 100644 --- a/eo/data/lerobot_dataset.py +++ b/eo/data/lerobot_dataset.py @@ -148,8 +148,6 @@ def set_weight(self, weight: float | None): def set_delta_action(self, delta_action: bool, effector_indices: list[int] | None = None): """set delta action mode for lerobot dataset""" - - print(f"* set delta action mode for {self.repo_id} ...") self.delta_action = delta_action self.effector_indices = effector_indices or [] @@ -158,6 +156,8 @@ def set_delta_action(self, delta_action: bool, effector_indices: list[int] | Non import numpy as np + print(f"* set delta action mode for {self.repo_id} ...") + acum_idx = 0 cumulative_lengths = self.episode_data_index["to"] for k in self.select_action_keys: @@ -321,7 +321,7 @@ def _features(self) -> dict[str, dict]: def post_process(self, item: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: """sort the keys in the order of select_feature_keys""" - # item = {k: item[k] for k in (self.select_feature_keys + ["task"] + self.select_action_is_pad_keys)} + item = {k: item[k] for k in (self.select_feature_keys + ["task"] + self.select_action_is_pad_keys)} item = self.normalizer(item) return item @@ -348,7 +348,6 @@ def __init__( # load lerobot datasets num_processes = int(os.environ.get("DATASET_NUM_PROCESSES", 8)) - # num_processes = min(int(os.cpu_count() * 0.8), int(os.environ.get("DATASET_NUM_PROCESSES", 10))) print(f"* load {len(data_configs)} lerobot datasets with {num_processes} processes ...") pool = multiprocessing.Pool(processes=num_processes) fn = partial( diff --git a/eo/model/configuration_eo1.py b/eo/model/configuration_eo1.py index 6f915a8..a59825e 100644 --- a/eo/model/configuration_eo1.py +++ b/eo/model/configuration_eo1.py @@ -55,7 +55,7 @@ def __init__( # flow matching specific action_chunk_size=50, max_action_dim=32, - num_denoise_steps=5, + num_denoise_steps=10, action_act="linear", num_action_layers=2, **kwargs, diff --git a/eo/model/processor.py b/eo/model/processing_eo1.py similarity index 100% rename from eo/model/processor.py rename to eo/model/processing_eo1.py diff --git a/eo/train/pipeline_config.py b/eo/train/pipeline_config.py index a440173..badccc2 100644 --- a/eo/train/pipeline_config.py +++ b/eo/train/pipeline_config.py @@ -83,7 +83,7 @@ class TrainPipelineConfig(TrainingArguments): num_lora_modules: int = -1 """experiment parameters""" - output_base: str = field(default="experiments/outputs", metadata={"help": "Base directory for output."}) + output_base: str = field(default="outputs", metadata={"help": "Base directory for output."}) def __post_init__(self): super().__post_init__() diff --git a/experiments/1_demo/README.md b/experiments/1_demo/README.md index e69de29..7d0a2ef 100644 --- a/experiments/1_demo/README.md +++ b/experiments/1_demo/README.md @@ -0,0 +1,128 @@ +# Demo Training and Evaluation + +This directory contains the implementation for training and evaluating EO-1 on the demos25 dataset, a dual-arm robot manipulation dataset for supermarket packing tasks. + +## Overview + +The demo experiment combines: + +- **Robot Control Data**: Demos25 dataset with 25 episodes of dual-arm manipulation +- **Multimodal Data**: RefCOCO dataset for vision-language understanding +- **Interleaved Data**: Combined robot control and multimodal conversations + +## Dataset Preparation + +### 1. Download Datasets + +```bash +# Install Hugging Face CLI if not already installed +pip install huggingface-cli +huggingface-cli login + +# Download demos25 dataset +huggingface-cli download --resume-download --local-dir-use-symlinks False --repo-type dataset \ + IPEC-COMMUNITY/demos25 --local-dir ../demo_data/demos25 + +# Download RefCOCO dataset (if not already available) +# The RefCOCO dataset should be placed in demo_data/refcoco/ +``` + +### 2. Configure Dataset Paths + +Update the dataset configuration in `experiments/1_demo/data-demo.yaml`: + +```yaml +mm_datasets: + - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file + vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL + sampling_strategy: random:100% # sampling strategy + + - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl + +# @robot control config +lerobot_datasets: + - repo_id: demos25 + root: ./demo_data + # Optional fields: + # episodes: [1, 2, 3] # specific episodes to load (None = all) + train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task + delta_action: false # train with delta actions + state_mode: "MEAN_STD" # state normalization mode + # which camera streams to load + select_video_keys: + [ + observation.images.head, + observation.images.hand_left, + observation.images.hand_right, + ] + # proprioceptive states + select_state_keys: + [observation.states.joint.position, observation.states.effector.position] + # action targets + select_action_keys: [actions.joint.position, actions.effector.position] + effector_indices: [14, 15] # indices of effector channels in the flattened action vector + weight: 1.0 # dataset weight for sampling +``` + +## Training + +### Training Configuration + +The training script (`train.sh`) is configured with the following hyperparameters: + +- **GPUs**: 8 GPUs for distributed training +- **Batch Size**: 32 per device (total effective batch size: 256) +- **Learning Rates**: + - backbone: 1e-4 + - merger: 1e-4 + - vision tower: 2e-5 +- **Epochs**: 10 +- **Chunk Size**: 16 (for sequence processing) +- **Optimization**: AdamW with cosine learning rate scheduling +- **Precision**: BF16 with TF32 enabled + +### Start Training + +```bash +bash experiments/1_demo/train.sh +``` + +The training will: + +- Use the Qwen2.5-VL-3B-Instruct vision-language model as the base +- Train on both robot control and multimodal data simultaneously +- Save checkpoints every 1000 steps +- Use gradient checkpointing and flash attention for memory efficiency +- Log training progress every 100 steps + +## Evaluation + +### Run Evaluation + +Use the following command to run evaluation: + +```bash +# Set the path to your trained checkpoint +ckpt_path=PATH_TO_CHECKPOINT + +# Run evaluation +python tools/openloop.py \ + --args.repo_id demos25 \ + --args.root ./demo_data \ + --args.model_path ${ckpt_path} \ + --args.num_step 10 \ + --args.train_subtask True +``` + +The script will visualize the inference action trajectory. With the following similar result: + + + +## File Structure + +``` +experiments/1_demo/ +├── README.md # This file +├── train.sh # Training script +└── data-demo.yaml # Dataset configuration +``` diff --git a/experiments/1_demo/data-demo.yaml b/experiments/1_demo/data-demo.yaml index 1027e97..f217180 100644 --- a/experiments/1_demo/data-demo.yaml +++ b/experiments/1_demo/data-demo.yaml @@ -1,7 +1,7 @@ mm_datasets: - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL - sampling_strategy: random:10% # sampling strategy + sampling_strategy: random:100% # sampling strategy - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl diff --git a/experiments/1_demo/train.sh b/experiments/1_demo/train.sh index 9fced30..c81033d 100644 --- a/experiments/1_demo/train.sh +++ b/experiments/1_demo/train.sh @@ -1,5 +1,5 @@ GPUS=1 -PER_DEVICE_BATCH_SIZE=16 +PER_DEVICE_BATCH_SIZE=8 ACCELERATE_ARGS="--num_machines 1 --machine_rank 0 --num_processes=${GPUS}" @@ -15,14 +15,12 @@ vlr=2e-5 chunk_size=30 epoch=50 -model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} . scripts/env.sh conda activate eo accelerate launch $ACCELERATE_ARGS scripts/train.py \ - ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ --data-path ${dataset} \ --chunk-size ${chunk_size} \ diff --git a/experiments/2_libero/README.md b/experiments/2_libero/README.md index cc42f34..be90ca8 100644 --- a/experiments/2_libero/README.md +++ b/experiments/2_libero/README.md @@ -25,19 +25,21 @@ pip install huggingface-cli # Download all Libero datasets datasets=( - IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot - IPEC-COMMUNITY/libero_object_no_noops_1.0.0_lerobot - IPEC-COMMUNITY/libero_90_no_noops_lerobot - IPEC-COMMUNITY/libero_10_no_noops_1.0.0_lerobot + libero_spatial_no_noops_1.0.0_lerobot + libero_object_no_noops_1.0.0_lerobot + libero_90_no_noops_lerobot + libero_10_no_noops_1.0.0_lerobot ) +HF_LEROBOT_HOME=YOUR_PATH_TO_DATASET + for dataset in ${datasets[@]}; do echo "Downloading ${dataset}..." huggingface-cli download \ --repo-type dataset --resume-download --local-dir-use-symlinks False \ - ${dataset} \ - --local-dir ${dataset} + IPEC-COMMUNITY/${dataset} \ + --local-dir ${HF_LEROBOT_HOME}/${dataset} done ``` diff --git a/experiments/2_libero/data-libero.yaml b/experiments/2_libero/data-libero.yaml index a4ec8bf..fb08f31 100644 --- a/experiments/2_libero/data-libero.yaml +++ b/experiments/2_libero/data-libero.yaml @@ -3,25 +3,25 @@ mm_datasets: lerobot_datasets: - repo_id: libero_spatial_no_noops_1.0.0_lerobot - root: ./demo_data/ + root: HF_LEROBOT_HOME select_video_keys: [observation.images.image, observation.images.wrist_image] select_state_keys: [observation.state] select_action_keys: [action] - # - repo_id: libero_90_no_noops_lerobot - # root: HF_LEROBOT_HOME - # select_video_keys: [observation.images.image, observation.images.wrist_image] - # select_state_keys: [observation.state] - # select_action_keys: [action] + - repo_id: libero_90_no_noops_lerobot + root: HF_LEROBOT_HOME + select_video_keys: [observation.images.image, observation.images.wrist_image] + select_state_keys: [observation.state] + select_action_keys: [action] - # - repo_id: libero_object_no_noops_1.0.0_lerobot - # root: HF_LEROBOT_HOME - # select_video_keys: [observation.images.image, observation.images.wrist_image] - # select_state_keys: [observation.state] - # select_action_keys: [action] + - repo_id: libero_object_no_noops_1.0.0_lerobot + root: HF_LEROBOT_HOME + select_video_keys: [observation.images.image, observation.images.wrist_image] + select_state_keys: [observation.state] + select_action_keys: [action] - # - repo_id: libero_10_no_noops_1.0.0_lerobot - # root: HF_LEROBOT_HOME - # select_video_keys: [observation.images.image, observation.images.wrist_image] - # select_state_keys: [observation.state] - # select_action_keys: [action] + - repo_id: libero_10_no_noops_1.0.0_lerobot + root: HF_LEROBOT_HOME + select_video_keys: [observation.images.image, observation.images.wrist_image] + select_state_keys: [observation.state] + select_action_keys: [action] diff --git a/experiments/3_simpler/README.md b/experiments/3_simpler/README.md index 0964ecc..bb30c6c 100644 --- a/experiments/3_simpler/README.md +++ b/experiments/3_simpler/README.md @@ -77,17 +77,19 @@ pip install huggingface-cli # Download all SimplerEnv datasets datasets=( - IPEC-COMMUNITY/fractal20220817_data_lerobot - IPEC-COMMUNITY/bridge_orig_lerobot + fractal20220817_data_lerobot + bridge_orig_lerobot ) +HF_LEROBOT_HOME=YOUR_PATH_TO_DATASET + for dataset in ${datasets[@]}; do echo "Downloading ${dataset}..." huggingface-cli download \ --repo-type dataset --resume-download --local-dir-use-symlinks False \ - ${dataset} \ - --local-dir ${dataset} + IPEC-COMMUNITY/${dataset} \ + --local-dir ${HF_LEROBOT_HOME}/${dataset} done ``` diff --git a/experiments/8_vllmeval/README.md b/experiments/8_vllmeval/README.md index a6531e7..4f0d2cb 100644 --- a/experiments/8_vllmeval/README.md +++ b/experiments/8_vllmeval/README.md @@ -48,7 +48,7 @@ pip install huggingface-cli # Download EO-1-3B model huggingface-cli download --resume-download --local-dir-use-symlinks False \ IPEC-COMMUNITY/EO-1-3B \ - --local-dir IPEC-COMMUNITY/EO-1-3B + --local-dir EO-1-3B ``` ### 2. Download Benchmark Datasets @@ -58,18 +58,20 @@ cd YOUR_PATH_TO_DATASET # Download all benchmark datasets datasets=( - IPEC-COMMUNITY/EO-Bench - IPEC-COMMUNITY/ERQABench - IPEC-COMMUNITY/RoboVQA + ERQABench + RoboVQA + EO-Bench ) +HF_DATASET_HOME=YOUR_PATH_TO_DATASET + for dataset in ${datasets[@]}; do echo "Downloading ${dataset}..." huggingface-cli download \ --repo-type dataset --resume-download --local-dir-use-symlinks False \ - ${dataset} \ - --local-dir ${dataset} + IPEC-COMMUNITY/${dataset} \ + --local-dir ${HF_DATASET_HOME}/${dataset} done ``` diff --git a/getting_started/1_load_dataset.ipynb b/getting_started/1_load_dataset.ipynb index 8181d21..904b603 100644 --- a/getting_started/1_load_dataset.ipynb +++ b/getting_started/1_load_dataset.ipynb @@ -7,12 +7,27 @@ "source": [ "# Guide to load dataset for inference\n", "\n", + "This tutorial will show how to load data in LeRobot Format by using our enhanced `LeRobotDataset`, `MultiLeRobotDataset`, `MultimodaDataset`.\n", "\n", - "## 1. LeRobot Format\n", + "```bash\n", + "eo/data/\n", + "├── dataset.py\n", + "├── lerobot_dataset.py\n", + "├── multim_dataset.py\n", + "├── schema.py\n", + "└── transforms.py\n", + "```\n", "\n", - "* This tutorial will show how to load data in LeRobot Format by using our dataloader. \n", - "* We will use the `robot_sim.PickNPlace` dataset as an example which is already converted to LeRobot Format. \n", - "* To understand how to convert your own dataset, please refer to [Gr00t's LeRobot.md](LeRobot_compatible_data_schema.md)" + "## 1. Load LeRobot Dataset\n", + "\n", + "This `LeRobotDataset` module provides custom LeRobot dataset implementations that extend the base classes from the `lerobot` library.\n", + "- Subtask training modes.\n", + "- Selection of specific video, state, and action keys.\n", + "- Dataset weighting for sampling.\n", + "- Delta action calculation.\n", + "- State and action normalization.\n", + "\n", + "Here, we use a demo dataset `demos25` to show how to load data. Please download the dataset from the hub with the following command:" ] }, { @@ -23,7 +38,8 @@ "outputs": [], "source": [ "# download dataset from hub\n", - "!huggingface-cli download libero_10_no_noops_1.0.0_lerobot --repo-id libero_10_no_noops_1.0.0_lerobot --allow-patterns \"*.mp4\"" + "!huggingface-cli download --resume-download --local-dir-use-symlinks False --repo-type dataset \\\n", + " IPEC-COMMUNITY/demos25 --local-dir ../demo_data/test_demos25" ] }, { @@ -31,12 +47,12 @@ "id": "9f1c6661", "metadata": {}, "source": [ - "- meta data" + "- load meta data" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "121fb6a6", "metadata": {}, "outputs": [ @@ -47,11 +63,11 @@ " Repository ID: 'demos25',\n", " Total episodes: '25',\n", " Total frames: '44492',\n", - " Features: '['observation.images.head', 'observation.images.head_center_fisheye', 'observation.images.head_left_fisheye', 'observation.images.head_right_fisheye', 'observation.images.hand_left', 'observation.images.hand_right', 'observation.images.back_left_fisheye', 'observation.images.back_right_fisheye', 'observation.states.effector.position', 'observation.states.end.orientation', 'observation.states.end.position', 'observation.states.head.position', 'observation.states.joint.current_value', 'observation.states.joint.position', 'observation.states.robot.orientation', 'observation.states.robot.position', 'observation.states.waist.position', 'actions.effector.position', 'actions.end.orientation', 'actions.end.position', 'actions.head.position', 'actions.joint.position', 'actions.robot.velocity', 'actions.waist.position', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index']',\n", + " Features: '['observation.images.head', 'observation.images.hand_left', 'observation.images.hand_right', 'observation.states.effector.position', 'observation.states.end.orientation', 'observation.states.end.position', 'observation.states.head.position', 'observation.states.joint.current_value', 'observation.states.joint.position', 'observation.states.robot.orientation', 'observation.states.robot.position', 'observation.states.waist.position', 'actions.effector.position', 'actions.end.orientation', 'actions.end.position', 'actions.head.position', 'actions.joint.position', 'actions.robot.velocity', 'actions.waist.position', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index']',\n", "})'," ] }, - "execution_count": 7, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -68,25 +84,29 @@ "meta" ] }, + { + "cell_type": "markdown", + "id": "c09eaf2b", + "metadata": {}, + "source": [ + "- load data" + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "901ba114", "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "One or several timestamps unexpectedly violate the tolerance inside episode range.\n This might be due to synchronization issues during data collection.\n \n[{'diff': np.float32(-59.433334),\n 'episode_index': 0,\n 'timestamps': [np.float32(59.433334), np.float32(0.0)]},\n {'diff': np.float32(-66.96667),\n 'episode_index': 1,\n 'timestamps': [np.float32(66.96667), np.float32(0.0)]},\n {'diff': np.float32(-59.033333),\n 'episode_index': 2,\n 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n {'diff': np.float32(-58.533333),\n 'episode_index': 3,\n 'timestamps': [np.float32(58.533333), np.float32(0.0)]},\n {'diff': np.float32(-61.2),\n 'episode_index': 4,\n 'timestamps': [np.float32(61.2), np.float32(0.0)]},\n {'diff': np.float32(-66.0),\n 'episode_index': 5,\n 'timestamps': [np.float32(66.0), np.float32(0.0)]},\n {'diff': np.float32(-60.166668),\n 'episode_index': 6,\n 'timestamps': [np.float32(60.166668), np.float32(0.0)]},\n {'diff': np.float32(-63.366665),\n 'episode_index': 7,\n 'timestamps': [np.float32(63.366665), np.float32(0.0)]},\n {'diff': np.float32(-61.0),\n 'episode_index': 8,\n 'timestamps': [np.float32(61.0), np.float32(0.0)]},\n {'diff': np.float32(-57.1),\n 'episode_index': 9,\n 'timestamps': [np.float32(57.1), np.float32(0.0)]},\n {'diff': np.float32(-59.166668),\n 'episode_index': 10,\n 'timestamps': [np.float32(59.166668), np.float32(0.0)]},\n {'diff': np.float32(-59.033333),\n 'episode_index': 11,\n 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n {'diff': np.float32(-57.266666),\n 'episode_index': 12,\n 'timestamps': [np.float32(57.266666), np.float32(0.0)]},\n {'diff': np.float32(-52.933334),\n 'episode_index': 13,\n 'timestamps': [np.float32(52.933334), np.float32(0.0)]},\n {'diff': np.float32(-58.933334),\n 'episode_index': 14,\n 'timestamps': [np.float32(58.933334), np.float32(0.0)]},\n {'diff': np.float32(-57.8),\n 'episode_index': 15,\n 'timestamps': [np.float32(57.8), np.float32(0.0)]},\n {'diff': np.float32(-55.7),\n 'episode_index': 16,\n 'timestamps': [np.float32(55.7), np.float32(0.0)]},\n {'diff': np.float32(-61.933334),\n 'episode_index': 17,\n 'timestamps': [np.float32(61.933334), np.float32(0.0)]},\n {'diff': np.float32(-55.433334),\n 'episode_index': 18,\n 'timestamps': [np.float32(55.433334), np.float32(0.0)]},\n {'diff': np.float32(-57.0),\n 'episode_index': 19,\n 'timestamps': [np.float32(57.0), np.float32(0.0)]},\n {'diff': np.float32(-63.066666),\n 'episode_index': 20,\n 'timestamps': [np.float32(63.066666), np.float32(0.0)]},\n {'diff': np.float32(-61.833332),\n 'episode_index': 21,\n 'timestamps': [np.float32(61.833332), np.float32(0.0)]},\n {'diff': np.float32(-54.033333),\n 'episode_index': 22,\n 'timestamps': [np.float32(54.033333), np.float32(0.0)]},\n {'diff': np.float32(-54.833332),\n 'episode_index': 23,\n 'timestamps': [np.float32(54.833332), np.float32(0.0)]}]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m select_action_keys \u001b[38;5;241m=\u001b[39m [k \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m meta\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;28;01mif\u001b[39;00m k\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maction\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n\u001b[0;32m----> 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mLeRobotDataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdemos25\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../demo_data/demos25\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance_s\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelta_timestamps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mselect_action_keys\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m dataset[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/EO-1/eo/data/lerobot_dataset.py:82\u001b[0m, in \u001b[0;36mLeRobotDataset.__init__\u001b[0;34m(self, repo_id, root, episodes, image_transforms, delta_timestamps, tolerance_s, revision, force_cache_sync, download_videos, video_backend, state_mode, select_video_keys, select_state_keys, select_action_keys, train_subtask, delta_action, effector_indices, weight, chunk_size)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 61\u001b[0m repo_id: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 80\u001b[0m chunk_size: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m,\n\u001b[1;32m 81\u001b[0m ):\n\u001b[0;32m---> 82\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 85\u001b[0m \u001b[43m \u001b[49m\u001b[43mepisodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepisodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_transforms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_transforms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 87\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelta_timestamps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelta_timestamps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance_s\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance_s\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_cache_sync\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_cache_sync\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_videos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_videos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 92\u001b[0m \u001b[43m \u001b[49m\u001b[43mvideo_backend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvideo_backend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 93\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;66;03m# set weight for the dataset\u001b[39;00m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight(weight)\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/miniconda3/envs/eo/lib/python3.10/site-packages/lerobot/datasets/lerobot_dataset.py:492\u001b[0m, in \u001b[0;36mLeRobotDataset.__init__\u001b[0;34m(self, repo_id, root, episodes, image_transforms, delta_timestamps, tolerance_s, revision, force_cache_sync, download_videos, video_backend, batch_encoding_size)\u001b[0m\n\u001b[1;32m 490\u001b[0m episode_indices \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhf_dataset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepisode_index\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m 491\u001b[0m ep_data_index_np \u001b[38;5;241m=\u001b[39m {k: t\u001b[38;5;241m.\u001b[39mnumpy() \u001b[38;5;28;01mfor\u001b[39;00m k, t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepisode_data_index\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m--> 492\u001b[0m \u001b[43mcheck_timestamps_sync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimestamps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepisode_indices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mep_data_index_np\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolerance_s\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;66;03m# Setup delta_indices\u001b[39;00m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelta_timestamps \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/miniconda3/envs/eo/lib/python3.10/site-packages/lerobot/datasets/utils.py:574\u001b[0m, in \u001b[0;36mcheck_timestamps_sync\u001b[0;34m(timestamps, episode_indices, episode_data_index, fps, tolerance_s, raise_value_error)\u001b[0m\n\u001b[1;32m 571\u001b[0m outside_tolerances\u001b[38;5;241m.\u001b[39mappend(entry)\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raise_value_error:\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mOne or several timestamps unexpectedly violate the tolerance inside episode range.\u001b[39m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;124m This might be due to synchronization issues during data collection.\u001b[39m\n\u001b[1;32m 577\u001b[0m \u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpformat(outside_tolerances)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m 578\u001b[0m )\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 581\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: One or several timestamps unexpectedly violate the tolerance inside episode range.\n This might be due to synchronization issues during data collection.\n \n[{'diff': np.float32(-59.433334),\n 'episode_index': 0,\n 'timestamps': [np.float32(59.433334), np.float32(0.0)]},\n {'diff': np.float32(-66.96667),\n 'episode_index': 1,\n 'timestamps': [np.float32(66.96667), np.float32(0.0)]},\n {'diff': np.float32(-59.033333),\n 'episode_index': 2,\n 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n {'diff': np.float32(-58.533333),\n 'episode_index': 3,\n 'timestamps': [np.float32(58.533333), np.float32(0.0)]},\n {'diff': np.float32(-61.2),\n 'episode_index': 4,\n 'timestamps': [np.float32(61.2), np.float32(0.0)]},\n {'diff': np.float32(-66.0),\n 'episode_index': 5,\n 'timestamps': [np.float32(66.0), np.float32(0.0)]},\n {'diff': np.float32(-60.166668),\n 'episode_index': 6,\n 'timestamps': [np.float32(60.166668), np.float32(0.0)]},\n {'diff': np.float32(-63.366665),\n 'episode_index': 7,\n 'timestamps': [np.float32(63.366665), np.float32(0.0)]},\n {'diff': np.float32(-61.0),\n 'episode_index': 8,\n 'timestamps': [np.float32(61.0), np.float32(0.0)]},\n {'diff': np.float32(-57.1),\n 'episode_index': 9,\n 'timestamps': [np.float32(57.1), np.float32(0.0)]},\n {'diff': np.float32(-59.166668),\n 'episode_index': 10,\n 'timestamps': [np.float32(59.166668), np.float32(0.0)]},\n {'diff': np.float32(-59.033333),\n 'episode_index': 11,\n 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n {'diff': np.float32(-57.266666),\n 'episode_index': 12,\n 'timestamps': [np.float32(57.266666), np.float32(0.0)]},\n {'diff': np.float32(-52.933334),\n 'episode_index': 13,\n 'timestamps': [np.float32(52.933334), np.float32(0.0)]},\n {'diff': np.float32(-58.933334),\n 'episode_index': 14,\n 'timestamps': [np.float32(58.933334), np.float32(0.0)]},\n {'diff': np.float32(-57.8),\n 'episode_index': 15,\n 'timestamps': [np.float32(57.8), np.float32(0.0)]},\n {'diff': np.float32(-55.7),\n 'episode_index': 16,\n 'timestamps': [np.float32(55.7), np.float32(0.0)]},\n {'diff': np.float32(-61.933334),\n 'episode_index': 17,\n 'timestamps': [np.float32(61.933334), np.float32(0.0)]},\n {'diff': np.float32(-55.433334),\n 'episode_index': 18,\n 'timestamps': [np.float32(55.433334), np.float32(0.0)]},\n {'diff': np.float32(-57.0),\n 'episode_index': 19,\n 'timestamps': [np.float32(57.0), np.float32(0.0)]},\n {'diff': np.float32(-63.066666),\n 'episode_index': 20,\n 'timestamps': [np.float32(63.066666), np.float32(0.0)]},\n {'diff': np.float32(-61.833332),\n 'episode_index': 21,\n 'timestamps': [np.float32(61.833332), np.float32(0.0)]},\n {'diff': np.float32(-54.033333),\n 'episode_index': 22,\n 'timestamps': [np.float32(54.033333), np.float32(0.0)]},\n {'diff': np.float32(-54.833332),\n 'episode_index': 23,\n 'timestamps': [np.float32(54.833332), np.float32(0.0)]}]" - ] + "data": { + "text/plain": [ + "dict_keys(['observation.images.head', 'observation.images.hand_left', 'observation.images.hand_right', 'observation.states.effector.position', 'observation.states.end.orientation', 'observation.states.end.position', 'observation.states.head.position', 'observation.states.joint.current_value', 'observation.states.joint.position', 'observation.states.robot.orientation', 'observation.states.robot.position', 'observation.states.waist.position', 'actions.effector.position', 'actions.end.orientation', 'actions.end.position', 'actions.head.position', 'actions.joint.position', 'actions.robot.velocity', 'actions.waist.position', 'task', 'actions.effector.position_is_pad', 'actions.end.orientation_is_pad', 'actions.end.position_is_pad', 'actions.head.position_is_pad', 'actions.joint.position_is_pad', 'actions.robot.velocity_is_pad', 'actions.waist.position_is_pad'])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -95,7 +115,6 @@ "dataset = LeRobotDataset(\n", " repo_id=\"demos25\",\n", " root=\"../demo_data/demos25\",\n", - " tolerance_s=1e-3,\n", " delta_timestamps={\n", " k: [i / meta.fps for i in range(0, 50)]\n", " for k in select_action_keys\n", @@ -109,31 +128,103 @@ "id": "8ffd7f05", "metadata": {}, "source": [ - "## 2. Specific Robot Keys" + "### Specific Robot Keys\n", + "We can also select specific robot keys to load. For example, we can load the head, hand_left, and hand_right video streams, and the joint and effector position states and actions." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "76454ac2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['observation.images.head', 'observation.images.hand_left', 'observation.images.hand_right', 'observation.states.joint.position', 'observation.states.effector.position', 'actions.joint.position', 'actions.effector.position', 'task', 'actions.joint.position_is_pad', 'actions.effector.position_is_pad'])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata\n", "\n", "from eo.data.lerobot_dataset import LeRobotDataset\n", "\n", "dataset = LeRobotDataset(\n", - " repo_id=\"libero_10_no_noops_1.0.0_lerobot\",\n", - " root=\"/nvme/eorobotics-oss/DATA/libero_10_no_noops_1.0.0_lerobot\",\n", - " episodes=[0],\n", - " select_video_keys=[\"observation.images.image\"],\n", - " select_state_keys=[\"observation.state\"],\n", - " select_action_keys=[\"action\"],\n", - " delta_timestamps={k: [i / meta.fps for i in range(0, 32)] for k in [\"action\"]}\n", + " repo_id=\"demos25\",\n", + " root=\"../demo_data/demos25\",\n", + " select_video_keys=[\"observation.images.head\", 'observation.images.hand_left', 'observation.images.hand_right'],\n", + " select_state_keys=[\"observation.states.joint.position\", \"observation.states.effector.position\"],\n", + " select_action_keys=[\"actions.joint.position\", \"actions.effector.position\"],\n", + " delta_timestamps={k: [i / meta.fps for i in range(0, 1500)] for k in [\"actions.joint.position\", \"actions.effector.position\"]}\n", ")\n", + "item = dataset[0]\n", + "item.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b96dffb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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1qqmg2ylA6sITm9PBbPs41burhisBZCoXCsNhDwRu1ViDY6LGLXBYG5+4b3NRLxP/BLmSzBYSxVI0LBC3BjiSSj/DkIel8ZgBOZGZfhU5gae8WPKF4iXkCzGfCgLsENKkrBbrHEjStuNcc70RT7Xg7AukyJ7MzwiXvJKRYOQUSbLlMpCa3+Zxsus2rCURuVCABQrgM0eZdW1Oth+0PLkY74shKhrCRPaae8mxtxxZNBHn4RNTPh4YyazrI22N45BTr8fkinSen/HEVQ3AgYWITK1W58ozNG7QL/gMXyDQCAItShQxnqby4GU6OXv44MNbz77MgwHycuX5fKs63d1o16dDng6x3+3nrpcZ4CG4Bk0WUzjyjvs82apWxnX8Fitqw+OKNrspHFdWW4rXwsJi2A5dSF3ApRBKPdXSEVx0hXjKxf+y5jT3vFyYe+4UWcZoXbU1MwVw6GoKDUa7tSSokxyl1eX8FtmHXFRsMSMBNXWrkCrmTUYqZIifIOPSIQ4g++Ra5sZ0VJ2M6jE9XfB9RieIHCR0kY2sCmFmJCgV46BlzsltH7ok6CoKyzBTKCkvpy9CVPys4Au3ObbMdxaQOd0vR2Z487HVejCO7IcQrW9OD4b/3lxRV2l+K2SyMtFIwllQL3kSjvPnCCsolvh5kq0xhyzHU60sJ80gWdUbmHIhVYpH0vydQIKARUnHQ1afY14G0HAS71VUat4z0vse01gwy8ZZsQRVPbLH7qMfTp6JqdsiNTjCoGQKEhnVhfKqSUo1VnEtVz+ysfmGlFudNpLkValGB3Gukgq6dCPlCmVGVjY/rwawIrmQYYYaMQl5UUg4aQjPx2ByS55LU7uIm9LNkSGMkajW2nj7nXc++vgOjwvw4UJR+mMBHOpVZZXmjQzELVvSJ1qybFKjeZcdiG/N6zJSLeO4yeQUDElFIMmpvi5T+I1C0FL+jDjJZvGsXguQJ+Vxp/5OhL9IM9izmqRagcxPDUIWQpOQuIx4irATqsY3HQ+bzCOnrWdutBqV8Xty9GvGOzHTqiY807q2w0bjyWAyHrLIb7Saoy7Z6zCYVOgpiDKHINfJyQiHzuNjmmFvOhlqYjUZMEfHNRG9MX8iFxI1a3V4Y73V6SZT8/5R9AYXFSlPj0LlgEvFyeVGijCK8x/f1YbWb95VJFWbF9WeZKtqX4PlbLvTbna67DHXGm3aj9TI+rfRYiFxvaZtRBy7wR/W+3L5vKpNkLsPB3222/rycsUZHeFqbHc0OpBkW6TTko/d1tYu87SjffZ9cIkaX79+ZXP3RqOzCf1Bv695JHM2ltI9nItxI2LWiteUREbI2Wx4VbkxXaQa9Xtsv03a8TCdPMqlDZ0avn68cMAOzVi7NucEsig1+mf4ojTCOfgJhwgBMbjCEK8f9zLqbQoK59E5h8WFSagNfZZjBw2gyFHy1S3l0vIYFQixxFhFusqjl0Zj47nXnvvSt8ajKe6sTfaER/Qb0NOajTLqjUmm/BO2+3mpsiUXsO7VVz/7pYOP3jg8vTOuNfjjZQRV3+kZq/O2FhzYv1YWw5bewqxXW6NG9WwkP/SWVvTjzkYH4q1OOxqxNM9eN/5q0X5rvR5rWrZQtU9ab8tzeX901G51P3jvEYXY6I7YgN7e6Vaqo822BO2O6/uPT+/fGYDJLjB7AQ8PDtiwvnewj88aT4rYy+7iJ0KpkI22Gb7PDx6+z/sQGPBofPOFZ5/BRGOVOuHNgNPe2dtvv8muEO0DgkcnJ9ibHppMJuzvslc7HMgXj5Gd5ffj4z2kGsUcYFTrIhsFxgqbMTPExlU1MTeuhyca6yPwqzXN7TEidHJydICT4PtvvImLbj3m/HjQAcfBC3cR1lZgvvjCjddff/2bf/g6nvs8MlDVDLURiaPz4Gy8f3RIuzumhxr26cToB2GagmpADnAqO161OD+Hh1ExdMIIPzte7GD7G3XR6tnKpO5gQS5YExxX0+YxVa2OdZGLq8SQicgkiz+v2KNb1nwIcBYWbknJITOr1iwny5ZFPbKf3z8kdGsAFshJNXLrjRJ1EsF6XesAHxwyJg3k8SJvKMczLjanwDRZUp2Lq1dw/E61bJdZeKQI5iIeZFlEUyFKpU8hO9S4KpQLsbOzqAGwp7QU7QpR9YTeGYviE/RWv1bnnZxb13ZPTqjDwWR6iq/i8y88K49O21tISw8pGnpbjAETBqohyRnc3FWU7yVLhhCQoUwWqywKYb1raicQ/qu4lBp+QmGlu1Jb52QtdaU9O5qSKhoL0Wt7LSKhSxnyaDg5Pj370U/+85u1TRrpM89t8SbHF7/c4VHQTnfz+eef/8zLtxi4bzw+oLd5cHSMf/TgpMeI1dAbX8XWCLzgQudGV3aOSL/pJGlmngcQAoY7D9YdcF3Z0GFDKdu3AYiuUjsKtIKqaEUwjmuBKwF8B5JScBK3JBFP8OUIVAEuEwdIRuDMSkinU8V5XH+0UI3+zKns8c2cUD7XIPOGCC89EIFphSf5VTrvUX/QGw2G9JxUG9SQhT8GOMapmEQM2XUju8VAUEeAJOCyzJeEoPpc+2v0YBSrSI9+mTzX6h3EwKLqzVaVid223qhAEXRV7DDyx2NgqPUHego7Gm9QNAqEDQ97g5vXribx1nBM6b/tiHWrMmYN2UKmpCQzkBRyQVW5EcBcNrwcc108sTAF0BIEjs5F7efZLYkhCWcBId0aAdlMBOJAfE04eSR6qJJxnrAyLurzDUrSF2KH+YIBTlz5YYbAVAYcoUVRHUc+OkeMhqWD9Bi7z/w6Na7i71yQF04E4nt7e1dvPEfekqDcJTY2LnhUJXKfIlAgyjL11C1enp0wkYuJAlStPxVcTajoOyy8eaoyypmHIKENIUcwzoVXc0nqNzsTcV4gMCIw403UCrRStgQvI5L5MiEvzjn4UaC5kRKIZorMb2KKk+ctZMtBS/FlHAqYsIrUWXHDCFPybyuCcnI5MYxcyKcuRcmrqDvd2jieOicRzJT75PQlW2btKf6klMoiP2m+3zj+EwkGsvFZb8tI4g2GSbzqldQctqQ+JPoSR4pSOG8qUuAUtZOSUiShLUdgDdAmGkSEspAxxCgM7EJjJm9ZycvcFikv81rOk4RJrN2m0m1OJCHndFYCc4QL4zh2vfHGG5VKl6GK+fsywVxF66iBQ3AqFIibzjK1dRQW4IlaDl8AoiXo+5rsKsf/LcRdU7Y02FnCVHYijOAAI8jMiCxLpdQly1xG+y1DLJRkK2VOcUpNkfOSMDlhkUBhr1+/Ptl/xFYLZSeA6ewkTYbyKRvX2lreV+QNTRI4vMIINSJsYTx6dMrG1t6eFh593nZu8S57h4VEbdKFwtbGhpWgZhLsQciXhebl6+XVdXl8pEJsrikLcUIYYbwkG7M4bkFj84qkdncTnQAmC3CkQngWS7WN9s2bN7ev7tDu+uMzyv7g6B65Dg9PwaFVsv1kq1ZhI0gDW1qEbHR2OAhiZ/sGmM1ah2Vzd2NLRxbs7MCRjU4C1UFGECRJvLBPnADQcN8uX8kLN7iz/6KNsHKrCEy2tglsfhKnOGAuZzcEpmAupya95Ummw5WQIyAAaECo5YjMxv8FzERtvUQJ5ZNEaAGIQQgx5JdEyGXI4zkD4Pgybz/D9uuQqTw6sfLpuoAM2c7s9yGKnvHJwxJIpY2c1FocnbH/wSu0I5yf2dRrjNkW0du+4HAeAS2i3qpy1cONdrtZly9zj8Mhej0khYglxNWZRxq0NbgAgctmR0shpu9cAZKXe/CJcMUZGcMjgpvz/Y/fI357VwvjK3EiRKvbQh5rACDiQwRLZh+b7NSR2ng05OOTY1rxnTt3QIMLG8q4y2Dt3IpvT96FvAVP3I2XfVaS0ChCMq2HBay5IjERyCLYhMMt8KquddEnCiGVAARpy6g2oMwCSBAcv/POW8i8//ghLt5sTQEMZFWE4pxcUKtxag0+kn/6x9/A63l7SwsuFAUCD1+Q5+S4T5z9KQoy5BlaZDRTcyfuCHISVEGlhr1EhCt0fDxFLOLUiYGZCwNxcrHbzvY0J/oggIhE4wXNdBR5kvEOZCj4SuSpBJfUpCwh9LEEDI8yYgaUy8oBboTz+eZoeZxc6RY6lIRblJ+owcUISg1epBNhDgMORpHgZUQUioqraFA2KTa8FCZ6FaA8BEOPACkLV52XFLVPubYaaiAvvaTzl64895IGQHGmmOIFNa7QpdPmKS2SkpSkdSrXBHQEmZ2XJMtmHAuXkAH+ryGg6qSNC8sbmKpQ912MTcSpyJSRCqUGoYlxdpotGtf2SZX+cKvTpdeid6KP+k9/+wg6HAIG5rCuek8CkBFaVAGGLaBdXhL133wEpisNoLSc6C0lpcI54hgfi3LRUgt1ltzqoANy4kuEYHyuC8I4dYHIOjFAJslywpFgyjwEFTyOJsvzgmkEKlF1EE0JGaggrmCKe/T8JIHDgMKgQCo9p+nQDwOkqapdR1/hHTzwyWqCcCE4nnNfFyejk1KEvIR0uy5jgps1V5cCgT1eeLaGz5b1jMAohbIwLhDAwYZ7LZ3RAZCSPhHHxP13GLHA6CrJEJorbNjxlOQIWRysE+ILCOffOq80WVq+KSQ6KWIEZKClEyeCVTjC9cIAHbLkaMsQHGdiXJpDkxFjPJEzGjDnDjN4lMMJcPu3+sQlT3OwVsEnAx6oVpiA0RhwTsC+kZhHMtUmm83MoNRfxaYzCOw7a2zR5BK4/PXyJ1nIwOiqSwjS4kkbz0OrI9wimAK+/PLLPG2nwbWmvVaz1aV7VZPVq6l+ahrCr7hoxCYsVVkBj8SiRAUiG0Q8ZMHzfzSexuljkzjXprnBrK509Ioc88/TUD19iSrNXX+cf134x2XanitzcLzMpaQzI4THglixsSVDQZUFmQJzBVGXcjlB8EnInIgIKbwh/GC8oJ25QVJNoKAPrkx/kKARD8CQitDon7I0qcViLyk+THM2PFj/5AwKXIrAE7syWnbTcR+Pc2WTv7VgDyCzK6ylVLKAefxTyRQ6XEMhlFakuXZyyJpMv2OwzLLsIvP4+WLlmDj0UM/rLXkFpcRRPc5FQeP2xfQX6XAv5Qd9Uyj5LGIW3U20F/VTyqUpOt1VjQfV8W4Ur42QDcdFiPBflIsGWtgDELcEdEHpCAVqyZUFashT3F9ojxfpE24FDSwtZiPmH2LRP6iUs5Iu9yaFtLhdgJX1FRJyfTg/1fkug5M4nIMsjUUZwUF+rh5fcbfrDYc//MXbeNzV2zokAXcJCou/FUpDeinfhYgRyloQEQ3qKnfUD5ah3yRJ5JqVveCVkp88khNPuREjBVjknsgJ5+lGPJWBZioRAqBOxJMAMdEBYmkjSaomkBTTTSVxu7I4T1fUT0PNfT6n3NJ0G1pjh/UsGbMKpsLrj8Y+bFX70ZTrrXb3xRcZpI/e0xvZ7AmwpsPLksVAnyPGj0ec6cB+6EaFgwXoFvBxrnPyFwKz+O+dTU5PRifHeB1KZ/i/cGwAM6rRoF/v1VhhcBoE4pAPLaJzJJBXMKcNyBGGYwxlk5z+LA3PDHCFMkL2FfALQWRkoOdaVGJseJGLUqinYE4WHtADbd5ynEKNTeHt7V3QUQRZBlXOHtFMsNlpIi47jfbTnEwHp8fHg54O02BHmWuj1R7gYltpcU47u8EspjjVmaVIpdq+cevGRucKG1X46sGus7U7mFSvXntue2eXZstKRj6drN/k/MQRH7gTyLMU5xqXjmNAoD8tX2VdWWTy8ocL6Hg0qI6G7HjSgyhXnKWLQw6bpEBw1azEyaeqvegwMBoTREWE1CkQNzw1ogW+srpAkQaKIMGJAmnjJVQQUFqiViD+Vn5U/CgFRUChqOYyYpxu3vjCd//8+le/8+Z7792efHT79m1eL6R2RsMe1ylX+ZL3B8Me7wDw2mGbA49ZADQ5fHb3xrOfrbbeOJm8B+b1Vnezw+k2vHBQ363XdQ5Fvckm9GmrzoF5U7ZNOnWOdO6zazrWKagc1oJW6Plpj+16szfgYcjo+PToyjblqG5u6HhlPJE5joO3kbBVPtlAC61WzibTHq/wQZ6julnzbk432XTDab/TqMFucHqy0W7ubG/WOLvz6KCpvnCCCHjjd9pbqsHJiK3e+48+xlbxofGpI1gLB3FygjZMwdeJ0Drzuotr/3gQZhnjJscaIDAOyuB0N7f0OGp/7+DgcPfqTqvZGcXGtErdbIFEKdiQ56p1Fw2Ql60mnNas3YfD/UfI8PD+3fv37x8cHwGBXRdtQx1Ljbl6fahN/NvPbXE0yp9992scu7G1IdUc7Z1ItZyGMxxMeUY2HD+4/4gNdEyZvXG1KBZoIS3IBFVW7CbQA8j3mZaCwdJ1hjWHVcNc3QWFUo1wUOnODqoWtGwUwFERFLSKiNdd2WWAFPDC99nYMXA7V54XtHXhkmirskuryLsqqYCZuGVmdw9VUC4rBAxSKXUSgPgCKVQBMkDNe1Nq5Jphkt8PxaP5p+mVKEeHpk0oE9ZIpVlnsdKEZPmQshQGErCTVmuVQex88UiiwmsdvK1Hk6OovIGA3zmCIUUXm2u26p0uIuD1fnxwwus+rSv1W9e26O4nU94Y1qYCqSJF96h9MTp6KlriR8FC6PC2Zj9JiDGfZAwJBOHRi8pSYuRSNofSMPgQEyUtZ7CuEV9LzIt+RZ8yLur+omy/o3Qe87Alp2da64PfbyBdNhCbzurKsB/qEM/lJrXJAA1Az4MJVCej8Gm9zznD+72zRyfHjCuTZp2+A/Ob1jY0jdBZB0wiKhxp3MO5/fSUrqlbZ+LSBInzrQijwfjstDj/Yb10F6d4IPNuSekLX+TClt1LxNpJNaduJDZXECAKjs1ozqU4VjTmoBj9CTOCIwW57EfqgDGmxPZTBCcCdUTjO1sfdMBMGMSZBE2isD9mLAz0NLNCICSJt7npr6RA6zroEM94zqLiHTYf/QlFwP71mRkJFe/C0nGCTQL1YJHi7TcalL7z0Ts7AtdWQWWhIhIIxiQCqW6XcbPC6SmiGQXobvBtgCnnWTO2NsdxrkDIQIEtJ9do6atlhqxbjZHNMQgXfSJ81IDLkIQpAef9YmqMY1hre2Oz1d1AfuZbbCMyceOLGM0Kg/0GX+9gJKZ3FaEx84WzYbvV0euD1Z1tHmFGSUPtZs3VITFeuE3w31CEUuT6hPsCI0OSkRChdFwdQAaBuHNZeK6YmZMWqF14a1KIhGDEE3EiiUUeSXCkchZSkzwXskv0l3NBRA+lV9JaB7fEOdcck1KR5C1gJiTEmekhtNskfZa0FsduFBD2diMI7lhxFQeiohaDMde4VTXofRC8iho6/IWuIxDWthYRehqBglM6gg/m4S08hoRlbSRWFj7dEiGvX7rMgU8aLyqrtBVnB0iPjH3wMA5IgfOkpC+Hn5VrsSEtEEAMrTZZ8mXNaV2eQuY1yVJdFoQ8j/kbLXLGeS6KKhLfPA4SNplklsbK7mMu/xPeJF4p3zIkJf03EPnEpctM9InVsJbpmhpchb/cFwWkaJsSycsw+81pgUjfotN9mG0477xxP3EhLpUhN1EyLCiNXjUZcE4OtAXM5bzGX9bMMiSnTDwQ5soefbuwkjALRM6/XaB/mVuppVJ59913WeczKjHNgXV+2r6GKmY+5R5QEsBqWTaTEl5YBfgpy2XkWcAhLwR9XUjiFjjScnUECLdJh8v4nx6SE3c8rylEtQCWObHzrTEZ3sl4TqFSrt+HiBXLpipBzbho1KrcFFdSGYjLYGIcZKnAxpPmLoOBP4VHKrMI9qTYWAHILadWGIhOeHsaMviOsSXEHhurUFbrpPIiPL4ebDTAEeJsuRKAD6f6DAvTABTbbFTAISObZRBJlVLKdfEvBF2ii1GDPixg7e0hZ0E2ioybAqHPIx08Q+s6y48tNjYcb968rc338Dbd6G4hKhzJ2J9IFayOSKLdyaO5fgXix7VTIBxIScOEIYxwXiALC0LpZ/M6y2k+P4gGCKGBLnl3r1yDywmrFPyuo2kgJfIgKmh1L72C74WFtXigOa/eg4VRX89RxkN5g7J7yn6ZipkZwIL2wILCzD5Ks3GvsoCcbpNsRAiwcBJFc0RaOLe+lvulgnguSuL3JJHElwgKQTmXyY0ZUDvPv/ACxfnw3TfJ9fndZ4g7L3RCh3J9rY/iYQzfCdzcHHVHXDkXkrq+Ey5d0GHjsjNQL4dBkAtSxAmmhmIJaKw3UEMYxWctEyMgxF0pXNEnUmnHhZNu+mOdN318BqmNbhOyJ4f3sWecgnkz5rXXXuaogQ2OM6flPriHwXZwwN/YGPVkmQSZajRPWGMVdx7Kr5CDypHf8sAOadk3hxpf9oMyJ5AAoURWgoSM4yZG8clu++/TmugHIMiBGzdv3YBUDN3VBsecb287IwID90cp9x9yKMjB2eOHbDrvPboPU5oXaDBywQXR24ljXHyQ7Zuf/SJbz1/+8mc4BuT557Yo3eOHe+BzUAnxaOI6fJM9dDoWiIzik4NwhIxNCSC3XJHBwbfauCkDrEHgjpZLQTh7mgOgn3nmGYBIooKXAe1BhDu4jzjDJFofaERSgHWJfqlfMoLnXE+2bXkueQhS0kQceREeoHq88owCp+ZkgFgVKbIyFeACgkk5b8oinFLPxk9o6VYFx0MpUzKQciPMlKIKwnq9nUOlhABK5XAP8lIo6oWWRtxe+VuN6QZe6nU9RjJxZCO4LuGZOLp39K1nwkG8qHTgBEqB9u7d1wMbBs2kWJJAjoGRoVFvzHB+DpLwRFbC/TcaUCmH1fBGwmXKh654JITm6YXQDHm5coANqjs5PqOy6Kzo6AhA6EnAPOvv/dVf/RXv+vGGR4WdW7pQPrPNM+ORqvL0YMBrCntHythu6cFbu7vlfszyYOqXEeyp4MiiMtNdoEkSYhPcjRjTWc7JiMagwxVFEXEuCMnq4haIgYkdSQ6QTVmIcOvgODgIs5A3ETEOVxDUvVWLrg8KFJEr9KBALVIc/BXArFU1TSKdyq1WRnTCNBN6UXpAiVh2JmZBcYDQgsgODpttQIgA9LTQ4sEFiLOkK2KLXWiG1GWEhLkcMf6TZoEO/SRmiYSM7zRtpB3GtraetsX7XiSdHOnLBxBHNspFsAD0EqSmw2PBB+6rEfK4IZ/min6snPOJwNTaOB+NVGMSAd82kxSYIuA4wNqRC8kuIECKjCaI6oiDkBfEEICOJASyUB3OQtzmARaYppYzcqrhvkKHiK85ph6wLENzDMX1fBJOGmzUUvUjxkXQ4yOlcaumhnBTzodibg8Em+ejvzpkSqGu7//i6EJJOKeG1gBrAFzJK5rEIs4tEfKT6qbIaE6TiyPhmOFNHtx77/kXvsDTvfq0RyPzExjOF4Z74TNbDHwzOXOA8S3/HNyguPqJIutBDkezVBCXYBVNZBG2MW1M8YPm66Lx9BgYcJ0H7SJwjWZjCeS7Hcm6xtl/xpfAZZGVdG6QTiSCHspFXFc5mdNZ8JVa0rz4Yc9fvkXqTGkmQTKfoi1DAiUuFMe/S7lUI0XRjO4HXX74HnH7xdv3uTroIxcHhmGyhe+zqoauTpJENemRpULmW13Wy0xam7LryB7WqoWyVkt8CAap9Zff/rNuHpBa+PVCpZRZea39lJAitj3fYpncusRoBi7WakJ+mpHCVp8myU9Gy3V9mXq0bmSuTxJM/1Kr54zsKi7LfAMSRsxuG7m1SKD+3IKYeODKN+3XOX2yqVeGsQaToCDcqq51Ty+nvHomnEJQwPsojIF8/M4saGZVCT8iJQZtnehqLEOFWT7QLvtJUni06AMUYIeE0c/Ms4h8klxOCREUPzesQ8gb0QIOt7kyziW/MpGyWKoZJdj94v27b3/wIIamWo/OPx6o0uR4+4bHqeRRi4sqSvK4msyDeBGKaixYg5zwoZDiKyU7B0hG6PuaowHJQ0qCV9mrJ9hTiKyjiZschcNILAyiLjMrl7vannYdQm1loZbz/s4hyMy6jDkMSzTKOBuDaHsxM6MsLg4lGkxHHAyMSwfj4BGHoF6/Ub/xTG9vj5NaNY2m7Q+r/SHevxt8TrDabDea+iRas6WlSHVyQGFpaWOO+BsOdofsRGm5eOPqjiizs8zxBJ02XxPa2tDBecMR7Ka9OI8Yv2em48zmtfwIz32e8CfVWdXzt8UddMruJ6VfHCEXBeRqY6NZql3RWnBMqbO11XjuhW29rY/fMv6AzQ7HDrzy2c+y5cRqFkTLc9qPLebTM62OBrriaQ7CaXPIhtdbe8enx4cf37sHF6aVHI3AJj5MK1U+17Z9/cot6Ez47Jw0xgxTYySmBwJrbHSlxTRe5iodi2tcOFGVOntmlQR4kYQYQJbDYhL58Sk6UxGHZ0XBKRfs4VXoITpW9y8QNV2upKIi2kbOi7iyL3F360BkZ6fTUb8Tm3T0xkjFQSwpkxevZd++XIinD5FgzPxjE8rUXbqcU17MHD6ut+rdLc4h5bHAaFJ5xJf0Do5YQzJcsKcbKw4OseQQmpNurd2obPT6w9Npf6+50dpq7t74zMuvfeOn//DXsNtodvhrY/XsZqIe3JzlMTXsciK06qeyQUPFFRqnuKHc9HpVHcCCO3N/cDTWp9XwkOabWhM8nfkjgnMnRzfj20ul8SXAV1+6RZ1OJ73D6WD/3sfsAb38fPf67qtXOmz9n9XHvBk5efR4n0M38TRjxbx3fFLpj1q4bDHt5dnyUf90tM+mz/7p/uHRHjN/ju1mCaQJOz0k7b7dufdwb+fOfSjT8NmGaDU3cAa3yVHJalUxwoYRTXE2PD0Z9k9GnLrLgTNYCA1M5aQeWHCx612pnJwe0tbu3n/MpvOJntZU2MTn2tATb9UZXKp4fpM5dvI4u6TLM6EbN7/+9a//3/7P//tXXnmFSR84I3zszgbNaUt4E729wNYz8UPOhD3cizMY2NwKHz0oRysOOdSTS+sEysqyD+sOEwZOucBx4JZ+gKOl2ehnAxogqW4+CYFOEgpgwnfI1qdm+hr0wXQwJnFnL1pKMaw7UchFLDISh1qCLEQ8pSjpKDFvU55/eiYWTXwuN2SR1kVAf/S91gPyAwcVSdTOZ+LMZRevSEpjirOQ3XhkT/ECJyAx4AqFVPRDxAVkDSJ84VT8URE+XcutJVe1KIv+9Ngd16pKU0fO4vGsaVuTLpSlr+hGgBRbkMhAn8uVPWiaDOViXQ/8aP+4Oql1t5sIFjSZm2oYYEyI3DjiQspLaUPsoBozbs4oRxLcO4fD4xOdKv7he+/CQp9LqVTuRy+NJgl+rqHXrAn1Bj1Gh1cgaKjLG9AqwgXBOnSdXoD6W09WVZbVzTjuXf6VUninIksiq+ytDI1ud5OHvvQGj/cO+8w6KrXeYNSutmhVHFEfBtN88Pjw4PEJ29yTwRmPsq7taiuQU6t4fPX4UA/heO1Le4Jt3iaZjHmePBkzjmMDtMnD4xObtNtOJgnRbEUVLonzqct3F9faQh4Ki+0ApMK1iaSXI7A5vbZY1KwMUm/Sr2t20WwZNtS4iAdB2oGs1CWCIGGZL5gFcpaagCmynDcnZQrwJQBHUtql+gnCVG/2RHtSEcGkrmkFdPTs1TLBo4cBiwhfD6G+2HZTLpoN1TMc8Z1YGhTdUUzNdIb7xgaVLl2RkTrFgxhsIElCx9OtqEmkGQK3bjUusbXqNSluyinkFKgUdEcvvso8ihwuOyJp0Gh22xs7TIRJgw7S600KdcOadYeeCk7EgXRp/c36ztamNsGih0FgdXiMPFouEy/+VOQSYsboNsl8TkT0fvMBLhSWQlFqQmKYuBMhUIroCRXP0RL++ZE8C3GIwJQruUI/ym3K6UokxcniXKD5QaCTuDWcq0jMh5VAMhYe0IkEuYhzhYgj83SKuySB8XPqxNXwCazGFI3Vb4wbaE7mFRvQTKqEUxabCFL7usDROK4VrAUd0aXhTfD8C0LkFgRvT1uYvJtBAGdfoPlEt64e1U0YITTJTsOGclUTxRXqPp8+ZQEhVfZlKLgUYFqtZFcuGlkE30rO8P5Q5X3SYNmK3MXUoRBVHHPlGmmVhiUUhOI5JFe1EvJaqlX4ubDge3qUA0tWq8sl4r+VQC0s8BKkZB2p5c1T/c2ZEvcU86ly+A0Ss+maQR7/DbIM0lbak+pqLf4aG0u1nxVnGTaDmH55VSbitFy82rB8Xlok6OlWGVLVJ+uyDoE/XWXm1BLTUgr9Fnwj4qVLAuZoK+MrCeaYFyKAvICzcJsjpKQUyXktx0HjqSlw+mTWjXwWibrAl4d4jY/HwTp6bFVNbCGBzpxmmQ6QXI3pNhdDrILayuyXBEqA6IsSOyAEDAniDtyGALOB5pLEnzoa8niYXqAcIs/M2DgAU6HAdykWMv4Oby2P312wGFgOAhNUnBCYOEkUR9PE+OwSKcx8JsM+S7tbn/vcr3/96+nJgCUEn+QDk5UDKmJjDIj9OCpVnRxar2rbmr1S6ECNwAoQOtAnC5s72CcGRy6x1xQrkHhOGxsSYJKU68rC55A8bvlzyOXjyhv2pibDYjUmgMSRhDWRhKvItZOtPhZL3c4mvqLs+eJX9d577+HR7Fy80YrM7DyzndGcyruH1y0hu4/n1vHxveE+22qj+gQVoRRybW1qLq7d5+vXdzZ3gET5dViBA3mhHDrUDBshgCCSBAsfWxRm3YIPGnD8xPJSg09QzjIYwvRvEOswdiAd1DkElhFK9MVfVU2w4CrlMNeJK5IbVbexilzMWd7DzlkAiKn6nGJuZRQo5wKX+Z7CrxjNNCGCFAVe6I24BUON6OFCAahEhAcZe2AXUh/3295GcgwAagTiXCmpr7Qn1sx8uw+E57ZvsVX6/X97hVWAGVl7rCjAJ4A50cMXNSuvwIlAECGJkIV4WIWcBNvx8rXMsts9O9XnConz2KPOe706SF3LmI3uJrYKKWwyHlXIg4xtssneITTlpk1FxKIY1qDRFhGJHVM2sqsc8xEZubKfB19aJVdmx+AQgctbb72F7yGFIu4SWUiI0xbsr+rFGkJy/CBAbL7K1waxlkYdYR7t7XO8Bt8JQ5jjEzlfk0AqqyPiUBNmlHrEZ9VDD8BxY0T5zz37At7cn3nxpW9/+9uvv/4ZFD7o6xNPg7MTcEDmSqC8xGllDx48JOKJAD8kxW0x0MALbRAoHUERmYNCwnRmbin+rVu3UIiKGVIhp0QN2wYBYSBCXsMdAUISwVkcya9pcDYQNIeUJb8tEz/VrykjHiUizhUFElKPnahbD1wTJEWCiOAUljLm8IQPToonhBRZTk2QFEnIKUKSOSI29UU8kFWD6B80OBJolG6eyhgeslv65usGh0thdRghcSUJV1ei0MnrQh2Iai0MJqpPjYIwHtLDH+7vceXhHhkRI+Cqdm4Rg6YB9zjQhYM+RHzn6jUsp9FsJ99/gP9thNBgURTitNN4JHbJwhUmREZqkB6MTuPw+BQKGCSdG10UWqXFgWCvWNsat/R7E01Mmq+99ln6ZB7ZsiX9/p0Heng80QGk1BdGYutu1PUwjwPg2aS+pGSfGA1GtoScAgIjiixKtqJAKpgG+jbHJw6QXAtA35KRkHDAFEcfJhOUjWYKC0S4tRS29oXUnN06qQwnIzaOGDxcFkRnSMnJiHg845OPMHFSaHFUKJhULnIWDunTGtVK66Wi+fwgV5KUN8bERkOHjKlQsQFNxPFcvBQXD7ffEmRIAEvQml9zJBF2BAhZsWvQF8FkoaVDhEiSkFvU4fFRRKMSGRmwZ/cMzCiEMl+3xvTVbJxxkeX8/WVwyIFsT1SueSZzd3BUASNA1hFjJGGIEOBIM3R8jsQT3sACImRyxNfzS2R880EMCRMVRN7E3HFSU8WlpJWRlR7QHvmiMQdlniOFfDJcjIr/PAHmh3vJBA5/oUGdiEGYsHbSgxWqqDHRYzT5PGOHNfxNtAktMnrcpEFKBMJrmOKoTsOARBYw33YnxNZAtcFkiLmMRsc258FURg/e/yVTsVb17Mpme4PjjSYjTv4Tfjxnw6lQUf5ZO/yyFoRsnJDlx0uaGCmDAryDqUpaQGJuzYcvfItRUDi4N6K7a1ROKqcnLY5gpM/q6mWBwWY0AE35qjyXEV88LSSDFgloTLUS/KzfeNSMVOeuNsybayFUZaTny7iXuy7CUvEg4UHQKHaf8dcjadLCRZpXdMUy93AsngkrbyFJkJ2PF9IlzqIBf9eLKYTfZYGgE5JIFs1GJRacnL6EX4a+0zPi7Gy6UtajSavCLTWMRSge1CgWpZBfSBmKnd1IBRFwAQkEy5NIpUiZu6jNdOunduk2RVz2dHthJJfhQuRAcIlcf74CTpEZjULvmW4tM0VLVpriqMqSoDaApUZn1D5lzG0BIqoTrDds4ElpFg2qzMbtimIX9rAypcyZ/S7jJVEzrN9edHWRVvF3Lbo9RpspPWuKdyPkpIW+eYeCWQYKj8P4aHfaQiq4uGcLQlxSrbt1CE2dsOpL5z+qY13WliSzRS62c6UomCxDHDK49uUEH72fUl2LEoi/ZJhFWXLK2oTSucneDVrHLViG5EUsnGvKeGF+4jtfloXbhH9OxGWhW0zU3LAgRVJ9olMa98ad7//N93sT5uXtAWVsNbTDh1YpKIWJasNfTG84hmqlhSRKDCZ+Sqk8jAIqV0geV/V2/IUbozLNlHeO1JdNcikkTBSH62VzfiK88zsEp1rhJk8cIDpJ3CwhEAtsuHF+08InGZ4wIhvGx5Jr8R5D6JiOmFLELqv6YhK1alHQpmdrih8HEJp29azRrnS6G8+166enJ+/fRSeN7hatBI9LfL847o5PleEZytHCnFDrzzCMOVN30sO1mflWtcJ2WLzgj3MarbPRkvuoPo3MBEpS8V51vTYmSfurHCANSr0VzVBGzH88d/UhjosC+hf+pQPIBK18wiFFtR+ed6wDWSZVN3g/Go9ub1PWT096J6eDGzhHHx19+OGHh48esfV2MjyGG06x0Okdaz/6ZnzqatzXiRZHdW1HjjsUjbMY2frAGVM9TXXSZ/W1u3Vto72t3bxqi9bKrIsmxoK52dCWIhmhJmRCJfyC4rVikHAPZJqKOjijlitqJICpnzKiTBGAOE6hAHBl5wQI23x0H9yOTk9wdmcXhhS2SnjYgKjxbgriSPv6pxmxQqJm4miMCaTjvoYAEkbvugUIHLof0Sq/lQIOWzJy8gkEz2T0lXhyFX18JMQlTFUxSlaWaT4h7uaSAjnAuiwkCQRRFWq2ShxgYfNzG89hpDrx5UelobAvb21+9blnRzdvnWxs/qrfw0dvEA5U7VGlzzHNE3pfDi0f8TrdpD/oNpqDswGLiO3r16jN49Pe5vbuK6++/uDhD6hvXL3w+dKSlSMBqNThZNDjoyNVvqs1bY2G+wdDqoYX8iKQPOVEiyotio3g6JGm9Ta+9O3mlZ2tj/Y/+uD9X7726tcwiU6bbeIRR6d2apMtvlYzOGpPxicPHxxxxDTbYPRbfPlwrI2ANkdED/rUBCWYtgZ1znNmWjbq4TAMz+r4lGMCKlW+jsMzCx6sDNkyGwxP8aEnddIJh5yNjbt7e+Of//yb3/wm478OJsfR+BBXUH2/tN3SGQ6cAUscFdKaeqcnbDMfHO+zKYwrM5tEPc7fwUIq2r+m+wgL0KmlGm+kfDYypniZTmutemWDF5xx9Mbn+vlnbrNzcXVji8b7B1/9Bn/VyYMpH/LheB9qEjvVAwAduBnHfvSPT0YIhWs4ENy3tScS81XZHm2IthdB+okNaCICRM1LlAgYADDHkR8FUliAahgRpJbyyQHis8VAR0abhxpBdhfBWYgaoqZSxpO1k5SCU6FsSNCgd9SdrTSPu9V5lHeGvN80JvkScY0MagjxUAGbHQ6Oz07ZfWZbBLjRoGb7z3Ipau4FsMBdQCkTXbCyLwJqOXMKBYFQBYMIqnbBhaOMGpWwz5BWFUGqJVREzs3qO1RpHNWiZSz5ZM56kBpdkEVhPVsocFrd3ans7mw1OjoKA7tXLrlwMjRJJ4RgIdGhrThPZrTQV2WfHezzGIkDl7AoBi2Eb+qR00h1rFnZGJ2enemRCcOsNpv4OChx3B55nNneZOu50U7HEJXaLVapkhQp9bMUCiMIeK69smaXMjxlQM5/kTQai75VDZfuHf3xJlCzXn304B6Pi4xdFnUxr+pOzr9aBijeaFy/cpvndvfuPcI2ecqGbnmUQLYxFUxn3dsAl4lJvb1V71TORsdXru8+//zzdJGHp+w5M41octb81s6mXJ55pEcdloHB+HR4REXcffB+rzfA2rE3+mH6BqNQh/OjQZlz/a9b3Mpcbj6mSfEVkVUUf2KlsV+lDtuRddWkCTURQcAOJIw8Wg3gYsw1KayX11PUoZFS/GGk0NBaW4KFq4GYlnzVMlwTpZXR8/JHn6bHk3GmE09l1MrCl5+uXU1MrRZDo0lFA1ENU2c0DXoPHRoQTtwhrDjCGgJMANX5jxi/OEplb/9IH7BVeWFEuzg5OkZ+obFwQyjeKuKTF/plQlDlMxjk4h2g+x/fPT7Zpx5PHz7qoqtyiSfdUCoXrVpl6HGztWaUGtoWhno6XQ1x+5JySrh1Kjd0lTAoKrEIOR1A5sKVOMLH9w+YTPNaU2ymyYYpDRt/TbSGEsGRJnV0teqziTsr59v1zra6ne2NLr0VpFIoWJY/5C2jxS+YZb0tpFz2Vja2RDbPTHnBgZGBC8XPMYmDqYE1U3VCcKHgRS07npI+WSSJlLLDFyAyGCJVB8TXhGY4t5IzHjYTIZduI0CEOGg5MGU334SsHd50k5CImEoOAc2Zc6DjQo66T6TEO0YoIuiUp5ngoDyuBOVyLLY5khGk7MssgECKTEkvfMSZh+e73cVyniNn0C9MYZmFMxbiRcMhnotkhTJQCrMyZtI2jMn/WU9dQDs2oNEDlItcRcEKvZGdRma+iUsq+7I850OCRRg3dNGMrzYC0iICDI6Oi1ppIooG9QK9jCepAqAL2k7xS0Zgqsfa6AYdZhzPzy5RV4WZ8POpCZ4i8+llFSxAl24vr38zWms9QXnBYJa4PTEgL12Kr9PVE1P/jWVYNqQFSNy6H7AQM72mYq6UboHOSpz/GoEudcwOKaJ6ObZoiAD/xEV2xvP1WWi/HCC5NdNzdIhotNVlqQRZau+lDOfQK5JKOWctcp2dl5hzNFcC5zDmbwJfVueM9N/MGn/6yze4cmAerFnFlMLPiXRhl5hrJpeKOGFdoealu/gOUomRyUKZIB5RC2XqTPiLiV4aA0YX6kF2Mt+rI1gpVWFmFhW2CRMEh0vL8jtAREIWWkx6mNuYPcaSyuJiuvjGxF8WTB6+U0wyPvvssw8Pe/bfZELJyo25BL69BJxWINiui/JgIH/MWI9rxQgaX2UzO9Biv0UuaeAQYOSrDDoClFlpEAWiVBZLqwJJiAqKMKMi6HlWIV4AIzvyQ40rZ25AkyKoONMa27J8W4YyfnjnQ14ErnPYSLVKklQUTjqcl8AGW4MXSfEAHcvTCgQCCILzXqVeEpTJkRJxSQgjUmm5xAOo5gwOOoEXrnFQcKEsG9OS6GDlW0eSuMRxJUTI4o+wrVvYu/Cm5ni6whpJCNY8lImv06CRU94UQewUzyPmCE2AxH3rOFyiFEKPqlNFEzEyVwJFMzLvCIYhCPn8ACtn5AomGhPVsB1dBctC7DlSKIMccUYgRbYMnWJKOfJb32HnYm80Yv2MYafiU3HkolqxGRzr6I3ZwqPiKAgVbb/p7c1t8L0XAzKG1Knq8HGLzYMEIMPBCcIMm3U8diEFmgOyUCKSQC5BcnmGBUmYH63y2pXHtFAgcOFjAKI8aUMHyYnrViNdUWSIeNQ2NSiLI4dvxO4DBcFvhuvo8LEVAtlkHNABzXCunEjzmc985sP3Pvzggw+gRhI1jB9ip61PFOKCDWWyw6J/dqpcGoe1ROfK4ZlIW63rCC9tDePhGA9auCXwHXVCu7uJpzN7dtC8ees6dLyMZ7uBE5+/9KUvQQf6VEFfLyEMkIBbLIc6YhOQhzq8gUBgkBHfGGu4EtcuDK017I2aIvC0AHkIxIvticDklqKRw+riihgQIRCHeLoawkKLCLmEEQYvbmGZ3IJsUokgkZXBmGRxKreGrET+BEBEsngmi0qxJXdNpkYqyrg85cCf9QmQdalNYSZ82cUlygkTfOKmI3yPAqExNChI6JY6UjwCP1GKopESB+yiUTWYELfefmKDCTgNDRze9ofCFqct2W+RXOUDBlMOSax5AHqfBUs6PjqgYXKgDAQYSIGzcypMHijFI0PlZVM6AjiD8SEPTlptNkyncEK3BIRHpLgudExRnv+aL6jCRaPsRNAtjZGyG35OyTwMgIl66Wb17dP796MaNRygTtc4NUjT45sTkAIZc6X/4brR0kYzn4EFjZ5L7ZfPHXOeDI8HqJ14sZ0siutJogIS8lIXHyzltXaEpBtIMsPiHFGfVhLyQ8pMHXGvYni6OgICwttyrExf0TBlcSpXB2fJ0coUKQ2OZHHqyk1zI4NGXSYi7Y5mLNHzSTmGJ+Fz+ikODiIrV3TRIiiAVoiYCf9piaQCp1xs2RKUEJNGJGy39fBSxzvxtpzmZYuVYirKxq50hKSrFFHqpQO5lLG0YecTZFUITLVixKMWGKGIW0QiiBPlLuqXgQn5NWGNgL3R2TKuYbQucuKwcLsAX5ea0H4TkQWmlA4uFBANcKVcRBzM3fhcCSDb2Ih/StnWUYB1oowwZppLQhwc4FxVNSV6XpBERAjRd6UsiTgRyxAuxgKjiIKYsY1KVyJOkaJHNvA2e3t3Zd67crwVCVDVIER+ygHiWHuLz4VP+Yq1pqryfzZjFmzg0rUpV/gPkZck89KTk/nQGJEKqj7YTKnU8irDBx+//+zLt3nVrTLVwky8oaDWrt6RYGkV0Z2CI06VCEwPl6rTMoCssvMoKSLCjKkSzkoYC55EfI2eGRqpk16NLxK2rl5jzkXLQAaT1B4GIQb4ABbeR5f1elbmxcCcAUmwE5SAmw0/nFxPa+SxowrO0zvNjaf4YvCcCKaoTSRCEk8dcooLkCAs+Y2DrrEwPXE+L0g/eu5NQXGdQIb+GV2dTrmT/n2d6R+3FuFHUDFwQbJ16cllGcKu/T3cEjT3S0YLlSJ5sshGACmVJUd40niyh4WMha3OQ0vmBdSWVgg0j3nOXUE5o5UKRb0XNGMJJEzQnpTBObypNdlQoeKwYBkbOdzLnJv1Eyam0l2YX84bMucLjPJCOusQrPmFdrEOWZJkdVSiLVdGyBzJlpv9Je7oxrjSyBSPKmxEnfKEi/2tSSsmE6BQvbF0cb0XNRLLzqIZhBdS4cwTXAo/64grv4LzzcciIbWRvCyOow0ZV3wDmsYMJsNOfkJIECguZEk8lD3EzaXKkfM4yEmGBLfJpdvzI5bWOCmeIsBzasDLJMAKGPbxtPsPv/zlnbv3Wu2OZwHqvVSKRUsDH4KmUNKZSbdcECMvY87yfOpYEoaIxfvUJD8RgbDDPGfqMTKpsn6+RLX8xkGBTLOcslKZZabfza9FYkOHohGXO2q8UyU41hJ+LqVk2pDBo4XmV623Kd2EA8QbzSOOHbh5a6dfvT/i2I0WPl3dna3Ds5Px4RGNq4+nUxXfygqrRzZmIDUZseXamMq5lBcjtCXUG2i7rcX7ZLV6B8dLza9G4sLWGFMuTQPUvTCb9zvRWjQiHV2n2nMp3SV+KdQl7daGp4VBqEViR7PSzlVP8xDmEqwcDo4OcYtjCijmNQ7OrTb0ggfOqJqu9Hunp/3qZq2C2E1OP+TNudjaaNdH/XEfty3t8ahrxCFMG+t1PsHR2uIwZ3oo9t44MJHOifVgHFCpF7JkfmRgH4NDEfuDUWzfVzkusdFkmwNpmfOriDrtFKHQEnsgOk2YAH0X3wVR/ZZwkgi+NaavYIAG0xAThccfaUpQnKuSyryO+CpRl0LCZDxiSxMCQBIF4u6PccpXX0tyhIBLEq+oUQhgDgdmEyEMSkilCMvdW5HRS0Ew0RIBq2PbNnLqkngRhQuUicAXTCIeQ6U9gKsMjrXiNz736h9/9Uv3mjuc7v1v/+6HWCz+uWSpT9j3HODYD51+73jQP2lvXm2Mat2tbfabru7sSsM6iK9y+/az29u7OEQxHx83eBeAM9nxgObAcUxsMGLbmQOma7XhwQGGxaI7rGeMhzRiToaDNm242dU2K675veODR3dffv4WX5M82X/80btvf+PLr29sPkep3/nP/4n98XvHfVz1G9WTDk9DYlimPolCjJc++byhFvkd1vbTG7tXmXtz2DmrYiyOvDLEUCPXIhKPZ3B1C4CW0mz5YgDPPfcCLop8YpHjNfB3RrH3HrAf/uiXv/wVG/GaBMSuLrk4e4PNuHpLj1hocmiez0cAH05Ek74AWJsTrTHaRgdqW1eu66SL9oZNAjjHotMy2uEhuLO9/d3vfvfqlvyg+4MT/Gni24qyF/73znBBnRydcK2wsT7U12xkPiM1HBkl1Fj4cOVjP5QCIeFCUyQiAcJYkLBoF8SogHgn0pbMlYDlpAgIxH1VceK0HEnTlOqhmRBifoIMshaAXFcGJ0GT1JVo+XzPcY9Anl/FRKZYn6zbQo7egkRtDPGogCtVTyNK8iA5rK2QBDw/gp2SK+GU7TsBFKFHmbv3jdZVegcXg6T94age3AM/tOeBGtsBhNrEhR/3V0Roi5JWMP3JKRS/fOpde5d8xZNV3Qhf0XJX9PQEyAHqrV+9yjGzdQ6mZAxSO2Xs89EB3OnB4dHhPu3i5PBAR22EiyjPV8nIrjOBK3FkkfnE7AuglMbBAj3eYBgcTyrb3V1wq12dSsGxt3L2l3zZ1NMa+KRX3EXhmNvDJ6W0Lt+q+lqHK7hUx5XWRPM8D7FMs53TY3z00cd37tzde0Su9rTGi1X1Ku/5sUWgXRwOr1d7YQeBOuXs7+G41uIrhN02fe/h6ZB9H0yAs05gzQHyvGhx2tcbTjg3mz55NzZ3OHy72ejgovqjH/8db3X84R98m3PBcZymh2eZQPWVQl36t5hDrmrLsySnxlUzCyrMjNiNKboCKjG4e7fEmMjg6TG36pqi7dAk1WeG4aEJWbXbqUYxYuVQF7WgyhCsDM5V3gmdauIWOHGCcwHRSIFChiMMe1NHZ/P5Cu2cMqxL/0GWhqexTTs8qHnIezD0w0FFNEtqp3wZAQQ6VwYEMuobADzwR1B9hhoF0Gmrpsy60dygF7JU+BHzlb6d6pXZkSmolL9ZgVQUelp4KTYfVgKNMtdPotYsu4p2iX0YOiE+iIBrQru7sb17hYen7BRCnNGNBqM1p1oB0wxPY3hVPvQ2rdAzYNAbnTazSQqiE7NRu3YWURD7hFIof+Sn8kUkUiW2CIoy1ydtk8p+iUAtJCzpoVSLq9LGZgQgyWZSFkfISJBphnESX0D4BLdJkpV5EwtLBY5ZW+w8S8I0EDQXGTjIBOCGEDfQmCmj3Gdyio4bewEOIcuxAM9vE11RiDZDL4BBY9TKHkKAD1rCdHaJcZFmwaGWkIG8wq9UePXs+Lpew+HWBA3XtYTk4q2MO2+ikOMApDvmqgiLq7QBDf04DMQbuxi4loW9HnMOWxx5kLPYJgtRIQsRroJfWrZcGMfJiyZj/jYzWWTjmaPIBhciDmLq3iVYr6jpkkFeHeQtwaopG9A5Va+aDb5UNHpgOZK4X7KtmIWkDdbn5EqypUgSNWU/B5IXM0f7NHFo5sIs3H4ayilvor+gKBBSUkL+9BFoJkUV9KNtJu7nsMAEbeEJB2NKOrFhFbdzNlnU+fnFWZQq8VgfKex/PcLvYQpKoEWzF62K0FRj3aqnkL1U2jnt5rxSisu8DSdskgjpNkVcEak6EjxFTDDySqqEuY7aSniilqcuxtdInvLmkZTX4uX2/Et2n+/cYYIGDjaTBM6zXxiPXFJXnl0Ey5A45ggXkj0HAcJOXY6ck+u3kJQKaMHS8MFtSiKyIDb6cSo2X6ReNCv4LZRlgYUl5OoIlZ0jpBIZgYLQlllRc2X5BqZ2UiYTpgp4Jx1EnIUlr3NxBibAaUfHoTKXIPC6MQu52D8spnGsJSArVxY+uhTfNMcbBOeXWluejyxSkyTcgcNOLgIMB5qVWPPMHEhKaAsRcAg5EOQFSJ6axykgAaaJPsUkLxNAIuwp4vUGPiIxLQeHD+ewDcFrkw7mAibYpGIwiQ6RCEIkwtXI7EiyDUFcG2e9M1jzITmnUmooaNt0Ih89IuBAESB+ieCw5wHcyKbmK5AUgJhdghABSMaEnBAcgT51ZHekPFeKO2/Kbjh5ETjhnB+xAKgaNDLCkT1YZZmvOOAEDI8KBI0VL1Khh0z2uUzJKMgFPkHJcVyGZYPjjENmI6RSKOODE5EiWVQiQMfCEEF+Zuxss968uXM85LAUed6xFAcOLtWkegmCqr7YfATOfgq3jYMD2oi50HwwAPa1kjJBhgtCAgEHOpxyCkGYimbMTHivABwLDCmycAYE9CGOnyCf+COVxoitvva557BPREWHH3/wMe2RLolUc/eJnMQtMLbHWeRcYU3ojFTGo2PJHI4rIEoe1sDKHo+cIYVglo0sFIemgTwEyoXCGI9+9JOfkmvMq/IVzvnrQJNAVcbhz/QmMkXsG2k9Ztnsd7a2+azfjVs3kYcNaJW3pp1QO75AnzgbTJTa64hvfetbtCNExULYZqZE8XSGpw7qW87O5KVOHF6EKJ8u1icLfOg7sAFtAbjFmRAuBGHGplXKCBE2oE1K8dCM7iMYDWCRNzpMqgD9k04qYjsSmEFfuwqZRQbfdHESWRLBPJLQnkoE2bAiNImPOcpHD15+QRwB8uOPLBX4FsaRhbhziUgEsiQ0Q7iuL3jUr2s5uizoJKYmla7IZi5ACJA1hLgjXKkR7eVQHTzsAB4btdxSNVgUzZNDW4Dv1NXV4DpLnIDAZHIcZCwc2762u0N7P2Lxfsw5Jceyrr7e9eFZKwGrhGanpUe2MMWi6B1J1YjIMdObuxDhcBwEtsyQhcna6if50kEyFEPMU6F3acbziBajhHGHMNrZVGyVDZSYxS84xNgbefPNNzneqlbTJxNsOWjMLdS1ZkwRjYqmOggce8ItG9UMZGwPQYpHefQwR6dn1A4nPid2tccHNyJADfr/5b/8l35vzDdFN6/sgOl9zWIUSXl+KxG4U2TsJ0qmC2z5D4QIotJnWRCrBYSASTnCDjQjkFBkic7fQEMKsvM1kmzSqWACMV/iVj7v8dCN07PSu+rh6PxUxxTIThF4L04F4ZFuBOjUaj26lziYRYJRR7E3S5fInehzbdRa4DgLt0RgRARSxIkQiEANVwUieR1xS17joBOlxpWIgedfrU/TD8o57fOzql4oDv0DQ+FKdtZkkofCEhitWu0m+jQcHkQIjlzAclWy865KWQ2LSinMaTXGuVCyU1jVRQTiBOeIcuhCwQm+PZfYZRMTi+UMcFlORTQEANlJKe5jYYA7lyUEB3xTttiGJLIpAg4Pf2Z0ncfXhJQLJDnCi1ePNOA6nXVGTIbIyEMZZbfVhRAShgOaOUtoOMDVgCbOXBuatEsQEVPxuC4beO7HV1Dm0eSYFocixIfrgwePcBzQYxzmJfZF8tBY6FHa1Ec4NV8SIw+iIWUhZtApmhlPvSwPuRzqA02W+G46hDhQR9PbeF0Irx3N4fSVYLk5yXhODvlgyWm7C8E6YCBhOWhANEMnyqDBIJQVGniikROSVBeSMAtGPJU/4rDXLQoJLtLbhMOCYIsHhJasfukvEsFaHfzU1y/lzTBEVcbEXAA6cJslxSuxHBVGORrhMlE7O+YYbgYwcFwurpKheHQ5y6qni1F9AYJ+Ya/F8/w1grqMkTHyR+YEjDv0MUsKLRc6kRhl+zEmVwPT7dOIWD/SwBOHOS0VFVoSiWrAoAq96UfODa50mUGJ+DR+o6ILPYbaCkaXVJesNIL7KaKWM2Uv6ijGtk8mb2E/bsxLJOZsYCn1MgDTf7p+EEXdZbbtE/M9UUMZEiwutQonYFanfXkI1rusG6RRtGd5TKfovAJkNej1h/nAE7CgqT4wNbF5FKcboXwXwdUX76bQHtUk6cH0KoPHQJq7PGdSbS4TBALHcCPRp2a5tWTn1AvU8tRkQiaVWOQ4Bi5AFm6Ns0wNNAO51mubvD/467fu8hILChc8q6PE+pIRl9QELAxXByjkNZRaxyUpL6Dl+kdms3ChFjB/Q7fmNVeKwt5USuSxhFnNFqUnCYSl1KKXcF5GSSaX4Lg/zfW2rjjZyLQO5anA4UP1MhHgitEyvmh5QKukEmQ6MiGtOjB8LkzcwIpTF1mg41hEa5qysKjxruGN6+PBkPX3weO9vYePGESvcCQ0PohMlphnTWv9M5oPrpAtrRw4H6DdrDYHDP14HEl7uMnwkSh8hqeVja1wPZZzMX3HzHxxqGRKPx6dQeGpFH6BiGvZFYo9xPbBjDvIJDmwtGBjYsDRXOwwakxGdVKUvhZCu4g5g4cxu3ADo4x0QqwlNe/ieVx8/ajR5Fvn0FUuzv3rtLdwfIZ1f9jHd4bTA2GHLy2nA9ZbbZbTtV4fqeiLNH9nh5pdodgZ4/0xtjU0ZaQHgCLn76uXK8ZR6tixZFSu9bI0kpwAyaLhRUYqHaNF4ZPJcbmdbEoIJIEJpuC4r0CIqEJXBQtA5SEnGOzO4+yurhUbo0xTvOJhtdg+oBYByggouq02YxqG6ve4V3EqYWR0o7bN+JZyaRqNQ5z1kglLzVIEAFydxWsB+QPrMI14pBePUfUuNyrCZ6vSurqz/d7bH37vl++x4Lx+41mORD4ZnuloWDxsK+xow0vKpYH0jg/7WxsfPvgYAev33mX13qyxB7WB9yOnNlePTykiGdgroZUNByPeMmDE3GzoQQinWPXQT9g+t6IQ312vj/n2IA6qm+NW5YTGMai0t5ucTX5z9/nd7rX9g8ff+/f/I/tgWOzVq1rM60V1Gm+Nd5i1EwQpfOopyxmbCBz2jbvaWa+xeb3V7XD03JDXPivTdgcRN7DAg/7g0f4e9UdVup5Qi2oz5CFixXP7aO/x4fFRs67HTu+//xH74PQFpG5v6QuNL774IvJsdFSuUe+ELTw0BalWrb2ze/XGc69oM6Kp7XJeC0c2p475YgH1El7YNAKoyc+Oxz/xJhnf5/nOt7/91ddeabDJfXaq3eehPpnIK09DPlbYZ+9petabDIZVHMXjT69ooWLoVBt8p5cXQPS9CgiiFrzLCdYPgoFD6YjkAYgDpkKEJN86YkzDiUva2BCBpqmRFD7sVIdMTpaWmSI4C8FEyAXc8TyieGQv5lQLmcvbi3AkG0tcyr5/cHjaG1zZ2aXjRSUoCd2jnJKSfi2GgcQXUnPM8+PuGdbh0HjESwIgfhhK+FrCXhrLZs6sFrVCizEL/18aHfJSpfKDDkdRbSw36WN5v4ePY/CdAhtyvTzpl1dZVMDe2WB/eshbCPjmb1zdZicJqqpCDYksWxXlsYSW453a1nan+twNZLv70Z2PP/74YG8fq+ufHMXDD7kxqcfmYWG9pe1sHc/Pglvj4Dtvv8fZ5R89eEznf+v5bW1HyNQp4Pn6gPnFIVVNMf/3g6JYPKOjFOZXB0X7TalrI56f53JmM/bcDMqSIA7NAxuXF1zMw3kGEBHxkNXloZRDT8j2jvb+4l//v+/e3+czJnTn4ikd0XHpTHm6CAjzjEjdNTLoPelhnVROfVdQzzaidTLixl4Hx+/wxzMGjaTRk8AXCsPx6fHx+7yiweOWV155pVlrfvT+e3ikf+2Pv0V/xStH0e7K0kjWy0xCVC60AX3lyAJSBcEiNccBOVcgaEB8hUBed9ySRJq7lCBvqXT+hmnC2IyUKmtXIInguK8LtzlQLBbk5/kMp9hXqjz/5iU3ZjMUB47YEkDqDo4E0awO+aOx0tHSfCe8c4Lta8NHez4yCGoNX3YaVNwXMd3g96vdnlqj0ygcEupnxzxQHLHZDRx24ghPqPhM6uj4uE0Fo+yexYncpwgSj0YJXfU+FwfY6TEGLyVtbbe6G+qDIsh2KSjKUI+mjoD5NE/yOs3W5sYmJnt2fNTe3OA8eObaIKZKSZHE25B0vXzxlkklmo5QcQuQlbems6xYIASIOOK8IDtgJ4Ty7jK6XMl8NRCOUOaamDoO0JCUZPES3PKkVJATHSIIDESGWgYg1hJZTMR51ROVOJf6hQpGXOTKs5aakRxByZzAx6DxEGZ6pJMZOGenlABMNTf/RAEsAYCc8IJYucBg8gYfNN2zFUklwYWM599aWnBMBLGjzXPehvx3mG9CtTzyZCadc1nduJeAKWTJTwptJWZXjJ2lAUGUOMaWGJ0v1UIqJMnJle7KFAQJw+U2lRugg3F05f9lQ1mRl8CXimKI0lMzqjgzOHJLnksQuQwKxTFaiqzMtZBa8F8lxNMTbaUgTxPoQlHLCxowfKHIT5PxRbRWs87q3DIz/LuBgE8t5po3JPg8iYWuEWy1PGuQfx/AuSoW5AnN6FJ04n5dd5UlL2T8BLe53pKZQYc4w4hSJUiaEoiDJVeS4oI4GOI4OOk2RUrEFb85PgaTJMnhebZlmsuQHN9xU0vEtdR/7yPO3KzV9GmpgR+e5UVaJnERJBcYkZJUiSkE3CLAvIjYxemJfo76VCjnBD9BfKVg59NxFhle6a5V4D8NRZ3P+olS3SqtZM8T4rmwmgNFKBpMSVFlYcUf0zJGSSb4pFBGFml8XpDsBFxUOBOAyGSqZVurs8V8id0M5k6TiU69pD0AiX0NXpyP0X80sGML9JkCcWXmAQVaKvgQkSQx18qNIY+XAha/SooWvQC/5C0cg7uYesIR9HRL0ohNPncmcS4h0rLNxWZio6qvw/Un+giVVypah0R1g4OPKlsSlbGOxQANbcihNwJJeMqASUYCyHjS4XZFBEUBIQubmwO21Rg6pwJ2t7YAkgUCbOPCSF/9ibmyaZJ6YQDTujUm1Lh14PP1WuCFk+zs3ft5iggGcoJBjThECI4keQpTKlHBBIMrSsCKdBvB+CWWfgGHFFoDgEaI1Bp93XAgT6jzA9kTTaRN1bEiV/ReFJkSsQ0JQl406wFqkTF0HmXHWBGJbQs85j7+eJ/tqkZLbuyICTuUCz65IEVJgeDEJ8rh7gAQeTotLa8pDnmrJw+UM3pU+/OSzbdg8lSBuEvkQrHZ4lu2sKl8rALnX+K4+HGOJKSwGcySZcW/+3f/jq2uZ25U+YQXhy9jXc2u57nap+DDgxAEE5E4ZManM+uE5RttaJ492AOOhWO9bNxAzUIiDIEkbotqiXUaZSHAgs04XtMmjYMKsHDOuYbm1Z0b8AKBvDiBIiEs8HHuDQd8xnMwbdAQkISK4DNjYhCdJyUPJtKAmRXNMt1Opxz9/LWvfY2j/CCLnzP9CWfVoHPsC8nxOQaSfJ+BIzllJ8gqtNHNRoh6HiBc6QMJsAOzaKWSQKEssiKm44hlMw5XkoCQNJM50mDELyLBNyGD4x4DiOJZyCGJWg7McJ9OlIJjQjwVoCLYM0Vg6MIR1TjC1UdhWEWJa0glbSV4HleuGA0T/mUiidQCciIVTOcT2eCEUTynI8GYSRJVaNnxAiTVmYuNq4lOEKb+sZbJIbjjs8kAu61vbihXtEFn0V20R+BeJNPEbt++jc3TzO/f+ZDr8SGN6ehg/4iHN/hcUOME0HksS6dxMtBYsHPzJo9nrt2+TUNYrHtL9uRXWP/0p3rhgMPQ07f+npzME+egaOTx1Zm5TxVEH0jwIHMZ0tbzX0fg6Ay2hslF1UCQABe105gwxGl/xc4ASYwp1ALIzYD5m59VnkuNx3QOdGI93JtjvZ/EYLAljt4cOL8e48fturrZ/sY3vtGK07US8m8u4nLl9IGgB6sihxMnKUGIE5LmabNoJqXmEXAIqVHnlKFgTANB4zYIFwN0opMw1UA4fybeHiAVpvQMrmjiCY0k93ue1XBHEq/DAFfZGETUHxctsd7UO3a8FkeL4OPLtEcLvLO5A5z2RF5YkJdelnZacpkVn6RILdB862uJnMPWxuGbpAJJKg2drM0QCZaW8jLq0d5VOiu22D0oignc43W7qpcIe6eHQBj+PDha+VxTPRLPb8+XgVSQL8T5lAiJhbVEMYmoZUWAOLeW35ITJ+LwKVkvZ4fsMhBIofxSIUk2I1tCgIwXXE0kXRfyJvoUhIygmZrxG0XFJqz5iLF5dCZmseyR0z5iBWTVsw3oeZSBkJ6C6OEFxsQ5hoPKkM/MtJBYz8zlMqTnFcVzPEnDIxNlFYVUAVDR+YUExiIpS88D8bgNwQSn5piX8CRfQulEP4ZQoTtYQogqb6GponZBWHjOqXzxzJOnr5DlrTxMo9aX7zMHPYpyQVU6q+I3A8cQG78L5qT13llnvLWvvXB9Y1s4EZf7Mf1GuQ2nlhVfIhaCSqTr+bUAjoMqOw51RiQgelhdJuW/Ui21NJa/ueEuaY6z8FQwT1oXDxuQHvK89aE8jKr9U9xgOPqLvHpUiyIVWWEgtqJIB0uYGGXKpVJlAY7Z3axVAMyT/KAdYNJGLqG0IVPT9TcSaIbQ9zOQYIAthHixjYLBqhRzBVkUI6t/62eudJG1KEWpEBB+gwUqWorELCQJQ82lWixCeb8CJx+QyvHVll9mWvzNiZyD6XrP6zoRShTSoislXT5inRf0S81fPnuGqWVVGdQkXSjTL/or36iXkmcjOFOehBN4ah4NhLbMr1pfTPu4EWZMmObeO1GeMszsqjS/oLzcGzgDLdF6SzpHwyCzsOdKu+bKf3lUhPXRI4OPFIGfzZwsf2jMBEuBil/wl+E5xH2CsQ3PUw1fSWSB0cJtZCk6HJI4t/PDDz9689d38A9odeVfE+svuliPNQu5526jyHMQ36yTKi9RPrqtIPHkIJTjsJBVQ370yQvw1bdRa+6Ty/55NeJloMvlXZdrQWMUBEyuVAfzUevZI6/GiVXBqatSnhgG33U16w6X2TRE6ROYNOK6Qtyap+WkuHZfJjptFiXQWvljRKDVxPdjWOZp1xMUlgocHFvnnAE93x5zmt3e/nhnY0fzTRw0CXy+75SP+MkjkqnIkDORN/GcbuK4i2YmvVb/BMcWudzqy+khiMZiiMdTYZaUHBwtUZkmqVzCoJWuLKC4faouTsS9wrHSRVDOPdJK0gwFZ3dGpz+znYSTFV2LPVXHeNexN6qeBBcFuc1SCFx1JhVumpyHyGZfe6Pa2Rw0uiyGKUSz1m7Vu/gdxvHXtd2dK3LRguJ4vLHV4pznEe7WlEmzPHyNGq1OF9cgJEFXcGezH2db7FynBTRaekLgliKB1c2u1AZAqtRJciijcr0lh9cYusVjr97E1YLsorIy0CenbrmYv2G/cVprgquHl1HPZFC9gSRNygmahKgsGM14meesvWMIsZUfZD3JajRr+jj0uoZUCpzIugHKXKmXao1vbqsVIIUECRHLVTQIqEJn+xqhJGV1NTi1mGIPR00+g1RtTofTn/3yF+9/9OFedfOrX/3q4eM9stfqfC0GykOVo1o76eF2d0Z0MjjrHfN1vZ1djjXg//YVysIS+uT4cGuzy/pCvTUnQEsquLHWgJPsAE3wo+EU7+0WdR/eNrFQwH5U9frACTpiA3ay2dnEmXf/8T4nJj978+X+ybTbrf7y539f+fxn9veOD/bu11i+sKfSkr8arOQATf1zAHt1etzrP9w/+OnPP7pzt1epsgl5eHLcp/t6fHzCaZv94YQVCvasGoxa40pL8XcdKCC3O1euYp8cSCAXw2odj8fuxhanlGxs7VAFg3Ef8eRCxwMYLK43mHBq9fExjsofffzB2bT9HA2+3sL+eLECTXLaK9xVPkIsEb0ewUcMdhxRraBTpHe+9qXP1Xni1TtmR6RyckS2fl39DB8w5DOOOEDi0Iq94FnDDc11rDeacFPTTk2V07axMB4blasYrKIomhgruPbVlspAEQBKvCh4CdavkUO4maKMBguC86opgCwrUSmIR2x2EcSpGE/ZWo3mK6Rm2BFz3+4pmJMK+nGjnhtpI9O6psMmHY8Z6NwIVBmMzCXoTIt+UP0y60a1pnWSmPu6q8kW492suyjQLSc3SGr5YSMx8muoXQuzHEjf5TWpNxTITS69EEJxIcZ5/DxpQAEogial9bkmhdxpcsh50BKArDQmTrg50zH6k5NKjTd10AYHniM2coSUcS1rTDCan05jr93q3rx5YxfD4FVpwhu/ehO3gA8+vMs7ARg3BI6OTtjoPObA6Urt69/a+OiDD1989VXoqwdQFx+UzSQ4nX8BEZo5Dkx/8IMfMIiwacsnOkkq9FnoudB6nkXxrBbWYODGGpmiZ7YVlUTCmiKVKXYJnBm/JOT8LO0b1sYcE49brDrYYlGf8PMIWb7//e//27/8qw8/uPPM7VeqFSoh7I0aU8U16BjZrKQWcXkm4zBcLnTAP0dlDXTkEef10GraeolbLnf0tHwpEuWQBHFehCAXJSKKKFzN/eDg6J133mtt1Xmi8Ouf/qJdqX/2i59ni7Co9lzEpxqnI1IbRySkjTgVS1nU7cfjZSTU0MDcQtarIJn5T1GUqbAYOktOO2PrEwSTclJy8zfc2WNAJFoEESyDcxmSw0kniW0hWpI1ybwmmqe2jjRji7Nq6TqCkiYJvLrDO1qMiw21RBqXLCQ+DatvVdNH2z/dXPgUAHWkimvu6i0s3nvTU/lG7AMxR9G8lcKBfHrG5xGOS3mjgWc2bDHK1BW/eQFXJC81q5U4OdAEmcby/YZKvd3qbscBzhpoomhqI+EFjQr0abMr167zCLbS09E9Zyd6/+nZ27c3eU8i24HJ6V8ybjEuifwp0VSbYXhE8kD1QTmsi3SFFP+UHBeyQ3kBsu4W8VYmISqykQop4+RxZwHBJUoUgBDPgWpv54ScfSJHflw9yJVSU3lSxDSTZJi1vDaiF0NqSR+Pso1vsYq8sVEbHXMhV6LpiGkSp3agQ1PjeenO9lz3TRpo6bpcQJIEXNJtKXB0UDFPYuhbzp4g0EEGhmKKQAGJOilx59ZDHVRmqcGcLBZDRBLFS0REfB5NdObFhBfdmsWYx73g7sIsINCVmgp86Uz1PE2fvqWTkx4Sg0RKWSJIzvXBaAl5AXFdKvB1RJ1lJdNzkhb4/vZvLZv5rixdjvDbF+9JOeaqxixtIS6Xks41iXN4pWo1gf9adJLEPqdoeRI9p3TlZvWb9INOvRPc4UgfbTEE1/18/xJpCRiROYSUlJeFuIu/MpWkZfgyZIFgul3AXLhNaKmYsGPGxtkbZ2d1NvjwkwRCEMK6DiVRWRMhO8rjau5cUyTlsP0HZqGNlHTJiPNeEnkBLRXfYnCb99gLyBfeRt6Lx66S6RPwst4YXOzacKEkT4pg+uRKykyQVGUp6RMQh4i/HsF8kuKzuUkAyAKjydcDNaXW5iAK9JrHEQqLTTKlYUgVjrweWyAQWJUD7PPRJV6hbQuCQxJx9nJcg9BnQ4pJCOcCWnK6DoKFhzXTDXDOL4szgpzKTvz8LCnVBSSj8sail+Uu2ZlGgQOQOALjbYqLDvFueH8/3rsPkCIB6Q/kmEOgdBQqpNUcCYUQ5/wMgNN4yZSlI3RYhxA2+MZ7u/3tP/1Tcu2fHP/oRz86ONoH4rKTnVyoESKUBOJsHAJhsW3JiRNSKS6MQAEcX4kgmMobARYUZRi8KqPVvsYL2XNSJuIraKZqvaUk2BFIJRgYCCl9LkISgfKCnLSBmxdPJebw5m+ci6vBjnClaFzzM0BBKKWY69gtG8gO3KqaOI68UsVFbnNDpxhrZ5Z541W5/Z5V8NmSSVA06huyPAikIbCpBwW0ypXXBahEIqDt7Oyqrt875jZkiNaFbYdmDCFBBLVNJkWRl0MACWTkypKBDVwODtfZ6/0hu05bbZ3aXGlr6xbHarZR3n7v12CyCYbMOLfCC/Eg5YDNQJnv23jZD/CNN95gNKnWjmCx0d1B+LuP8ZvWxiUr/4lOh5ZICrFTTiHJZTqwJoImXEAovPDyS0DoAKG/FWdDczSHDKw6RnUff/QeSXysiXKNp7JtIhBmXWbzEJ1C0rkf4A48ouG0VkoKHfgirUrOJkhsc9sfi2ZJZssABMq2AQyKCJ2aSatAbhFlAaEzA1Kb3IQFWraU6luSiJgC8YLUfBODHUlIYgTzTZjpdiFi+gmNvJR9Aedp3XLkLttt6tyyDw8GuzktWYAkiSP51fIkhMuLR5ZQXpljvqQmKJQY8dMt8jhOtllqsZgs6kK1E29CCCGrIOCYKFe/c6AjWlS82NaMJtaPzbTWJmdoFEtdZ9f2t0J5hSYI1E6zSYsjAWPmudRrr73GBvTbb7/90ft3US8NAfWyAf3RRx81uh1OpAEZgvQVEutpVK6sOvoQFy2EDEEpVlGNCfaUI3CEIqUQ69L4uUW/mD3TBK5mubKkAOmCQUA5eD8zCBK3+7NzqWpU1+FUq0+7qfsiibco4MhzJuIgcFUvgCvGWCt6nrPRM5ycHoNMAKLnD2rRXNj6E74l53rv3r3GURX8m9d4QozP4fTrX/+68J48kH05kxkBd2qOY8l9NUJCBpgwKTVFW6YMDkG9a4xxDJncgqZrKQhJFNXEE8EUMRz8HFJQKAWGOx8L1CWIg5m4uEWkvAnufk9ykCV2iSIq2UAuu1uJyEgEMsTdXbv4khlvyNJyLA/Drh6VcbMqkDHHByVJtQp9NSzPAkFmIKvxMiiSIy2BNp6BFY3CF2KA0I3XfY4fDnkfgme9IPDMmAnGcHC6kPH39la1GQFFoR8CEUISGAQrJFU3SUZIeQ1ZuE0Uzo/kuYyZc18JWc6C2DoBKYJTl4mQmJJShKKR1xl5JhIk8i3Q7FmckcL0C+0UT4PjaQOOOBD1/h9w4vbOcy5LgyMx2UHS9zz7tbOTaWtzU74cPIErheNIHGXBSYiVQHD340ym9kEqVQwtVm0Mhwb6BXBxB8J3ZTJgYqTX0+ryGWSmo9ojJ3+UrSCh1g6deEyLqAVddUbJw1oFcJs+O2Gy2hhNKV5jXPT7Vp+vyh1+f3oIyHjJM+1JZXc6Ofvwzvbnv0DBj/XUF3pCTM/SrRAowIVSIE2qMMsj7AgzLiUk/cZJr4VCRIniRmma8gnAHGTKuPDRUHFI1kNOy1kUuDAXqHEYZZIHx79EX/5SSd+GRjGKp8FRZThJKG//BDNqj/pNRi+fCicZClIudcplSlkTk+ZZowc84hGzSEb2NYfk8TJVtYPHYp6FeGoYxAumYZ8LaJS07AoWU6j5RdDS/coyJixqOTUzgMvCJ8ykfXASWooktBTJtWqPPJzx8kBe+7mYsvHxVSEwo0mYYUvFnVtxtJjkPaEkrIRr0UZiIKHU0Jdz2sqQDTYuQrRFpiwhC/ZRdi/SPD1GbFWYEjI4y7L955A8HrIpd9lXLOrZLdr0MYsi8iQ/1t6a0q4ktIybahj8qIbIV+pcN3Q10iqOXLhlTcPLIEyzJ4+r6gRAPJyXH3TkncRZbHKKxv51yKyeDhNM05YZiFzEvbQZ587lKbH45XF5VDfUCr3RtHHuC5e7argOZtgpWlBz3QlaNDlFzU+xCDOcErL8m7caCRIhR0vSp45qHdkF7lBu6IUWJmVaIQ+O9v74i5/juHzi79w94fNBp5MOcY5ngx17XVyn4S+JoZqFx06kSorKBYu4Z/ZFhxuIsxrXACerT811ziBFNpLNK6gtXRhpjDZLoTxUePGHyKIRgwLkqMe8I0r8SmAx4CZiLHVSPI8UsuWgIp7Kwn3ou9QV92VBBIcC+i81kAQpqJQ/wrQN0hrIwrKT7RhyEU/1XiIXv/Oy5XU+y1FKspBVt+ckrcBeB4oGxnTSqmdYdiOYTHt4EXHmH3XEOcY0sFGceqyej03YyqC9XXtxe+cOhzUOK7Wj3rDNob61SWxfNpoTpuNMQfi+xHZlNJwOG/0Ofr84TzPRadZ3u3iCtHvUdy8+VtOfVPjWH6zRM6spttXYWoIL/tWclcxH11N7WVeIZXjRDywlWOcysyDKLYzkCrDDx9I6monILZV6q7Eh3uZrb8dnPV7Qpn/C5ZsJB/Mf9irq9d2d6+zusRsIhGM9KS+ngxJAIdtkxIfRBpot1RpQZIbYbrYQadgbcuJkgw3u8bQ/6t+6devzn//8Z156Bo/Zr33j6//z//w/8TJ1aHjS3JiO+WxPbXA8POnUdqDMDj9curhI93uj8YgZEgJrI4933dRcsZmZ2VBuHTIqSKE8DPJs0G9vdBvTbruBOFPkp5vkTG+2VqUEnTetpwsOUEBgzJ3zC9Vwj3vVk357V9vWZ0x8sfLQbbvbwkI0AnIrmBQPjtLVYQVSeQHO3J+X6gSOw/rLFPJpCKAvoMXg+hXegbJJk3Af4THCWRLhvFOx5OJSrhNkRXLRwrnfzVO5rRH2jtWu+fIR20Zf/sqf/el38etnK/bnP/0pukKCazeu/cl3/jEV9ODOfTS/yVHdrPa361T6/rQ1Gu9ValdGfLOkxjODITNpUjf5aGf/pH94yMKy3plyImnzSvfZF19EM+w6scnHpvDpuH/66N7nKyeNYb/W0ucEKxUd5aGPpLEN3NNhCONGfdTC7ZRdlUkfjXHKTefmpDnCFfu99x9AiuM19va0kT09rl3ZaH/z29/lYJD7B3v1bvvufu/kZDg6eK+/w0sJ+G7X8ZDb2dziGRDl3Wptwev9Oyf7B5xpu31SORxu7gBvtluM1f1qjS8JHlROhvgaxtKaNgg+ptEb6AlRo8bjEPWcDN2871+vtLUJpC5Bpxlc2b2CwUPt4cO79H43rm5fu7ZblYpqx0d7mxv1Wzd22IY+PN2vtSrteuulV17ktVCIjZiIq/blklptqKM5ZKKGVMF9l66nWt0YHnH9337n9RevbV4d6WiR0XGf3WcOWueR0GmjhW82ru40Br48eNwb6AwOmhkV36BNs7Kgf+KFC3xP1M+wfQWvmH+pwRFTGSIIGktrlbxcaQPJcYyQIEQcwCeSX9mjf3j2ACNkXaNQILr1xE2sYZ1SvM2g905FxEaua276kUmppennic5iJqg0hRJHdec5FVaEJbc6Gx29+a7RkxKKJsqK3kOQYBF+0Hw0MwbBjG/QZ7YRvYEyReC3FIz2W5TTvUE5mQOvjEYtzOTkJZmQIaa8ufyiQxV4hkM8yh9KU7tDaMyFZ3SiHB0Mt8w5WUSyCY3nLJhUGXaodSLfAqHGWZKzZao9ar2XwDcv5TNfbQ55N+D4tCfBJ3q85LNTYgyf3yXQa2caLvHJbQ7rW5vj0xNUWh/xsdDKZ1989jPP3frgo7u/+MUv/suv32bLiVGAlv7RRx+jP77SyfEdGlOgACOpK/SA9GVIlVsCil+vz8GOsktXeD1/+zvf4qCPz372s3Tg6t3CoqCJtRX6d+7Qcw7J45YBaYQbvVlRpUXFjmAp4jLhWPnqs7sTDfJqLNGOgxu3kBhXOTeJ0y90MA7ETvYP+cSfqil6j6Iw/FAPVfnAsvX8wx/+cO/x6R/90R+pFEqhiDJyByBUHyMvESCcpQvl+khaPe6dUINV3k9hDkyPzMsKevFBU2WC3ihZGaKgOKOK7Lh7ut//4OxD/NZ5hYKpzOtf+wrwmhpilFjXQhEribGaAR4kZ+moghuUTF6SJE1EAPJJBE2sot4FpNGNh8x42FzQXxlIyt8FxMq1ZpLSGFIKVSAm6JQXBoq7l9NIWmzWM2GBjupElkZUBUE2stBLEw94IZ5nreSNahUygdmfh2SQ9QQPgafjbpt32jrMnTg1m6eMjI2skWUzMhMKXkz4YwQu6oLOWQ9q+IIBWKEsra+nHNDU4Auy9eM9+DJ09k+GHeYp+Ch4tyfsuVtrbPCpjNo+eZEihLa+JQ+TImZEjAUGSeiyvoiogFF7AVZNISPA6A1UbSG1dwukzKglyhJxUxRTh4JpqK3KGIPCefNGfVOMfVQebYL9N+lfCkdSXMWZPZ0c9/aag7O9/UfNmp5M8yJUv3caB9GrYYBKr8i17KtLhqt+XWUWZVX604QlTZqpb0OTuhRGHisdqbQM50gAimqwDKGk8ubcX7Neh7IyFWCwU6aEQJuyhXMFnhASZac6ybnAIWJ84KtXmyn/QiSRM7yQIygu8044aBYbdEOlgyMwDWWSmATSfjFE1m8PkUqAYDwY0AN4Q6AAWYb/yq1rpNKruhbNevkqjpeoJdCEmSk6kTLcwjiekhyxYAvA82+X6QAxi+WM6pTmA5imkNMhTljWBsASv6ADZLG/n6cvBKmiMC+ThbJWJzG/nEd/CnfmuECoFGOx+MiwgMmtRpEISY2Y7kqy6+xhFXKwLtWWmIZ6MM0EKNqnBVtFZ4a5LvbJciVqlBozz4kYAgJtLcGFFIEInXXADVlUMknCsSUkNqsiifhCoinkwARJWRa5ZtgJGVgeTygzIkV9FCkJ7owJ/0kjy+3ufApJt+ejXSaVImBLMuno5XmHXLlKe1MBo2qWOZZlP0eva/k7L1cH8IisxF4HX4l8DjDRyUeZxH05Y8JPkWUcIKhOY2NE4jcgoVLmiKIfkzocap5//vkPH5/i8iafEHoVljWlzTtjfrW2zTrX/DphPOImzBRJNHNIHk8I6yKIqlKsqh2VIiv4AgWJNNsKLxLXbfOuE2mer4wSKwHZlUgkITiCSFbFgjALt4Fc2Js7c9MELUmSKDvvOvgC5YXbBSIpNVFLkIWIEELAElM3RbwQXDlM33CuhHK4nc1/2IHCieNo7z5rBhagUp3etdSmMvF6S/6/Q522wQRcpxtTq+wvE4D0enrZn9ULjEZx9BMLRvgOBmzdxvf3wjDWFVMizgcwHSA+n7L2bgGT7B59iGgHvM7X3vSZcjYEOduazQgk5Ni+nSt8qksHr7Pl919+9TOIsOFOeclrgtq+ZNddTVUSmT2qgA7ezxwFKOXI66eB19Vbb70FwsZV+cayH40+Hz96AAW+KwqE7RFSIWLKaIYPGwKBWqKclyKPm29+TVkAUnfsX3NGJll4lZxFO7snYpRnsBmU+kzZU2Qe9+I7uFDpUXb1UesyOIkrxUQEruQS08w+l/OCYOJO4paM2oyIzmQZ33C4EG7cuIHrIvv6Dx48kDErVJ555pl//s//+Xe/+90mm+WYN68bjyr1zcrDh4//v794h9feqT7KwjuS4hv9A3v6BAhSd1Q0EaoMxbLFD+XjwxPMhxpnQwoDgxHb98hJAMcSYgDUC5vOIFRP+5gKlU0SBWH1wSMtTluGLA2HJxxwYTOLdUR3S+uIn/zkJ+CTHRlw60M8NpI4c9ktDi5k5Ip1qXhRuSiBABz6lIWFGalAYAs2yGnAdl4LyRVGZCGAzEaaWwpFprxsMRMoCFvSYEIQNAoIkCc24ANE8jgDA6qznhaaTgUB1uYua0GsyMUXwxjsINs73YORtudlG8Yt7IRScyQIqe5PxCB6da6QpeAeOEtGEoZlPySABMeCNXHG38Av6PsHfGOmW0vLNY+YnYhEoEY22h3jnH8tBTsfKyYDVFLqugNdTNe0ESQJlKInoTowG3jRrVF36NukoEAokSOHsym7VKQjmUpDApPEdGsKhkSmWM4Ejh1rEllnNM66q5Dn6ZMLAVAnolhIX4OC5E+ppkndMZwTZ/jBihJ3Y5KXQBayEUEhuo1WiDcF8bGeW1T5oKfymmJ2JckFB0ZElRzvu3D4S6KPrdLnX71+m8dav5q8xVsLOESDjHP066+/3uJbZFr4qyllhIuoBDs3WACz/pM/+ZNBnydHcZD6ubkuTIRg4PDLwwDFGfh1E4+H4+kzGGpyPHXjGp5joLntzArC9j9FIIBTPKEKs1kpAHPX733ve/RsHMdM62bwFN+i9qUHx6XkWAZCk8LSsRChb3Eq1a04z8O0cVnYuSS8RHAfhX+aOzQcVHvjIVvhegb4WwnISYC7O5ycJ0VGjTnEcWdx3LlUZpRW0+zLOgHHEdCIcAuplJRoOimRTeyEHFvAqVtZzkuugqw6BgUgiYJpIg8SOh4tMdbyxfgPySofCSEoFpVOdga4yCJp2WnXXChsyQiUxtR8BehUc8+Tzo8b3zRzzZdcFnMbnq6URefIhfs54/VmoVspYU6+GL4BMjadHh0gKuVnYunxER4QNM1Ffp/i/mkRtIosiONcCZSCQCTJCEcq2tpIwBQB82mJlGieE0mCIRJovkWAEl4M9xSBVOMkagkt5UoUEo48NZQczygSdDGipysMYwUPMQvnFvYgUZv9oG3Jfh5bPnF1N6pOjHdDGb4mowEeFCeHB9d2d5hrSY+TMcO2niih9CBBQxH3uLg8QivLZt8xREYVGusRPfxuppPrqsZCC6DLN1s4CkFrLiKhBXC6YkXgrGeIyG2Yx31MCCHiUmvVWNBhpINpIVVBQV7DzO+gQotnsDwIv2NOwRCBgtdsUDEzFQIRknSGltfEogRc8NuIJ4d6CqanUTwAZOTnXd865zIjAp4R5Gewg5tdYMs6Elmewqk4SzZQernOJK/F09Fqv0dHJY8MpOQUKbKbAtCFx6RLGnYxirLPV4CIScg1eZxzdi06YgPW5Upw28Ysd8QwMBBclQtJT3xbak8E/Ypl2Elmfnm5iIcFZmxoTLaFJLO1lKFoik8wIc/mDGnHhI/FNqn2dG5oulP4uTcEn+LkokVRWCNFVoipIb/oYdLUkzT5VXEOYEyD2ApBEnuD8ooBXHl6C7L5qpAra8plkJhFkELUGAM/RriFfqpEhP1MRYUuUF/kdfYUn2UpDcY2Kl5lWm7hhuX6nOGV+Of/Ws85zjL9PHVeOZmthl9tjuk4RYss8sY1xBG8j6PUQ86JVUJ0cYJEgX22l32fqS/gftpcasOUbGmzVpzrwRhcAc60L6vVoIhNUnCt3YOvOkEcotlByLxRrZlCG2UrSGRnXBNoVUS8Sji8yOXiA8ylKlHW2F5KLiPmjoRQC1ewSQX3S5WFz1OF3w5mHL1Wd7JPpi/c3nr92S+88dHB+++/f9wfoM5mR4t/tYJ5/ZiDKmI+GFIIH3UaY9asEMtZ5glcfJcTV73QaqLhqJKSeawnQ3bJ4JrS8FQUwRGNw1mjI762tZYsshmnbNgv4VJvrJb18QcpoURd9cuotAoMDAvkqhUvLJiQcbUwpahzyrda5knlfayFuKQ9zpNZdQe7vFxzN4HvnpaPClZrOIrx4XIMTSX1DIdxEotkPKaQV291nnlxd3//cR/nmzNWb7VWk4Ngq31O8RieTHEq43TXgY4jgAuemGiYHdT+4ASFEACejfTORLiM0wHoARVc2t0OpxvfHo4/eu9dDAM/NRZBodWsQw5R4xJrLdcFtqF2L+3NqXiGvCoW8yUW1cNRv9HUJhF9ArvnX/jat15++eXOxhU+S3XtBvt4Vzc6baJsJCH5hx/e+Tf/5t/c5SjbszP24qIPk8nJPNnvYFOX1odjbKXWYjY1rZ71BsxZO1ubm92OfF74KM9w8t47735472N5117ZZfNuNOX82h67puwznh3LM8ta4tASf69mOMTlE5fyPn9uMpaWq3uzVcVbAUN+PvTEeQubnW226s6O9lnvnfVO4N6iZmlc6Jv+M0yPbhN5w9qlWPK6X4rmqMVhMSkVHwov3BUsA0RxOLuT4rNtSV0WdjiPDn1C2AyktPYmFwboYWQdZeDkYgFPc0s45BUpBAYkiy1SoEsgiSv4LG6blcbobPizn//ih3/7w0d391gcXruy9e0/+OY3v/41jFFPAcDVgML8unrj5rVvfHXzrV//6u5gxPR1MDgGAwPg7/SM814fwwmPsHp3gzqtb16h+nix8OqV24Pjw4OHjw/ufszBNCPOjsT7mv002tIIOeWbwm7g1s0bUe+jceOwPoJ35Tp+wY3KxrVNnhlAkJrq8SGo4WZtdMyuWQsP9nF7/85jXvyfbFXZ5J0O+xvj8cmwcnjIawSDje7WoKtjcPDgZs2C/D0+j6mvvYw5Hgdjwx2/2Wz0B3iyN3B04wRrpdMEGrQsZNFGUowFRRfkTgwggaoJVY43Olu721t8VObevTu8KM0c+6Ay+of/pEcpfHAJKpXpAFXT6SNzo7PJGRrjTpdug5kAc29UCysIgommIUsOjLBdHdGYmmO9D/HcTuObX3jxKgduD04wWQjhyYaP86DCWaNYpr4OenbKKSXsPvNIoKhspBX3CJCFDmtn/heSR1+BIQR8dgFdxZsBFDOQSLQO5Yl2EYzKVHA8IYApBMydjQY2oFGF9B+YIU66FKKm+1WRfERQOkYNealL+7Sz1Nl0ZJGK2oUkj+MLeMsETN6H4Gj+0HmsZWMi5nym6LZENakgkTf3g5YqYhQmglIUL0tnOKRcXq3sqKFy9hXA1aVGwmiewT+8oSmlyMcggkVqJqkZPtRgF9MAVUS5EqTSorqdhQPQtX4fsjTmKZLentVV+DGAxdQKj3gUM6xoA5qdeBtOXf7TNBK1Sk4644GSyuI5RiF48Nf6EfHa0OQ7mvQl4wpvIdMB6CAjnh/ubm986YuvPXPr2vXd6//5P//nYb3GHjQPh2R+qjULi3IKEyWXQzLaElD82iqNrX0sKRwZq7xUpHJFJdHKAlsmzHmWxEHQtZBcds2t8DWMhz0jQSyBwSFX8UaydQGyrE3zTYKywSAiqoYg5CsDhylzO2EBPxiobfOGBwl+GZFKFOpcwBT//u///sc//mmj0X722eej5UpasRTrGbLKSXVgaeqIdfQED7hpAXqvAY/iOAlaAxt5IxPWQQlzCjNalLfsFhCKsjNMNJhin/UfTR7RR/37f/0/btfbn/vql8RSVKghTWMimpN5mnHpV69uLtJEVAxGkrgeoy1YNdQM62FSGftoJGCyycUV5YDs6tADg1ILhqQkOKlMJaY2xsbqVrRRxiqbSteWEt0yLSjeckWzjLDqlqlUHfxPV6pM9NjRZVLzwUIbdFgc9BGKK9UKhI+pNeotlmIqoU5x45D2eOul8IeVQUNKbsW8HyvZoF0Zsv/Gu2C9PqxEUWFRR1KdFhE53FYAMpEyn+JqHe5nZhhli8gwxYZgk11Z7ypRk31znYWI/hlkefrrXOhOe0rhDATv8UgTNuyqr2Nh6FBHuzubbeYdVIxOsZdCqDUIElBZQeT39YdqcgidS/IUJP+qsA6+CvcpwBI75IScb4mniHk4lVIgv6/AsUCnguxi5hScpPHAyb5fdxVOmWYeUtn8238hVomU/Uo4/sVmPzLRUTJ47N58BpoWkYgDmWQ18LLys0kwwETShUkCQRA6RhCvWSNJOZ5CBMoLMuS3iQHrKBY5JBGSxlLqE0UWOF6YV2WfKUkKJxTWkMETHVIR0rfEyUC8BCSsWUTkgqBHUK6kGQgVItFDzfAjJvqRNAeP/nAO4psVmCFVIiKrK0IRWc5SIlz8CzWe6q+kcI4ezqG7ktQ5+CmJjK6JCymsRCjqMTM40OjHh2enup7yyLDHu8eU19siJsIzIMZXOn1aNGs7Ov1GhwkYr3Vr0M01UDDNJkYZq1SItRGyp9JBFyngCDZwdQ0RAOb5nQG0gvW53VTCMU3TMV2Gr5xsihcCpfsLI0tk0hieZ3W5csinj6t0oXl6OeKaHGl5Wegwp69UKzqDWrFJsKjZ1a3SmeYqPuoICqJc0rQ85d1T+BXBMljaHII8+S2IC7dl1rW/4KcAfYIm8kz1gjL0UY6tj9kPVPj6E23hp2+8S65ethdjSQJ5zlaTbi0B9ImQNwm0oNIE/zSRRN9Fs208EUFJxf+wmHRlowQivs2pLUNSKknIkEptwQxk9QMaSQnoXL5NFM6PGBki6NlxrtB3IE7IWbg6SD2f7CVTTQcW6/CN4LkvOJZG19jM5ccZrR/HAZJLKLFOwNhwwL//0QHenXjGgUMqiwp7/THNculANi/P0dnnBe4O/Kx/xrYUu71cyI4G2KElCY9pnFKP9h5rM8u8L3GFLIzO1x/CWM6cHkAEcDU5P6X4xxGa7Q0wtRjD6GKuwXYGmLxDzWY0ucBHD0DYhiYeVVq0MkBAIklHcLADpbLGicCMbjg1MLoN+qdsGlYfPYRjd6sDQpLQESB4hIHDiRFAqhxWsuTYC4tizyEv1VKctgYMIgQUy24metc4y16ezuuUtEuZCgB54bucKtZZgPJ6GnLjJRh9JS+4ADcjImbK7SrOBVfbKUxdOqDI4CuR5Zyqx/hzfnKBRtXglfzOO+/wuTAqglzskH7rW9+6du2qFAu9aA08VpDXAh7rG21OW777rpDZm6JQVBOBCqUtYDE4c5DAQ4shXxmq1XCpw547tQqpH374IawpHfiYOvOWSZWtYD2JcZERacAb9eEyT+rG1hUM7PFWkytksZ+9xydS47TBpJ21LDjsb+I/yEsF1ObeAdwOXafh2TxCKo5pYgMazNND7nr9E3ahedxwZqWBDGsLgLQcyIBOmGCBz14uqQzkUdFqX2hC11Ba5eSETXPObCVAEC53792FDo+o1HLZ1tGGkSyHg9CBEygy+dlG73RuoGQgqqNItC1B2by4stVBKgH6r71yW/ocHFBqoKBRzyRBgRiLKZRwdMjuOdNFMXIwTeLCjisRG2FAIruJldaSMBGKuAOMiBR0YlU4u3URgp+QI2J8rurNOGA9PrSjxLUtTNlcnCBwwcV8jM/w4uoD6MjKzEqt6GwodEWcykU2bwqTS6EUTtFVwfRhmvtBq0yBT2o5KS4yJzop40J1LDBJ+Avwlbemma7gIAW31gk6T3GS1EKhHnZrLimVW1oB19QKxoOo6Ib6+W58uFK70fM6IbulIpKSYNoIN2S2Qwkmi56xXl6wIPVoOMAPmoy0083aDvZlIpe/IhIFTNwTa3hBPycIDi292wmHuWizSWCS1KhpaOVmHEl6zEtZwrZ9vBuHL6XAjiQ45W0xB0iSACeVvsgsFJ/opYroXKmRwrdXfWUWTI13OP72b/8Wf3xeAAICTY7gMEFfsxyyNHoi0Y8NTQiiECsBZCk8JCnlkBlbYzkRAxcg5gV3+jv6rrPD47/4i7/431UnX/nKV/J2sWjiC1Se5Bax0YyFJx8CoDEEIPjWxHybCAu/LB5AbimghbcGvOCx7aVcecTIZHTERMyFa1hRUdEkQUdoRectMuBYZq6OgCOgn1CFSCURjykqIy/70Huz+wy13pmGJEYW4rK4qDgoewPWRuI3gshIqsUgi9ivCchAELn5dmr0dfCUSsSMiIhCaNWp51zRvIYzjtDa3GRcpk1J+HjfAoXI8MrM3BKFMjhESGLsWymqc5AEzjkIJeHf0q+VgzxECFY1EZfF0lLpyLxOIJB/h8WxqJYfCbHLBOGWOBXkq+V3quMpV37LQ++4LQ2Um9L+jZZdw8HGFDVRj4CmeGwjScL1IMOejyIVOSJTbdQ/2nvY3GSfdgca4CGZhZOtxBTNz0iT2YGD2oOiqkq+a+Io+8MdiO+Bnp72sN1CtsBbd5G0UIpnwu6AmHIHsk5MY9iIkYMhIJ4uwSKeZ5bUJEMyjehP050ecyn1cJ/3QLc2t2gh/canfZfHJUI5pQAz7gniiCRH9piqMmGlaI1xeNVwho6cr2JbIcqCViEb+KhBjZM4IS4kSRu2AUP8dVEOUSE0R2zI8OF5vQyoLNRqFfqMJpErhmHRKoMplHf6jYMJl8FKErUnDM5y+Yy5Js3KFrjMVna9GAJSgGepy1LHo1ce9Ob2u0jL9zPrmU93jSSYhaSYEmpaaQZhC9gK423HM/b2iV7bHO8dMAsZ9wZYIG8ekLfGkU7RfmBHM5yRxb22UuHcTJlAo9ZnmaU3P+p8NoTW1MEVZ3NzvNUl9YwTOJntyYgq8rziaerMJCG6rhzCd0B4K98RSZV1ssu1sKCBksyK37z283hRgcFlHf2iU1lB9WKQ6S9sQy8wyst4McVVGNURvgk6EQzt6eAvtd4KtTTB0UqdgSaOlkEHh8mHR/VrP+iZjcZ0p6CjZNtApK+pO0qnWR29By7Ynv9CPdypJExw8VlfOZdVJZiD5RU0lxA3aK/s55cTC8gChXTrCDLmOXmRUJYfbiP4kGjAH/WkB85+pTXF6c8qS7WiZYTc507IXuv1n9mc7nz11R//+Mf9SpfprcYG2Xxh51DAiUCYpQ2br2UQqttUZvMGnnMlbxKeOJoWEeo2SsS4+LQCPnlaLWukLYIYmUs5V/PtSo4rk0wIMkjuJlAUJ6wL/4YE19BbhrKxzGmyTCx+yehY2Ib2N7mFneGJF7fUrTHBsZBglRF+vbqYcaf2Ch7rf5zdHLkmYZZzuL9yf0LbQWrVIb2lyouB6ZvmxbFj0ZY1pyaCgLTeaoV92OefP+BoS+q91ep0Ym7ToKFzZPK02Rv09qPUyMPIS4hJV5hfvXZ4cryze/XZZ5/loFce7bMPigZuXb+Np2ej1eaU225n89F4D+cbap1PNyCVJcxtCkuWhq0eZjKSTKVMRU4RgEktAIn7Kmx2KIYVNmTUYlQyODbOTscb3Y3TCZuGRzQtZn2NypjdhI0uG+sVzijEo3Sj0+ifTWKJz4ZOg0VIR969yKNAT4QwvIPDTKPdqA17I77F1mmFwyZdWXUKhXazgb/ttFFnW23C6bQjzd9kOtUJ+0SHh4+Dksq42b3KRsZGnbMvGRz19nEIPndRFxdgX0UGiAmKZDgv40s8rvXxwB6dTSc68IS6wf15maDtrKBA8kqOVvecFGtvoDCEOzNXlMPgkFdkZArz0zpcPVWwk0dYqFN3Of5S6SVftCbXsuOqhQjSqjXDbXQhcIcLXwjobm5t7Vw9Pe3f/fg+G7+bHZ0GfuP6rc+99gU+SaH9DmaJIQQ0ecN72p82mluf+/wrP37vB5Vpvz5tMtawmYzpHHKG6NFJY7Pd3r4y2e5w8nmjpZ3jyvj4+PCoOTw5fvzg8aN9DvfY6l7TVmmtfXAy2ulu6YB0tnTHlYOzPodXXL9968Wv/8FnXn2Fc11v33qWej+iPemc5TrKOToccLbs9/+Xf+DLXZW6Prf1GL/rh/fP+mM20PncGV3KpK/5Dy5Wk8FG76RxfDDar+DvcoQvFqk1bbkPJiOW9DTc2IrS6bHey9C+MTqShXdbw0MeEanC1PSi99K4M8V9ntPIq91WbXuDEjZxz9l//ODw4cONaexlq2/wlgQmF056MdjziAadD0VIjn7yO8QO+FJZNODoeGSmBE78pKIawzPiz+y0PvvKs6+9eK0xPh71jnWqfIQeo1kdpQ1P8Ss7HnDyho59LvpO1brqN+wTdG4hRYRNHnB4iAPEcIoWKbHVUgJJUlfHVeefSjR1jaajHlC3EBQbksr2BtAQvuAoeAQ6DeIonP4NXSCVcT7xFapj3khTZxs0aEyKaReVJK6eWZX0w7biGQlH47LkRHieLXDwDgjqxTU/ileKywzxq1HJXhAqu1ZoYmZ9elh3cwxelFp8LQ7KAB9krkCJ0DS4EpLG6ByRGc0vh8gkfCkVzcc2jld8RVLxHrPo84iCH0mla7hzeb1PR6rah4LW8LKq6ohJJwJ4LCYvVs0FBsSBQ4RvZyJjY9Ic475bxY2Z7ST+T9vQYu4a64hYgmPdLmtcY9DBiCA2ro/x2wc6wXVmEqdRTet8H4FPALzw4jMPDo+3tzcPOag4LMfjlp8BLOthDQR56chlyZYAW6CcnDBO8YmTS+maE/J2wGmfDk2HPomYps8wjlAd9Ysq0Rs7kczMEHWoEHyNSW83oBLoQAxMGzgxIJCChpLEL6qQG0IUil6E6LCuTcnoSdjN5vUiDjhWv2GZJQ1E4lMZ//AP/8ARHFevXL9+7SZadutUtcXs15ghOpcxhSIXNYi/KaKw+UfV9DSSIA9be3QwkSOEJIauohvI2RYk/UMC0pp+mBuPaGq8FMOJ/PSuf/kX/5rPHX7tm38QyGuJzFG83A0GhX40W44h200GSaRtVwH6iVK4jRRzxIw4vbvwq8y7WigEai66+hdMmhvdW2aVUl7+KmE8Aygq1UMwqTXXo1ShPNgTNU73yoxLEuK8izhITCpLauo1mKBcmRlBZYm8VE7IKDrevYEY+mQexBNNRkmQ69geD+QYiMgWPSjcwJdVy6wUoukCk91xhAtj1nQw5JW8lT2GyJhQ1KZ6g0sEuATbGaog0hHZL1PXWOKUmSoGzxSCP+4lcYRcnhEvIJ2eTAZnnHg2Pjnl1VQUxy4FtgzHKl8uoKG5S53JMoul4qQIacq4vpjnJM3oXiIWCimaPDS5TcEFxAyA+2qmRrsE7aePYgHOp1vgUPNlccDP43l2CptuU5wIZY9WFInOnPCWI2a5gAYJtLaMvAzJNU4zYKeMBQN5k0DO4lGNoizAzd045uiuHDgymNoy05UQ8P3k2akwyokDXLhNRJYxU1KKQByvhOaVq0xwTaqktliilOXCyGX4LhNBEivHSiZOWNAquS5JHDQIUiiu/HBLIDtdHVd3VEBgsSyJMQ1PmMtoyxDxKG0XtkYoIYVNl7dKTPFUUsuTX5e5rIQkdlmqBLCHWgZcjJJxhQoWsebuk9gpMpe85iaVEY77+4d4Ho0f7OG9stX3S3BqlcxhQHOb8lDEIJjoMRKmOOt1uBPYtj7Z6/HsenJPL9V2n9H5pNUdFkeFC0DK8qQRUS87KaSyhmcSPAm5oHRehlR96yrDFGb9YknMplLefdrfJEYiVJpzAswi9Iqzm6VYaC9aXKD59SumrrCgjO4SVahogIV+mE6WrWaJ3nkA89I1ApYBNlGu4jbrRi5r6c57DkvLGWiXpZlESpFUd4kdEYWY5TP50o5MOCPEUtJpNBDJRRsxKlfdM0FsNPhw1o/ef8DGBMBE3KlWOPEcbs2QRHESnYSTQ0zk8te8HhNrsovRPC/TXBY48SK7ZOZ/GUjKaSbMlZFlypABk6uTTIq4bv0TV+DxW1DNS7SSUQ4ko29pIzByEaDgQBIRi2G0UhhtWJfwgoIRgmABKRGcousyJAmQIqCVQkUWXVaHoCYVsUK2/CYCnLrTNdzo8LvnjMvD/QNuKSCzI7bwiB/ytbV+v9XUy4kUEwiWydYeQCwTIHEcqPGL2T85Iqnd7b700ku7V29C+eRMpsvZFFJ+HOFjdqsFnYeS0YAUSemGQGoZglT4chY1zz7CeIyn6v7+6cPj3t/93d89fPwhHisbrfqrr7769S99idphO4n5ElfGnePxGYvAdkfbTIlF4kKEUpAEBXYSiVB2crGlYnxSO3EwNM9fgbNvAASC6KTf15v7HBEBEbaxgLAYM+Xl0kHt/OAsXFUdnCnHt+oGOtyAfRcbZJJ5mQ5ohGX4OVmWkYFQQMM9uM/hBHnc5rBj8+LKfvVKvnMZyxtKgbrQHlIpW5RUK8qweao9lcA1wpWAHVIvPPCgQrm1lWKWwCGo3RlyQpNInDcaZCtkoY6IU5sw3drUEZaoFMuBArMOnliQ6s1HCgspJiccAE0ueT8Nj0miOwPZbx/6BPCXv/kVHslsPXObTWqeg7DtpdXpdNrhBBvpWttn7Rs7nPr6rT/8RxjMgGMnTk/f++ADKL/59vs4cbMBTUEmbXnl84wB2fjCHFkpAKK2OG4DLy2W83ESNNRw2kRm9Ay+tw9gJy6xbQqQAETMo90owr5Pu01BHLiFo7wHKHs88wQnshR8Q/+iQCBOIIJ+0BJoaGZhIwCOTCmAE2DxyiufoWfo8N3weFiiLJJY7o5c2FGFNbZMnCTTh4J5EYEIQJIIaIBbxwEWSQVAP8Y0kSLVWUsKCSGRNbLzOm4cBAAHqZBBvdzjPcrSaOhorE8fzE7tGFUXW+Fa0up2XhS0BTtgNL3emY7nYveZ+nW7IAlIUbWXEAs6oFMueDmeMnFbqD+W94Ybp0wQr4QvBRbVlc0Dy60B0Ixc5FXjjcIGD/J69pgDyeJb57WQBspYeFqPi2K02UQZfIkxC+pd7QbBMz4ZT0vDVrWptwEiTTMQctGauDqfmZomyKIZnv44QEjO6LGpenqMaatDJ3B8pNO3nd25ZvwvjsFUbZkd/ETBAvjWBFSI6AxHk2H0YxIVj2bJE6G0GaHTiigpX94j1+mxnkTa95k9e3C1HwrLKKsrzIyKa9QLCGAa4k1Eaoe25s8JkkKcIGZZAJ/TSH7+85+j22ee2cUmS41Gk4vLgn64VaWE+YljUCPiQKrNiyvEdSuAYL7NmBdREMhryVMqoqITbtkZ/8u//EvWmH/4h39IxS+gJfxPFoEaISQsTN2lyKkByW8X4uRVxfEeQzR8Gr8Q+AghtV+srTTHcODdHXOM21lTcmqWVOADSUlEdBvkDYcjIT1QoH1xaqpCPIjKM1rDtHTGKYQTqYlGvS4PGxRUaJ4ChR480w46+MGXdVevtTxpFPlVgbFsQVHrMFflFozsibb4WpPrsEs4GqAgXD0aTmp6J9uEiEAnFKaGim2jfQ2RcUwHBx/J7SBY+nphD1wSK3n/jn6RlroOdRV1gWAUlgKqvNE3JtEWbhP8dxgptR32HEMMEAORKkUcR36LqoKVRTPObAMaDNK4unNc+YQkUcntFyWiOJhGajw7XfXcA35WNzwYwKZHjw8f1jeuP6vT78KAeLajAScY0/zMiysenxK6EM/DVfR0BYqa4qPHB889/1LhiakywuS8HicRzyJ0lM6loYIHwnLsPTdQIulrPrR4Rnm43xk8w1pz1J6pFz+FkErYoa75bBfdiVdZiwu48WgNtam8DFdguu6oR4K+JszbjnrIz0F5zGoRWeq2dopcoVsm5QuU3enGQ1HOdVInVxvh7jGCJpRxzxJ+UXehq3IQXaCT31IKzCCHOC6Clwhr0AqCaMh+oJSv7MFFVvWq1u0CzbFhLjF3P7tZvRto4VdRUs6VRZuRXBWjRBYrFQ0iTN1UX8X8QBMOpjJc2zxgnVbwemawnAw4ZvDs5IN7vIxW2z8AeYOn9DzP1QSIc9Mkv6c7RT2GgnH7TFLMYlRjeMX6gA6siKBDxyqj3tlHD976YLTRoaPv3r7B5G+8qQ2CR91sAVB41Jqwn9w6fvF1ZaXMsllmDWRzTS2/TXqb5Yomdk5dzLJ44ZHRJiknntP8ncRVd2ovqqvinC+O1KNy9Sh9UuM1XP1jdU2yaqQWZ0zbdvU4WM1AghOxMczKLnAZ5EioZ++BrkxyrJIdcmX6q/423CM43U0rEGYF5LSdFPOpVX2+qS9zdNWkqud2GaeUTL3YyngJtBWr1pAfoESN8mrJyqqFvY9YZ/N+troelEYBJvLgp1yhPk0o68hPuTjuAG9GTi6mrU32rnXHL13dunv3+IBDB2mP8erBSMaong5euakQF+tyfHVqLnyeVAo/92tkVjxSbmHwii4EFSE6IB3lpqpSt0NeZ19AnruNXkVuA4gSIbQWJlaWBX6kpFx5PAHXRXJk4sijMb0c98klEYtNaS3VEHsdqRxOHt8qc4Q8o0iWIaERMSbMicALrywDA8c9oriXniYBLi/Qi6SZHsqUJ/6tcmim5iWMySxHMUU1JhHXopQtSwSrj0faJOXN+D/4wivvvvFrjjd+YWtH/latDWZK+Fixezdosr01bTPf3ty9eeN5CvXhR+/Wq4PtK9scSrB9fSfeB2c36gpzefTK+pxS8woMmJ0OG9AcpBmlFnv+UTTEKBRblkqdAHGg2FM9+752iTD3i5Yg7qsTiPPKhN65qjVoMHpjajjeP+jdu3/w7r2Hb797Z+/g4/qd+zevbXQ2N77w6qtXrmzffXjnrffeODh4xHY6a23sujZt0snw2gVSargr2gNbjPRArECqfLKxe2UHXqzHccfjK4N0bcKaTljEbMceNI9j8eLkNZ7atMUOXbezzWb9cKCWTrtntYZLLuXUUdOxU0NBUtkUz24TPEWCSNnoOBhBrp2yZ7rbepOdw1qLzynKK1ZGaOQ8b7LePInDUujBE9r5EVYp+Lqqdy8eKiyhR8Xi4UojIw1GZRtaxPTokKA2CFTD7DUZAAJjDx4+hCnNSUOyIaZzOuNVNdVot7qbG0yoOPiCTgbR6q32jdu3ulub2LiwFdQdaIYapzpSYKpsJ85dHU4HmxwwO+VMtBob0LSIztbVRmd72N5gt7h3pI3mTnuLWegxR0Tc+3g8pTtunaHqyaS9tclBLi++/BInOL/2hc/j7+xNZ71JHkXH6JEantCX9OXkFTttN0dbW51pjdFz9wtfeHb63/3RYKhX+997/94Pf/jDt978JbOs/fsHiDToazMIB9kGYyVDCMXUlzVkQXwYnR2ys7NT/ONggajTarsy5WjpLjIjYYMJXLLk+K4DO1+YBxtqPLDZ3tjCZka90+P9x8PjE0RpxqvxaBdZZasw8aQz1hHcSJc1nvRUdnZvdrq7pxrBGLwgqVFOC/XoXTujPgrYHA9fvvXc556/vrtRG58MJjjBTTSjP5XbmE6jYff55Lh/coKjtBb82q5XN4VRwwh6BDEkQI2BVVqNQAK/hpNSxIudXHVzQCAxwwkiCxenLgB9i9UxMMPcOOjqoKIPJ2JsK/EvCUwc6RIoY6yLxEotn6kwNRPHS4aNw1uziHhhgp64yak/mCkVJ8GEyoky8VJROSPy+quUxI1aMzKrUMuxonsJD2vNOqhdtDTrgpyXtpnDkRlmKIL5fZLflDwTg6/rKWpNGjMjR4r2W9ieuyZdJS1OtbEPDl9lCTNS74stSSMUU0/oNSqEz6ZcnrF7EiK4dlAShqAZiQxbn2MNJ85JfYD9DkcnODU3Gejo3Pk6Aflsz6xSaZUFR8ZKD00w5RVebLjDGf98ipbtfpbSeueAk/9bzY2rN67fazcOT08e3/v4xu0bsWtjPRc6LpV/zq8xg3lp3GLLmwdUR7Q7xiPyU1/T4el40OclBYqGChh21IkxQjljNAFK1OmwGehqnI77Op4lsLUNLTli3Pdqz9WkIYgQ/u6Oq2dMrSm25qVgUQmbgeFQDxLJpner0GGYDHXywQcf8Q2Cq1d3B8M6MwH1SfIoImdMcVWPomwdQTLiDCCVzXaTOcaJnGN5mK0/ejbSMWhEhrLYqZvXnUpRxCPqXshRWw6lDJ1w1bYq2mOk5eFypXLnww///f/nLzcbrS9/4+t6OLpk7SWZJ/s1O9VScEM82IbGdO+SYkv8eaQjlRIxH7R0oEQBNWFh7JZNQkhdIA2j2AoMgaK/hbpaX/m0TwnaNtUXzjR265HwVEUTO6dpH4lWS6NDlbg5UxvMWWJbSHWiRlBQFkdsim+G8IleyhMnGrvGqH0g6BUU6p0S0BlZ7BCTasUY1C3LKspAo4A4DyjZK+ALAlzRC9NClRzzDglL3OIXS0CfppySuA2+BWAhNaGlCOiIj7wJcmFESqjxBtXmlM109jd4UqWSwirGM6oi7K++dbW5eaV3cP/xwaHLyVFaGmrjnRXKBVYu6oV8A1mWvTI8EamVFAzMNWaaKhgtM1M1GnAAgUCq8xI/h/JvKClxT/RzMfI4VmRpVZ6oLK4plyOGGG05abZDmtJWoqZUR8BBX3TDC/DzbxFF/UWM2UTYOHvp+rOSOzojReINU65Fz0YvqMoQVQHDJH11Lq4uLo9EDE9o6yrOOZAc+cmShyKJrrNklKc6Ds46ykZAAKZHE1yW4ozCRIGuQGWnaS7xTTiXjLiMIFsSVURQzrMjJ/0UXZJWWugwdB7Crzbo85NIZT6heWc8aHNnLw7IkHONuOFKWqMpwxPaEoEZIOG4gOk2YSRIitjdBmcashDg5WC1E1/OCyRlT6k5ZgKeE4HXOakXJlmAIFLQMcFlsgjWajEJ7t/l+0137uycql42Q9suKbyC2qykF3K/EIHa56XUB/1Tzl5sXg8HtO71C3OtREA25PQVBAp4YYtYqIvlW2tvJbuVQAtA0gKplci/P0DryoWN2YZ6E+YZSZkqV0xz3TUsG89CWYTgDjQm3E6FGgEbErto47oNo4qUIm7VRVJhacRX2lxSsulAKtW4JUzwBfHMNAEX0PLbxCIhq7Mqx3isV4yy8T6hLUeQTUqMIqPba9d2mdAfHWtm5h0i88o55vFlggmSC5yAl4mQcSUL4Cks0yEJYJ5RRSP46hF8HkGp0aPr99xgBLMwYkBmuUgCAhMi8OVKAJN6CU0KM5nBuayKRGf3NXjNwXMKCTME0AUIF3AKDZRi5nRIzW/JErlK1JzBfBw0ADdu3iDCwQJ6uK4TqhQiSU8/nCOnbwiqYKMNOILxbA+P+26jjgvny7vXsLqH+8xrjre29AB+XNe5eM/efu7555+/fk2zptvPXGfyvXttFyIPDx+RWjkOaWOCAZBc7J1xji27Nuolok4l66rxzvKQxcUxTn5rhJVXsoCZkig4n4UDyJfNCBSEUjBggcBeMCXd3d3C1wY4i6K/+Zu/+dnPfobjpyhwsMRYJyGAyfNNc0/y2FooCLmIg0ncTLllx4giw4WNe9wkgZ+dnKCf3om+gsCtNNDQya2UHgk7dR016OzpCoWFSaG4R/0mHJUrKpQIAXi6NQ5k4ch2skPgSDlErCXnKtP1uwzJU1fGzZSkXPNzmFHT9HkFsPxdMWOby6Ybl5gZX1arBdyQBTjCUARqgSsmh5Jdd1w5W0Yei+qKecOdxXjMgdlAj2U2RN1MpPmmfKhZldto7erIY++T4ZB6PDs+YwbCwTLwwj0ZgiCDc3PnJtvNr3/tq6+//vrV69eAFF+mkTmxPJrpZ2agKkoRpL2weQgaBIT5FeH113e+9KXP6bOD/f7Ro2NmPh99+ODNN998+413aFOD3jGYgz7iU5S4coTF6WmnK3802yqikpeiQRn6wUJSkDEFGPGCAlcwDx49hgLbHrLnTJ5C1shY0hGMOFaNwZsm7Fipuy0QZy8M1sG0wmk80s/VNrqlDQAkCUMd6izN4UkPMJ8c1CjJ4TfkJYKE9JfODk0K4VwkOfiWawouJamWDbh2OjTHS8YnIkAIjhjZkATMbxNayKMOnCxoOOEsRxZomqyzGznJ4FvwY7dNdySxGc1Vj8CSlUc/ZrWwtQccP16uvPxOllB4Ua0m+HSvCJMkcZxrYoGkKe6xm1tKxHCHYMYk+yyL4wWkgKssbpjOMiMvmwum6uXiS6lld6eD+lVqdcKBA5qIxJLLFYQMQooDInjegW1zyhJSNdSFpw5S2vYGUyoIEXJJseEHXR3Lc5P+RaXQ4wwl06swmrhS1Lfb6iQRtRjXuEBkdrMiJlR4QZlAJEcB4luKQ0uOQlFE9gg1svMVWyNYVPoo8+I9DqQCmVR4g28/aG99FvIHXT828BS8iGeSm7ULSM2Ko7b4pWSu2jhHvBAYyBtvvOGu4OyRPK/5dCvtOpUFyHK378Lu7u4wOp8O9QYPAaCUqVYgVXCveo+6wNITwTIygyCDgShBRS4nIXWefIcLOam8C/VXf/VXHJP1xS9+saTw1H6R2TKoGBHWkXbBF1Jd8CLJ5eUms4cUJxHyFNBcAku6MoLV5duUxbx8Sy4q0LlCYEVtaBFRKYik+gp86dYcha0gJ04CyPRIBOqtSAhmziW8OCcd2wGZ76RilgTLs3xFeEKCQyTFFyLLSfACGPBCG5In1hoLeS2VryRRLkyOgvh7D0Xxy50B07RttZt1xspK/5AiuAOhB2AaAJGcGvF0u8D6d3trqVwi9OyIhScJJSQEy/l7WIokUhlRpSdpU5wICL46NSVx6zgIBE48MoKufm6jCANS9OLxmGiG4CSu6lrFuOh0ZDRZR+8DCt1hIUbkd1cFVb3vBYwnhHxH5tG9j3hRblzR0+zIhTS8gSAGQMY6x4imrkWaz4oiGQiJcIzunaeietrPB3mOTw75XglpkYkuWCogG3SJQM1tWoNi3HBx6fzsUTCCiUZcElFCYReYLkoABFQIRTvqaz2EH+/tb1y/ftQQBZznlIRrYvHYjSdigeuhNEqa10KkzV0svydzTrAkkhxRtZnO+bziwidzpR26yHgSiHi1MY80mSLwYJDP3OIgi7skAwzHcMXwGTOvwjdqjmfcxPQRBy1aS2Pc490zfJ+ZqvDAmuRCe7MxSFWT0+C2kDyHXi6ek6KuyQSktEZ1kfgjaKQMDy+6MGbbHJfJIuH9e28wzt37+C7THR4nqretxiK/UeUoic0rOyzvbzxzG6trbci/rIaXZBgT9OdqYXEpCs8QI+TPemkEC1B+KSDljzBmSC4aahNDpejHZYSGX1sTTAOdiHLcHbddHR5Q2R1VWCr03n+Hd5pap2c4wu1weh/IGoXwlNEGE2joQzmhPWesAclAPBNJFaQn5uIlcYTHyjCWDVtxZOiuPu0zrRwN5Uj4/tGk8mHlCy+ythzd3kGHZzH2DYOnnHMJ9o5XrOgj7FNQiBVyFj0MNpYKv2KKIwpoQj9lsIRInkQtU2aUUhIFcmqCJOQUKTDiXhPL1WFOhoRSnM8V90mZKdWRdfAFtHNuzRtfDExBaLiHyOlvxCDGURE4z1Hj8uVXop4paqFPqwm92aKSScyULUIot2jLST9s7RDX8eAspFnHlraOZ4S8oem1gODehf7Tuq3oDbAnUcsn2bqNkOj7NomxAC+wV2VZSiokX4DjYiD5w2sJ32d6AE58RuaiXIU92s5jEikf1ZnZIxgtKiACdtp1PnFfO+7TpABTlck+VlYrWVLRkmCpjCnipAUKbu+5NeaCJWorIwuUF+lrwoyXQTEjFF8m0DP2Rb+RKC9Tm+EG0jJCyptYG8cZfU0QRm/QrCjqKssb9pPdp6iRbcYXcncu0IwJdweIECnfcnMHovoExjUhB0SpOcQ086v8jOIdYT6k9sKtbSxto15hQnw67PNK7INHeOoNGpMNFs/yEGUmcKZzV9vaCq5ud1rb2zuHjzk06WADL8l2k0ONr73ywh997rMcLNB7fPy9733v7l6P+PXbu5valB0/v7Nz7coz7K5ubGwxunFYRXdj4wQn6kate73Fl/iOOI2Uw5CbdZaM9X5xwCsLY2b2jJXeTYx+GLGlc+SggLn2GRxoPIxKLnhe2EvGx5z+ia8bvVO4tXEM5PHB4ag/wDWbvS28e1hCbG50jg5PPrjz8Ud37/2Hv/nrO3c/wN8Z+tVhf9CrH45PQWpu69iQPm8jaLKizQgQovownTHf4KGnOe0NRlV96b3dYvHPnubg9NHZ0f7e5s4u2JSauuBt+cPDfRROdjJOcVNiBawDF9m/ZjDD3opBEBbQZy5EN5rrJC84A3Xk4AF3r9puUa2MujF0qmNOg63YZc2Y/hSIYNVKf8D4fZJoUrrobNSxJOBcRD2q+p0AQokJ75B5zcHB0Usv8ehCB7CU+EttJ5OhxBGxTxaUb7bpIYn4w7o48oU5CZvO2BgHvY4H2oQa9jmkgrP0eVW/uckpjcxApR48/CZyV2KmERpp8XpjpcYBHHy9sRNOIZ1O/dGH73Wn/avXt19/5bl2a3q92WHm2d5oHgyHD9/4KaftNWutb37jW5999fYrr7zyj77zDWZ0NB8XytqUUkO85ZLig7YUVNuaJM8FxjK1bw57wDn52k7n5Vdu/NEfvT6d/hlvnRHefetdDsx5+8138LV/9HAPZ5rjg36jvsHuFG8k4IrIvvA//MOvkLY62YVwlY82jAfdThVPbXzzOaud3v76teubja36qMXmvA4p4JHJBDc8/Dx4VhLtN07j9ZyENo3OmG/IUMeStlrvbHa3r165TT3gMI0TmZsJDYFURmnUsDkYwfFPvvIVOo7J2QEf1eW8TU6uGUyrffb6h3xgjRM5eJtOs8eNZncQ9h2NgtyyPbUJfugVYmhjLQDE8UgiWgSVUikKZIlctppZf+IEaJFaUp5Djnw5vva/WNzwaM8tlMPjj05Pmi1tRhSrs3K6G3mR1gKL7IIkJp6z6MWjMjQLsBXzUooHR3nwo/f4hgqRsFbJwY4KJu8jHHFyUZKco8e8wA+/tC7LGRF3f1KkglZCjFauB4NLgLSy0xxG3QIVS39MhBT3zPSVvk2UTYcswolenR8mhOAjcYILzd7NcU1kxUtC0XNLdfS2zkK8FkfK0HMGWahSE7CROcR0E5NT01YAhgGwMU2fyI4Sq0ZUQgcpL1HOeOc9ieG43R+xDab7Ni9ykkkzSfjG+EtEIU7XDTmQZMJeKhWDez9u+hoKJCEVNN3d3tze3ELg6WgY7uqpkzSV4rpiOh9z1HCppqjhy2zP/TBeTskiZy3GHXa10cNkNLy6vbF3535z49RLebowcGhedHpno9G77757/+HDr3/96ztbW8DpDFkTtUrnOakVbVMs/qCmZoT+NNYUvs/lqkepgQN/IYWcqj6NH6pZjkw8HZw2j474Ulujs0n70buPDAn9/uHeYxCQh4VXvaGjp7kNy9EOhljzT+v26Ov0yg7PrvQM79rmBh+zO47XjnahOu1Aw7tjSCh5eGlEdqXqVn2V0kJ/LmhZQL0AE1pkiVUJx0lrr7yGbCS8+fbb9f/p31/d2b317HOBOkfjad0gPxJQVLWh0CqUZXXqFaN+tT5FTInrQJxdJp54YBBq/TLq6G/LNY4qQAF4kU11GqsM7buwWGerHWbUVTQFVe9kJJoshjX+SSd8eJf3Zwbq7mnczMBx+VUb5QUDhkENnFH14oCECCxh+Y01HS/eaGEXlhCrbFCQVuVTFUtsN0QGDWVTnckMFEBiN43Xps6KB6IGyzRD4Pw24mVG3cDRBEsdFMBALPYQZpqEYCSsvuBbL92GHo3B40S+61upD5qNId/1aPAN6opcEPhCj/q+sLdibOaNrvG0Wxtd6dYPD9SzbG1vYO3R35MDmQsx0CM6lBrXBCWtTVyT5xOBl7UBJAWTRBjsQeKWAjuSbsEnzhX8BPxE4nyqTAtS5bQsmyG5tIbnkIRDhFKv8IDO6T71uNp/TKowFhZsODXwcL4aL49byiSfRI+XIIAoF31J9CBGUIXFMsGLdno3jrq//dJLpJZ0+IWEai7RTHEg9K5sCDvpia4l/SLTwq1Z8AFtnZwbrPOrpdVgoCCTcmoJKWimH6emW+UpaRYZoaASr2jzJs5V3XH5Jia5DLcIRbwkmzMiTipXsqMrX+nOjSPuK3guECgo5FBe9clvzQJIijg1xr4Z4kIqCUxxDPSjMLaef/XLX+Gf8uDux/I60dYcb6bosZKGZJbflTGLBD61R9i6ssuhmS+98urLL7+8s32FVIpDAfNaWOYYHfGTtf9lIgniiK8ubJFUapVbK7u86v7Roz22nsd37iEwXm3SW7yES6EgUlp6MbMnNWZms1sgxRZhNH5u14UF/XMLixyZHRY9U63fYhlQXfrYJqLKQi4RFjC5JdNCXguzIIAxE4dc4IUkcJYhKeOnjOQC5/GcbM49t7Ech3iOtpBkhSxrgFKrdYcB51lybRi+DMnZJRszMklqDuU1VSQQQs4oxQ0v0tbgJGQLs45UkiHhEzkfOWWBckzemXBJM5wIMINkUhXSlpBEPGkJSAIyTa/VTlNSLtVynJpyY3QSt9BZsOeUax08ISznNUSFilUitxRzAR8gkGXiQBzo8fIJJsBE4RNHQrC53JBFDhNPIiE2EPoSiuAkK9YIZYXM0fHNgvGvwFgCpSqzrmBnjmoyS3O+lDsKIoWAU0qlGnQ8oRmBbSY6QOgzk2E+069N2festhqxL9cF5+qmPKPHwz4Uus1ngbABjUsyxw6wODk7OtYDxRO+knfIQpd64ZbN5f6e9ih5wsfsaF/CVtvhsEgq7LBrxjgcbdmXHkdN6jwPdwWlnIjEpIgumsJCFhse6CWxC8JyGfMM1uf5FWFr5Jp0jgxIW9/eYKOQM6IgsrXVxeuK77/hyspLPCpevPqGpze56MxgSi7Fp+Gto+WigjGBE1w0w4Odzgs+6w3YHOwNR3jC4h1D7UxHV9Et+KSSHe56+A6pkbxHE0HT8RUg+DlkIW4tWRskgawvAkYjAkgEyl4UGbgyewIifIF2LlPw4UupMQ/MoMN38SJEKUiiSCXJohfOIMocqQmnxF37mxNci6QaQX5kwCyxasQjoHxq3JkwUaTlLA5udfqM5qs6YYVxkLyITXHIQtthg4MWgWGQhUaE5XN8M3DQqLv2VIdBv/zCy/hEv/jsM9jS1SvNbrfSYS+r7MtcL76uF/nJUgqaxXRZeSkm4db1W9/5zndYgyAwn0Nko/njDx/SCZyeHWKBOP2zxdzr1XHthwIWyDE46GH7SpdHSnxfETtxQHu0UzpKrhCnpDsbW2SpV7Shv7HVofi4/IryqT6fG4cfjDlaB+QBmxZxxLm01yhmaOCjxqQE4l/+8pfpSWDEigs42kbDAw4J4NSbYQ++AOE7LTw6uUtBvbRv2MogAhGCI1yRAQSuxFPEcAOxSW7X9eoUh1xhBslkyacs6WrFG8csUOD9+/cpBbd+BE4kNVjjiMoCHYMyuAHwgrgloWgAsU4B47PE3FALKCdaWTEoiFfIi/LB9zaxqX2Cay58FHxRFYgHWesk7Zr4VnCLEoxRmxXlKzC1taggZC4oeICIqylH1pJ+2YkBLLNo4Es16BhXdEV2bZipx46tOtdaiFpqUrTZrOcWNKwUw8Mga20dOcWDECVHjwRBC5NEQnIDyQgP5eLUQSpalSMVgUkzJI4ZUxGfbAqD1ZoaBB3hSkBryMyFOI8+XBzaMo9OweQ8aF1l8pO//uu//v73v88oixh//O1vu5hKDT/T2DeWhThAzZ+gFDNClB0es9RIQBYoO5WkwAycGCWxfPUkrS4qIA0Etj7obUAjF6KGDqN3LW0DSClCyVmDlF664tmtele+CbyxQV9CdvfYcCGO7aiQYUJsXsMCu7C0FizFqa/gK4D2ZkIwXxOOJyS/+tWvUNr/4f/4f6L69CndsvgJ7VNGIGiZiUBb/1cFSyuhoy1QcbLJGLvlkI9uY6ZavE+zigIwGJFR2PQhU1m4EQ2Rx2GM6cZBEo+AtnlwgNB28ixSdQhsCnlSavsuHQhFp5R/ZDIykMQvxAmOcyVORqxULNYEyw+m051rDe4i2PQjr7iTl7CAFJAi1UmBJQhGSCumROjKApBE3FuUFgwrJcJgyphLKUDWZFuh4DOLlYzJvwwsE8/7/WS5Fii6IFwTNeIEWpYj4EcZdUk4C0S4JQn8cxCWs3wayDpGluEcSZyUpOU2iUGcYMpEEjxtQKtb8TM391WYd+DZWPNOx3mpc5qTXq8oaYGDmrhTX1QC+Z3lNRDto25kIS+eEvsP7m0/8yL2Z7n1nA7SQi2IsGNJ7WCZzh5X1Rb/odOMJ1T15vaDvcPxyypOzP0hrulaPAlRxzkreZAuJg3BIQ0PRZPVUzM9atZzS3UrYjjrv4P9ORfm2KS2+qe1k8PNtgbIg5oGLfkPhsq42u1cZwKBW3jbBBvSymCByzv/ho3GEyfkmSF4KzYm/BokqEYRo2yqQHwAwZziWYQXeZUvcbMrq2nTCIns7sRPoWllMFmmXyLBA2weY/PgjFGN5TH1pWd4oOscPeuk1J5qpyRDlDDbFvH95a+ZUSkTUpVcCpqSk7LxaZpm8/Dg4Bc/+SkfOHp8/x5DCDvMOgITM9AplLiHxoNElv1BB/d4nJeOT3u/vHPvF3//D/RoX/z617/97W/v3rimj9Li9KJHHWFpavKLlWJhuM6Uz83lgkpRqkjxcmhUKwjZcu15fyiOnK1sxkxn4/ER0+7DN97e4sNK4ffNafzBOW8Xi6IsaHIxeSYRkoV9RpGdy3TdvKlrnvXSoNC5y377aFw5etB/7961Z56pvP4i05cjdQaTXiMm8fqucmVUDq6LfG2aZS+RKbpQ+ILmbaAXlCV4LGRc5rsSYktOE76VOJ8GmEuFIleSYsK3Eg4wGVvR4mL6yxu2JLFXgzcUx9ZhrXSSVBBeOUFHS1B3x0UHU1LPhQFWtiy1Mhp45OLcZ7+nwiM6nq5rEgomYqgi8FMTAL8GOnHZCOarXOHDQmMTJBGlomXdc4F6XJBhLnnVzQI+I/gilrc29HHnqb4vRhjzTWROiOUpurwGgbsHXsy4dA86IYHZAtvqNNsNvSLtiqDfBGFWKaUZpyxE1FJieppj5vEceRaPNlh2EtIqSp+lzsfcHITj3iNGq9kzqEAWR32bXRVFfakGIciwqFS375l1RY5PdbEwkBCXNcFJsWjVa3f0twxJqSzlrGNN5kuATcr6T7UAUEqIIBVEkHI0hdCqY0FeFySHGtnwJAU0EZ6pC1tOB6ePSOUEATbUmhsd4oxFjC9Xduv4LLd4Rwsfz6Y4Ts+00zQ6OcR3eadTbVendx8Pjh89ppExBedlWAg2ru0+98XPHXAOeaNxcjQ+ZkNvOHj48F5t797N3k1OlWXxhq7gJcvGwOUIhRtpn6tPuZ32eRV4yJIPvgw2zU7TXsYYBBZD3yFJbMaZtaeikWSlJQgR6zOHrIrrLRl5BcvJSyUdnJ2M+mef/fyXXrh9+3h0wo7zlc74aP/gRz/8+1/+8pfV5mRna3vv8Ih5kzzlqKkKh6vWh6fagGNJAlMd06rjEyVAHQ+Z0WB//zGL8LPY/sBfFH9ONjn1PibDfQPHtfGozxfVWNXUu+yLbm32TzncVt0OAa9GdMJ+Bl1ueOjpeEz9LRhBWTayOOqISMyHlMrakd4OISFFF5h6QYCiPU+HXJCRVJxHH1PZkuH8L01XswU38ybvchyejnqDyg6zgrH8ptg9mM+Q+o95sASYh6y7y9BKkSPvEgV6EKoMMyKyvbG5s7k1OBvj0fzcsy+8P3wf+6RxPH68v7d3sLl7PbhFtxAjF1lQ1OhkeLy3t9Wo9ZiYjYedab1/etStjF+6ee3ZZ29dodvd4mTITdaZr3/5RdmD86Jb2q2UjR+maKLJdCWiqgxWwbS4PPH4rmIh5SkXO7twE7R8xZy0pc4B4/xVKs9+7euF4mwdjBd4RXz88cfvvXtf70Pcf+TGSI/HcRdsZvXO9AnNYjSZ4IncG22cIfntm7foOprxmGpruwMOu9utzoP7H9/VwtsFkwv/tFWtX9nc3uUBGCf5MEWnO41xR2n0JjFKfvlzr7720rOd6eDk9KTWH9B42HXGr/dkzNOp0UCzXVjhxUBp9KrjKLwj6Ri4xzhln/q4pgZc32qGHMHF5OokR/IrcN8a31fRVdAQBEGuGU5RiXAUipMKpeoWfPSDGVD7dH08VPNJ0BkFoREuD3npldfZvDs53sffkeMKKR2jI9n9LQ3anoZU9ZkydPgWxGP9JTOLgIzLc0hDjBA9Lgszz40Fo+RktiWVOPp10Z03TFt9dQqIBtOcsjRY0nFTSN2OEN3Dl6pGfvo/iR2+3iqXajpIRFmsNygIxxN+42B2ZOLo81IDTFXoSckpp0QoIGWs0G03aEqVxcMjjqetar2vysOMetVhbdBvHDV1mj+vKbMm1fiLXtSWM30QxyYnvM+Hjpp4rUGI5zS8HqA3A3Su7nSyudGmlfHOTfhAxhAYRUGo4jeUnOJlYlRinO+MYmgqNb0WQ5Yw7EkfI/AXUOrhekXLmgwHx63a/vF+vXFV5QrPaMQgaOI5Gh/xRcS9/TH9Mt8iDeZsrMLOdVE0mGBfbjdH5pAzm7OhARoxGUOxrn7lgoksH4WjMQYvSt0Z9tk2hgnt4YMP3wNlPFQfGqVgwIQ0lGaWInsjTDgkTJ914WntTrd9dWujMeg3q+ONWqW9tVGbbkD8tN08bdZZIxPvxfFCmlNLLOmNDoMr0ksPYRu+hTYNMyVF6bnDF1vI9C3ko8LJfHhy/Pc//hGToj//8z9vtDtwKZs7+J8wyFaQj0cdnvwjXPQtiZwsUIIXAWmJ5VfOdm82ikNUeOOKVIojhRU2GTZTtCa3OZGK1qAIgfdOYKpHYjSEqmY+TCxYLDFDIwPzEohSIWiNDS18K9lJoYa1Q8MazvGGWg2TF0qi3TcF8yoUC01AXCmO+uGoZnpDgC5LAElXAAIaKb7lii8976Lx7ng0kFkpCgQdKRQf94p7E0x584hlSJDEHchCUsI5J8JgB6/RoP/g3l0GQebMrY4eZrc6XLqDcY3Rn4kccN5zYqZ974O3zw4e8j4Rs0RmHdHEov84h8fvImlBFZQRSB4QCiDBlna+jKCR93yc31rqTJJ5kRDSMuSRGXKkLdymDegVwovK5YosQw/jM2N4JAmW6ZJKO0HrGuzrE57tN6/c9KwCiF5DIETLZiOBKF06qZimaQriF/g8CYjevdueMiPh2Yi/iUkr5m3UPJwvUo6Z4jAiFwHIwnwWoIUx8sKtkxCmfuNZ9QgRDIQWEdcSP4pqcaFHbjma4yuv1gzY6lLLQCdeRhd/Lb/G8ljtk8woIBmidFydIRXB+AnIrTO6QwSuLKusIig436e6wi7Pn8tjeDCqtpotMHkL8gc/+MF7b77NkEw3ZmSsBcwooE6LY8gnzgyIK4H+Dq9IMPmPUf3t3/7te++999U//MYf//Efs1uNVkdyrZAMy6wtAETypPk2GCiFfsofYeRxUSZkXIrUjKltT1sSk0GPref+W++zaLlup614yGFhfM1IFWCEzBEujCPPSlKGmxpcYkgTJorllnetOfBk0B7iUV67vh1oSiWXtXSOGIljjnMOcKYjG2FZpJTFgMXbUvMl+opfZ0n0FyiUGVJ6CYhfWpZLyp0L4ux5fC7D076BHRWhUHQ1xVQp8VkuTg4pq13owJVUXtVLRzy3pADokugTobC0poBIGwtJ6ZZciGn8BEyRBZoJvhA5B40kQmhC34ch0LdynRNogdxFt2RnGsSeID0MRYN+yrFQUuBGSHB30VBwlgT3LaQWIInyciTnS6pvuUKcK2G5jAXx2Efg+yrcIg8BrlC4POtlYS6EIE+Bw1iR4mU2WNtGPOhbP6C5LCXWJ/ylgAs5FyAwIlgCrkQRI0HyvAZyNTBFEg6UbRXMPbq8Y6Pl2Zj5zORIdsLZEAQ+ncZGSWtL/rl4azHinO0dgs/3AeHbqTeJP358CJzdOo1WnHbV4NyCJvtujw917iSzJPJuNDUp5zQJtkvarT6v+TMFIC/v6mtEizkVW3hsciEVRIanWqhbVBCYvBNXqTMbjuKXNZVKVVoXdNYhZLiLUZgiQ6Mte4MvdUoRgBD5yU9+8uaHbxG/uV3DDertt99mdNvclU90nXeZOcbh+ABkvL1VZeF7FXubMxbAUabUyrPndpuGrZKO5NdzdqyTNzc729AfVXReNpMqRkzw8fN6HN9uQiR0ziYb+qSqwGlwXgo6hA1KKws+4xcQtyxwkjbEvuxnRjGz4jbPBWZ+uxBfQBatEGABbfnWDYTCsimJokAgo8pyfkAWS+fIeaKdTwgDKkmViOiTaJRgimvq48Ehx9Fs3tB8/uHjPfTANBiBwaF2kCKMqsxc4bya5gsvvNDaui0jmfZ5jE2EQEZq7dq1TbdmetBqXcBGvLPobR32BqTnGK8sg+kK+NsKLnjODe4OtNaXXnrplVde/c63aQU6f097ZVFrVCA6OTrU+ezsgAEc9vrY4aN7d9mwBoL39xc+9wprGdzcwWH6x9t7H3/4EQ9vPr7/ALM9m4yw7WFd50djFeaIGKidOBEshPkA58V/9atfReenR6dwYfcZLkzcZELTOOKjtFuIgA8Om4xRKDbCilB6a2nVBshFTlcgsOM2XYmsC6ClunHGhJkoRGQ2zhrN5SLu0mFpe48eUAo2oEWzrPGcZiIIC8cXeHELPkpmg+Ps9Ip8zI8PIaXdILLEO5TE0J4eWmkWoTLGLaVI5ShGUu4XuCR2F0YgDtl1aHkB1+GcA8+zByPJaYXlSQsUnGTB0Am3IDBCc7UGiGAP7LZhMyAohCsYvyAbk3jgS88OMnUOHu/L4ZFNJWWKxkxesZgplXxSLHoGDkeiZIAI+90CR6BvV29PmwqmCe7Uy1wlKsLGhjLfVSQLJ24IwrNJtBQGT5yS0pzv3r27u6U1DqVTttAJ7xbQHjFIHn9aMySKjtTMRWVPkgCZ29AMi2I/OyGwW6u8Qdku0EWhYh4FfSTxTkhnYxOXUdR3uL9P50ADkXIiIxSKOpoRLqwUBAfwsXzQyMWVgRhGVAWp8umjjBzixbnbQx1nTwmA0yCEHKqGBRQAptLFZkZhJ2TJS2Qc84UyBsCpRf/hP/wHZjh/9Md/QrUm5E8ckcBl2YmknipGrIKqcUjNxRMw0gG6k+EOILdcFcIoTVt3EVLE9H3rK8ZA3ng0Y9yCGlvOuo85OI9KQobCMBCJmjO2ShGumUQUjz4qfHuUbiDXmdrhatWHbNyojmJnzGiq1ZI4SdSpGa280prMZWWqgYnaMk5KklDxJN4lBK77VQE4X1xAG+wmaGwKNxpsD0i92eLaaOKY3+LQMeKd7iYS9vb3UTLffuaKCa2i+nsEy8tOnIBxOiIlZZZmoYH8Hkn/6URZLgsFXyCpdUIGVSPJ/aA5ooZUT/gWcvoWsyjhNjao0cw0K3Vw3nwPgIc9NDj6SFWDnsjVDh7c2e2+NKnh1MNrNVuA6x18CvRtXjqFzuYW8Z1ofkzkSIUAtejucjO2C6fM1Kr9OweHL+1eaYTTGw9M6TcRD3QySBfetovyWg3MK4SQ1ThNVFIBYTTNPEwRdFmbSJU0Qw6XUbmZKE+rpwdH16O1H/P9efrrYBNvf3KnXqm0NMVyGZSsUCh2QcPuL3lAZiRf6cTn7gNaYJJADeo8O4qgx3P4S9LXTvgcMSqJAc+ejHgsAZF6CTEUNTjPkU2o8UBelhqei35E+Qiht9I3hNJzP5MCnsLJQipwBrsgijCuZT1BjRkP9sDTaYZezvxm7/hHP/ghXiGAtlpNdC7hVV5wJWFolRV6APW6mJcrkpP/uKOBQ6H3PvjoL3kZ+Kc//8f/7J/x1qcKKa+jILRUCpFd35+SagT/xlUX6yJo6nE2jD2EGJLQiNj3mdOkiF+b1E8fHx3+4ld7H398bVKhebRkZMzBJAM0TcTZF0j5VlQWggq9GEA22D85qcLQUpHLobUr/h5oq723H518dLz7xc+wcHqghls5a7Iw5zGRvmrNqzKL/OI+9QyJe1FjxrYdZjkBpJaSSwiKKJRFzcsneHid+MU3ZZ+RnU3XCiZBYVkDpQglg7jPjTm4iG2KOF5m/OS/JqhrlL1scSKIsw5XhmVNP2ibrBz1kEUHwAlu1BDZtmcNWOelDqIVi5isLrio2XNMLZ1mjXMk6ZpjD7poTegSXmO8LemEeVkA6qJtVfg4o6gG2qx5irJ5QFzxbC6utPlgOqVsszTnnd2XsaLvtdcwlckSkW4KHvjIEGe9jd5GOixTRUPOrAIpiMgmSBkRjv/gEsCavEvZv0XRlEqrcy+WcgMoJdKvOlhwYhlfaGY2RAohKWEdBSGFzI4sXEPCsrUupM3fWm+ayiEAzMq5DsU6n/U8md/sXZLKaknKgavlvzx7FyrPlVPLhlGRDDSsVPN1brlqUlsOyYlpTq0AZrbtjOycQpwZCdk584UsPMIkrq+ox+uxoNUm26y7BmdHrB45X5JRrNvRcvoI1yo8EpvV1pWt8aiK/0mPhU+nfoqbGCeS3rhOrsP7x2eVaat77daz243OfabpZKkeHrj16XBdAkMyqGPOWB5xHC+U2+OY2jGz0BS/xfqZzgI8tc1Zu1fWdQEiGm2fMLCVNmoPahzqx4RoRNuhj2BCNtzaaI2HZ4cH99kvuP/BPTagzw4e6zzcXrxK2d3uNLunE33Crk2nhmcmri8scRm1mzpES3NBSGk6p6kIrk4Um76PQ6PPTnvyJz0c4B3Xjmkle9K1SXParA16p/sHj8nVastxjMB2H6f0QpE5KATpK+QQxFnQ+mNHM6amFxWZWSvuf7gTcY40vaAmsbGM57qQlYZatNWYM4V9gbXCXYBqWshb3Kp/U5Pllvoj1hsO8POissXtMnUU/VhJLX4LegXsE/zIkpBnXGnzAsEINy/qZMJJjo8n+1d3d1++9cx7b7+zf3jCUuvh48en4TtCLWj4RVtqbWog2ibg25s3dnaua9gpPCXnpClFxwBCPyzdSWdOQitjSY8MbqGpna5V4xzZJ7gJ9zVaDCUuQjHuxJ3ru8aBtgrCYRyKeHHBAZBK0uBBE27XWh29mdTdxJFxCyWMx7dRCxg431I+nI+Zh+7t7dFaX//CFykLb3XrGsGrHkZq+pD7Dx7x4P+dj+/QXqZbO0DuHWlnQZ+NDEV1a5WdK1e++rlX253q6el+d9hD3YPTEw7gOMP3eTJEImydxQ+0kYTOYzgeUjXx4oLG0Vgn04I1TTKOqi0s3BADY2idlRpgGGlRfHqngBTVFNDQSNlS8lTi5a3mNh6v2bGBKcIIEqpgbOUYYWpk7+CI/fb/H3v/+WRpcp15gnF1yIxInVlVKF2EKMgCSJAg0WATbSS7d2d62BzOtO322uy3/YfWbM3a9uOOkWPk9gyHYJPdIBqCakgCIBRRVSwtMyt1hrwy7v6e8/jr16+IyMgSAMlez0i//h4/fvz48eP6vP6yBR+jPr1GsjkyEfAratKZDCSMc0EI9HlngrXAxunTKxtbgz7b/Xs7t9nmkHTQUuY5Y7qSOKQUSzWsV+gVtSwJ5bO0RdHOo0PMSVLDD3ilJ3rQzb+qI6b4MEYgOa8fvVGlN88qCWjdwm6OCiFHQZRMsjBAfqIfgIwvxFAG0hBjVimUe1EBsMuscCyfNJoEHSAkMT7roZj7wYZaMAnFACzQGdOz6QlVPaTHJgT9VMcafdgYRYuGehOVRT+X7qN9B3zMpMdpUhstbPRJQJuOkqG+hNUi7B/Gm5RhPdskq6V2s97jex70t8p3tdPeoyPm/nKxpDUb41ukDS/VxQRQSoy2Aj4DCGk5LEbDGBcpMrcpoWyNsELSGTLlZ4N7NGIMBY5iaIdRWg1MpXz48uX/9l/+S5rhJz/5SRgDYs1hLIGa68WyZfwVJNYjYImtWG7rWnw5cc6ggi++KofqEfTs/dadm7Bz6vTW5qk1jlw51+Zqo6tX337nnSswRaNoMmbqRYhU3bp4kyzhFflG2+EuMFSf3bvN9WVe7OPVB76uyK3zvsGEEwFw2XJmMqR9wWikep+IS69jekNvYDHLii36DbWFCLgn0bw6lS0NfC02YpA19SMtVbfPuwX0QK+9+cZ//tqfbGxtPvPMM/14waIq8f39unSkkQpGSVUGxFT1MES57ViooM1mIKNlJY8RSrHuk92OfCM5NVOkoqZAU62pBeHUztyaeEMLkaDz0iJ1p+r8VbMNfelAb49qyRabKZrcxMebmIWwr0U43sMCix4PzUCfpRn80d1IbKEz5l9NTGOohA88XDDCAQHopIY3CNE2x2kEgasl+v1eF9CsCKK3CZmhtqQMhUyCivqswsptJrGUa+KEIJZBKlriJH4mxLsRNfSWwqlR8YKDqoIAenvIHTea6nW7ectc7yIpVjhcjx2fAWDk1WwwYoLtCL1Hz1m8FyIhBxHIpKKOJh5wHPWIcxjknGph1qAdj7Aw1fsLNANwcnKyme0cKNNqzI6IEnii8EIm4M/wHFhIi1h6dqK0kRzfSWcJsbKpWxFdJdzHAwpflmNrldkYQAJQrtV1KhLfpGFVIsD6UJdAsYnN2NCJazdo76R1vjQq8JWROvUjpZbRcioHTu4rx6pWHKZcFIpjRhOpWDqSh/m8zPkMnCIHZLrdzyDNPZI7DpaIoVuDMo8S3xE6Taz7I3x6cx5JCDJyzLQnwAw6NjCPb8ixiaYiYRsemOVwgvrn3/jmD37wg8OevpDOSAqffJs4Y0NZ3IbLYQI4YDEaRnGkGDE8NBovvfTSzf/wH37lV37l8Q9/OKfNBGcC0FmIY/rzdWx48q35hSRniPMIJuW6+dY1bF4aN26oXcRSxlGmo6qpXEnfsIXsVeiTXyf0s2QYUyI/pqiq6nmsJDpJnkMsga4+/zwKv/6xR6MVyw6OzzLARiScF0lOeh8BSE3074h0C1EsDXyciFTOMnRDIyqTLMMVcBJbQT7w3xk2co2L/cgciNsyT5rlY3LITJoeL5g1licuyS/EV9FQQxBB6pemoUWX2r5bPUAoK6rqKCwFQ8ryJ8wEmmTjfAFn5glb4MYt4Sn10T/OZSYeoHsrpt7q4sJnvQ1aTA8Tei7CTPLjHy1e0uJmMOchgSDMXGogGc3Me8o+Q+r4RxJmIiWmCn7vBqEUJCffhURKgj+tMIwhZ3yKQw1GsZLiZZZm9KRUIeOQCgomYohxnND+wlREkcp+EJit6MzDTIAkmtaHIyG/JmJRszVJWU5tbPCot7z39tg2gQEt6FgEhs5yVgIOCDRekkOEvVEcUx+mPSunLsqe+uo2RcO2mdbNoEBPi3Uj1MDFAARS9LRM2DULiq9rkAUUNIdHN6IoPLJNo6xPpi0u0bvzyQIHV0zYtI6KjzJRCqw5ecfooaceZIvtm1/9iviPul5u6k0mHGUnCfjwDoWyDniUfMbassfJIiZUhSQygRk3KXt3qY/PaTQlRVDQYUsNf7yLDbomluYEOggczHasWVoImk9YVyMpGYF2lHMsPk5aKjYZ5mQBQeCoVPNw8EsgpcDNa2aJk8Nk5K1zCnIfmcJdKdAynEnPB05QJguWpFQENoCHh29SiZwrUAtUE0yqajh0YWNI8ywVXC9KR+esDGO+QW0jEn1jKRzScADfZQzIpFPNBVegknyZKif/BxLIDFMgsyR9Lhwl1rZkS9upGFpqOzjuQOeOQ7AomkpX0/twiJBPkD22efqxJ5/8QizX9+pLV6/eeOv2Hhd9vPbWdX+cA5PMp556ivfSunu3rPPaqg6ybHKJWsUJdcFpBs5qjEAJVG+yS/52joUZAvgAM4Swi5IDfjRa9jPQFPKjU+HnQI5yADgM2yctYRoLCys2i7MiEZsZI5VJ5UB+BFLm7lQQ5JiWXqLOS/idzsZah0O+/e27qO7hUId5ODBr7I5ELUy4ik1GP043sAp24l+yOGEPkHOvKnDB3ABuqRMLDXyvfAWSLh3Hk3GMkZPDW04VYbVfDTFRKUByLZAQeM4Aao4FEwXDp4xoI9vQaGOrLeRmg7d9eDfIVayKy3nNMMOhjWJJw3AZPSidDy41JZJVepgZOC4ALeGrJ2e7MJ5i4uGGED573sRSWkqhfqvGWc6+tiOiMyVJwJcef/xxWAITCDixj83+mTQWBuwLM1aaXnuSVFHRH1arUckNaOCnzTvC2Nqhh/s9jW6dFV3mSYBhbkWXL7XG3R7v78ZkQLKFDRiQq3IHPztRDg7pZOiuXRcASYUYqSDGxshd83MDkTjJeccICHuqABn1KAsNAjj5GN9Z+FGZp+f0SyptKEa/oVglleMVqK997WsYLV1+8CHHGv4efViCmqujnIebLLETDgNktmESl7MWDv9VltTzOOF88pyE5PTP2iiOVkBaNn/B5xHpZTQTNVxyJEE4IzhHSEVALJA2h52KRwdIQtjiTjhhMAocBA+tNgwCohoc8KaZbJwXOkfdb0VU+atfVabizZJLapBZXZgp+KgxvDEdAxMiqSBRrAiLTilA08EyAaDfh4vmuJD8Twe4sMgAcVbLVASKV7mfDqMfQK4UiGKenHA6NJ5ONmktWgdobTahaIuALEECzi8sPHjQnIhOE4I2LSYl4YqhCWWO+9Ujo7RcWDRq3L15rb1+muEfTJofYPrEw3preXWVR1TNZ/vc1sQx6rjG98dRVVom5nocLnVqyxstDERHu3cxFMW2leM3PrurfFFpfWIUZyaS76VZ5ivwspYTkKK4q0xFp5ugIBM5pBL5sCiGFh1RWfQcQtU5UB33bl0/t9LuDFSovvfL9QqLJCLMiVgSMf9UbBSyiogkxmjeTptrOgemCMWDT/9UJ4h0iPlVfShzjOFSfMSGPFQkzH90xicx2WYB8XIux11RVSAGfMlQZ26mhg++N3D5LWoZsDQhfmY9D7qz0EXPOh8UXF2T45f1sfv6Sy88/9WvfvXtl1+ju1/h9JY7k3Y1TURhQMMMUsgqY3IM8CFp8WPLAspJKRgUgWDojn5hEA6F7avv/Mn//gef+vw7X/jCF2Q9TzXZfrY4zcvFzDJ3nUMqQyp+Rd9SyFHkIplXaRKXMVbY+qAT1s3nxs1rr125/f0fMGJs6A70OtegiVpY5SMWaXWi41Io1vIyTWeRhUCsOQeei6A0ixwUhFZEORU1AowmLuIuQkiW7X8gW93G3ktvIUcmZNfW6ivj5q1hfHIEKwbQi9G9ILw4KPrhSlYtyaIrWpA2J3Rc8KmCQw4fl+FE0cb9SIBOZgG5f0ggF40iOGBpYPOL/nNQB5w5i96Y47NMSC9KM+RNDMIhsmIKNFWqsLiv1GTYo6/HPoGdM+z7oopVs/TttCFy1LxWx/XMElACGgiKyWfSUUatn6DLux9BXcppq41k7ucechETULAyT7F1xEPVmyk6tR1CdIlimP23AbRkh6L3Y4LzKJlr3XI7gvCR4AbWG+ymYSSlnmgyez4ygSNiHPR7GwmzgORKJMo4STOrergH8VBm1Uulz+CnXs69rvvAoKJmLO2QAyCflGpfUn4DAzF71qz8mAIz2RXR0d8WzycJBgNTuYu/0G182mOpEkY+OdkZTBQsQ0JPJ48uFD6OdSxZe7JBoEoyX7pJ8gpHBcH0hRk8y2uGIb6nwqyarprsPvTYBeAHzEsaUt5uX59SYJ5za2cPxpi6dA9re9wZwdeV6jp9HzAMcM9xR5t3e3fu8H2h7h3dETHE9I4GNxivtVZY5Vg+Y+1+6iVZ2cqMSKoNcZo/E6b9fXaoa42wuKSZAucCXRaZZBMNZ7ZcECQXjdr42OmgmVKS7FJXmZ+rQBIUuJXQ0DYmcLyQ1GatQ1UOD/bv3r6Foj/6yOrpC0+OB/t/1qQP6bZqvTZCoq6rfnh1dR385iC+k8ONisMBKo4lFq1arbs2lgHcqKebJLmkHr3W2wh8SemU+vL9AUN5v7vbadUaHS3UeY+aGQq1yrveoexLq6vLrNXZW9jd3d6/uY3cVuO6D07tYL7iXyXz47QEBLfYeT9JQyGpMBXt9+CE7QZHgUOL4w8pQJ/iKFk4w6unyS9WFs5uAipDUSOuM3pc5YIlHbUeG9A5U6VwVZSVVoZLmu8tLAUhL2pc13azVoar0TJrb66Y2T9gQ+Hc5unN1XXmKhhH97h7+O72IWOKpIpEZdZEcsJAxvGtkfoSX7tAhLrBXLUwz7amGysag0Jt3fOHkqrfC8ouvMLvm0tz+3ilK8nWbd9fa5j0A3zKRpmGteZShCdxR3MT+kZRVVpmm8w11eDQ1WYLs3zWC/RbqLd0A0XHgSDri5h6haWkc2kvNR++dO7S5XNLH3vk1vb4ueeee+GFl6iFjz3y8GBvdzzscrdn72CfWz9oEdwR1NCbjjWaETtKGi35JmG8MxEzvppsQeOEDNUCogqLmZID9sWwuwu4V33C92x/opcJgmLEWgrCKTU2RxFw2L7r12nwVXRlkRxhtdy15Z3du3xMcWWFZaI2cWQ3F87IOUkOVAQyHSpV/QvP9Ef4vPVda7KmaHc2Djtrp2IbWtbQ9CtszsVLZjXdeq57ICRDbpsnlb97oXtdNT8ycdVMmqsEVxqFCxmh4441fKa8gRkUIq1jreeiUlHOaXMBLSMYMxpwh3MAODAqHdWKVEJw8orblFYUQiaiwFpJsz2popApu0Ycgnh0uogDbVA1kQg0wXkSCe3eybBf5uRaYTE3UwpeUUOK+wcc/2HHKONHvS2kzpysKAWk4DGKE9SYVWpfN96Gae1wecwhhtWakB6urHa4A1qiLTQkcoaK9SGmwg57dpSiMazk/JDWgVlqj45agmFTAQtreI3etcaLOIKxwmQnkSyX9nZ2YdaTBHpgotB/ikw3xEkkC9KYdYoXrS+lVwRUjoBoLIBtQ8RFIWyNZ0KLli4xjw9009Lgxk29D7G+yY1EW5yTKGI83t/ZbbSX2Ynm2wkc6JIQZjhHon6wNGdQQuKIMKZ6sCFdijm8FvXMTzbXlk+tdmQQqzmDepXQB82bo3NhnqvpPFZPHbSb3fkGn+w1Y37JUHzaIQGzXRbGmkCBwLEieHUVu4uQlkAa8fPD733/9KnN/+43/83Zs2fJm/IiW2JP4twu8PVXqTEJyTTqRQxSbPnhcqAgTl6SO8lxkoLvMQ/lBUKUWpmIh8+j/kQw6XemRYvm3n5RU7/HK5PdvT2NdC3d9QyR0GeS8iKaFmqE6Ir7CJUkNTas6JTZhEHs7H0hIPX4YksLOUlenAQrUvVwUWex4yFNIy5xK06jsYZ1tTcw4FhaCj+ur8x1tBGlAILwJ/C50Iz0oCMG5hxoCHIOfBSAiSozZeqcwst0vNXhe+9VA5lqrbMUlCam6xGRZDKLVD2DOcM/MdYfCdUtpEJWVJZyATxhcCajqu4Aa9KY/qgwD+zRqWbKRs6P/+gC98W/VShtQFPUhZVkERwTNZ8wKiBqtZKfINGEKkD6hQMoaybd0Odx82RduhVvdnCMrzP/4I2XFJSMXjVcNL8AVJTJRW1MM/7UhUW+wikdQAiUkJnw8bEzyOWjsgsXAXHJCIEbtw2PUaXCIfaenJTEc/go9hK8op/xywA5InMvXdSfBQ/BRsISkaAAGs749AdEV/SFCTwl+MB+xFUQd1724eGFF174029+k1PfldhuTvCY98Bw7hMNhwBA1CEnrwYPKOGUAVHg6MiAvn+pxvde/uIv/oJl6r/41V+P/E/q5RznE+QoMTMtvSpK8iQcTnYu1964yvXWpzEWwE0LWzhzdJz8OLXO+VZDspnJEivZJofy8ZgwREoKtFzsd7Bu2Pz0h5lSbPe3pWxx/+wxRGaijs99PjZV5AyV4pEkuBk0OAeYsXj0kGYNyfCZQE4yQ22e/kzC9/iY852hYzidHtNK80BB4M1wyqKAixn+DNuZGmjqOUmMann7T0nlMg6BeBYkYuQJn3UyHW+JV4STehw9mRCFykGQnqZ6WvCbi5bjYFup+GHTWewzsZ3iOWOePBBsqIxKEuyQL07FLHaUjiEIcko+jZThBKZj7uOpTJsJlukzkICda4Gw0XIgpzK38/CM8EEEsojIt2SAlkh2QOwcLhlISlWArEUz8LI4kAJ9BqEgIN03GzQlAnYlwj3D6AatKDagdTcrk+koiL40YDfs6iXCVfam0SItEVUdMMbi0cTVW9b1NQtWA/Sir7zxOt8xa3UugNnn001xpTJ8kkR5sUbV7R+6y5g92ZCA7vjDMJBNE66JhYfVxhoTfIhDAWaOWmNYekaDLC7zZsZO4pMKNP6bQz8icEjB0ptvvs291dvd27zTw52VyqNypAKNVLaFaXCxBQZlQ92TaxSRhXj0NqxYKAVmCGzigwYcYy5lcX0bCsstLWAMR6YgAkQ+3KIDEVbySBihQdl5NWPZg0XeMU5ZL3LmDfqSVdWyQASyCH0CmyFoUU+iTxbKRHJgkg61WszyBOU9huZFgi0SBUe8bJQw9HOvLmKnaDi0kYqwHjrfqCDpfEnHyUu0o5iEJlGVyD/goh7FxHuGu+JcFsuBbg9lptUiLshH20mlAy1aUpqfExeyUzslqN0zdrDHS6dO1T7/+Y+eP687tZGkrqfjrWu0Pe60JUfwcdQIPRA43DHgNkIUjl4lLh2gKuRANzAi5Rmei254fiwDRPFo33DSEhCXlZtJXiJXKLO/MA9aFEJXzNNVMlve2NowHOySSBmeIRRRk9EQ2YLAloSIh+SxsKYuDpYlw96e5rG8pYLcLAGM9ckxtvlIpDkwm6dlFsSWj0eFlV2FCWXI2D8K/yTwqtRiINM3sMyLWLIzwQyv0gZ4UlGiZMaMCRqPhA3kEadBLU4cgdPATTliPK9U58DpJ5mCidZx6wDDIh8XA5kTSychLdmSyvMuBapSEEVaTncJ4IDzSO3vcQUHqkW6SpLO+ngfyvBDy5HPvUyiILNZG9MQS5iTcGKJCqdxloGMHOnriDUF9ioIeEBREm9oRLUC105iOJdCvgqXJZsD2l7HEYWOQQ3MWkM7c1zjTphta3WhcY0DWYAGJ8BhD3wC5gdfCZFDRTgEJXw7pEYpPGLqNQcY9hw+Uk34ikdJOyyjKQNhFqLm0KScI9nhgKhkVU0FTJ5yj3aAF/VrtkSA2JDq4be//W0uFfn1X/91brVGsO76MoX5gNMaXoYzpqiHA8Jvhh8VoGBERQE1V1GqCpVHE4hABlfR07/gIC5ShEapIa/Ea2dgiazUU86JgjFxaUjEyDPEj8Y0MMKOVTADCWA+51T49N5KG0LXb6DKj1OTg1DmxEFAS8/KI9RgA2o5tgxnIAW06ICAYKe0GeOIQGIsYqGAYuBzDIL0PCsjBhwO5qFp9S4pAeSRRScJS1IlznsMK4uqpt4jKSe3cGhvZl5s879yJ8zCaU+I/EGjpYKcoIlltnMA3lAe+2xATwZOBJKR0v5fVY4cNX8SC0qhq6LL6aTwY1C3OvqbwuRb0SOJ8mLw1pQkbtN746XnPvWpT+l1jSF3HraYyC536O3j6hxuT48vkzbHGn5aOkRK1yAP2mG90tQ1HdzWhRXTAdmMD08FjjsT201UKiUeVLrUN5I/Ywj/1Hf7nErTEYwKYzzgbjvhRw8V3cVU+y2lkYoWBXZ5W6PD7p27q+tnKGivpp10Cofu6YCYB00mOTxWgixtddNBgbMSE6SkDtjnSBXIFCgiau01DVHcaZWpxdjnzkQncLq3SYVeHuzzeeLxUF+ZP4wPvw7aHUgyjyVrBpngClvIUW2AxTSH30wbUoYaPsWsePPGaLI4hrpjzOXR/sx2qrgN7SFfJ0IdgUR+qbkKfqh15t///XO//du/fXjQ47rnjvD4bHVEBj+UCcbiBD3ZxZigjqHVozEl0omkhOfFf5xRyxYBBTQqNlZ89G8w+v63v9vt7f7rf/2vh7WVKFdUSeDM85+SViLSY5TIcOdd9squPUmL/6GashXi00t8zHmp9lBr+flnnx+8+NK54XA5Xn1ajoPJMHyR1TZI0JSIgikgkaNzE9y8OpcELX50B2g4qX8Ng9nkmGcpRGNSgyzLW6HAcORVZSB8ug9k3hAZlZ/zPV6oHuxcuzUanX7iic2HHuCj6tuHevnO2bpeU5bxgzyL7JRvVH2qQUcFZ2WiqbBx/IqvClU5UsFTJ/ZfGMauvvXWGy+9zvd8eB2eq0LYs4Qrj6DMQJj6sORg7bd57gwW3OtbOvry15AzvchIEwsgTkgAew7oqN8ohOYkC6tArxS8BzfJJKrIpa4vdbF58Fni4bkPKSbErdM6HdlVNQhcVyLDtHZcwkoPcamVcck7Zh7c9s5MdmnQQwPcxkPXoqVTTSHaMF6rgaO5aUzbeNEMaXAHnciL+nTrCwhc+HeBH1GSXtLMjEk5xLnFOGmj9M3R7fL2C6naS1gnDbBDpGKsS6ShbYiKVDK1bJUx8iap8gKHf1XYWkPNShkFD+GGtusOONnrTGTI2GOuFtYvLbmiD5bfF4mMQ3oqUc40wPacREzJJVSAsmKJbLJi8+TVsphVT0AHEOZMIAeTQocJ8saqiF5P77fxK5D7urDEzdJ2/mXGWf4pqvhJjBWQ6eAxtTyN6Jq1dhFTFV9I8IrTOBiONarkEE3VTc/wHHbVlBVkBPySbAYWAZW0alCpfknCms4MONMCX0FaAXArCEpG42kzKeHs8rDNDGXI9uhSi257hBGMXq9c2t3lFju+id4+xNBrdQ0KB6E/BCjC4QAD5tXhmhbnPX21kKrs8DH7w/5Sb9hgYX3nYK9du6FFaU2vgvUZkWOTgk9n8Ho4K9XDgdoyt1CJ8sEe2yW65o+eGVMzbuRojjGf5irku7dvklCS1FfZrWZJPqFDpEg6wCOdtZoBil9VeDl+CbVyrprqKX5RS8ybuVxzOMC2jV4Cvn70w7/o/s/i/GC/x+7zwc29FqZXS6vd/frmKfWy45bebaoNB33usdYQPV7a2OicO7e+dl5rkvFOf3e3NeJa7MbOYAlrzvYaG+srXKCBTi8P6+3m6pmtFY6Nl8aDZqu90tSHCrtcacubZ7XDve0dWEJ6PczXlpbWG+39RnvQ1Dt2y0tt4CC3Gs2DwTU6N4LgUHOqvBAUvDAJdOOKEgKmTveX11uDPvfl89mS5Y32CpWJoaT3DkCj4brtqjCaX1Dr6nvgBIKpfwpgDCax9I32K8WuxO7soCCOREeAeFxqL3fIHEtv5MYeDvevSIyu2FS9qtAEIZVpvXc/coEjKLdaWjGaH/jgumdaK/N2vit44cIF5v8YqGJsfvPmdfQuGrHmYPRCXPw4is+hpFZXG+iab07em/Fx+2kmE/2Y45UxepUMcqG2VYnL+CPCVes7InoCNm/YqQGqZn1pNJkglaFk+6zFD1+u0U6Kxxzdl0qtsS6RIxbngJ5j/h8W7qiJfldXOl3dhomtLdgqmfBFQlR4szIq0yXWWMO78SxWsHJRmFcoBoOHN1pcij7cubG5NORydG4H6PJqBr1K3PHK1h8tQ0bWdDr0I1oVS7XYbdMeBCFUMCw5CUKzdORNFt6qyCOygGJNzBDWLBCK0r1JtUASxksI4fxIQAWdoIsMTm0hnIkbnzBwcjt1av16d3D12o3Wsj70an7UchMbkBRF+6ajx2gJGchSIrCUUarlkPkhm3318crmueVTZw8wfN3d7e/vINylod7PkLWpGjG8JMapBlGI6nVetHa58E3ZWYstdv2KWZlnFOBiBAo69DGzVtrQn5AKGpBEAThRFgY5BmaEqbWA6KHAERX4BEjOcBjyEedkpF43Ks6aHSacSp5dEqZlVGw5Ucm8llLHCJy6o08DQffy6xcLXPSY/SELILSLYRW7WrRwSH9b60sG7JstcWsU+6dcqdHQR/C0nIDz0CUCVdaQVAui04D8qL21dv40n6ulp2XBD+9s03IL1XiFNSzx6r1T2jyxlJTs+K4J8yXufUbd93VL27DHjjNHC6g0Y7YUW3frhws7DEKwAdtErax0bt3UdEtFtjzRuVhdkoPae8WzOHfdxcqSR9EJceiGO1yEK/koX5LLj3FKA9Pycr0pmUiyoc+EzQymtqxNeHuBUenGjWtKxAR4xCdPpMAMWsgd1ZT0lZI/Te6Bc9E7l4Bx+/MK8xWuBh4P9HmGRn/Mi4/xiMDYcac2RYcjAYblvl5dZeLAJoA6i0NuAlMxzQm+AyFcKZIHUwsn+wRUZAktfCSqWh7xuRo2fthJ/+Z/+Tp20P/8X3yZDzo02x3VQqHtEKc1pIyo8KpY070Fms+fJGpMcgquZj00Ty02XLDkSlg6d+7Cysqa3nGMxpKj1Clr4pSSzPw4F5eUPR8FWFvVG4Nha6Wz0e91OSYPs+o0m2XsUwOhCNQVL03UOrU671MBlP0QWUEfOcZ5eaWzFT+UDPJu17DDn7o6UdJrMiSkQVJxKr4M2OXgh2tk8AGCMOge8H0SnlSKqkQuKQgsigFbPtKgXOYSUynlLMPwg08xogRBTQiysGDdlKQbKQjDiqih09FOg8+lQ6a/TI+5g4sPeasGGKbQ8FZDZ6jtzgUY6+7rPgNctBSt+xjBTp2S5QFhtyboEqAgKjmFVjYxMEUA5sxL9lMP6W8pGQqJcMqp6oUAhLxSFCRzaD4ALyUQMn6kXtKIWiHQ0/L2GHJ2Rjlh0W9boiW9heHZOYnkEWJZiH2/QDgvXFJjOKsE5jc3EgrFISSdDOfie1RKcIbJUMUsRO9aGF/+MawfFZUKPKGhEEBVeVUBxilRDKEvol/jhigwG40OL5w+8uRpTFTYT9YGkEZ6jQdolH3QcC6gAmquKkuAk0feQIyPLICmcMDNkZiTpIRGYMaRnYZJLeEqOc9g3OvRpSM5E5cN28hMJwkG3g1xU54mpicYVsdN+5V8kgbMoynfKBQByZbPJkVAbTYmPX6UXLT1nOgAnKEoiEFHiKiKnrCwUJjzaBmSA5CAPbYO/8t/+S9MO+iqKSxAcei+NFiJLrBieJKtiyuFdJKIUTKKGyzNlCyl/N73vocG/ot/+RvWvZKZhDH9UyLM1CtR85CMnwMwQ4f7ysuv0ArOxBWBZJ0zAU2Y0cOWwFS8DFoUUMLCuSIAUnXIsMylwDoyGAlnCpSQnRGleOf2be7UXl5f5V5Ia+ZRGc3wdmSu9xlBGXkNnkTMmOKl1Be4G3H/zh5sAMRnBlpkrdEXY0NFNWrf//73H3vqyY9+9KMXHrxs4bC1wd50SkiyWOnxaKfn6JLIVMFwZTjhvasfVxZJS8olpYBP6tdT/DwGGLNiUFQCUvjEuStAMvQITMQL6s7dlR39ipCJdxLmO5Iks13SlskKCscHXR0zOM7CuZRRyjsaeWr18e2v0SHGXPHhlSrWZXOzLZM7LBIncBkNDudJ5Uq5J6Xj1eCEdDIzx+DPRPGIg3Ocw7Ca6dyT7Z8iAtyWubMYQ6WBuCLKqPsKz2gaYplPjnzI3RmhVHRchsxjGkIsDt1n9UgAIElI3utpinwQ02heh2X7uN+3IZWqIlMDR+0NxzEdrjI+BIGPCeLbHHKHy1vZRB7L0InDHnxSQJH9KWeKGeDevnozdkXxvZ0BUd7muXH7FjOQkfZFe0rCdrAIHOfMoikfh1fFgVkWCjASwFl6SOagr4uA/+zP/gyr2JXlNeAMrMgf9nC3bo45+es0VzFP5qom5esl91iHgrwDB/HduE4EyoShjKgwOYMI94c6a6iBrCguZIsbLcEhSuTCDXp9ELhuFBw2SREFWxXg9EZj4MsdWTpUBVIuWhhoaTDbV5ARFCAJstgg0JKFBMhQYFMJyqaj9KIw60CGAjVIhJe7QHhk0JxFjWcRmZMwMdyxoNrkes7gB455lZUwexElnULdSvB7DaPzUObWUNhzWWASTWPqzpIbh7U7vo3N0U9kZQmDTzXCJ/5CJgJhYcw/JiAyYUZx5/Y2XxTc2/XVIgdRaqk4ZdTSmjV2q3X58mUUcHV1A9ViP02aGfs4UADiTwq75CRU2uolccLAQcYR4NHKSS5A0I3BoGcVxc+yI0wWtEp6JFyYZMUgXvVFygMVjeUytZQeVV8KuwiZGhkBDA4mtQkkIxDISUzKdEqEk4ednenjIz06DfoW9rAIkK2yqNqs0U5OfAaTbgQIB8DwT8fF9K+3t0xG9WFLdTSQ2rNVJ5wQgh4VEBkqQjIJp+f37CgLlQy9d12ooJD0BDpwZFKuHRg2j1M1N71+NwL4RnYAIjiXHcE7QJTh1gmHqRfg2uCNy6DRvcYwrO9jVEKa6s1Cl7K0FpbXsqXqocAUnlT0orSyzeV1lysnnwq4YoIVxk7xsBStMg5dGFHgzVvP3BpEKYJVfjWqujgOkzu9XKO5gXoYQjwZgebscoBN3ww30I3JBw8sCFU6vlvU7zMegcmjRqI1LhKV2MiUKBJ66ehPEXK4gh6yRUuPynY1mAyglAVRgI9zWvygIY4IwRo+pQKhvdzmgBDVhnm2fsyYcklOSUDDt5ybDd2KvtvTbSAkIaMKU79g+lHJRCwJwW0ngLxhpC0ma0KWIZhm2WKkWX39618/f+ni5z73uYOeOkBSZGqmc08ffNgBDa7MmPKdbjLATQfkyEVMK7NwZRYZbm3PUTlVhhAwDnRIhWeINFN3oU3mmUUuICbpORUPpDJ7jAtqC+GcXdWlGZZ8R8mvKptwMJDqrsR2lDWqHHiND6ZEVgmnTHjPsCmQNpMiSYQp2QJnth1BmJKiA84cHwgSY4hj6rvcjlvOlzpoHedZKA8uUySMoFyuDCRA8jKLMuonHDYb8CO2q+qGB+A4So1fqsdPmL0PKLtSDchi5nFex5qeI8/ukFXceT++bENTxwGxExD6r/GFRMakTRCO6bWDapXunRC6acsSU3Mm3h3F+JiDZW5uHty6+uaZs5foh5t823Zvp7lWx86ZO7Y4EO4cYhxhC2IOiKg4tg+lr2OdFmM/oXMlliEQb3MyCDOcKbG9iq0v1VxstBEBByQ2G9m3Krg34tYGurI6m7Nq3gmFqAXrCUemHRGVPjoBd8TcWBdT7b1tDug5tSTW/R+1Is5DIqmGKrGkzI774dgrSTETAZ0BnBGGQ2gIcrR8nKNsFEr2GphsxbsZYcp4yO1wGFBgr0pHwPDJglhH8r76JJWdyQaUOQ6VVEIyWSaJpypjElbB6jcAc9Aqdvo3iUV5KYJ8/+APfv+l556nY+poW2DMZXVx/ieJUujAmdB2yCzFEKP4wNFc3HBKKWAc8RhiM10kCM7yqP23f/Yd+r7f/M3f3Je51mxbAnJfLrc9ypJLh4rCzspQosRk6I2XXt378bNnRqNljlHR5rDhsu2zk8gUKzixfxIGcl4Z2RCKTAA/MSYWJAwBM+p0wPgSmBtzxIbmSisYRMVzfB/kPGdKV6/v1X946Yknli5dIubOEgYLnMuHJKfJ3teTmS+TuO5KCGxQKFaAXNjynb/+DkcX2F8wZ9W4HksDguh2kcTtUoAhRoY7ez/6m++++MMfP/Xhn3n66acvP/yh06sb2719kpN7OH1TBYIWHdlxQk/aRCWudwINyDxvyuN+nOkclcLtcSp20OMg/TBe3wNuBuRHzfrOTW4zFMuWwCEffD/kyJ5lRH3YA1rmSLgsAmHqkJKpwdF+Bl3NHnQAr55E2fFlGWmRekKPHYIe4YoWIVnZcqqyHBGEvPB9BitOeEif09Zkuskdr/CMsSicWCHp9z0JBTPyD7pQKKdHIuMy5pKak/yo1OGA4EST6T4M6IzOr/dABLMzFROXG4zphJQmPbwfjTnjQzxrTUQd1fIm6cxPVN8ESEhVIAdLytDzG55hiXBEhcWEBcWzxyxHyA+B63fxBK6iP0kwFZqlNhX5rh8oLPlSWAL2CeDmCc6wZxxXxwzyvOgCoWq7Xq1FV8bE0WSRZ0lExC3tALNKJZYd6JiEKwmTjiHvE4C2p8/6QAcXFBIdKEip8AOsvWeZrogCuxpg2qxqbbl5ZnNt50ALUWy/8OsYOqmzwVwLs8cWNdo/xJ5r1Ovu0iPJahJ7pTh07w+7Q61NB4f9g2GvjwTbtQaWUKy8YcL9PBlBMxib8uAgcMTkEeKawp95YJ1T3z9gqdzntk54RjF5QWPpQIZstWa3P9zeuXXn7g0W3mgmbxuzv0PxDnrdPg1K92DJqLbTWlldO7PU4C0ypmOdRnvE5Ia1KZ0OqsBHGbvdXU0UtQEhfPOp20Zplq12rP/FvGyn4GG81D/oYqUGfr0/6G/vDA9lZdZYXod5JgHNVof5o+ikdpTKtFA+nAOgEdYufBx1w+SJEUFtjQ+AqApkQ62JlN47wOOZGQvVOBzp2ugR8mEzke0zLqEj4c3d7U67We0UWhuje5E+zHIBn2fOXaw3OxwtNNodbmGm4IPQopm6+IAeLSQ4454Zik8ufG+4N+ju9/fbq+3l9eUzF850OmypsyPee+uNl8MCj0W1+yjZM6bRZ46/3JfOxSwGxJ7D4qiF0Hv3sAuT3Q+Q2qJl0/jiwnHb1jQOR21aJv0AjYp93+5Bt1bn/pz67Rt6NQG1RHMa3EmOC32+e/PG6dOnURtpVGimmgYu2qsWONHRE4v8yU/piOQmUU0a6fMBKCs2eogcsUOlvT8+aAZf8oHHlpTS4oKIhrggRo+ibDxlcP3SjVWB4IBomoE/NlH1IWF8nCb+0BRG6AbrBeWhJLOqXIEjbpGHcEykjCQVxUOYa+srB929ne29jfUu1wGrIVpAFfY96VvfGunbFU4m2XIOTxa1+FLO0qiFaFqN9un1M927tw8PDpZ62BLyMT1tYvqjsqmfCQIUlW6ETkpPkYFezlKvG7PfeNc2NYXAV36Vs0Uz1aWuK5TV8x+6HK1FYyQqhyPPsoyjlzZUKzEDCIJpXproSGiwYxERBoUw1JAkj+RIxwXQdtDGzCtlyzyoqhJFKtRGfLpaXcEMXbLTj+OTKJh2hAjE6ZhmbTh6KsQz4rKlJT4jz7eJlviAPK/Q6ZObfMtEUtKl8GImWI99hUO/4xeNoLG81uTtnt4+JsN+h1jzGXDNg7m0H+NpgqMe2Cn399gTYvaoUsvgV9vAMUpQk3K0GQZgHDsY+okmQWGhR6ltjBLfGFFvTazzEQLjjCvdoPAlJX2jRE0gAMLn9mV6fiAQ5PiJTd5KjFpGCVNRjBipeyMlCKgPUXzilfCd7W1aMnfuq9+P2UUMdSIOTX2DmHqN60GiMWOaNkCJt9Y7q3xBotbTZJvhlOUOb1IFX6RCI8wSRLzYIdvYeeaNn+5uV6dWvi0dtBBMsOral30u4lLu8CNuo3QIkzAE5ceuvfRGd92rGfSZ77TYnVl68803//gP/yN20E9+5En2oxmOoROIlEdZaW0uxbW0IaAxXWS11ZPYkNZQ2hA1vigkLRImRIIgnrhHSMGUphxQorxCSms3tTTClfopJlzsOKlcFSB+3R51HKWSaQbQqDH29TlmVAdOf67xX+sFSiEpsfJC9cNyJ3QmUYMv+nNmKCq6BKmfezpSgWk09Qbc8S8dQODii3fyiGKXjbkfdc1ijS9cT2iGDRFzE2wgGDukbifJcpJ+cUicz8VkJnMMhWXmg5ojd+Td7jQ3V3ifjFMSjj10DS9wuOZNt6U2M6WBXtvh3pAr5gABAABJREFUlTM1UJrecG1lWSxrG1AkY9VZFC1nc0TA+uN5+Qy70PO4MJ/U/e08/EgIE0BG62gvVKcoRwulOahFhKMGj0z+TzfCeptOWspiht4fKZGjYku4SVusaHSpeY7K2fmRroH+gmM91JErArn26GAgQ5XmWhcLylZ0Rs0lLTZa9BZMuVSJ6pjw+HCx6VOdjGzEElDWakr6MWP45JKrnIQRG2Sq1quHyoGPq57e0y9Hpix13hOJ6cQq1iLWXPbU84ZORxFC/p6UBNDE1KcjExqHep6Yxca5KBAoMCWRfMLlXpinY7onxb5bBzNOaiKlTz0yPH/zm19//vnnbURD7wPnquOKH1U2bk4mICysRCWsHEMRwVIxTEdqdqgLqlgGfPFX/hWDvVOVaSsax/0avyxgZpMoHBmhjGj+W6+8/uKLL56Lr5+X/BiNPEyER8L2J4EoUFGsxFJGS8+R0DIhCgUPXxwlzIlglCKAU6CElslVAWgSJQ6r+woo0dWwgz539qyGmWiAGp3fb0e+LpF5gDxsMKH50z//cz7QwWwAtcFUztka8xgWYJXkVAo1/nd/93eYGHx09xMf+chHMNPDzgJVzGmth84uAcM6ibCllPkBcs98E4X3/ANXmjtG8zAxszFTcdYlEEBUOBajjIczaCAAsV6VemAQPn0F33AhC+azKG3qfCLjmaG6VOmceyI9nW8mXgVEzknc6r2lN+bdQ2rKR2JBgTp2LkaeJy5IKKrLlckqg3A5CU85TABlMILrUSL187RP7s7aMs+RxzymclVzkZwk554hBACagVzAhWhOAib8VJvkgi1EXgg0hew70/z4Ewu4CHDo8pLv8dyWfIJZqtzJeSYhdMiR5Oiz++eScmYj9+RSe/HIUKLRBOUAHy1NoBhigFs9oI8jSn7MfwMgj1R2LEKgScfFdbrX7nZlQ8oREU5vUGqBR3L2eeENmrR3IFibrmI0Utf+Kp1AN16oZCMESEVVCcUerpBksD0rWKOAfBIHX0aLVEswxi3XAGMhJSM1YoniRPDu3T1i93d3mN2pNMPh5saGY/GVRFafsrpaba9TOpCBEyCKdQesxt1b6g0giE/DZOuYLPxIAaFJKuhof5mEwy443tgFzrKft53B4b4TcA7baeJBmIxwZORS+HGhb4mZK/PQq/d41MZKoasL0wK8c+cOJ6NwtadLKrqHPR0wbF95qYdtnXuwhZ1L9IFgOlKBcGxkM0W5du16pWUUQTm/T7PXBYWAMv/IHEFRU1YqAogXoCZp9SVM1zlX4OiX9K+//rpxFtD6Jwey8tD1Igesm5n837mNtwsc4VBcFAZpSHpR19p9YbszINXHDNUz3L4t3V7d6HgFBATNggjOMnPYqkhYyhwO3eZtB29v0S4yMOB6FQKyOLSFqCCrvsgM+DH7TOZwZg/GHXbuZdiQeR+yAIVpjaz6nJm0fpROLXITIpF8Ji2lRtNuXL/FPK3R3OLRrWMRpSNhavtVshCvOCnlzJoTCG9Mw8zy1hYt7vBgTy23q156NNDF0HyrDZ+UcIgDrO2magwiENkvLuORnE1HBJF7906Z8+nUeoJCjq1YUls2JvxLFIvQrHRGsO/kubwCcp8gcogP5REAwZgmzqOdJKZvaUqYOCSFNmLQwcSb2V1mz6mO8kGjfUX3r0mgeYY+JSRKgYXOeh750hxgRGcysBNhwADxBWefleFJ9EQKHzg+bDMWe3xh/yFzm9CCkzJn4FBjV9bJedQABH1Oj9nX6HQ0OHrDPTXGhAkR9lwnZQkTOmYAXGB19vxFFiM3b99mxGcocUs3A2YV34ypOSAQrgejUFxJvLJCYxHl4FOMxSyCUgtNcks9DLx5BcHLQnqTY3svSq0pB3XnXEiCyzlWgQRxFDRjielFLihijK7KDFB2fQY1vv0APu8cc3ETF2txg9Ot23cxNQNfmD5CUGo55Tpdv/nRAcqFy0AoWKudNmhMPDBbsTZURRROjIcjAKnqUUBDCtykJGzPE4WG4JPjDMFMLSfMXGUIAZLjxHzFQBl7VNiFzUWGAdwMMrXsip6BGxk5zMBP8ijZ+tDL2pu70SpxKfwKln7JFwmgaQTgnGEO2/zzW7oHPM5NdMQKnKNTEJCJ0SBYVe6YCcYMTR5BK908wgcHyfI3GzkjlaIa8R2FbsBkRvgnE6B2ynLxmIuGEOIxTbMNB0irmwJFhJLNbB9kQvOBnF59T+GYCyBjDw6+6zbOlKRVwaWZS6nFOkusWv3OO683R/vjeLkSTNQR6xQ0dfVAPvrKwMO5NGGOlHDtlqwGmBwQxdVGeAccNdVqH7q0yURhqJdhOcQlr8SLz7Xo3OF0erbirbEoOzEs81p1jkcbsfMwjVkUsgy6o/QZWsC5nY22tX3jxgNnz2KfDEy3tVkmVULL+XhltKScwmeAsefDzGESIztELKD0QVWIecEVso37XtkWJHn1vWbkyrmxehxmC8iKlynxhmEBTRrWqRzQMXZx1qTIqFcJjIqL+qx4j9VsUetCDgf9Cmf21y8TzUKLVE6bKVChXEnxF9/8FkbpK7oikC+mKxefbepqK1xkm5NErHoiBRZxkvmMbGe9vLdE1piXffM/fvOByw997GMf29abUuQbpY6PGpUppzJKpU8/6LbtGuiIRCFYsr/Gi1CjpbPj+luvvLHzvR9x88YaJ8Ljmu2IqeXIAp/Wq1mCc6wCql/Z6heuikogl2UG6AmnhBDmepYeqlOileGC/D2CpGpTxNHhcgxLF7GDvnJ974fPPvnkk73NNr1wj28C07KqXbGcSxVIIz2PZanKcMmBpaq2iTbIPFWrPha9f/LHf/Lqq6/ywvVKOz5hL2sr0fCN4QR0YpGrWRQnOWBKBh2OLIEik9dffe3Ordt33rn+uV/8hUunz93cvSv0wFfH4odqgkIlBUC+iMRD0kPCFX6AT+QtVNRp2czSYd+LNkq/RQfJ1jA8+D0GOMoDgDdtxRFTauwgmD2wlsoiT/3YLOXcB6p2whYj9Bkrih7rCaTPbBEbL9VvoKJPhP3KIbQi9wnNXIockAWBHHWlmbCFSVBthk4pFgN0TYIP9O1yFg8QFf/KJZQ6XTmmVRTp3Hgy/SCjPt8FxY+GKbBx1MyCBeSRwyw8QYuKTdVd122W6jrxpm7AEjRtMkZwyoPJkpP8aKkm2QbHDpsTAOYkYkQQzlAtktOaTKQk6yyNIGRc3CpoHPANdMDIOTxPxwj38Isx6B6Y7zYaDimR6yBzC8T0DCn9DM/IQJy8ZOGY8hJFWpLgI2dmvySsqGlRl+Y1QQ5DFkXRhuiCiJNiwRu/eo8Ix2IEUipCJEMBZRIRmmgtQqWFra9OsJGhhqi2w+0OjRpmk2urywf7uw1frcAKlq1b7llgzVyrr2xsrLSa+7du7t26wXKUlsBMHYMulvSjrsbu9oo+l9Tv7sKG5vJDTJ+C6ZN5Ksv9uMAnWzmtoyKtieDj1PfTPUTdgUOA9S02ZetrAxac7cYKC/N2p8PXe7ioYxAigQ5XWVO0vbh2gwG/S/NXn8NrHpsYjiI0JvTcA7syWhuN97WNMNLdi8yFyF9vacbnwhgaxge8FrPb7/Y6LDq5eHQ43D9ArM1mmyUf8yRqIG6yv1eRKQgofLId/ut9GbJx+zb51kcYRx4gcCMEGdc5vqqXdov/ztW3rtzu9bixenOdb3BhBkYRGv19VCbmV5XMp1sWNAuyS1yu0lldwa6BsrMBzcZEfElC76xlF809Pd1nTWYaCwKQQl/J6PTWKc3GsbSjFmpLd3d3WHbzx5CDmd7G5ilPLVTFu32+qpCG2VSuyVG0xqzo2RZkdiyoFMixiD+5yIolLY+5L2d9da0+bvFaVU+7lDpD0W08A1kFxpW3mG116VnGYwzwEWFXyhoftcPgeOcuGz9DjNy3Ns8xF6WvgIQSRsPJRXKORMlp04yOgQt2MbPUSIFpW9y2gUEP20fqGegb6GbY94OC+hung0osEfFRYGDEUi/0FlhrEva4Sazzne1Fok5zbA6oZiOJIRkOkTI8/+hcFvqZFKUjjP6zH4fVJNeVIHMZGobeE7sw+UKgFZWEUTxRgLLpgK/ZBTTDNpcLeDsr48O1LW5LGWxr+++wu4M0sYZGlGxlskHCxJpUhNQuYhBxS6yKbPqe14idosmSl0TLzJbOj2FC0YWsXSRzq6jKqX9xHhJsBYXzYs6JaRER9J8Z0zMNjhmAMCrhwy1yi/KqxnEpr0oaLoLRFFs45m/oJsoDnQa9A7PA4Fw/kKLnw2fck2hjR4Zbixujep+RsH64zKwVBtDaorRRKJDFR+V815s+bgB/jc6Sro2N4xzN2SDlSeRc1dMUoTPYX+JbvtxijA2Trk4eMoeEY4pDF6zGCffxiO2z88WnKUQY4aSNVCqdfk+vXUMzxny4Cxz15IiKsJ2pySonNs54ZFNC5miVltIax9XKDrmFBKgkCcG2YZqu4sLnfI/fV196mQ6fU1ZOkzzcaDpBVIhLn24K+2I66FpDEw/KxYSB09GttZV1RrrDQX2stj8cd7F+jgk1SLHtLh3QRB4+ILZ/sH975+Du3d3tPTbNKSG6QxxJk4PbyDdpcqSSJqq3ED+ayWiOHHpFW2AnPBqlNhNNYp3lL2fGcXrPdyz+9M++eebs1rl/9a+YDICjtTZSQB3l9HKDRkjRDcC0R0kNIGCnXIIDwy0iEYjFAo9uHNQmmFznFaVDc5QFELXEYCLQpcJIhyh1sBBOXIk2/FCKsIc57DQPNdMIGdZ1zQm0JAe9GIWqeL8FPSUXrppgvQShIAsigyfvUCnPkJ7SKtsFrsKRRGAyY2R4hjgQLHD3DOMLBCUoC0tP5BGmAOntaieo/OAk4VSw9+1XG3aaBUieD19Y3eQanVXeoE1bzBQMzjgd0nv96rv43Aaba+I4JHbI6y+oB0WjBgCqQqqNhXkWI4kTzkd+IBBrKQKUDBkFIkDvyqwXZuwCFgiUIQ1YUSUfCEc/CaIUhqLdMyfQjDN17JOThXT0lPFy1DzEUc41xyrghhTM8FiyVT4aky6DiRdLAjaXOc1e2diiRQNkejEayKZvaT+ZoIoOcw38qEd9KwfFi+K4ylsxbF//0Llf+qVfsl1GaLJ6DwhSzeRIXwYF/YYreRO1kCCYOCPcr58JOhem4Evxsuf90jkKH86dhenbB5lOHrnRa0XsYlXOyOBLFNrtkfM5rYFaI0f/jO8+0KJCgCD41IvAjBPy/TvTLNM6bJ+q/8Y3vsEalYAxF+ZQJgeB4mS0MmxgRibK4XnKrnr0hzH+93//97HKb61tYfXAAAGRTCHnclQgU3YS/AlnkYaMrr59BZOos4OBlnOxp1XSJwyRDMkBszFDbYaNEjlHAXTBCyFNSrQwSU57z4CJZzQWTrzTcPHixfGpM5SUVbrmIUe7d507JcJhs/zd7373yptXyAtSOIAzuQEMSKmrk7DxSQ4OswRqhEr/67/+68NW4+d+7ueWV5d5zARNKmdRZkeVmUhGdiAjz8Df30c4kc5Etzgp23Qe4BiNHoNmQCSP0yhHPhlT6ckoJjN8wUUy10xHQhc19y5pU3hCivj8kMME8oAtzmN651j7FIUAAwSxtlthA4kwm+mINE79tAAJt4A+8JKawwm74qcEOkp5cVlgvCxpiH1KSj9IvjPKNUNBjFXESTj/WNIkDPIsxRmMeIQrq1ZJfAYROuDg/HGkkmwZdipD8IPgbHuZx5/J64N+NAOUxQWHyeAzZUusEewDzYGSMYBOFaknGpJxVNfhyIhf0HKOzETca2XKmYFMlgSKjZE3xzqVSUHTWfCY6cAscI/aWVOMAA6rU0Y9bEPYXqTbATLkpdv9fT4FBKn2yqrmRaNDzKBwQJg/KS9e92dDZFTD3OnsuXMQ5zJaRrEeXwDkwouTOejgyBGqFsjJ0i3QYehAhPUXuaf2ywq5ur/IZCkd9lmdVVmEwbDzJUDW+L24RRo6JF/qq34JsJvGhgBl1KAZl+pCge02hEYUqRrcwUb/EBvNzgUKEiPfeVtloaNvrC83ZWYFYziInLCMKk4sGNhKYMuc7Cg2EtbFK9EpHU+KzdnTDzxF7uNTayqvOs76eP863x5YGrCRrT6ndCDyCNwBRzGllNDamgRzHzRv57x65TqIsS1Spv5AwmzzMA/iomfEy8KV2kTlUELeqdXW/xLXbl1il8QMwyrfV3j44YfY7MjcEHW8lDLmTKAUwkzUP6hHSscOA/JhVxTJcLMXEJg3/y47dUqjlRcbQIx6io0PUu0daHPnsNZFdGurVyGysn4KvWWzVQofG2QmYrKUnQCKpO0tNpx4Pb/XYyWFj05i9+zd59DPNJ8UK7pzQ3qVHfXosIXJo1dPuXcCDkLETtWggTmtyYJqOkWqnDzH3CMwQ9nYACkvTZYio3KEEDLCcUY8wgBhYu9BvYoWrcL50VkTVnZVLI80WyivN04j1d6eerbRQUjVOXL0KDGqzXI9jogkOSQawXxFbvo3onJW03FHP80wfzTi4hhxGA69KuQ2xYYZM2bOjkfwLR+KTMBh6PGIiHi0DybOuThMQgL4YDJyRVLlCNBEjJx9gIqtnnnkXRnawmjYFYVgg8QJTQGhmhoBIYBHvdj22X5AiKJdRDA2o5VGtQYcnwf+QwEcfIpDh8a40+SEAB2LDWgjVL7yIUp5ucjadEpaavaga+L4+rjiZG6gzdxK1YKHKAUTYuBMFCBIt8931OkwJjyrlNrTxym52FfPA6/4fHqO3mAjvrJOLDkyTQGTAI5uA/wIKAf4JIxPLhzqbO8ckAs8g8/kArjMWyZTLDFHDSvl0U7MxdidD/JpPgaSFxx654Gq5PFP/uRPsIT9wj/7kjjxAf7RlGdioAmrMGzi87HAYTfDXRfqN6NFl/AUVXQgElFKG71BNZ90KmLpCqQhuugxfb6ZIhAbNSeszJXYCKdcKoUGmX5MaRc585MTggLEZEqg4YsISHvppsooEloWBFS6d+vKtFlu9yQGpiTVbDKoMZE4e3bTc0KYSY6d6Xg9As4Pw6LScFMmuUa6fzAO3jIvpRCAUwrHAsdR0Q5kfAJA/EigJFXinDz83imcJK8yl8x/TljGIoEMJ1BG6SPAc84dSupoSmww5wVU5T2pgJIgyY0gOlUYBMPr8VEddcLInTs9dRqq+Ss6rVlFo7m6vqHTI2a0/ZiQRXuNK3lTN33oT+dGluabL7ry9Oprbz3w4BtPPX6Jl3yY4lHr9bFs5QIx0tL2ear+HBH7HjpPUWE48ebMD0MC+NH2LDfpOPWxfpwzg0EBKQ05rtYP926+s3z2IkDGZDLSyZvzo4MLmotqYZKLy5Wej1XQEXclcudQWDd4U16CFSdyPgMnEGIIDjywYbGl0kluCZ2v+VQu0QkSPttPbw4HwklEUlHyr+riKDvoitNJimf/7u9eeeklvjIvEN8sxsXAf8hdRkmjqCNNfVx3AHMtc92kCQJhUUvYjzBAZxe1XynEJMMUQgNBQ2ZcZbh97e4f/n+/8j/9P/7vXDwt40+d2UV/7SPUSDHVC1solSYQ73ztU+dAVmRiPj7dY9w6uPrdH5zq9U41lrFcX1EWOlSpkHVG6nCGmAJnw1aiDKemKrVS+pxvMJg8C4ckBFLCVLNCEJwyh/pXqaZqOMuWWMKSuUVYRdCUibL0xE+ttrU0fu373zu38rMsD6g/nWrHFvTxOl/lftJfRqm//du//eY3v0mOVBzOVs9uX5w9UTSXhG4omI3+zQYTRSYukHXJpq4gN9utv/7TP+ejEp/9ws9j3DHkkjyVOihI5nQlaumF9BNFylhlh6CMf9z+e8HIuwyGBbR2oVTakXZVDmOCK12WSXToVdzNKoGAxEv9TEkth8ShsiahYsNZGg7jA3WfIHsWwioi3WPYJvtUMKKBKol7GIWSk36EpuETdi4KhNooafBlOD51ByZXvRLmlVfmeWz5MKo3h3rjG2sOlVH9AhYpBLFHFb6/pUgZCHuRkPJNE8KAK27WOd8JdFzvdXlfX8WQxto4Rbm4eBNEQqXQHC4hMwhlyqq3KWEpDD846OQ4HgnbdyDHGpNHQZADXpEwU3DAUSWCw6IcyyrQytiZ5D+Zx8yki4aPrMRh4cChvRuQ8Yt4BUlVJpyhAEKmABph6DhHPzKHZDYS4sRSVtQc6+yojHTOEqNS0vyQHv0Pa0Xog5+UUVqvuIxG9VJVdCQsHIMgpRMPg16XVdnmWofvHrx54222ngcHuusZXrSFymeXWJJiXw1qHDaRubhl4ws71BYvp2888sADzKPe6bTYwpaNLl8i1Dm+6IuHaeciT8PezZPLiU/BoWlZjQ4He/vsme5DscWXAriqVl9r7HHfn9gfYVLQWF3bJDZM2tjJZAudGcu4N+zf3H771u4tmcvpHZemxu6lGl8rah7sLq+0eWclDoU5ltKkhRz1ZjViONTbnU1Mj1bah70O/IDGpYyjgejIRpTq7Mtmud/VC/V8MogV7+L13yIxQJDl06nldS5YYBdKuQ/jZfzQJeQ7J2KtQ/hKIDPds6e3qBde6hPDY21hj7u10f4eucPbfJ/p/EmOo4Q8srcIz+P2OnWJQNjw3RsO2DHgAmyk+h6Wk4uKWsCCY7a5a9wvceHceWqjN+4zPqLAO3v7W2tbK2urCPbU1ub5ixcpDEpHpb/44suf/vRnTCarBI8UNspLiWlRalPQwRUZLghmBOHfA3c2+Qc7+s7mpsJwLsLYxBqM+lIT5o41ljScj8gCrj4YHVBf3di+ZPqnCj4Uj4PrB5w8rayiRjQj7QrxTgAWYwcNXopo+c1F0EBXi1EgJl21Foudne2D69dv373DOwM92gLaFXsjWsjTj7k9qs9B4rq9PDngXioHTVOG+BKfyxDxlJeUuqqfEH0YalJ1wHFOG1lM1pzOoERwuPQ1zKcpdwLTfRAqmBG8eNSEhnZMKvV1Wxu3bt65e2dn8/SW0Og5gssZmiJxL+eEWccm6DGvwAJXPNB1cEtSY62u1xDWdEFpl0+19cbdXZ0CDrvMT2r9rnJPQ7DkY131jHRCdi4EfbincvQGx7SDQiX8FAE1WLX0/VO2iDQoVrj8qqLMC4SKtgOdyFfUQENNbE7IpIpHzXyUTmGxJjKMkeqqQjsUpbmYnPt8dfsqvnoAzc6UnvZOsvjzHRfCZjTjZk1eH1FnL5zwFFC5XDp+5Nz3OKicDrnftjluNPk+gKaCRIhPJmZKllbrsWGKbo2wfebOaK7AYMbLzhb3ycrnDgiNp2oJVBkqSLsgNU1OGUQbIZ+8hUQW9Pnb27urq+sEuBcrmGR6Fui8RlNtZMMJpUZCIBBADhYsnxc2KqQM4dQyQ1xGHlUW5Z7WeJXdtyTMOzv0JM1Om7w85AEUZqRKZA9H9BG86A8OM+RmbbS5zhW7fNhJd16zkNbeXuxB81K2KEZXnDjsc7g7vLvHhf60wFiqY7qMYGOK4xu6q7xUucGqK4igHOzEr+qLUtDnkBcqAC5ywJAs5mt6OSDQeKf5sNNu7fU1k2cI+8Y3vrF2au0zn/mMlcc49lEenDOzbuOXj7DpcpnDFBt66H4sBhhNm9SjhVbjo/LLWD5yQmGi0jR4EWFrfmpgwQQVq2JFtoZL6dA4RnZqf2mMXXpj3FHtRwYpk6rdBmPiy9KmjMplzDe8CHjaGcoUeR3vicWq0ktMUzbEvQEqjLk8kgFYliglR/fjhjfFRrkMz8RzINGsKs6PxNpZnuQuBqryGse+4K4tlqLSTxmSnj6zdu78KcTAVIGGKkyJTNt2JgvbNJGA83qfemEeWmxfY7QeLmok4lHX6HDDAN2CN8pPx4f/mYZJeXNXAE9Jbj8d7t5NrjC8MFkJdxhf1R1lzEnm0XQSZbyMNBNQ7BG5glmmnc/PpJRrpY5ldjNh1E4tJL6HSw/LGSOrKCh49LLvxpPuP4gOkQ3ozIOasvoCjXZYqr7xxhuPfugsucToqDEJh0JUbR5RTnQ0w4UU5YIszvJ2EuAidoQDuYyJ1IIQ4Mj0fT+sCelNMhSrTAHCAS35DMZSVxVhxRrfj8Z3waLJaAo8oRARLvd0ESe5z4cmyau4nJfa5IkJcfUzS8RWU8MtC8qK2JG/zsWlo4NjmfTAAw+wBqD8aBfD29WrVzlAZrIY8xldMA2tzFtJN6sEOD/4wQ++9a1v/fzP//zBwWRrvkQ+JixpT0cDETDstn784x9zNEkWngfAJHVIrFNIVsWQkMkoeTxkTAWO1M1UmxbLwsJmOjmL4wMlkTI8nwrh05wx5FEt1GTG7ryOZnaexr0hX//615999llER6WPWHZVClyVi99JhhHW40y9OJuMyrw0Fw3K3/nOd9bPnuY+aFYY4OSoCCcOg/JidUoY9/ljgidPBL6d5Oz5WhSSR4hMoqqhkWkgwHk5ACwznXksoxyGPiKyb+Skt4VSzhAxS5lUSuVZBzOJYCB8ccJsmaah1+aZQ+sKS17MUi2nTS5XN9cyYSRCxagzXFALolYVNXKfNDRnl5nJAeDM8stYipljFwYsh4VRAIO3KdnOY+bsCIAPQg4YGeaB4Mq08ShkJ4EN5RUYRGW2CQPHGTPDMylJptqAzsCZvDL8JxAwqzmjVK4ou6PMWy5RDjiJY51KE4zCLSxUKRAQoCZl0yap74OWeHGZDGHQcEA0744od+MZJweMnB/LgIlQZyZFlAN0aBzdsXXFqyTD7h7wZnuZDWgMQNh55AoODGDv3rkBQhg8xbksV0+02sTCBnBsA3mFiFKodfDKc0U5ZwTEbBN47w5ZlZQzQZih8eqRDwlW1Wfg1uZW3HWrzVM+g0yR2YC2rBg7mACAhlEaEKZSjJUUBMe+GlHLrPfCNMz5guOFn5JwCQnvecZ5M8Vvr9Sxrrp57SbS6B7KXqk10rwiPg6Xh92pNpWZtw74EeZ5dBGoBcKD/j6PbDOAQHbku5hKJhcTUe8GsrinIFdef72InA3m7IhwvjZuHY/XVLOjHvrALWE3b9585dU3kAZ7ibMk3tdnKotpFWa5SA9+KC+dJI6Ki1pYWllZRi1l1Mb+dL/PjViszXm/2MyDT8XBpyo6bQaIP+Bp0V60r/eV8Z8cMYklKosyEuZmMGSFo4jYTqKcPe19Da7deIVY9ouoYt1PU6udPXMRGT75+JNYyp/abKFgK6uo7RZHC3DfZ+MsNlmQGwHwPQhAhEfmt0y0blzfRrwDndLy4SnpuR06a0XCtyNVdoZABAh+dn7MwBwwArwTMBAKOZwCFangU2QJlDiES2cKhlh6pPDjTL4AgUANh7jQ/0F/RA/JyQeQHEugpFmSmidIrNPiw6vDGei0M4/UAqYtVHFtRX3XaF+dEvcDUbMc0iB2tnYWZgSdDLe4S7ZdNDinaDnfowJGdmzmORM/JtVUNYecnSoTIW2mUwIznNiMQCC63MQwzPMI/7jASfVIWqKsvYJXGpL5BFiIQmnLrAkDMZB6RvJL7TbSziwtpEOWMEN7U41El8U5ARDmdvi+esNk2XMloC1pFSe1F0eRNTTo09yaZAKh5iwpggsP2spmFIhuzZYYZoYscCk8PR8Gf7qrnlR39B8JW2ICM/bWmcKSLxud5EcAYUaznpoQwipihxO0ETRGTC61AMJ2P2yAr4RRRqWOKiDWEwYmvKTSHjcfY1hKFIKaJkJlx0w64CJYTf4JQ8115PI6DAIORKSHxADyHXh4yGkD0tKgsFTj+z1f/epXuQ/64uUHTCT7JeUMJCDpVIqUZBJ9UcYh94yQgQREMJ4JzMChWkIWhhPNKimPVDQ+2SEZSIpsFZspOFXyq1wsSXCqcmT0SQBqmU0nd5zDjiv9HAsCFWqBZ2Cmq7qZFleOumfg+IS5UJNMo3iWDAoGAl03sdCBc12yERqVhBNn/KpQNqy1tNfMCmR8dHuG+DGsQs0Jy0DCn6udY+jcbxTZ2blSzTY+DlIp6n6JzuFDZw72fgIK+sdlBJrLlfMuEgoWj6ngGWeBwUd1guquUFMK6M7QSjsbsQWM8mRyGVOBgOorpco76U3AFEPfldlN1LgLicbL7U6MK61G987dM2cutJjV6wXx8SHgcKTlvUP8dO4Rqhlks6eZWX3l9N7B4PU3r/7Mz/xMd+8mTPBGCjVlvui+IRZ8j23XbGb8Wo0lwHEMma9snaLpMr+GB+/TuGAOO8sECT0ALlKpppQDJ2/rnVbvnWssI/sbG+qWqujM8f0GMJWKbDSclFwx4GOWw13aiAcTIGKVmRhTtpJzYgyrCSTAf/XL6ZQ73bbKDj5v1csYI30NOeCW9nRvKvpoSdCfLYFqaMZF1rZ99q1/jk/J031SkzR0T7xY+uJ3fnAG02Dqv9G0xSXTb5CcymWPekxaKFMybtRbW3nyiSfYL2bivtxZZWQlCX0cR9JME6++8dZzzz339y88d+XKFVYHmsRE6lKSmQ91c3wnoVH/337nf33s8qObly8wPNe5fexoN0XHWhbIHM/xG3c11td7I5YWN7/7/eGrb5ziFH+w1O5FOVQhjJZKwEm88KOw1J5AqeDOQUJA9yyKBQKn9k0o1MCJQzmjp5BWSAeq9h7Uw0tNdwJIOgZAOUqBkyvDHg+45Zw4t+jWKE68D5Zag8a1V9+or6yvdS5AYCeKMggfTPNfkbzHb5JCYNGWGYfu3tz5oz/6o3euXXEUFyyGCtDUIawygqv/6K5+shN6VXZrUIpCTxJigc+Bbbff+9pXv3ruzJmti+fBoYVQ/JSpfihJNMqQEg2O6StGIjm/n0xAZ8jsNsa7AuwNMA/1ze+hRzQjrpzjAkisBrGGdjuSbzlM8yqZJP0JS3eKTBXbFkNJoON7kEM7qEeQWVkT4SmX+woMIKLg2ptOWhvPVVvWQ1YAAkmj4vZJGQLRb0ct8J01pv/1bq/J0o5NKraTos2GQQff6tbm4AijMoxzNDWRGEjsvGULHCqAzwwnsrQyxGoj+m0ykrr4hdmCpf5S49buAf1qKlXIyi1A+FEWpJcbQqEyEYcM50bPKUh1yJGUE5LTnawam2DiiYRG41HqbGOiHBsZUhH+c31VtZCEDAEqEZ9yOOCyJF6j4DLdYFSMwqnWIhBJhAUkIxOYSV5GvS9h5wsPDuDP8EMu7nlydplVQ5zEQJSE5C6U6eQC5uQO5ByNpgWk1sIYqvhWYVUIDrI4IqRhUXcA1aWhe8CoGvTNnQBCFQIVIkIRFgUCWrNA3dbTpNL0RlOIlZU1YleaSz/z2CM/PPPt19/YHR1qWbiCEeR4d7l5MOxeb7a5OrnXWOryp2sfYSYywkJk1Nu/deMqo9Xd2zdZhUaWVYOoxDhXm6lcQmZD7JBvyrXg5H5c9CfxRio24wjcadk+O7V5pr28DD+Negd+CLRXW43+iHnRI498jGuauG22N6SpIWq9Wk9huqPtu/t39wa7wzpma9xWwNETr5KO6NSgfNDd6fZ26cbq4/Na/A8HWHmppMPu4WiAXSSy4rUuGGryXRt6wlbz3PpFLMfZqDq4ukM1LJ/WDR6Dg332BwcHI/ZKl1Y0VbinQzxR8/qKIIG1lRVqsX8g+24YY8lEMxOCXt/TXE2aQKVG2xSEm6fBWOKKOV0Nub+jc3FUIeVbzBkCgnLlHky9LbXGfKzfG2K4tTSs9Q50cRf25OwLf+6Zz6Gr23cPaBTbiKfXY/KDTA6X9DHG2rgPQfjCl6D0rpXWzPTG4jbGPdgiFbMiYlEoNRB0ls2XCK+vbnAdGbbPQHrazpAxvuqiT6c8uvDgBfSRdMCXl3WfI61T9nR3b+/v72JTAs0YbGrvvH0Nq+02V6HQWmL4QRIcUGBOAm+VIKL0R3tCOyFqRSSWDtXDB/HrupMEGIZVlr29/dvbtzDORSvQFl2m0e7s7GzfvnMdrVtZbgPhU0z4Z06doy2cvXD+0UcfvfTAeXEnAjioIUrVEVpNuI5hf3TFkmc47Ds5Z2LrmZMJBI568nLTkP1nfWItWwXSmfPqvfRH1Sqn7RKQ0QEgpkkuSd8wLqY1xt6ZPwyljiocaVIgKsBpSTWBiyyKk1yGVwD9klEJR2MrOoqN3jP6TFWy2o+Qg6LzBpk7TiSKuFxhdWNls3/q5VdeRD/XVjfEtshLaCJXOSBqSxX/FXjya/zSd1waZcIu22HNAXkdkwubm7yCstZqrzY7a5wmNfv7fMO6v7+jGctAF0SM+/rmKramkGL6pdwlGzGmjNIKS/kAIlazKc/kYDaGjzw3I7YskWdukhA9j/objTeJrGUoqhNX0dEvzV2cqJGSiVIpoN/IIr6Gojj0LTBTvjqcTm8xChUdCg1BJoRRIs2+4pUGKgid9+KrnJMESbhk3QkbMavUG6pppDBNIsiO3IvCmhdB6FajwHRN3GDRanaWD7ntn/koRSJz4lUq4fNeL2yMuzuYgDb6gwarAz5Aq4OBEfbDmFnCvDegPbPF1BJ0Wo7K5S3aWMVEc8E75JJxqu7O9l3Gmyhy4oq3QlodFcEMgygmKge1ZNEPi6F7gkQghCA8lauK4lHv6FCK2DpH6QggWxD6Ed4ZD8gde2r+aAKkIyqyVi0gCYZaUtPhYOu9ubG52m7VuGw5LLs5MKXgGpNGzLCRvLbzcAQiF3GIaT8DNG/6Ymbe4pooRrfBAfywvyDe7OJmBIKMD1qeRwXyKIUXCojouvou1Zce+UKy6oU/jizJLnQ1FI8vXvDi1uoaSPDwwgsv/dEf/af/8f/679g339veYaVMMzPFKCNY0nggSN+dEgH4hha5uxQiFX/CrgJ62Zu5Wex70beJ2phbzjaWN9d5sfV457UDwyCpyFq5x/vK2kDRicDIlxSr4WKSDxs6AoCo6IKMj2CcNjJyWxREX0VSkw+y4Iqr43k5aaxWNBCsM8+SVJwMZqr9BGVDI1WNJSYlZxpBxnQg+5lIhrho+fFeAVNGK9SJ8pbexinebqcjFQf0B8zT+E8nyQJaC7dRByHTr+JuXNve7d7hfCeOMfZW16UtcjHmNtkmikaEGC09ZfBBuoUFV+7hJEa3BX7cLcBPWnxJH3AZmRQ8ynfif2x+Yr4qkR9dCCtMhuSAYxdsQN+z7EgtyXiRuIicyQOCQAyfJJ3LxqloDNKbmuZqTJE9eqkdV9f2E6sOT0oWU/ywi4FYpmy1YyMb99prr3G/zPpK6rxm8jRXGYikSo01w5F1g9l6RnNgJu1M7PwjVrdMLl3G+dh3BzEP5hMKIRPJGUdZHHtUjuCQZF7dnZAKM0s5eabmhEr7rhpMJmj6x/svvfTSUQiwUbJUkmVh87lf+PzTTz+Nya2WUozZ8X12SNGloVGPhHv8iUe52/fll18Ghxd7c7nKHEMlUKjUkf3FX/zF/+m3/g2YDHIoRol5VDgzJm6z0OKcnNeib73zDtRYpmvMriojklQtbJpuppbBGSL6uV3m6PsPmGAmezxJZ2ofWZGbCokqstKuhEbRzAVr7I0Hz59Qbk5yjE+mjEnQ/IPf/0Msv2K2NoUOglw16BLnKWFmbAr7Xg+qpriFHDvoL/6LXyFMishg0vOUNIgiifWHcI4qwxn4PgYq+jEL5DAvXNCPUCwJ2HrmgT4ROIGcOzMVhRNAP1SmiqC351S58qNa/Sjk6HYoqROZGjUekEgS+mB4IhyZltPzKR54mDCWWgEEWZsyHNgCGit68hVjseYBn+zAYXLJ8MHGjtiLzfFgcNYzfaA5MBN2ApeRTHksy+vYeb+kNhMraRRyJtYQ/BnM8pEk8wimY5/YeQRTUIR3wKhoqixGTAFjsRG/Ub/BAI9OBVk7tZVgmLSU3eFMuWQyq8s08F0+ZU5yerLOQAfyY8ZZGMhoplDSoQviMdfpTAEztZzEAdCyvoGTmSGA09o01NISy0QISP4VvuHoKjTtSMukNNKqFqgo4EYrfc7RMSO9fb1Hz8NwhiEwcxtsJAfdPcIQBNlktXGDLWRPl8BiHptrkFgSljQ/uLA5cf9IAcmIQhFgUgdLS2NZvwKEN9oXpTh79qzCPb20PuBVVpJors6ZX5cLLigIEGCQZceJNs5VIpIe52xsK9R0xQHEhML+XPQJkG3GXdhcYKCUqi4JFseoQY6HB9qevvzI48wP9+7eUb5sSbNoj2X2PSXjuhbRcGSKc5ihD37I1QU0JmFiTRYeMn0Bx3oNC8d8N8PLgCmUqRAECdlwvH27TSlYjyn3EClvhYPpTWd9q2qojy7wWJfBGXcpxiZCcEsWpGUsgBQ3afDIUhWHnTLM8ENlDWOzw2M6H89TkrruzqZDgSsS4tghQa+oJjLyZrSg9TpCZlP1bvc2dcHs17pHDEQcpjaVX+ynQxNCjhXwaDclh6PR3l0MReYzEtgRf+lLX6I44jBV2n3Ty2VBUFglo6h///d///rrr7OZcuHsOSTDXe0YiW+sr6L82EmQ3anVLWqGD4dSLwvzy2WHMejzGBDuFejd5pKaW7e2t/WpQfoh6hqxg2bfKoSfUkR7jLB6LfAdRaYmSy0R0BFJBYmMElOEJ7lH9RERCFIgY5b4JpKj8iOB0omPKnkRSP1hJkjAuYNDAZEbQgOIkqNyCIE54cULNVZb7L8bs/QLyolVIEc5KM9ElRDCUEZBzBLBBud1XBDU0Txl1GlSL0tsR/f7tVYcStmUiq2qcJa50lZTriCWMgyawmRgAeR8S4QZxngEWX5EGHMe3+psTBADQbDIbqLrgaA27tuNgmTCMSa+YqPPcRhSBNRkdAinMAiwjW91NRH7mTF1Qbh4GZ8oUqlHSIyVKabCzstopEbmOYkoRCeTE1AXrN+pBQ4n4cR3QNMwYEycsQE98ns5agWcuYJv5qkYIMheqaJoRInbep3ujuLSiqnz4DnllhnLfXxQiHK58UVtOklK6AqLSSa6ANBOn1cLGUKBjfYAqkbYgMbxnhClpr8Cwsaok5RSpWoODojvopEMeTy6FKIWW7SQJEhZoAYpBhEJqprrQgpHLF06d9ApVdhcc2EVOA8//DBdFp/e1TVBfHphNCI5/Rh2XXQ44NKFclEJuYOMgxQcEiCKUYgAuZKE2ictQGLBgUnCyrfV5ELFsxcvffnLX97a3AzL6NmWKLqLnKvPArEPFjTx4zGFDTEBwnb50YHSRxTwVkIIA4F/btl3EYCABikCwMlWxfGwGh+B57jILEV22vFHMG5EJCEK/310yitlqG9UzNDnEUbxYTW4Vc4zOO+FGatWpqDsQg2i1FpNg4Di8QgOgjK+GCjkQBcKb3z+AHyi8MFEr1Y7Otxa6ii5KeAfw7xzzwV0kszbBxQgUzuyo3Q4AricHbF+xCec4ScPvLtU751+zrcsDmRzicosMnIOGBN/8b5bmfj+wjodQpsmIs7Jg7PF/JEgo8kub8jh3oD182B7p8HnzbE+5XRJFiNyoKq2nEBDN0+K4tcwf/mX7hV4s7H242df+vznPqa+Nu445g5a6XrYk470urYsUhMxUWA2w0UzWglwdsSiRvdNL7cOMe+ARFBwf4Jt40SaQYDsTEe+7SAiFxkU08X3D0bbt+udlVat0RciNr0FvtIc6SbSiY4bPGaMlLwp+kwVy/joXpsdrnFqky86LYGlpQVSg2cvJ+o+r6wkmeFmgjLm4ljWWOs4p9xMZgaEiTSOLMeE5tEoUzGMZ8o91U/aQDTGZNZGX6/hWVuf8PCzX/yFz372sw9cfogpwm6XwTKVo1JJPv3bR1foyJ746Ef5e+W5F373d3/3oLunRVfooeVTaVjqGYkdjQ/+9m+/85FPPP3JT37yxh0uW9SVnVPsLnzQIX+lD7Il4sYrAc7uDl/93nPcm3hK84gRnatlbu2iNJFK3lGuFHgOEzCFo1LNw0t85sYgZGo5TNVPEkbQONrZCoVA96Uf+jHnGnrZLFC3Gtb068Na/9r1wZtXzl2+vB+Ttugp0ob1hPiJQ0yMbl659ZX/9Q9v3b6l5l/0IaZBuWCtbBtIH8xKm8qcynqMSiojI6w3BijNYPjKiy899PDDvO/MxVnEoCeSRyzj1TopbsghwREm+6Jzy5g58u8PQLXvRVctNmIwjsAsxnY39F4sGNiOYRYVts+06JyrtV0WPVGiDNet8ZqFZ4CaQzwUEpOGS0GkEuFT6VIELBmlS8aUkMjFasODAxO60yG1ZVmvKK+6zD90TypWRVj9sT5oxQ31Mh0kOiavUEP3KJGmsATw45jSeTMHBFEG2qCrt7SK0j1ORv1k4BzKbN5sEbPT3cdoA7NMKDAWuBsR/2FZENSIeTcud7BmXu3lxHritPg4qXSwHUxIAgAJz/hCTbvPbEmDoMcj+a6xjy++QID+BzE9PTLruYiidHNxRwNmUlFYlSdcDuTUCH8G31HGJEyAyaO0v6qmHGVM9AMIigVCgkRtqocMfSM1AOHwEpDezqZ1gC5VpGOhQliqKGGMvJP6FEi699Bj5y88uLm3e4c21Ovu1th7vcnVlzt7d1dYWgz7+0wz+PqFSAQDBAZ9mrNeegVGubEZa+kNhcW7nOBkF+UQq6TKwBMGUBnaVYPr/HQZqPYFTO2wP9i5c2f37l3RoQyxX88e+bnzD547f5nKwUIWUdEFsSCC7dEhVxQsdXu3e32KzAY0bVobc1DjchE+P4hONtqrbAFg/sUVzEiJ2GThG0PLwZ7GhfVVHXnSTvHrXb5fyovYS6ud5bWtzVjb0J0ctlZWeQep1x90uK73BOXkrT5MyYwowfKhpzov6KV2h6yJwhyPP60+dTO3alDvfoGt8YHOxjFIAtvlOt9fZI+Avfb5zC29WThVMz68dvXtd668ThmZnFB2hBb11cIC9LGHnwA+6GqphnUVmwIYzWM1zk5D6Ke2LzVmURGMCOz/jLWJyWgJnXZzGUPcUbP95p3t7T1dLQKGtjDq2hVVEm2J6QVw3Youp2Xh7v4eExlEgb6RhLKePneWC3IxH6SQd27d3tvZvXjpPFJAPXUFSlfW2YdDcSLTxVi2c10EqqNcKsXLgSyBcipCRpXhZo5/TwFu7fvTP/1TSDz00EMc+SBDvcdwXy6Nd9qW4kwAv3fQvXn9xltvvcX7dqNBr9bhBvMRdxazg4M7tb7Gxg3nJtQLl2bQHSAWrs1Zqp2ez1ayil6Fi6fAt3C4P/rWrd07t3d3djiX0S3krMzZcGNjLW5ap6GkfSWYCefNEXo8/mI6oEanjsh+yD62hNSXSFfVYqPr8rqJjEVH5hxCcKqcPLMNe04m7b+XU0YVA9MEJ4zBDjiIIHwNfM5dYolxEA1kDxq78mvX3x4vnT+1cUabFMEsCJBVSaq+NQcWsgb+QrhnBY7SG6I0B6+/YjaI2R6MjBsr9fZ4qc1XVbnk4YCmxcso9Pi1QZc5GHdDw0Rt2IMhzm4oAjNEZ8c4wHVAEI8vkdNyQnKhgby5RnnjVTTGC8nEbl62tE2izH2MPhVq8Ut2colMzB+Cf1AQCzEEPBxBTHIOyTtbxxozy5CAXagMqs/UH7L0FLy4ppqq4vWrgoSDCH1Iq7NMV15vtFW7Gu1i8hcFcOkMj5kIUzw9aZEBofj2D1dI0yfQ8wCnLoK9kEBS0j5XP/OOCa++HvYPtPfq7UJtO1cfHgyNwioDAO8GQphKwSkH6Zy2F2WVWU01OUOikZIREAknWALTTuMwZQy1o/eg+YFDlOuCbx8Qi+RJbjjJ7SK7SS7Cis1BxUaY+z6gxs4IhbVzQvgAkzBA1VoMSeTY3cUGf7i1ubHG+BC0WIul3Wfh05ODrd6bTtu9Nx0I3ZaseWtNvTvE3jbjWB2pwqwGmrXVC5hvPf30R3gcDfex3xr0+vSWr7/y6luvvs4LTFzNxPsHjA68RU1DfPGNt9TP19UFyc4X7vQlF/YuyALLVs2FXFK+p8xQwftEneXlRmeVMeJbX/vmuc2zX/rSFxEdnFIZheJDaYHLEpuJA54hhEO5kqqj2wzdMmCiISuGP3QMX9wi1agmaR3G7/ID6NhEU/Mu1t0SKDMeWrozYGdXNRJINCPydT0CIAxZdMDzJeuD4fTsiey9fnKhQueFnQM5CrmhHC20vc+XIRZTRqmIyknulW0Vn0Y6lcVpyX3iQueNCrDgDTkIjGToIXhlLV74M2LyTdD+wX4f5Tx95hxMdk4NL5/aZKKIwRkTDA4y9PoJwkyTAKoQ0ovLOJVByN9czcBP/ugiz+CXNHMpwLFYXOM5SaZQpsqx/1gClKJqI4llasohynuUXuWyLz5pnyl8xp6Bn/BRyY8YzhdSUCfLe9b1OvMwFhgr8QE6eknXopPkOnOlllHRq6Y61yy5VkNfuVGINyRJCzM57cLcS2DGZMjRpLBwM0IvYhYHaW8czq+cu/wehQl1cRWNOJPKEAIIBJeZME7qfiedcI5fHMgFXxy9CHqSJDDDhG0+9cK01N0777zDrvC8iwKm+TexdG9MNNdPbXDnxid/7rNay8XRa1X2yDE6KcuH7l46FlOuJ5988t/9u3/3H//oK6wQWp3F5zGua9tTf+tb3+IFSegA9IA0z948xJwY7sJiv4Y+XKgMaVWKaCMlZvSnidg0fD6Hk0IyHWd30mQFXkkhhxUfU/wSQs9ETSF9gAxwoFBqamfp/Ol3nXtmhFdNOTlgitNZSferZ7V3rzeTRY7NFOYDJDxmAKMUtGKmUxzOP/HEE43VTu5qZ0iVQoAN688MzgkfS1L3kUQjvsQuDjlRq2ao6TXAahPtJAQhQhHsl/jIM40zAZ2RNvg48cCcrur8E2S67yppEs7IGU4q+MdRFk+1K7QY+QLPHILDE5UUjyGCKuuSGmFjGmiuohfJWApQQNCYBws5ps6OnudwKtkRD+bwiMh3AzbbMOkFj0mQi53gTMxiIADCJI9wFblYyFCApsmWDJWyKuEfRJjcxe374Uo6M4XKUcAtIjKkmDNo5gKtM455A42xBkgJFEH+V5w7Kj3G4BORNCKPWak1ObvwVWRSHdWfEItBH+PO1devMQ8Bk8GRGwtQTo5ZoYBzdgTMtnMkDIT1BQtL3v01Y0aY8Ymar+hMbQb5+EdSWZgzFUkWcEUU241QAI0V7Yc//GFuZmDdT9dKPMeKbN4w2WOVCkKjH9a7tY7Sxuem9WkQWOVFZOqrPo5jY1vUBs2wLmRNHdJI8nTR1HvQJCQo0dzaUu9NdnDC7A476CtXliN2hutUVqLKUudHix3mcSiKKJDfdN9YJiRc1rKTg0/93rx+fQbzmEcnpNciR04gwPR+Etv0cAI15NOM7Zj9/gA7tcuXNrnQjN334DSWf7xAjDvYR4ZnT51Cr3oHe+i2d3iRM2Pr4Y4+erm6cYothnMXLonPtXUsr4nCzhSbEBaHbJySO8Misdpr8MbomLdrNxnrLSgrap76efcZtskUCrxFT3E4IFQdRS8lMVataUYIhpuswlV1GTKDfJLHMiNMkmURv7eHYlAcypJ5PgmphThM8zB8/vGPn0W2jz/6KMaDVAcVhNbhP/n4Y5LhWJs1DHGUAuUsWSppEouMgOjAKloQMuRjg1gbHvBRTl6Zr0yeCZCd/Ug02WJQoaqunp6BWD+KbAz0BEpnBCARG2NEJEfyJmXkjObHo4qwkHJmidhMZz5QpiVMFq4j/BxAelTiO+9c4f1C+gEkjBRyuUzznpRnMlr4qAIWXUKmacaI5RZ7mKm3ea1ieTzo0DTGuj9swLdQ1cqwhNI+nPpDjmHwoJCJiDgu+ivnThT49GBy1dkDQUQQIEnDAfsmBVHhRFRGyIESkzBJHOVAfoS3bAdtsqA5oKjp14mIIZa5mvOFZ3YxqR2A+KTCkSpzhYjo7ZtY/1QFcRR8g48HcgQAJycSnlWGfIiFB+jjcm+gcJR9QF/DdcZxFTLjAZJHejDFEQ2UCZgaYVvmEjCH9smSLgBHSkOoQQ1P1cxB5auUVng47epSZh0+EQtBHEnIGh3ge63Bm9qOmAmnREkBhOxHhOQwKNwIAin6VtHk86W8X/LQZUq99FowB7RoxcSSBb0BBUe89Daqo5F66cyPKIf6gin5x+4KvmsTNMoXVKU/mTjfvzl/7lGS8DIHS7l2a/zMM8/QVdJnPnDxEp15oy0939/Te0vX37nCyML4Tl6vvf0qfRTTaJMCgvShA1/q/5MKq3S+YIGXgWi5e3e2v/71rz/44GX6zJDTvT0oQBxHwOF7pwlNm9GxnKqEQ7Z8BMeK1+KjFnGfFXIb1sIaOhpdTQe7VAwFTQ3XzdfEo/iYQKot4IQ4vTAJ8GLPyDNxOZcZOGyjaSgDgRxVUojqLlnLWPcOlHTujV1hwGoonTzDoCPxxha/w9lHwVAGkrRWtuhLd3d1X9lg51pF7Mhf1xd0SAtSDuQcFYgoQ96dD9kgM9f9Vo2a3HFWFWfhJPOp3h0DH1wqSWyOemZ+LmYWgPxL5AjP0luw46ZzBGQ3izlLffFznIpE5z9RaKQPI6oEDoBwkZKQxtcilyrHaCTjHgtoDEtu3b528dx52o9bvlLRX9SkteCLpCjKAcFXIXUUqrMyqrze6jD7eO7ltzbPP9RurdFN6CwN5KoriZIKEImUEEdeyM786Oa7pTpX04077aWehoSwCMC4hd7U6Ef6pY1he6l7sHewur/brq11a8tkydCEbyLHU3L/POk8qgwlvcmYW0EZZvhkH7aOh3pZhhuocS4LNlCUN6WoOr6cDE4oXT2sIA0Ub5zcpQcKTJXpyRDtcBR6IiEWThVxYhdcKa/kIjDsD7r7B0xMKmj165O34J9ygcvEm9dMf+O3/gdGu519vgbPRB7eZUAgygVjMKWRN07JmDgQNRgPzj/2od/8N7/1h3/4h6+8+hIDtocEa6ZrxxlzDMubqm+/+tbzP3zuUz//DKsUpFvxtPg3C8GyMhLXlSHn7TfeOovpE/adQwzxxUmF4/K6FZRl10Tng3OJ1ahDSywzT6YOAycw0Tg/hHgVNMtBQY8Bl0Zp72CpjTEuS/3dg91X31jb2iDc12d/OWSnC+JXztJOkq+AgkesPYYppjW3rt7+vd/7PQ6WGJDEDwKcvnLBrJJkZsYARE24KpFp2jfDJcRhtx2SQUoXU9VrV99++9WXX378ox9GJVQGWWLMp5uCOFNA7ARORbzfD9gLq/vSdW+IRGylbzuP9GVRswEUJkrdjhiMhoMb9dYOpSaZHlO7Ew51FH+xW4Dg6M1SO3Xp5HM5Pz4skJ3aadCcUBaZKWf5q/YZrfVWCtcralLSGeg8stkbjHWFX595PZfBR6WLz2g10cyVo2a4zLakpmgXpGLllHBc6QDDgQJH+p/gMXZElK2c4IdJ2wHdaK3m+76ZWhLvkcIJQ5DAQvUjbelBoUIrwe81bEFln45D0s0DYTQnxUZJrf8oHbzEwoTcKxEEI1kCVSDzLDTkrxb/T9QhJQmqclHYSvMrIL/ALUZExLIMAQECHlJVHSP+sMGy8isF/yXyqIVIJY1NRMJig/6ezVJpcMpQbLD64kpHAqFXREz6/E69+fiHHvnxqefYcmo2V1le1roHEJA1KpwEFXPC0pp8gSq7cZ/Go3VRzKCAs3glS9aXkcuUZ/ZABgrORC56zkWbSjL14AFA7SPuLnCqKiEWRrp2gIuJt7d1R+1wyKri8ccf3zx9Xm29qetcNV3rdHT1NI1uxMb6Ybu5hPlWe2WD018Wp3DVWY7FXiz5WFjVuLEjPlooiWluxo3VOM9VdFs3BWdM97qaqlIVs7rWl66X+cbb2qkLsNleabHGff21F9haVcELlZgqYPEglYhKAmZ8fEon+iFaA93zeGIjeUacskBfZBMVTTHw19dXZ3pkzzPpZ03WBEkrOCnjdX5zRK2m7LgVtNfjwofdR7bB3NsZaIe33vjI0x/ZOn2K60L3D7bB1L3RoTYs/jGRg/4A22P2BBgzmlpAX7tz41b34M7OHW52Rnqrp7bWNk9TDfTDV95+B59dhsyPCEpiki0mzdsHe2sbZyjyGax7VxmdVWvd3v7O7l3tRZF3ZXhI77q7w/b3CEtSL8/QvdZheqkWrjQlMvVcIwQQV2XyhDyZsKZqCTGm8DE/U2otZQnmlYAwJsmf//znCbPhriqmBVXkjyE5FeVWIOMGak63wFy//s4bb7w2Ghx8hOnphz8sW3Juiol90scee4x6p+y729xLQyWGPbgLWzUcES94llm9Oh8dxXJJ4d27e9g+88VBagTBVncMcLRAkydzhIJOTu3AUibvO1Pe6An04yKUAYmlgk9itb0oWz5YgrY107EL/UxwPjZHRXEkfHBK30ngsEg72wu57qCgtV61j4VsL168jPHKrTtXuXV2ZeW0llrq9iZZ5LwK4icKuluMuUVaVaUXsWI6FZlIo8RY1CO2vfSIteZyZ5mZSZ/7g3Q3NOvQ3gF94NKoR7OpN7QrqntjcTGyMIKoOFi7RkUHt5JDKr8GfI0vQLizWC6+hESQfGk02ZlbxiehBFShqlplwZ3hYa3vtDFtUcNI9aHxzNvHagyMMiRDC6AqrmmNQYhcQtO0ddAIajRn6kU2xRwwMlVkd5F0xI8PeSuEdzHECxa2mDCzAU0NxkCaJBzvW4AgHYs5l23DkQtqDRxHYV2n4NAdwq9vKudNRWLrw76mgjt3uQll1Oui9LBLgqGmk3qhmdcI0GNQ4RNHEv3EkBz1oDfmFFC6Cqr6gW24WNrd5SQ4dbyRoUoGIkKAFCbd9G8wxjOl7nVlvkbu2AtLN4J5S5sssvN+OFwAIR/DeezKXHu4P9bFMh/55Mf5XsLuoMcmL4hiR81cJxmQxSFzrF5544SDjdNnt9bZuKC352IrHWBo9hKUKRty0ksarTbzDliiuNIv587rR6gYRvyM1wzK8PyJT3yCay173fEf//EfX7n6BiY4Tz3xKOr8N9/+Prz193UkzKVNvM/xyMOPcfvTxmNPcqj27N+/jPHfJz/xDAZeP37hWXhz541SMIdhFIYZhBnMq1+R2vOWcGeF6lxbP33t+p2/+qvvPvLIkzW1DpVuxiEiIPYdZf5LCGHLfCat52kA4SrvhGYcD5F+hD2IICACGn01gIvXhr7GUW/q8zSyGqeF61vpWoZQDmLCdsH3PsfBDCOC4KyKcLxXyXSy0mEI33MVmXkrA9Y9LbTQ+TkR0fao2wFnH/0DjPgphE8eck8KxCNySfODC1Nc8VnXTV/43MLCy1SqHfbrtFHAgk1tVHLmnnbuDNNLfa397hDNXz3VgdvO2ubZi43xKmMolmcqhxZAOEZMzc/pfn7KDiZxbr1mRdUdruQMQPn4Dyos/he5o+CLcI+ElURSB1qCZtIR9b7I6ZgsZnLkkfZM99Rud9jmY5sJY4oZHNemqzDC4pG2rf4hXMkzpJ5//vnPffIpBp6SjooWSkDA8iY5REqcRI1r8trtEYudiv5CtPmEJYTNMlY4sgA92h3FwEwK2DbL8qMUGSJpxJnwCd61TVSr0s9kMtWtEwcaDvLGI1wmmHkso+bDJbIJzkD8qG49uph5ChlCcoT2q7/6qxzPstzikbRmzT/pIScoAkTR36EeGE382q/92u/+3v+CuQqWCwVKCrpeNHwu1bgJ+rGPPYk+cIp7EjUoGYA9iHCAvKF9hCZfdiCDUgIV8pRsxUSp0ImpyY8pTJ5PEFLZ70WzYiaR82MG5kyDlMSeITkwLx8sU87u7R3fCo5h35Ohr3zlK0xuIJIzIkkOkyma40fNNSszDRZpZp4lihHAxIGJK/lfyAB06GJUa+Pxs88++9hHfoaqjO94kHphCgHvSfbIlO82wnySL04MRMNl6nQUPeEfvc6elG1OJTNBic8daQZVgQSf7iuqyMmv0fIzj4n5mPGrHwgLlIwQsamAZRaAKA51ShJqiscZylHYTGYSCIKTR0JAWEIwYiiKuXmMC9aWeWTwM8+EQTsqo6k83tsDOeLQ3+BQKmgIail4HMmYYc3RCh3N/Dsw7See4L9CS8pdPb43pn9Sqe/JrQUCmmvWfBWlnmIUuNHAz2EweDSdUrzurNk/M1DaW/ROAJ2KgMOmg+9OiVjCMw4gCzl2bLFjPdjegQdWOqwcPDmDDviZmZwWhJyF2c6PGWc+VY56dwHzr+yK9OTLLI5VK6Pt1avXWNYy5mLT3WlpW1OHKIyMfMyLlsumCpvNSyuM5vT2uDZfXWw09mOJO9BaqseVxUAw96SAfKfGq0dSaVkYLoKiaSIEAEcS1QUOBDiEFLkvj/WelXZjizYSWEd6ol84SGXIUURAcFTGLAjwwckVWnKGzBMhFbmAIDqYqlVFCEiqehBYlunTzS++yHb/4VAbnR//+Md5M4zDdSswQsi9vVnCp1I42Bj0Y8ck+Nzt6hOFrPrYUGDBh6Beu/Ua/WG/GxudQ42k2iEWsnJH8lDG2g5SMA+APQh4gAEkzF4J03iXDiUHhzBwEez3+fwUvOFISBEc1iML9HDA7ciJAL5JZV/537+jUCRCwUSzcty8UQVTe+TxfulnfAIsYVCtn/3Zn2VTmyUA1JDq5cuXP/TIw4iIzXxXKzwgwFTMzMGiADjsqFJf3LJKO1KdhBiRJxtSiNcbCgTIHeLgs+efKQEBjjMCgRxVBoRRzRkUrtByAOTMOUByqVBKMpNwmTCHTcGPpe9kGW1CZS6EgrhEMEAkj0aht+QiDubzW1ttbOgoDPBMcD6vOcJHApTRnMycuzUz52Igvi3WuVyd5rDa5i6xwbjbRvPHQ7UOmh91wQ5V+EE6tt2RqJiI6qLSCGIbMGHLiG4RVV0IJ0wcEj8T7KrszGUqbU98Bh0xqXLpATjhJKIYuQirpvxZwmihxgFZwmfvMmjiI35K4WzRZ9cvcHQyqzdAO4i4gXMDMWw5R5DLntCkon3iiT3ikzSqVqsk4WCTX3xIcUmQ+hmfloUlsjQ+Wge5EwAFOgJFqVOU9r+S00EoGLGjBYgi4AOBB0Ycmp7fk5DKhfEWmHQpfKrhzTff5M1avkUEMjUO0wRgDN/bY9CBiA8ATFNR4oqn5NBlIBrg8OPtpacef5IJAIY76ri4XK8SLzhRaiUMMUiR6BPo2ehqnC+VGgVNfpJq9Dn0TjxG9hoQcdChUNAnCWV8/JFHuXaDDoc3OX7w/efYxCCWD/m+c+VN+jTGdHIZdoc//OEPt7evky89HmMQHSmXYa5unOZW/dvdbXJhuEcy1I2zgzEqCN9fYAqw2KA4y1zxe3i4vrpG6+BDUBBcv9fHgeHZhSKhA6XvWPIqgTlMSVWJoVoZCGSGVMrCuhikwAFIvSIoHGlJ4oQEOO5QjrEbzZY1ucCjiAQF8iQJqUkFEIduFBym5pD5OSZAqlT4aSRoeuVC/0K12nTG2WVE8YmSV9LL8A80AMNIiaaBX2bk4pufkCireL0cFqKTiKyc6GSrsVXIKsmthJRk3/dwFlcOkEXOXYxSm9GYDaeYZewMPyDPQH66j8fwQylmYqksuMV3VfrR/JeYOZwDxsGyNwLRnKo4KCaJVBAjT3x3o9YddmWION5UIJ3MxFQ118SEXBEahEVDB1sJrPgOdREHr1PxrUx4IiH5hi6qpdK0zCVNmBmsydJtJmKcRhLSxgGfhli9eWf3uVfe4uSQzoJk3O3Mr+/YihFZ3YQThij5rCn9gjoUjj8hotuJeAVgdRmTEr/GXkko5XbPH99n2rt1vX3hbKupHn/AYQ/yDeVLZhO2WKzq0jRBKCWmQknayt9hjhRVzmILCaExQA7CTrymW5WiwyOJJTZN0LmIJhQrW05fXMSoCjwxEBYZ2B5OCh5UWSNkCscHrDPor2gejxqxdE8zWImC862s4Kmvn//FL3z+C7/A/VNojQ6Dg2f9pHIrOO3Ms0qHLRDKc3fYO/2hy//9b/2Pv/M7v4OpjvqLmLBWqYSPwjBm8LHfd65defH5v2dw1TWVnJjzNt20syq5ZkmZC4uEed9s7Y0btRfe4A1PRlrZ0dPFeljyFLMgFWNT8TwdzC3XGeXI1DZzrjliUSBrl1PNoITmVMoTapPxjTlRrUKpAML5JCrUz/3D2gB5HPZeeePRD3+411JftptufjM918sMF6mdJg0fLX3lD7/yyusvMwg5C9m6qqrdGlJaovQ2Nlode5FYcoF/+eIFXhZjEYjOMKdhctM7OGCJwqjGYpcXimczXvQMz9EpvfPGy69iwSQUN+GqlZnKQnkuovd+wiiXa4ujeJjEyADqddmaIJyydEyNJvkyB0ituHqpNtddQqqsUaBD14IffT49tTYJ6HzpoYzpQKqJIgsIIrdJlkeHzFj0PzV9oGR02OTTZLoAus+0mv5Hq520mAyLgWhBtDE0jnlUtFyMxBj+ZeCBHjIweIFR5gkz4QQjUEblMDN/Vvij8TpdCn2EPp1CYTli13KjaNVKgGyRwJQGmk5S0UpD5vPyyGjxlWEwAfqP1KIeSLJ7kghS+6K8wlSL0wyA/kkSYDtPYK15ci+asq7KGiohzgMepCMqbzogF6BBXOVSQD+lFqmIAVXgH6mzZkpoMSf2rDfJCvkj+UJioEkBNW7rhxQqtSylNC6a1JwckF4SIFHgoKf6k0ilmOTlSwwioTITTgg8IIkBWjYvKj/88KM//vFzd2/cZhYUK1OtfKLe1dHBh7diPV8P/oJDvfDISwVCNs3Sd3YzEAFDaaPJz1Z6ibwwTKHkmD1V/TJX/a22W+xmopCXL1wm1dqa1sa7+13WeTxyC2WzIQsXLOLwD4Z7TOXaDLeqBV6C6LOxIcPO0Q7yatR00YRuUB1hGMW+AIVrDQ779UZc2TXW/IrmTxn6gx6vUrU6+sJS81Ab3A2ukKanGOgypcPeHmvg/h7vOWCnlm6VJ+1CV4qCsHFgw1KlLDiAKnfheBQ0nKIkVxBomxM0Ep5k+5vNg6gU9eoY0lEWX0vifD3PYNMB+O1bd7kEeGNj89z5ix/5xEdXTq3udbeHY8yWYVtHs3K+kqWhrE+f2uA09/adm2wcIEw2Rvfu7iNBSFFf5Hvrzp29gwNKWutJiw6bujkUjedlM+ldtB1EcenShVOn1tn0Z5DtdHj7OzrPGsaAe3fv3kLqXC+B6LyMpEbYrVA9uteKeVR3X5/ZBEjWBPDZlcWnb5QXLIX0JFSVIoqdJWw5z/mxNRBIXFhNrKuELRVoIrE5/PcAKHpIOmBMP7kM/eGHHrh0UbtRiI6N0Yce+BA+Akf1gJEZUTgCIFD20JAFPFBcOh4w93d7jE3YYHY5NejL8BlhBg1tk+GgYEcSaMYIhb5VUO1GEWaAnGw98EyWbuoeBQxBY3HKOpzZIggm0iPXgMg3jhHwU/Lo/nI4B4QWaQ0p/UxBzKfWdHQXhAoEG9KV2DMiFe9ScERz9txlzB3ubt/kWtbl5dVM1nnRlly0DD9JAOIzaB6XEzzeuPKbvkYDrqhIJEteOrVGq9kaH66stbCApltj97m3z6kat0JT/Zq4INuoHmZxCjflN0axGor3U6k9iHu2QKVTbSxigLgmbBPNpAyItzgzJwpEg9G4JrbCxY+w5VTLyC8eRU//BVG3xfyHzGiCYTms+UYgq4unK8LIFtXFOFi41Jt8GTSSThbQ1BIv99Bj0S9jjctMpcXmA6rJTmq6cwZJiiuNj+SaSgQsbJ8Ts7GOkD07bIldZcYvVLUJCxIqzVxRrwHt3cGC4PBgH6tztUOaQXFBDWXSIGA5awjmnRuN6ZInj+poNTyH9ikWR3IBYZlPEMABF1t1e3R05GpDIl69vfHOtTdfex3jGGyxef9jc32TVxLVf4lVjfvIVxREeawjPkknnaDEU0DAIrvY1N7r612T5XMX2c9df+ChfRYtZM8LfsglruCPBE4KbWkaDwfc/nw44h2ULczd+nuMnioYLgoleYWDMqe7NBZiECP9obqUnu4YOegPtFAajD72sY9yyeG3v/99rj7g3ZoWRvnDAbpIJ/6zP/9ZTjfbq01eHNm5udvntvv93uee+cwjDz/BpvP3v/O97u7BJz7xSbqjv/nedw/qB5vnz3YeW3v5jZclyZhA0XGxPlNxdBWDKhL96vWHzQNdvtpa7pw6vdU7GDz/3Euf/cxHEtP3+rEswFKhTuCoSZnwN6ge8NM+Uk7nUZoahBrahcTgEt+tnoVIaJ/kSuetlVSoLU9iA2WnRbBOIp53Z5mqAaRydae2zpK0klAnnNDVNOAgVlvFPkvmxQFlUCm/IQt8aCYoozw73Zxy6YjL6WB1Ihm4OqGgZrNBYDgKGY4SSS7h1A8b6hZaya2CUUYN7vFmmjscJcux8CM5RYtr1PmQxHqj1e5hecC95P6mBEbRdItVHmXaTOR9DFjiiWDINUs3B6QbgQHndE6UgCfXAoXFfdBMvsfyThTmWEInRDuWxlSkRm0A9Af4yKjMoAxPJSoewHEqtaRwRBrIT4GoYEYm09m4GdToPuhsmRzTLTItPn9uHRRza8agQIYAGfactR8rSkmhlWmAuC+fDegqdvbXNIFOExGaMo1vqRPFdJyN8dnEJ3g2WSa7lKWxunl8CrXAxL5yt9yOSRIIU/HiOTpNoC4aDOSAUfVYNXtRCGhGmyJ3rweX7hgsZ30MAlHw7K7H1BA1tb/Ep3HCHUWBVL/0S7+EDvTjGqaj0I7Jmkx5EQBl4/sJX/7yl3/7d/5nztzSMcN0MhGPQ2MM6tmAJjIgQprKt1Buw3MsRYs1nrpX0ybKrqpwg+/hkwR8+VUNZh5MmWliJpEg0dIzMAcgovC9si8zymkdIMpECJgdfqSB7lICCYhxYOb61atPPfUUqy+qeIbUPR9feeUVTtq9zL4nMplyVTSLvY88/THefj134YL5sw8nvLnKpOrHP/4xNum8JAw/DIrHkCUJ5dL87OCAL2Rij5Ckd0yan1IUrHqSpPfv5pwLApiAIguNncFVq6xArlOecuVWMfq1VIuAVOoowkbO0iOQk4tWOCCatPE/pm7ASvzqcTJmgR8IE01jcDDEBFXYmKD7MfvCCcW1NCivdwHYHxk30wsrFJk2Nc8kRBYCQU+ydStwK8tZvtsArObsyEKSCd/0HIWPY94J0AXJnJAcZ+QMnHnMCIb/I/VPXgpkRRnxEUgWV05e1qMQol/NsSRM4aof5rEKqpmw+rIAA65eUSZVck6YVrwlTiZe1qyBjFD0ZrevXafzpOqhAsORlvhUrxFSFUcmU906/CvjcASqFAYs9sHJpV6MMQc1WVIpYczNzBI5YufLmN5c0xYzvQjCoYw4HrUEVSvkhWxdlwmEMC0OIjxSWCb2ON8dGetVZQwOqSRk1ll8DDDed4HjgMvmiINF+nTwkRj4UGNbR5TjHm1drhPOEIfn/eB2HiwICYmFmgNGIuwoP+KDk2fImRpowgzfFDJ+DhBpfAJ8I5FNgeWVBoepq0uyEWs19flBRMqASGHB5JGwv1eBhDG8xXgNozwGOLZ9V+Kr8YgBarAMMV6AJVXshB7ySNpudx9SrP/ZR2bLjD5wd7+/s7Oj+sDFBnSzIcvNekvfG0RBkDA/FIEDBhLevHZXtdxqspFtOWNozolvVKuYpDZxUICe2I5bTQlKUFwVFS9E44OD79k9x8OEWMZnZxG14ogii+u+AggEziXGeCPKaWHvvogchQyfRJEFAb6MR1kQL1b/fCLyzOkz5OiEyIccwTEnVmx8A0vi4ARvNSS5vb2Pz2vViBQxujcglkccJSI7p40kChIAjnMA3y5HwQmxTmWgw6Dlx4qHpPYZuYQb2anmKRhiNiYz1ExoLgDlgB3XWYFj5vFxlB0IMsQGCcnfvn0TY9W8AZ1LffyMF7Qq6yme5uElmsO0LNJYAs4OOA7egPvqDLZVaSO1YYumOuq1qMHhvt5rGca3wjzn0akyE84YKRjMCTP4BNnoc+hQgkn5sVlZMqqEoVoGkruRCYiH8C3ZMhWQjOl2YHzjiAHRiadQJ8MrmYsfutvIKBU/E0ctQaMJmwh1RMBhZZFqOfrC6XWKus1MZbrOXCJHmk8g0MT2mXYnO2Ft66s56GCn2H1WxuwKRlsgSUTGpjoP8Zg2hVUIOTDBIYCvglB5IVv1kDHGsZ0htJFebsDwGTR6aE6YAMIDVQyThHE5UwIpHI2OMDgkVEBXecjqmULRIdNjbD30CJT3MZmnrmO3gz4cHsgC+h5ZSK60YVfrwY6umFYw6IltxkhiwTQn+Ogj9CGC45FyQYoS0Z+kPrdW+9znPgf8L//yL0m7w2Umzebjjz9G0c6c2aJNbW/fcnIGlzvX7mqX5vx51lOoMLrNXIW3XuDkF3/xF5c31r773e+yFoMyExjsoM0GHIGg953CqJzSQRAGcEaggDd3bkBzaek+NqBJK1Gc2FkOR6FbtWCsUlKJi2mE8VWpVV8R0hDYuQNH8v54L2GAPEKNDWiNZW6DUV/sVxOLogPXnIUaKZTeGR3vm0lwyGgGkyiELJbmaGau5lPNEHkfH+EnhCndVkkr5yKYEwRl3sBBIhVK+iUKh2Yu8Q3DfzAOlnItwBSP+LmwM2w6dgb4U3m8L04oTolvPYdt12N+BFKileGZMnJOHDOVaAyxDZ3UN7rUGeTFj6ZOc9ToZZTgcqJZcJOS6lyHVYaSRLTxsS/OrhUvbmqkjc0DFLHeGL159c21jbN0fDxqctZkYaKVHA4FBZPJq+s+a0AcLJGtdrrRZXDqzbW/+psf/MIzTwuH75Yy54v779wpY6vDMKirkKL7CIGKB91ISNqGJmcaP+MdOnWQXjtFwXSylCVe1FBmhgDHxHgrnBL391tDda+NetxRGNs7ZK28REZHxPotLCn0hHILqm3++JVmAyXsjjCO5PAkFBHDmqfR5ru+OrHGUUhRUFousTUFSFCqTNraQzJRkMxEHD9NVuJIx5MBZZ1dOgcLfjKwCEBBrFOsoOaKLyqcGPUvzBHonwnQUcMXv+1m55lPf/q7f/FX1Dj2U5Bh8iKE4F+Wy/U6t9/923/7b9dPn4ZdpgZRD8o7CUvBY5y44OwTytwlyfjSb9ae+tTHv/T2P//a17525uyWOFflTxwiQipowWsvvz7m89qqeCBaxFoHJqgRggBFlx+1udLtd8aN62++sU4h4rUpy0Rpx+m27oHbownl8uixkLkeNIwpvqomp8g+N6/lsKUtBS66e2LTBmXglXUKwzntTEC6VGk4AdV6ODXHsJ/Fcokklj8xObn7E632Dg+Xe4ev//DH5774syxrl7kHu+LEbz8kSU6SJg13Kf7gD/6AfgAOoWMLfUZ75SKLeO5QpIeIMV5Sl4Z8+dd/jZvLzl04T5j7wV20xBe2dsudc5cu/jcf+whTqO/+1d9wrsCbUlGgIyVAViI9Hr9z5crO3bvrp04p9zmnfqcSo7NL0ghtmamIudQBCJ2p6tGlX4xYQDFEUSPirQ/8SvwxmSvt9N23uIhFQWXFopY452xPHa8E6mJpOsPon41HvSujaAxuy5ScqLINuvZLfXParCHIUyYBIqOK9/si6xiq37qFoRBrGmwZtdUUDmRhFpanhuMThcTYrSB3+EwJqj6yRFPYfZfIjlu+d6wWtpPYlxzWX98d3m1s3q6ve6hiyIFFtUIVLDFeSdiE1VPSZfCQShqjRoqLfoZkZt5AYYbNFAFUIkZigp7HCBOCQZPIaFZxUQBhtxGaAHfXcf8evb1zbC7LeBC7UTKq1zS+sLmklhISww8nCUve6vurPAEtcqmcOSpJLD+//wE4zJkSXqCNWfpF5mDmJyefPOeIewWcMIk6JIWAcqIk83gNmQbGZAkI0q6y1tAQjh8oMeCFTgfUtUOVEKgvNXlxRjqi2knknTUDHd0XIGhGrPwwh6Kq1KKXW82HH3zgxz/8ETtPiAY7USdUhlreS1psSpoCg4xaAZyEBmPo09bb37p2gD4uspCdKeoBhPNq/JA2oxWTsDC1DyGkLIg+3kWvoiYCccaCMAIiBWZucMDgg5nY6LA9XlruLvFWsvJnuoJBVV+TtNGgO4I90ta67Kz1sKdj9dbzPlqdsZYOf580mMsy5fO7HWasvbRMm+fyxf6wz/SMhXptqcMEbaWxzsp/7bSsrXtYGlZSdSrPJPlOGJjMHrG/Xl8/tbR0jXcHKAD7oKBVlRPFVumAy2iJEIYbg/72aEilbES05eygyoZLD4t+zEOO4RFFmgHmWFcQj6PD7qc/89n1zgqYK/EWXbMhLcKpT1BF49b4Dz12obnz5GMf+xgl/cEPfkAs4ya25CDyZTQeuRTyzt1blBYJsJtJqr29A+5yYF78zvUdDKjZRFg/fYpdjLvbBxjKgU/BeuwYDPvtfUxKl6llcDRXVL4Ko0rsh9Px3rjFd78vPvbIY2SE4zrim7dvoXvcQKB1Y+UIU3B8mHRTYt+a7oo8SNWmJ6s2oxnrB0vawwKOdSEECCttf0Kt6s9FnfkkCNSUasLijfBhTS9903goNVbEq6dPkz174abs9sLXEyoG02RcKnEyN1PtcMhZNdso9b3hQ1vnLpy9dGnzHBrMdjOFgBN4y/UrtvnmAVeSqwmrPWoyTAv1HD4Gn+3tG+w97x8Mur0DtvVpKf3e4BDbyzGXDSQ7aOueCl5w40d86ih6/gQgx5ATKk9brIZYDcba1pSLMUJR0SnxE4zFKkMNQTxHvxHIcx6pEn6UrowvU6WsyujpMLLKdHKMiadHCUrOPoLl6neuGj19+ix1ffPmdeqivbJKH+vyChMHVRFONexMyCtglFm1UDr0r5KRwBmxDLu3J44hgfWFcSTumEe5pFp5QD766mZb+yj1M+eBHOxss0837ndhTisLErGDiB+Gotxwqows0iiobZVpgxryo1elTOArb1KFhbvw6bXkMepoTGHhF354MbVg6JAwQIsmpsTxgpnmPOEAkDNUPWNQO6yEZJWgUYEIEYk3jgMx1eJVYv7CxrRxSJ/CRCsuee+MuS9+dLrV3Dx3hp43GUlQAjn23BG78i2ln/iICMtQ838NwXrim0dodm2kOzdq+zsNdtz299jLrI1oHXQv2q6CNxyGk1UToOmRiHGGTC08ZKTlZYhKxSaCrskJCbDaA4+xBtbWV1Z3tnfW2h0ODZAa0iDlcqf5M0889uEnHyctqcajPrMArrpXqaKVoBuCxyM+ToMfuxzx4RIyhFuIAz+o1TjAe/ATz3Cv0bDV4XSCwtAbWEhSglZjZW19fPsOAwNaRhGU6XioU9vh6Mknnji/tcpbEnvbO9QIZZQ4ZTkNBZ1+AaRF8MnfTqPDZdKU5frd/sGoPWq1hs0Od2A89dQTa+utl19+I76uW3vg7AXu0OA4k2GFDU3MgF57461r/9v/rkG93d48rMPn2c1TkN3bu86AsvXAuc88/fGPffKzyn11ddhsf+Mv/5xvDSzXWue2LrI3Te6woUkAOzFLzXZdc2+uAz+1scXHlTjKWF7RlSYbW603r7w47P+SjmpGXSQThQ0VwG6MNNOORSWp6BynwelporQCoF/S1bXVDV7QOlxqIfiYR4kxTdUQEZmpg9DihFpXck3yx1jw47PcRB+ErDpFsEy/AY+5xlP6pijar+YSDCng4KDJy7ZCZ2dpqIEbOZAr34LCcfDOHCbvzNKvQ6fZXtYWiHNPfa1aCpwrA+/nRPPQ+AVILtiSpbBGy7u378SWNkhizxj2zVUA1TOUUScJaw1BIvU9WhhJPHpckNSCQtuZdC632vzxjQjeQW51dC0VXQR/fLENw2imQtt7fa49h59TW+c6Kxu0CvjsjLqceJt0ZzhaRRFSfyg5xLsRQCJzdMq/gU04GoeEAs3UP4f0FnEqGSl5lZfTOl/dEaASZhnmgCjlFGiEeOKCL328JOMIIuJHyxn53Jdzb3xfSTLyDGMZ7kDJicti+DyD6PBMWh5LtKPCoKU7oFVDsV9DNWe2EFMOg2qpGXM+P0PAz8Itk5dhMEErMY+iBlzNg/vn+33elGfKSydPQuPnjHLyEuJwhZtRlthp0i1v0YmYPuLLNI1PWrJ1GnInEBvemmoRFXNuDREaNoqNmEke06EZrugc71y/zh1wJbyURhmGEpwsrOCcSYmfC0ISmjIcUl/KqBDEPH5OZZqBn8iXTJY55rA1DbQZIgXC4uBCyqCaDrHMGrnW+Tt//n9Q+3RSyKHkHEzgGDj4zj5K6mwS2YX9ymJGkmJDnKphYfClL30JM9tr168yDCC2rAlObW2BNx7NamI4+rsyhzIWOIyhwG+88QYXIlqFQHC1RACtvh+mi5wSlQLiYIZnrZ5DWQyYrprFXJU4WT9zYDHdCkpaJiiNu3eRsDYGTuy4dyzjkldZNYQZyKFMDbp2OIHnAP+xJ54Ak4N9CdlDdSYRlQIPLBGZV63FMP/yiy8W8YuDosR29s4OH7rZ2NzkcTHeTw9qlqbYmqvGUnrmtEzl+rWQ7dMPEoCmEsa6qqRP2qwSBI6SyUzUQjRwnKNWYuHIkS5sRpwzpIg1stsRsblQTplryZlm34EZ4uToNxXghChTg61AO7J086RcEBM3w/Nsl1nn2HlS5iEjZ/5VKSxV40AUnOzAVO5VAnAqJxCrqirmHr+kugfGTzz6eJaOjz2GWURHrNdFqXXEPKGsRIhLG2MeQoCoGYKWv4GBmeYSJl4iB0SyJTBDB0guBWFYGg31LUECXPvDqMdtjJqnB8OkddfnyQjEoJmTEwALTnDOK8fm5ECyEzDIRkIJhADr9BkOM/5RARQyGXl6FieV0+W/0OHjUDFei08gwVjqPciLXTRi2figG2evAJwQhhBw3AkBBbYXwcSRlvUEjiGVBSo/euBbhK3WSmeFLVcCcAJBMBnfcWxkkvDOziGxZ0/pHifXOMnZbO1i6lt1ZUQpj0X6b2FGKSRbsRrOyGKhckAmDyUQpAKNQvG4sKWpvFF3vMTDy86rfFgbTYjmy4vnFclU44gEOhjFsmRl+kQZ2X1mqPK+BUjgwy3Wa7zSCuVmXH8BMnwiBzKS6JDkkt5PAoJgNzZi9ctL55XZMiLFMpptfSQWRu1tHumuceSOHTRXHxDPhVcy3Nvf7/X3mfmw6QBlqoDRn+xgg3x5ZJHsAkqecTEIcHjj0Q42WD9bRNBnSU0YRyyPWQIzG9BkARVIMXF09wgdS5goGCZTkhN27hC0qr3biVjBSMUVNK1+pzc3kQbVp1ll3DGSOSeAU3miomGSR9NyAB8mMYbAMVdBevBOw+n3ZfuMdgMfxP4FARcH3xTKR8ciNIC4zC4QwmRZACexGY2AEQq0ND0uIYTzYw6T9TydEuJwLniOyqQyQobkQEbOuThfbQhR++7Bos9BFdFqqgA4eSFq0pIKyZPEcjYPfsyU7xmY53wmSSAslmrGBIf2guvv3GXrkL0rlEdbZLi4jmNJO1TsgYqOV+LVNoEhk6FEJwg4rghEyZU+bUmjWXqoqoOCuxHFbpu228wMnDCoiEAMTwSUKuYMCuAqZXDB8QOmeYUjg46AMKB2VzkwEb4qIu5ehwEPN+CblJIXbTroTDyoZQTjmzWyQVY1NYg+25b4OpBBsaPzZzOQsB3JIYIATApgYs1FDP4dRSIhVubPBOidwHdaeCKhsqvKbn7oxN2QxWciYJkI33czEAjC0SgiR++kcqNRUFNZeFeCNyy5Vt/UwKfjc6YWB5icbBnirMFhP5ueDa5IRS64IJi20TlMMpBUwNlKNnHw6Vjw1VnFG0Xdfo9XPEFGD+vsq3c6mEe/9tprdD4MQ9/+9rdZI6MdUPiZx5/gJuiL7VXyPdjlSvruqbpepmlvyU6I986R2Fe+9iek5bJ6tbg0pvAVpAOzgU+rRCsIuPdjbxVOyBqWsCZhjOBWE7a/QTjekSMI9jOmJWARZWAZQAhORY/hMMhh7D5kn7RMKMpV50wSohwryUfW5t+tkikbQKpaeUVIgUrPSUIY4UjmlTW6qMWF6X4TFxwQ0GHJbdpFvqEP0/CFTwiQ/Bc2q9Dn4LBICXHh38sFztFt9YjklIVJBY5Kz9LOkne+RDF4E0Y4aJRH9sySZV6Sz3RK4E8xnFmFh3luy9ifIpPvMWsrsCUPKT+a5lEFnIenDWiND+qc0LvJQn8ee4Zj5rqIt5r20YZ5TKqM0N0i1bKrOjDBo8h6nl7OwemsIYmpEfu82zeuXNhaa22cpUtPNJvYyWFnQ1Y4fvhzTGKTorg0TFcBuUG9futg/Natjz5whjTjmuw+Ms/C0cEJY/Gk7XFDFXA+V6zYaGuyMVte5pmeg0GOCqhOoUE50rH5BdEWFiRcxXf7evPsZmNZg8dkARFJJRw3/mBXBZt2dAwZqCIEGj5QIQbjacXDooLeLe4Ok8GW4lLtgM5UQ4kkI8SD8izog3JdCIext8Kf5gh+E0fpR6KeCFDhauMPFnMNEU4nUcEVJldgwgREnBwJP/HUUyy2eQFnv6uJI7H0U84Fxjkj+4XPfLqOGlQOxhy7sKudYduPzotREfFwRRMrrLWN1V//7/6b//f/8/+FOSemCcjVEy/O+pw3XTZ3Sjqh/JC8H8ssXFILAknC+bn9wcsvvb42rLUgHRGWf8nJyTnXURyVaDvoMuO5sOUMdt5HCJYXZDW/VvYZY0lSQkAmoRziQHoXewCe2k63wZxQ6AhBNVjXVzn6w+7NW1uXLu3ZwiKUt9COnG4SYC356quvz8s5VQSbElKpOnN2Zomf+cxnvvilL6HVWAXR4sQxLnzCJHFJQ+k5ZNCC9sKDl//lf/t//o9/8BVW7KdW1TbdXtwwmlFfliQQNbfDwytXrjwZ3yyiw1DmziQw5yUZBO/PU6sMmveXbBF2VFfqwdxjp5ZoG5/UWytl+tp4WIK7W8idAwFel2Z+TfEpJa+ESJiqVbik/cYvJLzTH5xb0JIMEotuIdVFMOlw6m5DE5pKW9voa4tkxBGF6KrDl7ULNEK2MzqZVYJcxJUuXeM0naZRWe9EXvaStgQnhlgfhkusVGnikkq3vszu84AsY16YU1cZBRMBNSTLB1gMNTmFAmmELjZ8nWNC8kIRtr32CCgZkEpoSbbk4wYnCVgIICY6LGCGMvdjXKJX9xe9gowago4guRNbd0ei9qoBc5umxYkJ9b0pGGRz+L+qQGiY+ggJUjWSWl8Wjmqh6t8IW5iu34gKjZfI3GdIl6gjAcKBI5o0H+o2OghiyctpjQk1cES6Smu4NEGU+qfPbF584PJbV6/QLtjIAp+REZN3krCXi48DqC1cMolalSWdixN6JZ0IrkIHwJhwKI5jTuLsIWUSPMKVSxoo9/aiFEzWhuxqqMOR4rGk3JZwuIc3RCGaFFabFkOYHsX30ENXPaBoEgmrtGTya7T0sTgZTKuVJxccEltrMg/govYh+33MInosmJkadg/6u/1blJe9ToklXs0exiUS3KHcWeY9B22zjtkSl80Bix2IQw0KEl+VieYzPOAbktsgkM54wASEfe+1Vmfc3hKDOnE4fOWFV3JyAoJXXIdkKlKpjbNPykJ3MpEUfrjImaZdw77siSd4/Xm5xW3gDKLpNtjJutS94mB0QIng8NGHH33qyQ9jt/HKi6+gcJQLmoyHOzvb3b1dws2WII227IzUbY3Ha+uN02cutNoySeZlIfJHov3BbrydlXY0Bkt8N7mPydaQS2DYT+HlscZZDL6W47WkK+/cePnVNx5+8GE2INhQYL+1jdUIVsz19p1b23fv3HrowcuMzxCmKqz52ruPNzaoJnJkrhXaqw6Ni29D5aiYvjegG/GmI5UBz0ThCOAsK06FHMBnk1raxX2z3QH3lDBQSeOGA92ISRPj+4d3d/iacBvbM+4hzW0gKLmDfV9Gc0axbh/7xRV2n3GdFV4o1k3tuYcRM7EP7rLgO5C6GQbD2CljB4G9GES0u9PrHoz6Pf6oASpKOxT8aKavrTH1+KglDinTHej9g9j1Yi9SAe7spiGyzgnrWkQidQ/VJAlyo+wEnLshepT0JrLNQoZzwkRBBEfGfownwXGEjWZgiXAMZBKl7kPOpBSIxzJQATQBQwUCUxUpQUTTU4toYhTLKxhYzHOsNeosYYQ+9QJ4JiLKucg+wE3lK1FS+CjJODpUMymnyIo7i6rocwNOLJtQ+DQn/Nrm+Y2tCw2Ul6OGru6u4WoOPNnzqo5lD+h33YbRD3AdPqksGtUywchHcyBXHD63CitVCIh9ynASRjhslYnihWg0k6BckqSe1GnDst6vG6kvV3+tegDHxQtfnsiN9LZOrNPVgYsclAGqe1On0VlusY95unGW/VMuD0LctuHVIowwLTeIz1h32cLxUHc9k2mUN0rd4F5Y8Ht7HLs12OJk63m/y5s+auzag9bwwg/S08gjo1XpI//dUiTPcEzQHJ6JAs9yU+8pXDZHBQGbXuVgb19badbSEBu14PPR3BJNXympF6i71XDxo+yv1RbFfzQTzQu47KLeuPyhD53/6Kdr6+v9tg5QmcnRidLpikLoiWtHprvUNBuY9He0eDrkwXB3e2fUO0Bc1Jau246Ve7wjB4KGQoiIDmbGvIC3rGNivp/Epc999r91nnvIyWKt1R5IYPqAFuanQyYUzcagXls9vfWX3/3O1atXOUVjyH375q3X3r5K0bBdxVdx4KGpM4YlZiVsQaydQaHu3NlW8eltGo3uaED9rayv8WEe5Ehteq6O8MDkv1pxdDgcFlNpeLVm/9bNO48+ot6eLPCjBIs9ZXScSw0wo5AtS0uyjBkZsfyRi8zRtEVOl63zKsZH1DO0mzFE445yQbYECHK8pUdocfl8HJdi6EtToeq4kxyWhES5lmQtzma7co++GgmoKhipQg7MYRgeIKl71oM+VUaLHg16TBEEqDoQUTjaZREpa2aGB/u0Ldawkk34PsBWdak5qNbU78DmhKa+ogQdooCBM4mJkOEEc17uNhBOxnTUBCEikA9zDDqB2GSneXHlC01a+ZM2OOHlJzoMnZTzBaDa/h6vaUEEwh4cwYRjTtLjvWdmKfDGH7xr9hX9VWbh6EBaHSrf411UdULR6lQiVFlhVeKMwaKQW/AmcYFSgkUhUhyf208i9r2wkQtVVvQJmbbQSuS0AW0Qsg0NkKpZrA7g51yNeUKfVKqtcKbgkgtAZxTTx2NIua/ROyrRALB/fPQj50r8GRFA9Z582gr18bNrdCu0RFM2TVFze4smBxBqJU0Q3A6B04+AwDPDDPfMm8JCv6RgmiTBVGQpNqAXJslApyXTmZJmhBwwphk2kCQMAKw9Jgyo60kiIgCa/dy55AKWZB0GM9yCnigj50Dmdr46IJKBDgDJCXNeLgj+b/zGb/z7f//vM0GzShfG8IChDd9AoIyMZDMUTv44wwOPzFM/9KEP/eqv/up//s//me6MioZB8jVLBHAYsM+zfUympIXnnZ1b1PtmWBuZ2jFJ3mPUPHul5E18HjKTKUzO9qAzGDFmZCwVag4h51XGkDVGEEiSpWIJPyaMAEsL6EibFJhULi8tC1X59Kee+eVf/mVIawpY9j9VaYLPKU41PQ3ML37xi3zl+cbVd8iuQp9lylmT0VtvvZXjxMA9hZWxjwiYcooMBl0uWJtJkWU+A7/fR+hLGlVvUJLNURlIkZln+RE/O2fK4yyXFbL5F8J0YwdC2kpsoscKCCEw88NqbyW0S4uTk7lMXISqaZBLsZCA8e2X6gQEHaDFO5URCHu8cFmMb5TcOwX+Ym7BIQl8mVoOZOKiX/WNxIKMBpoBR8WUS8LHAck+Q5AYYHYb46n9XBzWHCD70QFywZkmfripenNsFZUyyo//5AMWrIXPNGVGGkl0lVWX69QyIUp1o9opxFspPLFGyHVBTQEhbZVqgWjBMX6QFQLjEZdCvvDCC73dHabwmMsBZEPARi4iqHy0D0USAqUWCZN1r65tUXeHC7KLW6URXJYqfB+/MEBeMEAm4euWSfYd4HlV23DaD4JcWTqj2TfzxILpstOiFaiWSEThgIBAEqjhD+LYrL1Mz6Hrd2nF272Ew3xvNe5ziD0RPry0jPUuQEmYJXDs40MwlzDgsc2RGmaOUcCYlAVh4lhNAeS+UcVplyZt8evxaAfzOFe/6Rz1PhCzVqzhMGc2MVJZSjO0ARKFnDc3z4KPTH70ox+xB4rOsO/EZtbe7jZigXsw40XypY1TW+CLD1kmNrBoYxuXaqL4slze6yPe1bXTZAQETKtZ7Ggrc2hCH+DFi5qZMyD+1V/91fIvLWM3gPDZgEY/KRo3BGATjYMlOnbxEMZNIoHAOHWsums6MnJEgED4Fhs+Dog6OG0rx/ZxtC8BKyciSDKqTwFprarSS2gX0NvWyIFlLWxgXodUkYOrUoO9pOoWQdJ36Zw77Cg922I9nYUwXbywtsURArsSIFCekjqFEDB8B8RSdMk6P2fvkdcfwiEWCMI8e9H8D+NO7z4TI0f9Z8VwAFIuIwEcOPg5SjyGc0BcRb5G0GM4P2YgFAC7IUbqSDOHaXwjl3Qcfr/8kn6mmVnNELSFiqhpI017cKRic1KaGY5H5E8q5F/6hmci71eAXI4nBQ84Nr3gmc0a6nrQ08ef9eFytrRU59hEaRua7S18LiYCP+2CVfMpIOTivPC1wUUriwNvokJbBCE5TgTp1rRNzHGQVo5xFa1iQY7GETSkMGlkEf0YbZyLM4ILAhCHDro2kWq80RKyx1hf73Cw+0yHRlGi6bEzr7zEoZsOhKIJF4IKrUuFkt4m3Q1LZ6yeqdZGX62DJRz0qWa8dEITas++nuBqJiq+ih2lM4RzMeA5ajYc7xmwnACZGal83h/nBY5uHKSFBYBZZRLstDwSwCeXGR+4gLEBTbUQlnC1XS+HQfGTTz7Zjd5e1a1BXBs1k2bmnPQh3zXVoFc6ceQ2ONR9Sr6pgITwKbJBX3URE1YJWQ1dPS1dNPiIDr/blTkdTkmCpviuVIgAzHz/+98n4IYT28faCYE+pINPDdbjuErLl8tBlli2HCHLiRmxlIVxk7IzRty6c5tUEJQ8mxpDs7OE/UhfjQU0XR8opIUN5RIJM74DpCI73Az8+Edowh440MwSQybk6I1g3qVBVmpCUXHQ5wuQ4NPq8GEGRwAczyg4qCDMqQSYtFCidPKjFi0NJzvhR2XrKINuOeGAOOVAy32Uy+uWWCEp08i5BKSw8XlgjHahKqTJL1lbwcTPnDwBmghokzTvOYRkcKgNRYMyWYh+HOHkHMkEII5WDPLGqTVma6QwtyThZP09M/JeCWQJm5CZB5jhkmrhMryA/aSDP10e5nPHbERNiAhcHI7qHijNQirlmxFiKbC0/K5AUGAehRpXSdRHo7kQ8wata8hhoaF7DDdKIxLzOh5MyS6bcZF5487d27vbdxm0yEfzYRKTSiNWzB3dFHONa9+aVCJtn3sECft20W9+7zlezz9b1216zaV9fE6blcBnPjEcBH/BWcX/gPlLk1k4ZJZsB61PwjJC9DVRNuX5Zsz5HixgbEMu5m6jPtq5+sbZuPOLL7mIHMxrunx/jvLDn+WGDwW+0gIDdHYiSNXqQueYuIBJU7dAYoqQOkRN2kGOOmmKFAUBEmdlEr8Yjtd2NKxGhyDKyLVyIeXJI0kUo6JG0ig085mq6Iy2kjIWwETLLFuigS14UhjKSoaTLom5Rx5/5Lf+7W995fe/omG17Y8XtQbDw+W11V/8Z1+8/NDDyF83AijPSChpR03H04m9eLUt6h1bAC4F/MKvfOnFN1978dnn6RrpK6k/bYRpiYtJV/0zn30mlgjIjT8vJ2Zrj4LiDF1B9v3B7dfeXBkgW62vUisr+FOd3Y9DPkna06mSBCP36Rg9UYdUsWo2ySuhVHKfTzELmcd0P2A4TXEmgbQ0GCVH4vBWuYiML3awAlRbmkw7LDFTK4mEduo2q+7ePvDQWMXn4isQs16+0PHPvvSljz39Ea4AYyoAnPkImC6sJ0xKWTnzzDIVgHKvLa2f3vxnX/7nv/v/+W2xWqHxm/s6MROTFd5P5p1iHJZNcY5dYC8KmlvPr21TP49V5ugKPEom82mBkEVFQaWu0oplW0HqFH/GpcasSXmgJVkZy8AwptSpPH+2etZ0AfVju1iSVZ6cOyt9fPhOJ/2SnnxXFlgm5YoAjnMYbVTqeMbbOFD3evvGDe7caasrGtILKNbicFjPRzqJIDqBeQx4UB+YSuryyiebse4Q50hdbza8vbt/5xBLS00TQ4OCARiO0pnbUIGsIglhPscS4q61FAV5NYt3YCyQiVi0XKClUHZJklLxv8WlihruYlIbNc7Yg0Ji6CM47SAEpUJGMemmogrEoSERE0ii+f93sxKQpKIOmPjGkClZFXWX8EOMoUxTfSmpqyZoMqqspPyWvOs3apNOWENOCedxpn921pwmkw/NAfJcA33x3NlXdrb5/BxMMppAQbWMnZnWq9BU905CdJWwOWZFzvd/sOyJBafyTCVJxdWTgSQjTiWBPz47bquPjH18gIGSrMmVd8bhVh9ukN5iW4fZ2hBDHvhptVmrtVjDYTkV1DD4gVsuTKAsGB6xicBegWI0nyG5KFBCVq7Y5sIkaIz72GlxESbGAOwH7B/Q09OcdHPx0kizOVaPlJRrSCVhrpbk67JM9pBXh20X9oxPsXpfixkFtoUIcnmFWc+APo0lI9mRCiceFrkQFHf5rtRrWrQnFJmhqSLY4SSf2XQqCAQhHvOMin78ypvQSSklAc1Puft7pbF1eg15ggQBcqFXdF4KxyYFATD7h9qWPX/x4umzZ196+YUXXnyeuRZvu7PGPegNtnclvVZjjLmbLaCRJOthgIjrsDa4cbOHUSQLfnZvKMJwzJLvcHWNspB8lz3lYU/bXkO+QqhVtAq+3+vyxyCIOPvDw5deee38uec+//nPN7nBud7aXN/kom3OTQYHozs37gwOBiPuXMGIjJQhZHzMOuvwVCkk9JklShrROgjASSwUsH7U1oOu52A7I66F0UAUYzNwKIUv+aBJUG7Jyg+nQxeSEIvgqKHewf7w4KC1vAxaOjaI3EPaiFHf9/C7j+X3aYLUPb1KGUCsHd64eW1za+OhDz1wps7xB8Z9lKXFtSfiiYoBEjNEs82jqXP+yqRbco5vDCIyHJUChNtM+gN2cry5Rny8Mh87WZQaS0m4p31AB00EEuOX+wf1DIpi2q2AdDsQpDYE4Na5x2PaZHSU5BbygQHEWOFrY6tIEhgmVfklQsZ8LwGzkRhAhsFABgKn5KrisFxGQdgq1PsTHEdoEtPAFJ5NXLY0UPuD/kED26HKuWiQcmFzkav4d/OrapV01ZksTL8QnoszjNkaNtFc3tds6QNIWFrSZ2Lbi+hr6gJ5vU/XdGDYqtiG2qY1HzNd5eiujGEMR8dGFTFTUEXpPgcskgmTEGrMSKEzYC2L1SF1PBjx9ieOExPh0xpUBFLpuwJ07UBQUKzuIax5Jn+e+1GkcGSDorHSiKLz4kF0ekvjg16X2qH34GaBW3dvjq/ow328jUKeXGmsQwJ9OQNz8OBWfOOiQw6fSQ78cOOzShrf16v1DmTVzPcGec2FF1uQButDlUVIkos2JVEV/hzWD0SIjSh+Qv+B8BzzJbhGJLHiU+Z0nsiARPynRNroFyUt4riLfcR3F6OrogjQ9C4yMjDr8smMHimuQkmx6uFUDSgr/9ymeNprLbP7fPajn97n44HRX3HOoAWzXhqFKakrFQhxBgFKtby2ri/B7neBU0AwWCJxtKiFNK9+MiVkxg8nqhFnyM6flkU4RoXNjQ1qeWcXYR1iYR+rmBh3OdCN/gcbZ1JSOk2AGpobN1eWKbgOxFgYx+vnLIfRE041xQOjOrzFWybRwSyt6O4HPh3MaBK9szjREan2oZc77bVV+jrddD4aMndhXYgxHwmkrBhj603LNCzysinGW5cuntEBAzmFxuHTGYEtLQxHWqoAVwGO/0We6s3QxlwFkbtS8QWCwzZ1z2EV6kRAwxBw2w6rBkjssTiMlFVuXtppcE8svbxKgTF05oRhO4exjRadUAXOXtkgincGlqq3EWQATmsBJ8ZQyucxRRWpLTPNIuwML8OTgicF1GDXHfT0ChENlKYQLxhAM9xI51hM8mA3QT6AH69DTdgVyitceouLVsgZla6J55Uq5Ms2EdMA2ppGn/UmH8xqP3TuPDMLBswmRtAxzyGK+WXnwdNLda7miBYRswmmQcinEM4HUJKKJCxUwdlf6p0GElqkQwgCxyDPJv7H8xwFnLCbdRvQTNQEaVFIY40TS0z0NGrwmljQ6xG+L1qL6AsWlKEkgjjTrCgfWZGmBieMd1QklYrPRXJPPfWUSBQNxmEolwyUCCWcsAa5Wo1rjH7t5z4htBjSCJBX1phIrjZZMZzUSDipO1CUVjt0JTEeaUt1kTMnJXemyWyeM8BFKe4BgyAUjKRAiMLMAxTxigBhd6yar1RJHGmuCOeely6WR+gQhSMQuqTpnIAB1ihHFoXw1f3P6RzJnR1qJH6iVySAYzglx2leRMEOBNJWT7JV4fETn/gEnfRXv/rVO3duaWk6HmFL8gtf+Hm+8A6C5gGRCp+ETpwLlUkdFYhiikmlLbKGAsbXv9f9X+Kuq/jYTthB854SH/bhu3Ygq5WcwEGcBRT2RDfeflsKxrv976tz8U3SxYE360dZIqO5pCfMP+gkTSuTZFInp2ZM/OxY8eo9gFOyVjvekcQF4bieVmNkQ3IBOealXX/uZzlX+hy3WzKtsfKQFnz7R+WSYqvax8CQ2zNffPFFFf8ITQVOLNJmA/rixYsYoxxFfAaeGZ6Bzz9q3nm0Ozmdo2ncX0w0W/UnKnusQlFmSFRvlWoyhBRCYtYZiSi1RDrxalJ4FOcWNfLGzh2bChLeM0lZAMh6zIIOzrLLeRFIHVtI1XD7OReVJRxqKWCMC451KUwT4sYscz9h2IUSe6FsJjWfNgmt6pHID0xNuyptJDmMIW0CAKkI+iXoNNoau8GKcohfd9yqpYqaszMDDjvW4ZxFFfVf6a/lQEVY1JZCCI0YLR2sGIYj/1JuZdgIQCxw/BxbBhxbQnIYCho1IqE1kH1k3tFhLsRoyKeKlDtflmNzNoZLkyIVHCqmOm5kroIVCat8KPe624ybWc3MZOkr91BRiGS0Mlwiz4TNAL6d6YDjR1ODFFpNmIzk+LIpvAYGT1rQxqXKrFSNT4x1P9EMamDyCA6dP5ccQoF1DZQRC9nVYndJZqdsKDLoBqaLQEL2poE3x7IpZhWJjwARkT9XyKOdsyAcDLhbUwyP+PCppUaEeXQ7hWEcCYU35wJ9koR4J8eH2hy6AJBiLGOgGQ31CSZ2bnDah64cy9wqyNZpizfD+KQB9etLPDE0hJ/llXWI3L71DiNjn49ZIeRWG5w333yTUnM1M85EMj/MWPb0EvSw0VxDtgRAIJYAyoG4IAJl7dmMx9A5f/78Ux/+KDiYXWN/zQf3oOwpLkk83CN/VTR7EINUs4ShCp1UBM8YYxVHFwwcJ111Fxg50sEBJCEu9nI96OBLDgDxkQlq0Km3w2xclo9kjYTZX4NbigZLFAFMxGKWQEIjSa+apE4XV2Bi84Q/MIMEMLVebUrTYADOlUXojFiNJu3imCZ8EoB5ArCNbu8d6C5UBAir+vp5WD7bB4cn/MqJhmuQAEAPey4+PlGAhDTnnCr2OMQTjzgLkwAQqMGnaFaPxAopHp3EVDPEj2VUhuTAPHKOyoESJ4czJ6BlYA4DgS9JOwxupGRRHKqAIxpEwZkXFHIW8wHjq9Rx0DKPcL8QMVPpZxmYoWOZG6gymPMYBfSCPhRa6uXYgMbny35owngobdFX/tDhsO9O5Q2LeB/HU7OgUP9BOehECIFIMULdISUycbEku61wwj6hGQhcMRNANrPTph8cuv5RVnOei4l2QC0Vh50v1ssNvUVxcCA9ZCZPvleuvUMXvbK5Ra91iq8xclQWd5HB6Ey+iCF4UD2SEDqcMRLA9lkSCKtndZRcMcH3y9QqlDsIOGq+CihtyEH9mANoAQGHY9+VYGhLXJIQMdJ58MEjAFEjU1IeaZu+SV/JkE+sCoFn/h1ml9SpVIojVo4M6/ScOzGvRlwkbMQWsG/NSHSUPpGig6UT68VjFFdnh0Kr8UXZvYunT9GrsyMejAVvYUwGApCVtQ0mEjXM3aMU9IFL6c7iyKDqY3mAEzNDQpwhqI0OlRWhVra+sQKg1tZBAqcHyIRbpKkLJi6EaUEkRIDkwk62EkXpNCHhsKOny6BBEJ82dxNddZLg4whzJx7D2aWLXwjm5AE0MxlCAIhdCbxnmCxKHCirdOH63T0e0RmYh1UwOQYAQlhJ4hKYEK+ezBI+YRAgk5nxeJ0efSwUaKorJVAW0T+loc3UMKKGuOG6JCRCyunEDoapBRKSwnw6bAKEI9/Usk5M9T0hhmQkYWed+bH0IE2Aw2y6AtT7dPM0gXw4KlH9Q3UUJDuXAm7/QTEMe//QhKfRBYUILcdMBAbHdHZqXOpp0yQMtT+GbxokmAg6fJdQfjWjVFLHGo1HAopF+728WaBVHibVbeFgT8MSR4+7N29faZ1/5MNq+fE6G0eJok8O8ccg5bycC74ChW+7PHqR5c7qXz//BjuJ55otusiluuygwcVPvZF5DHMVeICGPg3PYoZDdDrNOGDWlUW6DzouY+vLFqY1N72bKTsUdAPi+PBg+2Zja21pSRPT98UFk1M1RdbYljAJYbxjksKhk8ohiyQ6d2HaVprfzAAveUGHA0l41DeF9SBUTiQVQD9CFEobw22VUpIpHfkgjYBWWiRDqjjaYwberL6OiMRD06wCrIktLkg5o17U/Sd/7rM/88mnX33hJfaGOivLmNtsbZ1F8TA1cqakKnM/eTjxqMtUmCFFGTVTqh0Mhisbp/4v/9P/7T/9p//0988+xzKA3pC6/dTHnvn1X/912cxWGfrOtfkcWaXhOC+lSJu7vZ03r7T6A6YLDClMCebx3wskCy0TSU02rM8MZP98oZSMWZUmE0gBL8794ORhb6Tiu8kRCIhQgo1FRZO6qWXFqpEzbsy0NDB22U3+0KWU0/TPDKt+5DMUr776KojWDfvkTgD3i7/4iz//hS9gQMeMqMmNnPHNcfNvDkuahvhslrREVaVQ+OlnPvXSm69xcE9eLPHk83+6TwvAEut5aU50MX7tdxGWcZNPdoRKZqaiqwfkSpCyiL1qul9FknxqzhRw4aed13h2zSJtnjBWEMx2MWFZQ2zwoIWEWRKC3Cxl0GgmspmhQesFTc6DtHRHteXH8gzjaMKyYJAvh7Y5KP5j+eS6mDkPD1ynkFj2rlxt7h+0ZDDAd8EVGalBcA0EeulN7LYkpuCNFSd7F7LEJHHgklymX+Kj6sGmi8xiS0Nhv9FkXbTDaw500lEib3A4d9Q3qLn6FHSvk6tSNCvTlsBc4EmSsSozA7w0aiSI2MKFgMJVNws+L5CovXDyqYn8kOFWFNjKiRGK5KgI98ZRxHhXiPWRSLr2ozIshABCt6Ks5+Qs29k+vIr9r/E32hKTBW2NsquHwF1xqmEULTSBXrysqSwmIVgzqBqsYLiDvtI6cFKfo0q2Oimdw2XCMhYEql88YO3L0nFlBTsp1u3cZuCoWp0+ihRUtJqdCccCMRpCqALE2Xd78KELjKGg4uCfQhX2QxAThfBp22JPX4VjTSiaoR4Tlolc5Ca6ppfDWDyQL3gWoK6kZCLB13k0Amo00KiKuDAqi27WaxKQSUs4MpfZnXoBccz+nRZ1PGskresTdqN+c9TD5JkKwsiZRTDfmIcgL80ebqx3+HwSuQ961nDd2onVEhOhVqexoothA85OBLdAj0ftZn1QZ10khsktlnwWxmxJo1CSjEx1o8hiL8Y46oq9ZK24+ahEQJ1YlRBfNGGPg5t79ReTVWWgdx30eXhwIFbuTiB08kKMUBoeaDbEwp6ywy0QJnSRq/KJqoTt5UceeWzt1MYLL7904/qt1dU1wkiJzWTq/c72NswsL6NNDWZg3O1w/fUb0Ay1gYac+vhG44B3iLCJrrfYwD13XpcV8II2f+gy9QHv9QHXk65peFInKxW5s727ffsOBiK3bt35wQ9+9KUvnaUqPvTwg8sr7b2d+s7eAR923t65w1SK+TYvmcEwOxj4nDVQcEYXeFMwHGFqgNxiOkYciHJ0fJScAFgqBkJ0ZeGHxZzsl5eWrt+68bd/+7d8sJFZwcqytmmIx3aYdQN61ds/QEO4aFJ1J/1EBrwtSIWpn+UB4gGX8BV+tw41xoIL6TEeyVe/IWmjCRQCqi4IUtRbgn1Kq9kwqW7evB4B7aBxD4f21aLRcIWxLmsFxhxddqu0JN/+TFLZWIrTmC1oGIAaZpxgUT96QFhCUMsKtaNwCkQqt3eHAdrlR4gDMXsAVUVSW1XcDOZMkhR9Pz9qDC4IqdLMR7ICmOHmxEDTdpRmIJFWfGkUVdOiZTEvCQTRwTAfK3R9Ybwqu3DVH6qTB02FK2IPQ6PilESxIBsfPz86VcQkDyIO5cDC2BI4H85p2YkkC7/rqUy1hoLfQ05YmA8sxSdqeVuBmh0PtCF7OOqpdnwkGWu30DY41kchMHuEdV9VjLpRYBoaukgbxJAQzdKXVFBOusdQVPqtKAzs6Fc2m+zho1vR+KVldiHtSglpvEImX6TKLFSa3Iqt/552xG7cvsFcKZphfe3M1ura8nKH97vUo5JgfKgrlbzxpPvKqaDYkuDGZ8YG2T5zDMZUHy76Q0aaWl+fE+AGZJSbzoUtYkyTkA/xsMcvYQgqIgISkaKlLAbqUVHugpmrimNFA4QK779GWnDgGSDlinCdVzo0MeOa4JBRtLAkDwkIR8bhcthDRkKq80mg2narze7zmQ9/fId3O3xEByF4ib1R2BJzliSygStGGeIb9bWN9d0btxVJXY1rdPLs6tK3sDAhwMBx2F+l72BCDQuMGkFG4dWNU6c210mmgZS5JWOnWokmk9QyjONLMTTIMasQM9QLjYQhe7XVRoC1MLtZ6XC+ttzZPAcmo6fSLmlcHu/tjvb3Owy949HBUPZA3AoNHFHSE2oIq9U6WEwvr+xHGyd5Egg/Nb4LwIQcFHIFv3Uw6n37B9998PIDfAKx29O0JzrS+C08iNgVsIVBas+1JW3G0JhUUtKY1xFwGvhsrKwQNezHF2sRBcogibGKD5TQfw0bvJkTsw64leLwKRY4D1Vyy4n6QpyqOs/b9cKiWxNy00aL9AqH2TpjkKz2IB6rPEJQC4F4xzuXSEVQnXHhmGzMIawnTNlFp6ZvCD94euXtt98MFIyI9UaIxSwdBjNagWNP4k/19qmXTl0lZSFTl2ieFFEAicXBVcwxarRRgBjsSwN5FxTZUeJ6bX15hQ3o9c4KmrbOFyJGOp/AkdY+gX1I5BzTp5iJ5c/yiAQ/WY8y0rjoTCo5qNI+aBamepJ7ZVa2sHvhpvjUEqaxXZsZFl1KepqJyjjzAfW07IVSl6ShyWUqajfR0wmSlGw+eYJYJ3J0JfoMmA0UCMrXzjnOolbP1Ci5wAzIdJF8ku70g0/QBQCngwA4z0OV9MhfUhHHXsPf/d3f/fKnn9CjS1r1CCXNyEKDCEkMh5l5O2jaEyyF8DQy5V4sM1FSgA4TcQaJpU7agI5YST6yU8DOjzMESxxI8VhmZwjJBYx9NSA4E3QAn1Qpj+kfwem/sBGISSePOM28BFfRptGPfMqYqdtKK9iY50U3IYLHOiNQyxznwvAnP/lJLhyMSTwVofuArAY5I4hZDrmwx5JPkc6FhPQeOSGPwOkB2W7+9Cc+yTd8ecTG54knnmBQdy/uhPfMApoIk+sRT1cD2z2TnBABysdIEPbmWxbAGV06YV4zaFlQGT4PyVEEiEWkzHQIIA/C/FCtyyXS0WGQYZttl4wyI/xnnnnms5/9LCtbMMkNZEa5GZycNgcSwlwvhxE0Ff38D/6OqRU6VSpYTkuA6RRKmHOpAlEnVXMDrYJH0hQ5aeAlwZOHp2hGsiLDk5O5NybytI4RwLniCJDS2kUYZkqIiRpSZuAyH19y9tTIIgs8F7MKiBdTqCCTHMgRp9lb8GwEhUPnPQWcYEcoZ2Q4mFwudjhOxoAlsmkaQrgKlCgp7NiSPUGO6Ot09hmtA5/pZqaMEIBoThmyBQ4Eh75plGGuptWRIIRxzO/ZKqI4gkRjcT+JvDInOeAkM48gGj7vZ67mo/5pQ1xwaoFiIi5ky+hXyo1aoGbwGaRKUYCThaYaic+cGlhGkSRopiGVJDgQSmAmq6h4AOHSpUu8eEFjIWtmEVzwFGsJOJWDGUZGTC8vXjrPiHn1yjXUA9soKw90Ms2FeZkH44CAQ0Pxy4SZwjEBtFGp1PcrOTx4zrPW2SRsvTbZFgfSMUpSHFoEsbCKc/O0NPDBYQQByBpNQ/9Q1j3avozGBX7sBdRXVjrs0bP8As6Ci4TewEUyInKoV4Z9CYQTwhX5sjuPXxaH7PKjw+YHoAPBCTFCSz+hJy5UTjsTIBUZ4djKIIpNGPBh3nRmkIlCIFgCcd7Q27mr7iIsH30k6UNKFiGudJA3tk5jQ0elY4aMiNwnME69/vrrjLZmm1GLXFZrGnvJlFRwjWQgQpL19VX8vb0DjM54BxYHGgmRNDxjXKjHsBrgMdICkBpDigugEfXTT38Ce22Oitks4MwY28adO7sgQxDG2HaA/v7BrjZHQgkgQvF9CyRlkSST5KVyUFYGMUZDJCLlAeeRt7YVaTRvHLOMXVr63ve+961vfWtzdR1jhY8//VHokwv5UlDKghhJrseqvkQlhglIIQs/EjYCvsOC349j/sBrWxIOt6egkNGOSgI0YQSLWKgjGKMtDwd7VMTZc5ugIR7KqLOKMCFX02B3MNpF+HqZnVS4XBEmTirDY4daQSDg4AhYdIQRG1EqcrjMGE85DILDJdD4ShijRo7KgZyKtBmYaebAfBSQSd4V3gwM4oaUvnHnIRSWqKr6pKV0IvQAwEuyhImBsuHGN4LDEHFsxdRP9BcezIb5lM/2Ix1atBfsZIHQHtCH+mFbneJIZlVosjRhoDm//qvHUQFxUCNpwCRMC50TI0rFgYfzykWmHSSMojbBgQ70LQgjqCupKhC9MR3lGzrM5RCEl4Y95L885sOlq08+9jiT7fWLF0DN2+sofZrkTcsYav7y5pgPcaLGfMWODkH3bbAZqj6Esxh8f3yZBkAQObmMUIpHiSEHaBM8wrBxIix8yAKkVWQ4R0CEIQIcKIFAVpeilrsk81glQSYVzzzGrq/UWamyWEgeKu5akIxqNXafH3vssd2Yu/IoZM5SsNIxD0GATtakyBoMwmCyVUfAtUAq+hOkSn9M7Wv/bn39YEefNWo11JN4A5o+gCSg0Tvt7u8SBpOLULjAKi5fijVUHAybLD2zuI8cubcbfAzgqcEOdj46ytGs1X0p2/AkibsqJRlyb48bDK8qSCXA/n6ffHMSsq7V1xh8IYtA2MkH2d2/5UOYQKuhq5/p0ol9+JHLrguIkKp0ZtJ+CS/D86mgXyKYOBAC0V/IXBf+IQuE+TcBiJCKZASoCdPEV5JKEwxPLEY7db2DRilW9CaGrvekdrpqRnwgVfeV8BFCYs2Pc+T8UHkFk5yklKzmMCIFodnROwTt+HAiR1Nk1BntZRx4gyCPkYvAGZJxPrgAzODc8cIqZbQewg6PsCTF8fHG6irzt07L17NFLUxXkMVSsgplFewn4ixDZ0W+DgDMzhDX10+EoxNlAnsnwvvJImE4pVkwzOnIRcIcMyljVsEHJpEg/XOIWLKlfyh4U1hDRjhXg5RANlmpSmJ2TXTgxDzF6UtBaPjUUQxJbVRketmPtLX++HCwX9c1PU1e3eNTJ2+8ziRbPNEjiCiNluW2aOSUREHVj2mS5NZvfqIsrfby3n73r55/kyHwgVV1MaMxdxQyssMSRzGqsPCY0op8PTpu3SlGjx8mGcwNlQXj/3IDi1dGv/3opzhrYqUkqzScj9X4hSVOqGiE9FFLzQc5Hnn76oUz5+mgd6O791KEUyzxrjLJkS+M49sUwGcq5KoiG6PwGdnAJEr4kXu8BkKnqM9U6KAT0tWMQYiqMjaXI6M4CtbZuKYIA7oEsiS2WS25xRBWpTm7kEN+Oi6AauFiJUqNke+o51mLpBocq/rVYj3GI2n4DKfrkvT2lijc3dmGSBq9Q12ZKRgNPzV4lf7+nOQQLvrEKt9QY5oEV2U9+Ohj/IEiacTSK44PJ7lkbg2iGAQsVa5XZGdi79o7bSyGEHUUMEiJc+OopijXDFHTeh99HSLISgiSHiarcs/mkQWiclVtHCTOcnOUQjgd41KBooAvCOfo1WihR9WvYt0IMGoF0grNPdsdY8LA/Z3E7vn+GtPxKXRqraJJLYjOeHTp0oVf/vIvf+Mb39jd0ReE6A8YtNDqL3zhC5/7uZ+DW/KKuhhJ08mUv9BwcQCHUbeBIG6zowuC/uQRzEaDi1ae+/6PQLbtFROPXK441WFaxCk4n6O8SA7WSTPqHo8mNyFYEHeNqxkWziwVgEnQeCV7VVy0qepBvy5dUV8JEtLGZhAUWytTk4RD6VQ7Ze6RUWimKCI9batx+E7jxCaBGTHWAopQt0G3msqo2gGGX5U0aJpDtXGRkiSVTMlxUcskA9LRJ45rp3oDjnn4JjriDCMPYkiIVmnLo3LKsZq4CMZ2kiBx5yMjjkYR/yly4qCVRoHC2oJWAMZ4STssPqh7/db2zmGjR0m5Li3KToz5LwPCX+QCM5WRcPBvPAaN1CfACIpE1oGAoY/evEEoACcqSKKYojXqbe5Ha9Km1HWo9Qy7XLqgcY6uTwqplaTqqDnqDnZ7zbUtyj90vcT1pYUaTipawljgXF8LDu3BJ9MFKf7pgpCwpaTaQcy85sX47k0cSl0IEDS7EFH0sdHLZdlEdWsk1dgvwQvHjihoZkxwCNvPQAdISPPBSi3o1DfWNp58/PFXXnppNNoBwqAKKTokluFYxXL54OapldUV7ojWgoQ1KpuPBFjm8SE45hvQDG7JzqvplKlbRJk1OgEGS6V3oQBQo3Sss0yQ4ZU1JM57behULLTIQWtjkNk/FqZs9GRxtbayStdDvixVqhUZvVUbhnTXYpiANZba2FA0IUADYQdG97s2OmuNzgp9VpOl3YqMhyCmxZ6mGPq+liyD6Mu0wtGFxIcdfZKww92wa53mwc6tmIXQTyMceGNsUPdOQvFG04vRDZH3D7qHg2GLu+LDCTsWqFEdh53DWlsG3BREuz449X5VZ+LVrEygVd3aNGFuQGWpR83KEWRJjv3upbNnz2xsXN++3ev32IDXLGLYC8lIPtDXJZV17uHpX3zw0fWt888++/z29m6ruXJnZ3d7d4/tYHU56Edwwka9Nn/DvO/M2fPki0Mmvf4OuwYQJOed7v7a1vpSTVv5/WGPP5WLAnocaTS7o0PufGE9yVXP4mGptt/tra+u/ejZ586dvfTwww+/+srrly4+8MlPfPqRhx974bkXoYnugdnrc2FKdHrqVZJuSFXQcJoDHRvbVCEovvoAPhKBNxn4SVrIS+OZclQPil2xuFXdxiMQZIhjIwZTlc6l+s7u3ajHSAnxw5H2fPtd9oW1XcUeESrV1p4dbqnPRw4wxtLymJcXRDZ81EeUY9zUoEQoMa5wRCmQnWpaX3/aO7W+yqqBLpV33zDO5KtU6NVov8eOMzqPtKGD7xu0YYANoP6uNvp7owPLG/XA7m6I0XMfC2Y2B3HstSElVuzsPns/BFz1MHZoR/x5H43VE1jSsyi9DPJIFMMkIxQBabrKRDOR7nkUEP+kQwIKKbWlPwkkYDF/yDgkjERTyIYc5aOZSi49qlBiFKtoCkrYlA2c+B7vqnT5t8KXPkhFYkjFp0cRzlxbA18aFaV2OGehKk4zq6Lic07vX2Bel0ybxVnKJOQTbVShWDUguHgph7K12nwDjkUHa1lMtLiUg1CNqznYjB6or6PuuacDHaTSMbNkkaq7fUVloLFFt0Cz/awVKurATi6aTGRkVzFiZdDHD7VRjMyiq5Q4E0alKtSpuvuRDhRXWiusjBkK9E7G+vqly5c/+pEnmWMPa6NVbkmqyxYyxkUthAh70Ig3umTfLc5HumCEy6k5bK/hc2TFRcXadJapstoyWYEbN9KAGfdJQEztQm0mlIcApOT0JoR+y1g9VlvSgsfGvZCUO913NBCUVFv9pBdFjelauElMhOEETYN/JVGJ0viqqoqtQyCeYFFXI6zal5awfWbz98LTn2ancBDFHiBRJht1qgQeSSnHwOMA/Z/yw0aZCWOzfe7ygy/8+Dl1RzoV5CrsAS+v8L023jOR+XW9Qc8huYxlUS7+tA2tnz4vtezt8irN5urKxbOnDvt7fKe2ubRc78YN6ZGxJhz0h+7iggG6Hfp8Jhj4y9opZedDu8+8UQN7XW0TLx0Mxk0GhVF9Hxs7bpNmvaf3kVr0WYilzxXm+3v0rZxD8oYKCr9fHzMmqIwUHynzugiaErcE0+pYc6AOnDCAcHXv+n/46u9/6uOf+PjHP37xzDnGd5aB+IyGHHEgtB43Zixxn/UB4wG8sXOAVtCfWyUpNR0BnHMGjTSiB6h3Vrh7i+0RPjvBbMOXQVPikLLmIap0SKlC4xQWDeFYQ2OD3kMSJpUrFpVc0wNKpA0MaQw/qgPhhKO1QIdC4SSzmNaAQzMCSlrF6nnEvgOxfT5Fw+VLw6Wd3QOqm8K2N3ShFshgBgstRmhG863z58mFEkWO0VVGj4EY1N3pqmUcdeTve0l5eYaI8RWZ+jcFoT/jPOrNAiFiOiqSSlUi+BEfFSazQOBLk9wG1kbgB929+ookQDkR5gpj/eiQGevW6gbHxhJjtCCSE4SjoGz68s92lzBAqDXiMNVzAImQ6lqwfim5mg0XpZ6Kmi5LERWCLJ5dTMSIMKObgT1xzX+cpPxPy00UJspFqXP5ZqIy/JiA1q44JEWVV9vNqmDqEt/T7ik1Ffq7cWRhZc2JDYFpqo7cpaFHO6HFQAImM8sHHnhgeWUDxugVlBYVV0vQBKIKH02rigGZVMzOAVx64oITqoMO5SHK1Iwe9JUL+RhO5lnZgdALCzMUMXYb9BRZKGBnIuAA13yXz23dvs0Uv4q/j1/z4QSRy5GqTiwOKeHPZJD4CahrGcj/j70/e9Z0uc47sT19e6656swDhgOCBEACICGLMGlJbCm6W2EHW6Gho6OjI9q+sMMXfed/QTe+8B/gCF/pyhGtK4cVtmWZUpAgIRAgABHjwXQAnKHOqTo17nn273mezPzyG/ZQdc4B6KazduWX78q1Vq5cuXLON18CasbcuOSXeGk6TekYi4s9krqaIevOrIsOoRYQ/qgd8aqEhAO/CPsYwEUwz8UZqugUdUYPdC3nsgoCDCHJfWdAzP8U1pXjxTMuTWp4UFyRrT6e9gtRpCpDGh46tZ9G1cNNIYvqy6sAz9RMSwhuCbMmwj45VtDzb+HgNCok//znP0+nxdu1fJWCnXK+Fs1YhO2odKiNsA/05D28hRtCg5AQZwy5Io3zsIyTUGxffVIzsFg6fqaOvdohLEy6qjYEKq6LMOpobKkOhQn1gil/db2cfbjG67eWZA/7QGHVXBJjFOPTGS0BCeC8KuCECdTMl8J1wuP5jTRgCt8DIAIwf++998ImCAVN+mxcBUNd0zmaIXx0JFULGeNuTM9j0RDq8o0NXu0q7SQBcOIHeUSOjj6cC38jkSOcJRq2Y4K46mkcjITMVUZdM6SWKCQIFrjmTD7FI9Y2M1J0lM7+MCoWlV8KySdQmiVC1fLeAqMpn/8EoeT/W+N6RSXjFAS5t8qHo/azdQKTVqbC1HAdNZbiSGxLyFFDa4mmG38COIpYDE+4RWGZ1unKlSt8H54JlS4wkNMJVoYTrDiDAEPW2nSm0sc/kR9yRh1NpCTRBOjL1kAJA9OG1sI95mS4oUGO68kRgMU+ZANo09YpGMTLuVpqhNamvYLQpkMWWwoPK2VSZ2fUerD4SX4HLBtzGksLazMnS0c6O+PPZ7EejWvCkGscj4xygXO5BL6Kx9MG0uXlZZ0lrwKP5QvCFiVRXLIInvNfLY9kAYbEBgIVrm/A9DjNjZBM9BE9hVnqnFgC+OQSctTCoupv/ZZO+3L62Fo6oVHdYdF6b291VdtsWWjmRmgw+SAbk1susQYTBWIYu3uP8XMybn5xQJjNOfizCgFP8tWLQTjqBQFyMk6AdVVWmT//2c9zmg/mDG6vX73KNXdf+dM/o6OnM71z585gaQ1V03WCwBwdF7a5giPZAgKcOyfwYa7ceVyhgkO9NWnQUHeThIyIm7ccnn/+eS4DeeGZWyyFk/0kYeOfQRKShidw2YDrI4Si9cSeRQ8Vh68iYUqhFLl2RNVnWB3C8GyfVFA4SQSNjZ9tFhJ2doCw5HTt2jXWDqi4xGqfwg41xgno+Q6Pqh52yK+bN3iuC2qhCjLhJITAOHDwYWNk660ikFNrMejyG+1YGA4tqgUaGZAkWX8V40RHGDb8PtCTND5BKFFV2hFgFbUnb2wbkEDEQNUcU7Cn1k9FbKd88W/CpBurjzqAhE2YD5IWTFquGx9yShso+KLOjbKAG5/87mzpxD37DjEh+a5rkBA2IVVziQ3Lo4M9bB4gPlpvzEtyLh3wpcmupAjnEX+XDTGuMtfw5HBxTrccrKysMtpfmNmn56KdoR1YXFM7sKwtf88mVCKq0WiHpFkuFzsvnbNBCB8WoJUX+7N67YctV1l4eg1WzJEHB8RVRI8wMKzUiMQymEpA/O3ySOJBFsxhJUFhqdWRVRPLhhCB4EUtPKLVga9JIWHXL+UCtYEfwgyOoSMWveEy/kMPWogPo/P8ITcLg0HzZgyOdhXLlradd9gQePfdd4na3dwkxYMZtYFsMFhytQx7vkWU913IIEVMv7niK4D4xgA4+pKhxBcfOhuFbbFgwg0fR8tM97u0oFdJyA6YHAYCjQ/rgU+fQOvHgQkwuRUCSHAgJ0zbKw4nsxgbEDhAHhwes+5EAAcQnhhpArSiX/nKV/7jf/yPz998htdrnr11g4HQ2voKag9/mZMHGGre+SgKFaQ2SoQpJtD8/eMyj0hGYI5DhmQTHB6F6RKkY0BC2RmW5iVL0AjTdUhCa4ZA78yveMBRJdywnoQhR06SRn9AeJSefSPZ3LxO/jKfleHNLqAfUgQZowNOgap+rrJqzsqtFqlZUIcz73D1KbYwSqCWwV8QqXDoSJEkyEVirelh7NQQCaWYiC08p+KdDsRmYJI+HQ7Je1jhUxfIXOMsXdm1wBjjhjkG/xU8knSTijBqwUXNpE4eW+yvQJhzk/j1KuoM8coCNBjoi01/lMguAtblPocJU+neiFV7TISOUGrT064MrQx3g8vIphZMbMeEBZsSQxFDmvCwKUtBdWHXYHMbIrAQoxKFtwSc23vn9s8/9akvqDlAXJ9vIQNUMHHBjda0JI9PKhrKCjP86fNodebev7fx1f19uoH1o3tQcyOgRpvGp1WoUrB/ZKvyGTo2H4HrSxZ0JO49mP2hQpo8Vm/ZZkYPGKRo/cmXrBNmYZfdS6h4jRT/8cMHq0tuJRFNG09csCeiNHMKScShziggqIbPNS7Nj0h15FbxHJmDJaMvSDguRJ5JAI0NfNNxzhXOa/hBCqCwo637s/xlUsr3AD6EFeu9UJoNhc0ZZIfjCY49kFYHLEGp2o4+m2URBf0NX4NpkjWX5gQTvkbgXOPoDkPbucgvj1KFMcGkbtCvxJuaHVKuZ0hjS+NZztnnIqBtbJn95pNjZmVc3slOsCZztqsPJROqEacw0snQaSUyiQ4LCunUVb2OIKWJj8MG8VM7DJAHbk56FiIgwlHboq3kgtJxpK49fLy6uv5IIxw3PLXcR5CyyeT2hx73i1/8ItNsELBDxhOsZ/hmK6dusmafFuk0DY2m0D1R9BAyePr4Jz/x/v17cDOfasmq17J/ZuRIsrS04sezUkndN5MuGYJuH6T82ioqaaO0LNBiNBrESrg1ALrx0E7M06Z1BZl9bObUoGg+TS2yDnsbVrNZNa8AmCURtZM0HxDr/kFGHl6ySYnDDVF1BKPiu5RVXYEkX2fXWVo5FJn25xIXX9y5M7O5w9DDm9ni0blS90kQ1oYHkrAAbjjRACdKuAdNJpc4ZUHlRVolVx1bguJz4BPE2ycLP7+/sctr6SB6F71QTPRXFkNFVtLlx7ZNA5lKQaJYKeP5q1evY0UpIyYDLHDTAEBILO1w/NWc/YcJIjtbnEqCg5hwCIScHfK2+CHf4o48SxruKmn57jH5TpUu0tKZIa7SPYBsaU5fKuccjZ0yTnyolE7nhmY20qV0GH+bgqh/qpbQgRTMIRcXHKpqo31rmLqgHjSlDxrA4Ccs321bGmvsEYiALsc+3BMCT6k1VvAvqZzwpYljtr7YJPvFz7WWl/dadLU+nwNaX2d5i6mFCveYFdIZbmlkfs702KnSEjOJpOmhXRuK2gvTi9TCTYxAJv20crJSqUrjEa5OnDli3dNvGFQCasT8oqZYGDgSYsScQuUFZwTm47FMtPi4lmI9PWfUoe+fexlCDBB6Li9m6gSfrlpcWBvoVM38jr/AwQI0H23ihmGyTmGxTHHw6CF0nBsVtbugE13GywHohe2FLZ8CltbR297ONhtiKiP+lAW1Ifw5OFJY1DNQqMq8NHvo2ips8e8d87oMyhLpNspBcu3beInVIlFzBU8/LVmdw5IAPoo4OJ5dIlu87g2N+98srtHiIf/C/OonPv6pq9du/fwXb9+5ew+i7b29Da5tZr1pdmaHu+ZnZ9bW9VkqxpvEYgZ8wgoroCSw3fn5xUtui5BtB/3x7RAeef9N9jrQyg6LC5iQF19oapn46mQZjdQMJ8hZXTriLtAbl69///vf5/vebAlzRoTPyd66dv13v/DFm8/c4u2Wx482Hz3cWFnhDPwuhxplt5qNyqqlBC/LIJhAaY7o0Xjp/oh1EZ1ns8yYIXVNzahKofabTO0hYUHApPIYzHMFDbc/X791c+9Aiy+gy4aP9UI6AXDgzDF5rWFT8N6uQInA3V2AwJhVDhl413xG75/oTSAadyAzi6UWw7l3/YAM8bxscYxZb27excLJ8o1nrl/j2+NwoBAYkMvayqxKB+E4WMnNmCi+LqWpXhzoDmjVZt3ay66AMk4LDzb5odVCgcoYDH0NoBTKpQSoSSY4DyvG1jJr6RO+Ln1fvhHJgTGYAwnnCiAwPBIbn6g+wKOUadeiJN8YJM+j/hDfVYbz/DbxgtTHNroAx0RKrMSw7Nl4AAgybQt1PKVM2akQ7ehiiVX5kuTIYD3xxSchSMynqIWIlt8R1As8hM8FEJ8Gxe2tTsNCzADEfhEVW+WrxFRyoJyNimUubG2yuLy/vaUzp55ocSoWKk5D854V1oRhqY7TvC3wCgeXPVsqmXwpbkLkKCMZKxPrtZbcm2Al/NloD1j8wkoh5LAsR7OXVgfsDTJQwbFEiIZBWF7kavbBzIBxFzG2McRG/wdaDeUQMmVBOyupDnUnJDc+q6/lDVoNcRjvqKkHoAvS7QCzpJawxHPbAkgQLToDgZngwquOMI2ua4qieP1NFVDMOeYtVOXLDgiLswlbNWqvdE37IVXHhwBYonQWiAUXH2R8FIwqGNuBz9uEKG1jQdcnP/e53zm6foNBHjhc1Yw79KA+w8G2DoB60sNSx2BHB8YPnwFZWl+9+ezzt++8P3+i26uwfSmENGdn3r793u7+4cBngd0h6xUIhNFyPYvOem1ogzk365w0w2srK6xI89knVjqPD/bW5vW1iYM2vYj4rMPq5RB9GXFxwCUSc/hrg4WUJqnzeQXUxYXGqI9SxKgoRWWHNodhub+pk1yURhiVwEump4Mj0pJWKrQ0gpoyltBcY4Y1Fq+9qKcqVye99d5t/sgN2+3MBzmHdHn90vY+r/8tbO0iiTpRfQ5i5mR1eZUND4Qk74vLq2wBPuKTCPoKseo4Vs+3H5h3yFi0tU0tIhPI4sxTs4iSZPzMsWCM1ilH9e4aYsm89ZYzI4qThaUBZ8NBRV6dv+ZWKezK1GpvUH1sCMWi//pOwzYEuwxQrB1EyK466csmPV9gaL+yvkaZXrpy+eVPvwY+HHB0eipNDstbJMJEjbmD3Z2Nhw+on8ipeVkzSGlGlaJR2fQqdeUEyzQAaT1kw4nC91+eMkpReZ3pZP92BJy0y1Tth1a9WH2+tLLK8Xcu1cECkFd/umRIZiHPNahPoQmvWLB/TU6loCZFxYsYvYt+fk1yPVmyvTKHlB+eVs1/vIw8J6mpoTiqN2aEcXh8RLmLgMagt9SKrl9IyuMpU9yCU+uG8Gu4EPpHwk2YV49AGBwc1ZK+kY9uX7v2Euc4xnDyCFoCQ/E6PMVWqQGDA4QFAobmv/PqldCq4eClW0f1TIh1DuCvWMjRzGBuuH3HEFVJMcNTN5aGRpI7nbRkigfCWJFmiFMh7OABCTfz74QT7oVcRCVRSnCMAAgj+N4JoasZKdz4ZFwyVBZiWMPtt0+CXJ7hnHHFkzqsCMjADI3A8Y0hnaAQNeW/bock0UBk7sWJNpyDHnxWmGNB5Cv6fyLCs5jWuKECK+RJf4tIrSJ39DCn5BqgmYwsqroWS0CF2z+PhpVQxy2RKIeZao8ImtLtHBAVRFj7C8sgBI0JHnMz46pkhobbkT9dkIrABvvXv/712CQSSX7X9ySN/3f/7t9tzCNSe5wMhHwEboZAiIJ1jC1oPXLC8NcIxq7FRk21PjrW3pgCRxI9/WEqFcxpPBg2UCoKu91Lv9C3IdDiinynJ9FiyAIOEur74/fvcVZuvZT7dB4gN9qpAVghpIQYNR6QnZRsL0ziW9ulbQPy7nvvUtCaqE1z4cAYI9yCopT8EuLK6hKDJ67UwxoFHApgmzQxy4UYKqNexWr5SF1qRmN9gipKLg/oHNRpk2PbUBEJEFoIkZmwpPLrfollcI+bX9ZRAuKCH7SEW4pK7sIuHBo6rFr4b0Mg+UVjGhtUWyJQS0Q6QEVB63XlsHs9T0AabSlWou0gb1GT+kwUPo56BwVnYViAvn/vHVa1WFdiMs/1ZFhFyjQBrfFx6odPLPv2cKiYPSNw0oILsQAnk+shRniaslZynuFI5uoIU02wW3xOM0V7SEsAn1lizusxVQoF8PQqcOv58JhVBd6epWaRffAZUslf0IeS4IaGjx7pqgrGNPi0EPJmFgHr28zAfXOohzwnOztbuZykSnrWr3WiFjuBoBLuH6fSkyhUCEBskE10DmHDgZzROD5tsT1P4dyKkt9XX30Vtj/96U/JCEls7uSLCENBUDvw3V1paaDD6NIS8kQYwhjM4pIGYFgJjdXmxi5Jc+oWn9gUXIRvTUfEoBln54OuHLP82l/8OZdB/5N/8k/AgZDT0KxHcwk1O3BcMQEmJDu7u3T9LDCRIvsl+GEIPkKCgAysCxDAAcQBwfGoPQmcxxqBIhJOsHq6imVbLtZjKkMuOCsmynA4KYfvwIQEMC0gxsPOJY7FOOCzutZFiUYzgrCkIGdJMsZetOZrIYr/hCOnwA5298kpqyRoJis1QrQwJEFhmk5+DENZrS7l5cxJWBCo+vK7KguuOXj8YIM0QmHBLKM9sqRm5FT5QgWEtIMdGcItvvGHhopaAo9+CAeBAKn0JAlP+g2/BSZxxiDGLOkS1WTo0QTstoqxEZeXyggVN0xY4bxKr8lXk7lHmOQPySSwkZwWmKQiOaSahJ/G4eLwiIdllEAVOI8MUmClhTTq1OXLeuPQl1o8fnCXKsneBLaFVBLMIxP6F+bCuztb1Iv5JW1mo11YhVukAnlMPDOQR2ucEQj5hYRGg9pNXSAACRBqAS1POOCDxgYLtQ9rIlZrOZRRvTBEsvnqBnaicLmD5li3cWjJzk7yy8q9rocPBX441xg90to3oEJ2jYpGDvEKVDd8oBciYVqUQ1S4ae3ODggCU6Ysh4M/76MM1ATgQYBdeIIWF7OEDBzUwlsafKthiF/IpvwgCIWQFBMNQ5SH3mh1v/Od7wBEPBCKkLOzDKrR+aUVNfIIqFROpLK0KtQKwhooasygFg95GMVuH/nlbE6Za2LlvCQ7zimpJCNQ4SjWFC5h+M97C9wzFZ4kiRKtLjjhQAmp157TbeAMeAPERz/hjx9dEQBe+ahkU5/TulICLCiz5ckbsf/oP/uHADFaXBhiZnQu65cvhwmKWlpZ462gBex7hUPTavmRP8jgZIyd2Q2NJfCUYFof5EGfHPRI1lAd4w3JaVHho/40+17uK49ml6VhvrfrzydYcpWg9K4T6PqAkOa1dHmqecxQpFJSiTxK3Y6PKPC6zPbujlK0i2JZgCa+HAM6ZepLhxttFF7dT1gp3dKoDkuqw/owg9JVbQDJOzWsKR84G8Z5hwAlgxnkfo45KQpZCBDkszEnaT8IJEoLB8LNNWEkz/8kHFlreSE8lqemf+CTsWPI/aORpSJterYI9txIjFZKdYBmVDxJk/+C9i7Wni6tWj6VIaZcEEOh7RlXErhkdUpJ1JwkLDgQyow0y4AsTNLUKKztGu3dg8fJhVnu8XvnnTeuX//czDFbqJzUk+ShOduPtDmGG0xBZmeW1268dfsBkxCOTqwd6q463nujOeAkjhRStKSOnHxYbAbxJOkhJvty7CFakTSPxM5zSRI/nLmon9OpOVYLO78gmbnWjSrIHYm00uy4qtUT+6g7o2ueUIcO9DVHVvljm7NhJqpo341X8qhsoRntzwFAs7oOSOsUR57s8SEB1V416FzVJb1yWxZDjXr2eVhGHnyfVr17w6AcTSXZUnJWSZ6kNcvsWLfihhT7ZltaGaFxP0AGHQkfLU9RkXGjCJGNaf3UE6CEQlCsbpTYmNO9lk1HuwAUgk34j1MlR0mlj4uFJJZzi46Sz81/W+/fW/YOum/TpmIxFGCrtadWuJTaBHwcb+KZjPe5UInwJwux5U7gTwf4fBAEY3oPcm+BBeJ62hmmjlNQPqnjY0lIvJSIO/XktOBsbc7tbM9d0iDAkQKrsuQUqps/eJJQkcGD2oxrktHwKevTgKolBH6a39vtFM1TyWdmn33h+RvP3Hp05/0wQTNsVJMXHIPcf/pP/yn76rzIBnmGLAXNMtgbTxzCpEuAOKyZ2qdTyXk2kJwKrZLG5v1k63doaN0+J8iSBq0K5W1LCIb8lEX0VtI13wYXvtFHPQ0lqfS6Ml5/XOC2xLycXX5N8YOaUU/aaotNQyVWHbtJrZJucEF0azm7xqXGO3tb791hMEjLRCtOMz8qzEWflBcaFE88oWmSNnrGwwkLky97c8Wev7y8z+77yfF726ze8bqC2nPFsQSTrDpH6gj08SyNEBmCMsxdW9MFZjq+UZ3Ls6U2DOgMgUZHR8+/8Ozbb72n1/xJ3/GUPl9Adzvs9pKugX9mqALFoZZmD9Y281eU6POfYqEzm2alJkVmqGLgDRvWxDkEIkPygE+YbiVUsOe46N/ynIkp5tUczkScGvmUpTyV1wcHnp9bp0F+KZfo0KUmaCAMl4jFJIyoUmrOYYhCp0Jh/kBsngvU2IRTISnTGihtu09dCqwYtY0nayvrzz/7wu0330ASTeUYg+hUH+eMDrmemJ4RbhtHXMPIRYx6V5lHjzdYfpHJtbQ6SZvIwwCaEWqfn2Hk9JA42ygJjOWUZQWWAdASTq3QkQ5oY+X7ez6Xt6vbCXKql6EK7oDRgF6tlYzM/tDwkc9T8wKrsqA86TpQ7u3Y5+JJoD7rOresQgGZ/LIWQgBy+TrnS4Sk2mf4pbNeyhw+sTR13E64uaHF2erIPX9pN2KxzpiAKFoDWg/USkXQwMxOsVOqhqhwSRE/YXpNgmRkagNC80DbgcgcZqen4NjbAN68v1aX1UgIU1lZXLp57WMv3Pr4Ww8f/PLtt1iWeLSxsc/FmHrpWGv9vJ7Hus/u7qPdw10+pAJ8+2CTUlgaXGUqTt65qIMPA87M8HGFRfC5WBXDwW6kImsJgaXVBcaTGqt4XQ+r5MDW4sLiEkS8DLSwOPitz37m4b33/+Iv/oKv+H75y1/e3t5l8vx3/s7f+epXvwq/+/cfZhH20ury7Nr6/oEOaLM+qyLgwgBdWT7AzxR0j7c43GrJKHzGmZV2lKYDiq6JyK/WVSpNGUX/erkEzozN0Rj8OaYufL9Gvbu1vbO3vbZ2iVuU+eQfLXmSgITVE/3JMj0D0uDfRUYjS69EmJtqZULqYziwRbqqpGiDf9X1IwH6srXlFa7RZpyAdoSSehEfPZK7GJQZ2HbIkA6ic5sSi2pkTqts+4x26eSxcA6B4mkJlZG7FsKOGNRTQxk+q+nQAUf+Q0gULVLRzEhjWw1PGnOP3IRvgdI0gRDkqKg9BlgeLT/Fx2OBJFT9hhxA/zgyqvToxrHRCOilvkjFdlJ7TQiRAux8Nc48EkVLzH+1pqVmoUh4a/ONur/Lpbi6P3tkdazjMyUYseE5Je5pQfCcZDgVeGYK0rzsUD8Sr4z3fOaIR3Sy75O5aqxw3hRhVRhN8TL/2q3n93e3qYB7Gw+p71nq1WlQLgJYXmWiShMpzhzm1LxVDPSoNlEBRhoMESkUHKu+XtnX3Q5MbTFATXRVl/VDQ8GsGAtlhLa6skY7Euud29/lNQje6hBnZFIK6p84z8t6Kou/+LwAph6LA7r4LkJ1Yaw/U4paefYCtCavebQwdSmWWgIUreIkZA2je1o1U8gjVrZCjpwH8gsqYZzktBlBjwBBA1PnzbUeoUkP+lfmqZ4DNUHElhJxDeK9PJRF24kO+VoxjfJDvha3uHjzM58dPPfcPmrBMjPm9PBhpLrWWiDFqwThrT4rIc24ZmavvfDs0eL83B4NI6sVXEyCZsiPXqUmsMsERZtXw8s3NNO3kAf7Bzt7B2lvySkdESfml/ic1dHghC/0cKKbFy+kNxp7LbOyyKF5pS7ep91UrmkiF/XqN/N57GOwOzfPxwIQUVJ6pRsCqckFazAkcnqTh52/g/25PfKvoStqxmno4L5bOMo7fy4XDaFp0NRXzvEGGPlzY2+K2b29nR//+MdXLl3mzBD7K9BiMiBibXwOYs7HkunmmM2xDq9Y1j5VInMsuMP1nbfefvalF2XhFCXOkpRSoPitLIElEiKo1ClrXok5PGBwwndaRMTQnd5kZaCdFdJdml/0F3L5MMM82+xblAp3i1kPsie1RVgYyuRFG5JwZpBYrxyw1zvHW2zCOZlnA5Ve9frNG6zOIrZuqkbhHoVy0h4le3Ck3oAcjbSoUufMzvYGwyv6QTomshJksWBg4OpgrAt55LTH0xgA7bvYNPMa6fl6xBIGHx0xXtAHFWRV3LemS1o4To+FXFtdX1vg/nopuIx1VdClHilvNoUECNN7yWLtqJKkP1prTPAr8RCBBiSyoAXcmKJ+JVKcn0gKL1oaKchKaquvD90vGeyenix4Lq3Gc2MODUKGElErYcVagMAbsqJGBRtDCCasCpNGmUo4SqtEkk81rc3MOhoHwZFzwmzespd1+dlPjCEp/gLOaMOESJSRJTx5hfD3XrtJ9klF1VvNjhTSS+XHCFzgYNJ49cnSGMECnoyAZZFMl9zqgQM5jgA8GWrnseff87l4WBzcy5Jcz42wBMBpPqIsy7eSEJvHCJAffGGe7kLSxY/kuoOPB0klqRMhxboNBRJVJFX18TQu3g9XbzPa5I1z/Miem37GBJjI+/kSwIo7mK4bMTk9n+ZvMAbZiWtW0hfrpODKsk2uz3ujBZ9NXXZoZy/r9aievLEV3FFEk3SP08LghDgIjbYhPEUg2eTdrq++96eUO8NomDBMIExn8w/+wT/g8AKSex5+IfaRER96CAhE5jwGciFGpm05DYmESn05RUUX5+zholQdp3rKf/zajDSBQSAT+BK+SyAQWNQsdnE1GOb7O3u8ArKA7DBnDEGzUBEu+NuSCMMk3Xxi4VkUXTk2kgB45KMCymDFkzAaTjPepiXV+RF+aagHyznuoTWINKZaCHZrhl+pazL1F25whgM+R1Y5CehjiEcaXTnLFXHEvC22mDepgpbHDGEDSWapHDxWqlJNElURxH+MWzj8//2LayDliJ4bScLRLept2m6qbgFIGiFAXEMeBmqJk1DPOQhJPYT4ukZ2aYmDTnxHndEF/ABqxU0nZVSbGOtr1OHFHTgA4bHJ0LLwEQUQJi7CkzRb3RzJGWxvIwPfpmLhg6VztmT4OCLLH3wFHkzuiCZTbPZIqlmtVlN18BFf9Y7ZitSotYGs3pFEG6kmOa1QeuyKDzJAscI5wCO0mp1q2lAUgmKZfTJmC+KkDxWyhZZYJAnzZA1qgEydCvO8kzvJxRClbscTv/rvLJyCLnBEFXJ1aIhESR1HcXOv8QuffQEEXg1Ewyz/MHHlpWBiERU4h9UgJYNgnuzv0ZgxzdOZ3MUryrVM43h1VveBEoDWX/ZCe8oXFkVPpxm9J+3wJKtS2IIWmFhQllscMPOHkNg/+qM/+tf/+l//u3/37zjMceuaxtJf+MIXeFvx4ft3uZTjhReeI0WOYOJDjkM8peu7vzMeUGOL88SeRMkCNuDU1ZBSeEAkMaNuTzxdMhQQKKpiqIvjI1gUE2+YQ4Jjqg9zjE12aAIOgIjALlSgKS0O1fskdR6VX19OSABnYTjGoUOdMK8Myi8QcPIAJo5JtVIHs8KJbYQlYD5CNg5AlrNSFsrkmMF0ScI5dgisGUnwkwqs4gSsvTSQGlskIbZwLbLXp2jWLAIKbYn2T6Mdizr7sedw8TD5TXJSaXVF4bWVAwyE1OnG8eN0Gw9bMdpM4WO+KhHOpKPwymP6L7RhFZ7TkT4ANPxh0AIfgNmQ1ApRvgmQ3/gUPrmm8eORQsbDgHGrNAFXrhzvXqfdONreQkub9+9RU0BFP+JiJ7Wz2jycWo4aSjU/mIAJBbRUbSoxe/RI5jQlIRzigqZFZtVHVVJ80LjEz06bBGkTuHZDEF+IQduueuEbOLwWDVbWkVWyPOjZLbyZUN7SQMtCovBDlSqWWOxJtOoTjtn5xKfmEqX/ZRgp8aJlwd3UhHmAkDtroQpMPlQlz+jfBoxaWCpde/ZZ8x5vQ4aUXUMBUDVYTiXIj3Rlh555we7xO3cQAHnIFMUHAoVLPGF8NrTwdbGIfKEpAJ+iG3GmdgCBm/gwaOCtKe1fcg5a8sMNH4wkDWYyxWMcPGEAW37TZajXcEIA48BUolV4YiWqb2KiAYJhQwjP+NA2EsJax8X5fUTySip0ULB6/fXXWVAmgCMVHF3A22+/zb4y3SVHoeHPF/1k5zZjuIKDLPRKP/kf/0dK5JOfeu3WrVs6eYM2fPN+dMijZKuSQ5W2GrHQFbdpCKI1+NIja8G+OroxcBZllTJU50UqckD5gEq25jzq2cqhFPQlj7UrfMaD+2bI4+LCCsDyXUwPbDLjo2NMfnsO4YMPObH0Kw3SAsBxU6kazhkBCE07hfMYFWgoAx2RU7KA06NUpzZ5lU8S+qA6luCzPRqomLc8ddtdUz/GeewRtqkhY/CP4lFpucgiaksCeFyD/P9EIHn56EQ9jf/ogDu7plRe7XPJaAhAqd9RFwhLhGe43DRKi0ITjhmNYabxBAhvapBilRrJ9W1QWhpBcEhChSHp9Amzcwc//8WPfue5j0FFVSSFiDlqgn5y4oT48zhWW2k4rjxSRjz20vEy9l5nL/3yvce7+3vc7rp6cJ8OcXDk5WMGkI6HirvzLIyamKiVlIEc+H4is+FJJ2LVssB+YZAR8zKNlkc86hCYoszo0uhrS6v7C7n7iRTgLDHN2SLC6QndsLpGkxmD0aTqiM+AHd65BQ1taQxJCGTaRG6Jw/FaFn21VsprUyhd8XemyxAfFMRNWffopeRcvuQKdpSvlE6THc7VunIiOCe7hcgQg8NR0Mzpva2eD0clWxLcxtnCsCRc2OZlqBb35AFkHCOS6HX4OxbF44illmjBVui+H29ymxousg3zXtD+Jv7YAl1CtiK1B52zhUg/MZX4OrdwwvsIw9KxUYAjSka/jUFvU0t8pP3R9tzNK9XoCr6uzjKoLwggE8XSuDqVCZKR6Cd5SLpf+tKXHtx5n00pmhgGAex+P/vss7/3e1/87d/+bbp25t2p+xF1lL00FnhsVCcGMCG2oypeYqMNWUWFj/6KT1qtHp50++rJ4Icu/WTZry9ZD1kcD20tPyWu1rI6pwszt2nANSI1DuxYZOFOdn0zWuegfcOyWzw27SsHtFQ+JBU7Mdsz+gWyrMrPq6C8rsdtcXfvzvNRJt/xzTkXuBctyN6iGPzIrsdJPZAWMrChz/WdfOCaBQRuaAqlzY8gaXrYV9oNFh0QQh3fgd9feW9j7+7W/sH8Kmw4xMdQdXZV33Djfjvqu471a9itjKmRFDHznKLOXpOxduFVl9ZMrResfYiHa1Sfefb6u7fvwIpehnlIyZGX1ZSIRoTSKEHlSz/FiqKG3A/Yzom7GVfZMZwDj3NDvH7BuuP+7t78Kps6tDnF3GJsME0uTFI80iF2NC99/IcbLqr7cJl+iNx6+VpVbfwxBnpMNEZXTqDBMSn0jnZlMtXJ2hmseLcVGNM/LhUQFQVDFCANd4q1BpAyT2HBqi+sYg42Cs+LDlnUW7t085dv3VXF8eonv3xniJsxQWAGxaFhDb5cB+JLpGpdSaXUFktBNUqKEhsCu+DXp5FfkPv8jsT5AQS+h6GbE93CzBzubjy4y1sErAPubdKCbjzeeCgOs1p61vczaHYY46lpkNh8mIJ5NOdSMX5Oz7GqMbt0Bbiv/qQ+ezTIR+K56dZrJRo2sAS/zzI1Z0VzeADGaNqjNQV5yLqm1l8oA5a7D31uGm1YElKUMifz0iCgSXV1XieZo1I3XmKiNVPGLQSKNQUhHKSTbuKXUmjMJwNJi8kryzGUC5nhr1CpcFXEg8HiC8+/svF456e/+CWfH9na3ONoLI0hOeEFfEyuyOxGjGkfqSxfunTz2WdWBi8xCdw5eDyzzysuXH55tLe/uct1LidapWW9BHOh69M5QhssRya49Rry2RlaL76ixxcc1jlctrZEayPb5lDIlz7/+d/7vd/7q7/6q29+85s3r9/k5srnnn/m45949Zv37nMt6e9RjjN8AFCTT8YVCOY56SJH20mRg2UwIQrboHyJWlnhDnEqWt7Y8/F5WWnmpRRTGa9C67ZNQkeHjHIZnTZ9UhUxEu6+ZTpOomOlwGE0FyldBZcS6ww1J0YkG3eo4nj26jBBYXCG7gCLLZ9GLDWojnAoGp3oo4x09d3MABGYAOg18yKN5KOftWRlZuQWmCrCWww0JKl8SNgWyAiDLp+ul+TRdeTlElIyRtijd1QLDd0zeUl2TuZZ36HHkk3ibDZq7dG8Uxmxc+BFd83AhlTV5DoIwQgWzkNdj5In1nTFO62vkXolQpE22AJ2BJzFNFypEdV86VROsW5LdZErlQZbx5w4Sovy0Bwby+yZ0MvjKN8yOgqp6Sc9cIhHYaTG5kQQGHdIKAsQCHJHnsbBBR3W43oewxx7bBxaYCpCV1wNcVyGRIDpV+1oE7O+g44Kps4yo5a1K+vrVzFY8Jfvye0+vM9+0sqsrkvyYU2yT9NGozJMgjku+LoXg4GIbldnV1Ft8jyX6nK2kVfpaZajNZmtUnJF8hANoz06Zi2KDtXULEOKlmZG5n1wSGvCQVa6A+artAk4jI3VPvkOl3VpbyowIgrQdURmiSPXVAhJyI8fgWBfUIOcygUcbGH6gLWzosqlKqTDrvp1vAubB9Us+QdHPlNs/WJHRTy1tLnHTeliOUShL36OFhbJ3qOFZfqda7/xqcuvvLLtTUHWF8Cki5Mvr7iYPK9akFwpfdcOHbjG9lR+wiSWZuj5l158+N7dHT66QJq0w5JdY2KuDxEmb+L6HREqEmGVpp3p53jLaO9IVy1BgrQDjGBh/lDfIpzVwXcaW383izxrQu6XiBjGEKYfdVcaCxezZqXwIXnWE9U26pNX3FzBoWrMhSJ1P6tC0dUq/OgjWXSm6M3rtuaDOipbb6MiNQLa14tPTkjP7KcyL6P0CL9z+61PfPJj0k3+6DPmdP3LNu9Y3r0zWFi6/e6dZ289g2zRMwEc2WLpk3O4fLn3Jz/7KRc3/cZrn8K/urZOu/HogBPdzFWkZ+1IoHDLpcv3Fgfcr4HbP9oBvsA/6oA6B1mNXlqStjXtW1lGsctsRe7yeQrWXmKx0NjZUul6yTLvAqgzRGa6P/SDbSytLWFFJ/Ths9z+f8RHpoFzwhoqTta7bGVjDHyaTipj/W4+3mDY0EOcF306QLs73L9DM6YcKWu9I7+tNHt4HwYhxATiWiyPhHu/f5SxxXR9EQ0b4auDJbTNV7LcXphNDLxagUHjnsaEGhvIJa2EP2qftLosaOSfR8Cu7uROJqZcZtz7kQmUvvBc9q10L4h/LkMQ1HJWl+zXpwv+inx0AdqETbmYtUadWJKbUZr9MH66kjafpgdxAoJ1IXoYEqds2OAasKXY7JAo6qOqnz+9HYSn9sWsc0jCvtPOzsa3vvWt3/+tlwgf72uwXtFG6iRA8BOVLCAVY0ggMKWVaCXEUB2EDGHhRmskTJZMcFzIZSduJSExkCZch4ex3WOAk36Tp48KH3y1U17Bb2iRsKRnVSjhYX7VtNXhtVi2HPX8LxJWiqN4sII5sKSYyOBQvjyiQKXumqztxV+ri5BPkX1IyAiyw6GpnQDPydCYwnttfKQ5tvKHLUjSkmC1KW/STopBplqJNIHBF89xliPUJbMdDAjb1AEQnkxUwA6/D0ZaEo2G+6jGsA1jgIR5qCLJJEmDBIGa+/f//t/nCsvHjx8ydLhx6+Yrr7zC8TzCjBIa8nSxRytsjOfcdBvPswNjfCYFOJuc2OhhKhpRtEwqTfsZZPSYNp7SGpzNp9l5Iw8+rbfuYvNlbXBDObSbDefigUhCKROQ9VSdE0BFcT03IGOPHNxjAWVrVvuDJ/OalB4sqLnOFgiCiclpJtjzOj2sRG3bmA1nNDiuwplr7IdlOIgieaOOnSgnrlAJSAZLzmMCOjmidS6dUTJtmWJpm2RFPUupViwYuZlt/McCMATiVMZizn9U0lXh52P/TwXDOqeU1NKh2wQIow2Xs8zwjLz2JKNoQ6pWykEww9I2Qp6ekZaW1un555//4Q9/yHwQqUBW6ds1qVL6eWRbBRExQpBPazNDPuk7dyN1ZxIHyFjepZF08erxCJ8wszqceZ+ljZ3Hj7B/lguZcTG5E/8j3T2ajCCkFOUFETggf/RGJkHkkZxmAVqvklIWHiAkimKJJNKVBUiB8Ih4WcDKKacxacceYdI7mPcIJAqkISiqG7MlFy22D1QhBRPP5K5j1SMnHBJlM8pkTY3jQqw689Uj/MVFtiI44cXbFUzIUR0OTPCRinkdGUddYHLWXO3GsRZ2wdcZq4VVGoqT+WVIdCWcCREefCxEOwF1ABNJeExB4INGI0aR4bKQSqtOmfI9lT/4gz/gLDZfUPjsb32W6eWVq2vcBP3X3/oO77vwQhhS8XVM5GExi6whIcwBIhU+TOBMLIfjkXZr6zFhEsIBR0ImnQTAREIdMO6cpPJFAcTiiIE/JIQhIdwqCIkyAwcfJvAnfWJDkgxCRYC1KHyO/4HGL0zwQSNW4exldAIkCCsCwdTChRvtMJ/AFSBR+JVEFo6DD+nix4UWeJslAUmU0RlqluEXwJAHoaB5XC3yakXJVA9JVBMVPi1MgEcQcAmL0I1Wg7dA0D4U3zyHbWPj2QQLJCJFAHJP1jAUionjPoRzynJtbRUrot0MMrGNW2MyBknq+EkuuqKYgKCLMRka2hiT9gg5tO2RwLkkPfJY2LmwDLVcxhDOZp7Y+AgGN1pjPF6swS0eH/Lt9Ldf/3GERIc4kEkvVQnMJEeO0CQcwoT6O9Dicxl4xKTDJBeMhRxWdjrRnM0dVkbhmYVXzrUSy4UbJNqYE2YRglQC8ZForbwB4cOASt6NFSwKb/+IyitEPBGOA1P87cQR56VtXXHDTqTNPAj4yaYCJgkHZABCk0UsYuNYJo9hEA6QloIAQByTQOAEmEFcf/VVEgzbYLbwWAA0SBpQnC0PSRPGEQCH70j90I2ShKxtAoREZTbNFm74KF2QWt69P81aKVR8rtMtobcWtXHJiTV93Yq7T3DaPyCJY31wzykXryUnDt5NXDK3COjCVW9CLEv8qC4NtsSo1QdhwgvIaQ4EolIU8EROGnA6u53NDdOqE9ne2qBzibjBx2e8vbS6SB+3t3vAtsrD+w8Yfq9fu0b3RzlLHtvzwTGXo3BYX18Xe+uXb4Lzyosvcdjo8nP6QBfSghY5eSSbhOUXqaQ9cMINzCAjLQHQcPwIx0VzcKQjiDziWoAwaOn70DwmzkgJrdF7cisKzON06Y2H/XDmm80RwymUgWiSBicBVARhUmlpEcCFIZinOXD6KPBJCIipFSWE0gb0iONh0BAGB3looYvM+BQQZcGKPaNT7dRWhypiPBUw8quk7QiklrXoJBe/AT+KAEngWp/rLErtH0Vap/GcTA6RJpFBmwqfxPwQIRdJcWQBOju9DPrRobZwtLVGbjAfWhR2c9R0DFvN8yQtpw9cHBGlTONdLcWYKocRi4+55qiO9/bY5ANeX29X5WffLwlKGFeeWOfX/+LPOKU4u7Di/emUvXzok2hKyGGZhvJWXboZBqcAykkEycPJh0s720d/9tdv8Arh82ur6jmOt1Vh9C1pGOdWQe1zgiz+zktORkdMN9b1FI0nUbrYDW2yoMr5He2ocer5ZLAwe7Cgu+S0x8WfpEPvFr7s+BEJTGZNmfCgZkonxos2KtAxxIFi/ehH3PClYF5eRQMnsyvIdLh8GcjJ+2/SoCwkL5xv4Y1IF7XOvCg7Sot0cdFSLSNBjKAAzihVGkgkZmnvHC05yazY2ckqyKn10Fq0RPFYkHzGQbuY9KAsCtEcG1Lsx9aCnCBnsEKAdCNJcHLzeOF25o80NJqjoEvK3rEQjniqFyX7TQlMUZQ97y2Xk6EmnUe7lPXWAw69LmhLWlriJ3wjrsrEqUc/PFZt92mfFaYDJVpK18BCYfM3P5tlI7YxKL4GCCNRUmYBTxLBp4vlAZyuNC1qYcg9V46OAsWW7slaoqLEWkgfnJRU+KRGgxkmyMwVnvffvXPj0xqQMQQw3LFiT2UQQId8LJtjYyStHpMd4TPVkDIjkgQP7bg+wak2k3IQy2I65j7m8W7X0sria7/xyXCGHARpWllTnarZF13akOyTu8RLErqQzPUIfF7Xas5nashZNfsWMaxPZ8nWoUtNsRxeV6S+HDGzr0WbNwYSm5TQF9nhnOyQQ0KlTnkxS3e7sam/IkXrgDEMRMcnS4TrNo2mSCXowYRjFSM7LE4BziPj6yL96gYaxszt3H2L3Xl9N5zdfb56LAPSKcjJPdGSr8KgMZcM4B/yrj55YSGAsTIZVyq2B+FjjgcIRF8DX1pvlb5j92eXiZY/O3PA9745T7CwDOtDZZ0lBp0cLD1O8iu2w9ZG4WRztIoBr31WZraSJC6DZtZbjg/2ry0vP8bat4+XZ5f1OjAKrmj69Z65BND3B2KttMmqt7I3GKv9ZxJIB4QUUkitceR3X5nd192pK64XOzoR3XD6ZEbCcG49IBFt4c9II9KNkCFsqugYVHqYAD0VgFr2VHTnE5FlI0k5+h9nE6xRHbzG6/yRnJoaTAXMIKNkSkHF1JkEOHrlSFagFPRYnVgYM0DCBkSk4svy1a1U+krLwmtoSZGZEiN4Zj4M4jkxw/ruFhdA02OCA54/qrbIFzP8QnRuSKhsJE8vbYMnkNNS1O7OPF29Kl7siiQA2AITMTKkDE5hKG1ocou0OCalCCAYg/gTvfZLYJ415/lYvV67QGccZ+ajglAx4QOb+61Bw2kafHi8t7t3zAloTck0veHgFqsY6JVOh9mc8JGeU6hq61h61i3ZruDULK0dqiHH8ueXkJMXrnQIizfTXBeAM0zRcEg9ajFzUXSFCM7SvLYhM55xHVURA89MfpjxhOwTS2ZJnfbfR2WRnJskBAgtGgeHASO+Wz+GZ4c6YejDPvvOEZ814vzxnj7fxVtHKy+9+Imto8OfvPXmg4dHu3uLHCpDmBx75W110tvf1P3aMyc7MOe019LKMqvPWI4aLNaZdaU2pyOXIeK6DeyIs2U0WVzOjQzcO41sZRDqsuZTTixRMTeGJ060XjumUeL81nvv37187eo/+of/4D/8h//ww+9/7/rVK9euvvbaJz6+fmn14aP7vJzBK/8b+9tUnrz6Ut6ppj/V+/hlMRq2y2t6K2VmZh2fa815dZqAFqk5T6z3wWXADqRK6jgeCuSMpAYi2pegvF133ADG5B5ubCL2LneR73K50gKXkMLkcG/X+ZBt8Kgsu0xR/kAnaOdZy1Sd4nYAKhXn+rjVdvdoiUq9ouaAgjGJ6nhouauAcGzgaEk8ecRhjTgdzQdRwZnlI7VvoUKGYeqOR7FENRdMHiFQefjPN9kinwbW8tXF6QAnJ0hFzihUrNzCYU0Ki19aFeC4JgCBOCOMhKdAyDXZ8UiA9MAuOIFYA0BKjhQvnWRWaKumlxFEX/Gpjg0Pa8jwrvUFswlQ5pX+fkOEL+9EWppDn5Q/0BHXk609apmu0uK9ptVr15KIWisKMdqXzG6+ag+cto60uqEseSy502CWGErcrT25qEO4mgFwh0GH8mydlBhDSL9ZRUnU+mlAIVcq67BQj/2o18YcRqAu6wppekPdYp7UMyozWVL00rPHGBDSFM7MbPEO6vUrn/x7f49NoHvvvcuW+c69h1SiJTY+uYTWC2Fc7AAm9s/f7tHBzv7ugJcCaTXnGadQnzmmohONCJS6SfnTRGDn8EGH+CcHA2g5JAsf7i7GF9ATfPygBVNnNY9nWCiU0ylmkFhE0/4SCCFJoOxmFj4y8Z6PphMwgpptCR1ZZZlbB6RdadCIbIMfqNQDWn14kp8fC4aQXB61s7VNBecDcTziVNnhBhpthIaQ2qnCJPF3ZrVWsLeg/bO1T7x25bXXttD/3HzOPmO9ohotQhKK4aA5VWh3D6hRElAvdPmymylVZDnaNOYprGzuHOzeWOPNlhfX165yZz1dQCyZ12JQX84H8W4jWaSY9EqAGxjkFuPDoz325Ljmn3O2y9oJvuoXtgBiA1xig5NaQFZRcD6XFduTTYyFySCtkTpvOoK5VdYbGKgezQ4OZuf5kixXf7PFzFL2Dvt/FIbSYvTE3RTsN6of93Zj2l63gan+yRm2oTkTt04TpfTp+mfnWHvVtRt83mBen1k+Ptza29rkBPWStKoPS3CzMvT7u5uDa1eeu3br3lu30bJsbvb4/Tvvsg/Kli1lpyOGMqX9Zeq15tLzKwOmPDM7Dx//4OH33vjpT5997hanoZ999vql1Uu+gJ8JFjsVFARKGixdu4ZmjneW8LcPN7bu3X3maJtla78nQbvGbgkjjx1++UzD2uLM0vJlNmg3NnRB1sncIt8tT/91cDJP78ZV/ydLizPrN/gi5+KaxlF7O9vv3n00WN5Chsxc2E+2XakElgZcXkhBoAC6An7LIR7UyXI89kMl23j4gEmHm89iKirotPuuNdGyOcTYBUADBZuHYUiVIvjNT0vejBXZWpRInRbMCbPzh8aWudjQu4Mcil+aX1hdXHpm/epKuVSgsTGPvGHmlrbWhSFvjtb7tTi9uM/sjSQy2slqA+Zl1EbXAoAxF/VKanYkoQSOhHo+z6FonAwYX5s7KF5/PGJg8AKBQFNaEcRsO0Wel8zTxkfhT0qt7KQFmKBMfitYrTQ2QCnrzYrqRsnLeKapNBzS8ZR5ZXTuAeXIbAGGykCnM6XksTLwhGui039Py8YkdsNsgeAkM5PhngM4QzdzwqsTH//sDSDgtGw3/MDb42kB0BotOeWRTTPOFi19/BnaKa6poYlB+UTJuoRcOCmM1Sl1m7WLJ3FGq3guOYZDxiy0cCOUdPFpWxvhxECmkIz9jCUxGYtU04D63gsZLA2Ku+oI1os3RnjxR/JyQT6x5tM4E4vJSzOdVk9D/kjhRVFPngY7kBD1CkEzcWFGmED8J2f/YVIg5Bg7S9XZpKMBCnNadUNLmhd2zsjleZK/7R8UuY6oBAOdpCK6AR1QbYUVLdUkkzGI8ZVWOExNd4ykPTbkBPCbGH1UpgO9wahJrkUczEbYmH+QQEsdtkmXEQfhXsKz+Vd5CgmP3O/JcAG1nk2o9nC06ID0eYe8F49HELi19sQfniKqIVcZzkpwjBUkcAvPMbJgNvyx2DyGFiZxAAlMxfzgQJTZM2eQevuN2+043iR/kBHe9Qz9ElQNsVdwrbfhIIBHsgOCArZMHgOcZP4hQnqRGts+pw34FAEp4SnILkYSDU/FjfxTsxZ8EBoOATCDjI/OmUo1toltj32gceiBTxSGAzMobImjaozvOYDFNEbrhjrUWQaCFk0zeThjEpDgzk0lONDGihr+B7So6MdX28DYbUtmwJaSVOCP79QlJ20pEHaOMgqVGHNaruK4moIsXzDR8nQCIkFSBbw9AMvkAviYSxSxqMMpK5ssN2QODKTHP4NJSwKChjZGPsYqmEm0RYlPfWh8ABDGBVl35ae1LC2ejpWxAkC5P3PrGS6XZ4D65ptvbm7uoBNNjOR0uvDAH+uan9uXxpnMy80yDYYh+T1hVQC2J/toGK2SHMeZaaz2tx5By/o+8IX5fHDVXyuZOeGk0tqlyxieDpNWByHsKSwca+JvvPHGl77wO3wmgfEPb7q89PJzL7/8Msf07ty5g5xMv0kNOSGBAXsHzqxON2dBMNyYUROgTzOOTB1hyPXBHtugKqu2+qwH1jKUQznwyRFAfSnLCgSCI9cAQQAIH95rhkMgzr6EJxYIOmE7Aj8HOwkThT0RywKKMs7FelqiKqbSp5K08AEqrfr2nvA9d8gIoaG1AMjJEcIEiAA4CHnEx/EIW/8K3hxwa092FBJ8udSghKsPVfg3+fMY2smw2FTXhwMzpNlvxau91ST+EONJQuQa9MaNLCSMr+USZVRCbuV1Ol8mwD6HrtLyh4K1tPmBHam0thMBKK/IENk+MPthGzLJajKJJB1MYtujhZxSHMGM2JP8G3lLiICsV/eSLb366qvU38d37lF/tx7c1zsT+lQzq0/SOav9oNWzwNQenOSh+SaE5TYhWwB8jJwWYN93L+bqDzZNIkYqI+FWKwl4AVrHlAmzAE1GdIcAlqwvbwoTHwcENorNArdW6kRSokDQzRkFAVocjQWOfkRhmZIyhU87Uh4SqLUAIDpJ7shIMgUwGsgbNjzCjboPAo5H3rRDh+uf/JRTCVFp4ctD/QEZ19gSKLW4pKUWIAitmJGH1pUNRT79SmHdWL8MjgiFJ/zYaqi0R0VUGhMnIzSl6ERTm1jzZZOPe+3oVfOmyM42fLwMgth6lWRvfpeWHDRo2XkkiXJQbEYLo+icFomL5tSP6L4lvWADN5sEHNRiQwI53TY+jziLM/SAYIOg8b1EoDySFuaHo5h5FN+Dg93tB/iL/sIBzT+Ly3xfACpIaPN/dPdHyIMLX6gI0/3xxS8K5cq16yzfr6ysIx7fJRbOrFZL6ZKB6ED0W29x1dlLL7304sdepUmhVyFrYYWv7Ps7XgveZoAzVGXjk70OBPb4hL0/wuBjJIhHulpx9luMOv87WEGGbafOXjBoK2trYK4sLSL/EVszWY1VqamkUqasO8nZb/IQiLUTQxFB3kclTBR8Wi7E4xRHUk1vp6CcBe45E0ZXFIrE91CNYQbb2KhCJeXC6fFB4zGFODWN8MHH9YQgB4KP8CXFmsdJ5IbfpwIaj2NsxxDAsaoFjqgehowoM3x6wo80jBhPkeLTUfUZ6RMlPMZwGDuiGzEYX4AGlAE9dxxRZOy6MIjjyK7qsnb2OLAIN1JI6qrhY24sbSytIVg51VY0uJShKLawKw2EYD6fwlYJtavsk5dXSMxNe2IoelHmNXtw7713rj/3MrWab7iSekvu3ED2QILmRfmTBTcs9KuMQo9Wbjw+PPrK6++8/PL8b750fXXt2sz2Pbo8BqOQzM/q6MT88fActC+j0Xk2WLE9Q9aUQ7Lo6VDESn5blY60RQlovqoV/pl5d4CSmzMqZMEw7blaOF69tnv4eGFH3QOnMHQshm1LyawF04u7YelCU3fsh+SGsCuFPKV0h3El1LTRYrTzLLsxb4rYWyAwOfKdWdFJxRGRy5EUhF/05k5XcR+2S2G1IoN9UowtabcaOWzU8Q8ebQ2YD9JtaKCjXl/1q0o1ya3G/Dp/+9wpN5iw9/rK/pVE49QQrQGn051VC8tygOx5Soui6NpkxF6GVrPAGT1e6zrQ666kgpdBbduAIXXSaPU6Vz4xrxTYlVbl7zJoOBZH5aK+uU7/AGJpUOV+RsqL8BhJCJ1ggvJTsmhhCHKo0YqPIwko7LFxUYxfKWqx0NUTQDIBIzdb6NlPrytDrfW4SJjUvc2tNgQDy5Ax51w65FjeyZxuD2uOjHBbHI+cxZCK5hcZVR0vrnKUmoVs4B4Rcamq008dL5oXj56VnsljMK3w3DiWusDJMZI6eu/u8cOHixxUowXfd051rFejUPzSGriOQAUOMDHt00EGBpHz5IN2S1K1shCeS6rw4cFOcPSjoxpKZc9U+/aZJciArKueD2jNxQaGj9ZI5a3fFrWzuanBNDeacrEAbZYWZbBV2iaNpZaOFxg6I/PS6tKN9fXv/eIXMzMfI+uNvAXgiTDNb2nUs2BKMVqVfnyQh8aFDU6eNDSng3I56IRU53pRCzi1AH4jiB3NkweVypN0xH0KpxVBj/NrCg+LiaIkj9K6tCb75BGTo7lRY2CVptZH1FpSNk5Zb6ngGuBUi5XSprsg44MgP4ZBXebv6rUbL7386vv3HnBCidHawfHcnufafCje1dDXC0hYMWHuBxC/zT2mJ9hBn644yttgOgFEg6075BHKXKUctLG0MNiY09fn0CLZ0oIyx0s94HT7oeko+BpLufPkiT+wsXXgOcHIygMVkcxJIRCHhMaMkFs6Mtvkh5a0KcJgpm7OL2gxnNQtCHMVGgeVsoU9tT6kbcFHjUraAojKFMgBxMrXWJrMpvwAgoO9EGDpBPNQXyap1SIFU0BxG9b9nOjRy7Z8E8lZZ/GFUiRhkFg9ZdX81kufXLp861vf+9Ht+/fpSBHEBnZytLfLxJ+VSUBbM3oJmtPOiM1Ni1uHDzd29x4yOz14Q/A4GQirwZJTB4RIjuJjpkrp6M1r6Wp1/RZTZVY6vNoiTIC4MOBoMPwXV/iY4fEvfvEGx57v3b3PqWeOT0Ly/PPP/vKXP2ezhPNc8NXCke3f42TJQOr0QpEn03aASULmoa0dlqFXZldXmLr7Ok1Uq2WIyIBOEgCywHE4F03UDhwCJuScemYAsH94wOyX8+PUmgWGlVmG1m2n7N7pRBwH7jhEjnPLSjZVu0kMPlQjWM0sDbjwls8jYNuk6lSst1hNTMLGm1sx6GU4EspaCs3zgCIcdRFVYqPllcHuBpeSkVAsE4XIouJkKCkYL0UgFCcTEZvFHiNohKMFCwrMDRGLg4XSPyQLCcGkH4sl3CBNrgZpAaG5D02L1zATaGg5qdceQ9VwZPVy5A425Bh9pX4EYLiCuGG4jENcd5BdZZG+2xlwG0BIW7zLK5fEU1+DoDYLInvCFUtzOGWUYMTpIAZP8dLWW/lqYToMSkdPzouajgQqgjBpD0aBikRFlUQBGj81HV2uK4fpv4Whk67aHmqypxlLqEVZJUhrgJtPwvzReAFi5VlHQiUmFqWIyy+/wN/2o8dU5zd/9nNmjkt8iIkWhhZeJNxEv5Q7ZXWZASyODjF57uInSIFQQpr8uHkBIrZHR5t+JVBv36q8ZZmB48dQ2aliPZl41jNV7m51OZcUBC8Auef1ghpVFAT4yjeHttzMI01ogAq780CcUrWgJA2JEa1QdS0TUmV4jUIsM+0juV1cXuWcqVpLzVmsLvmuaxqAHw9WL8Ng2/dBH6yt82m7m69+/PK1a49pS+fb2WcyXco7tSZ1QT2i9C3HKVHySHYoF754BE/KGN/FTkgceKI1W7t8iRc7bt16lrXX/WNdzsDomPaktFqioLkVK0rADJQK53nFwo48k9auR+azsxvkK8vKGC/4VLv5hZPdA51ai6NAU+5Qq3G09CDS+mAb1ujJ9q62NskRJE5Ur2mISus/VApgfI4b3mpFyXRqFgGyRpgJwzz/1JyJPx+R1cI3HxDgViXem9InFrXGykd4iUUezI5bqZ5/9rmlV3Sn8IMHj/gI4c72nqaKvKukhPQWCqNkCYP5Pbz/+MFDlps/8anX1K8tL7Fjusf94xKR3rUsqW9tbP7ge99//Sevs4Xw8U9++oUXXjg8WoA/r+sIcXGef7PHqwh2wNcBmM640eeeJqTmLQESm1tS246M6n6WOKF8vLt9vM1rQNzxvHblhKX+k6XZPW2vbj94jF3NXVlBO0urC0url0iI497ze7zkyc3cqqDqBDBIv0wg46MXlF1g+VYTBcz0iGnO9iYMyans3PULTI764zNa5HuGqkYoTQxGnJRzpgOhtuRn4RU+nlspU5nec/P7kqaCDAJvXL3GoXUmy2ldZQHkr3PKSragawObSOD8USFAh5ash45qDL6o0LXTDT6SjHHuElEwoo7hFPk7VLGtzq1NIcQKZNR2DZ2nc/TYUD/6gIRRsbqiTiRH3AQsljUCphaPPPuBfHeKcp9he2uYMlmvGFR1wEQlRJIa+0462GWJJ0JjOrQhGA/JY0WEzza+mtVxxmIrWYbwYPb4JSdWx4glDokUAs2tKD/q/Lj87vr16ww1eFYS1UpGifJ0BtcRdNiSceow30hdPnz82muvrXuvb+/QLzM6iUaQdC2UukBa5pa14CgKklJ+KlqhpdI0LlX+lovETKJ1FE8f5HTA0eZQGxH46dmNUn6I3NR/UbnRmG55mFIrPqB+UjS9+GOQVutULj3eBcLsc0IVDvjibEsofrWilmIiP2COItdUJlOBwY8M+OA0eYhyGE+itxwX65WZj6gEOGWU/IJM2OTqPXraFqb3o3xxU0t2iDYRcrqFJ+EkRFoS6LS0RpkErYg3GtWewrA99oExQh4DSd4TjnwJx+85fBThkopNLtM2CrBXCOFhKbpTl+qqKLR4DFo10Ol0+KRFU5mN/CKYR6XH2w8fJqIZCY9JoocAbBpLoD0SRS6Y5Igqp03rUAMcVzJVI8Ky49SojltShwMuSQvTYXx4JlzphD4pmBkX5vy0QucSOi0irOr+ssGybpROJ4s86JbhfkuCIxisxcA8ySmZac6cVYlSRqmDLbmeQijCG7ZRk5yBCO2jdxLDaU3K8NEn/lGlINV19SIZpIhpvhIGQTga/Z7vCq4RTVVIhno7r6TAZJDC0ScomaNQi7A9Hd7RjNSneHRpGMNDL+252EGItIjdW3UfLnJ8GD9I0mwVaWGZR+oCAiTXzi+eNoEQL1FJvCGArKYJcl7GJdeeQLKIYLQsvYErB6QFCJdUnPfAWTaGVTCV6rDvEDKP5ikvjypOAc0irYox4JC8hEXLJqslYaIk0rCYdAi0EigFXtuWSF5SIZR04ZNxThMEMYRc8wV3iAACAZe2mjDDjO9973tK0Q64F2h1PTazZC2YzurKeL3ODO2RyHe4XJnXUJgy26F5q5/vICm/nCpj7qqFciXHsF+rtEzRL1/VlS8kCh8OqcBHhNZb8yGE6vbt2zmejwxcvvmxj32MQ2Tf+MY3mOETy2o68Hr2uejc8iunIJhb0SSYOsQgSXT5MiLiOKhNWItjduDwq1pgt7SsOUtOokGIwOz8oQfWyMDEEUZycLgoGA8EeIIZX1qyybEADTIrN8CjeVYUiCIhSDjGCZNSykm4+MqRs4PdqFDwYZ5IZ6SUVEGPjbF64hu9SSs3mYZDWBG24BpD4iS5zxgSRg8kFjThuHZIauvE6PJAAJJAkPEnXUMgqg+PYYb5GPAMkqmsJoBFRVPZNpWhfy1VoTQKY+irZWCfBJ+lGYWrwie5nQ2B9gwEZEYScNAAkvAU/BboaacCe4SPOtwEaIGIPTWP4CAPdoPPMBE7xCBT0PjYPAi8IcEtxi8/9wLz4vffeRPM7T0t/3HoGR8XJmhGVx6pIpe6TBRMiA1DAtgtS4PSpNNtC9DBAQ1HmGs3EsbgE6WVVC/LBkFwf6eQogBCMzHE9KcFg5bzznDApQMh4RJluSV9kX+Yl6mmQOb4XBzIdCT4RZk2G57hz6ooikKBtIHXPv4JFqBPVteV7nkOVtxbAjmvCdI+ky/4cEqUs6Jau0SfsfywUlbIgmZPDANw5Ii803fRSnPYxS2kFR5M70MTjBQtgBLgACGOL0nizJaeTOadA+Vcx014d1+Khq1wtK4Ec/laeq0LhUQAQQb8fHxYS+rVYRUE0V8F6BdukYEDjzyKzsqUNnzvP7E69rynTw6CAJwGnDAOYRhMiIvPF6cr5DwibwWhNIBYC4edubTDbI3XiowAXeP+/o9+9CNKikUkCOnlwKRvcW9SJEc/PP70pz/9xZu3+fDGy698Cv5LS6vJIjLwupB6hLrhBZxskBgmhk+8ws4dKqYTpIgB6vMX1Kx9H8BSB3eC/KS1dnQjHPBJF/2w4yst+ZAQOgRHCNVFXYEwAgmY8+lJl8cemUfgcKvUH/kv4uEoSlJCcsLoKrd4OUZehEDOFm4QyntMxOAkU2SEXrgRJoCPesZYNYZT4cSKprONsUTNU6VJQGVhy+eRHHk8Oxw9nsZ/jOHf/MfUSuQk0KRVuBYIqtB2UXVRYH0a+U0UmulxxhaghxbJphPq5PY4eLOeqnPQ2g9lbEMrqJLtuZBO7X1qksOqUSGnlKsEIgWScdlzrA3ONKnYDovm6r+0Vk50aWJglxk2w181kNxrPDOz++jO+2//7OoLnzC6xpHDVJ8kdOhWUTc0kx7VmBHh2nU1T+9u39n95SvPX6HpWZ1/REvIy4ekcjir1nDhRF0RYgPBYY60eZIfmasgwMXUaBmMBr8VMFHkFB2oPCuyFAALVyS4ibNdVoyyVxvIaT6Nr6J85MoKLojc3zS7vLB29Tqd3NzWBtB8NT67ncMVqar3ofUVBqM/9UjXKLQ+EVtzpK3IizqlqY1SOjbetGV6wKN2MmW0tkJbZ+WWtzgip09OobCq/YqT395WY0uj8U/2lPyEZ/QW+8kZ2P1Hm4tcpubL/3SqiHphC+eHZFKJ4vepTkKaSfRoH1ZYyfmcQk4/jUnlRzzZJinKrmXWDPVQHrnXbgtwoNgnTUXXVLHGIRldI6ynPHsIJRKxVAfMNJixrqwUTqdYiM+ekJpqgUY9OG9I6JYWgpIK4LDElSjMDEB76W6bGjP8kQAysVIQYlQdmI5VVKOqkcPflHumwar2XEKiPMHWvn4VwgumVVjEtO6MYNFLqP5YX/XhzN9gZqCj9wZIvnhSR2qE2lM7OM150q4zWYimNmqes88QHfOtaoYIy+uMZwXRcS3x5hG/tDanlA4IU13atAWpcG5tZ4/X2Qbutpf3VYZ8gBoqTuSpf+supbbCkE2yD5XJg9sZ3fjM2QRm/joAOCxx4uECvpyHv9E8JzIwBnIEAjfx4e/a3/YbMwdci8Tb1kdaEWNwj6C6B1b5lWFJLDuxHA3ksfnYADjXb90EwuIemhQ3GWYk5Ljdns4mz83wxa779+/9+Nvf4h25WGlj0gecovpadKB8lYxKHn1ie9QV8ThzRA585pH7S4XphrcJP0pUns6OnUpyLhBrR15kh3lkOJfkbzhC6jhrd8ipfJErO4YJFImgduSXwspQu8Km/IIml9rZ1SlYAk7vVgzOOhxjYTTBeCNtdf3y2qUrWtc7OWHudPOZy6xBX7q0DM7uziYXIOgEkA7JC98iK0RAz3YSpHPt0amUKIC4nqqjGAbB0YO9pEDDK0qDZBJmwoQBJ0RbMhcCSmPMvOkGuLF6joO0WVYuGzaCD5Yg0SGn2jJz/R/zzwMuPNZkwDWCwqh5AU08qd1qaCKh+iN0LrhKjFYyf+4AWJx155KeIgSn+cqQCl58CbOojV9rqCBaK3AsCJOjtZNDruvc2/LXnXxWicH1rhjoxCA1OIvs1CAyMSwmDn0Byn+/0TvHCSyGqPsz8995/Se3HzxgvZmC9foAa8esGOwxdj043MEgc1urlghIha7i8ITb4vd26FTVnmTRuaarFBFGVFpeXqC5Qa7l9bVL165yLkxFoFKgK/Z797CzziWrFgD1EhMhIh/c35i9rEWQd985fOWlF25cu8KNoDubj4/2dzNC5mZl0gJZMvhTZnURwznXEiJOOAMNhTFwPcNRUBVioSUQgfHjtrfLwjqYZGFweLS5wwL0gV4SOdS6OeSZnDMT5iZo7jRUVnVgEJtQKiC41QWXAcccXzwDglM/yfDeFwjMsojNa6EsdyKJzwmml0/fkZGMSkybkFqE0euTZFZWV+o869vEkhPE1qB2oBsPWGtg/V8QO86sYeMSRoWic6CceuZo8yFXd/OVg7yUikKJ9B+n6zkuDykLQaISby3MpRqkJgKR8koKSUd+wHmWAJ35AcTKHZW9K6+AYOnqK4utW2a3kK7XqNGxJtJiXT2UltwrEhf8cC78Q0hcWi11frIM9c5c+Qk8VoHxJwxcsao8bh9IS7TinFxzj7xlA3BRR7EXVJInI5Y2GqutGf0wwyjpoQkMSf+ouiAukQKxKQiPhwvr8hMSSVh0Mhp93lNNfQpxoppILTCVpUu/iEAYHXoNTmKjDSyIgC6XpT6uLb/86deuP/8sJ0zv376tBb69XaoPy2XgZFSpYRCoDII0LxAtxRM7RB00s0BUIzWPVrlTraQBlGVTd6su9ASIClwg7B7nTSxicbRjypoXpoNJ49zgQBS2kcpUecapariwilUnXMqL+Lx9QkAiubUBgzC1nYoWU1Zxuu3CZzwJ8g5386+sXH7+ZbTBFxpY0zxeXqQyH3pEyjCTZkd6lt3HPhVm/ws9PNrc+PGPf/yT13/EB+hWl5dZRGapIV9thdUXvqhLjXh1hF5PNKqtpM5FS7wIzrspK1evX3n/7fce3r+/sawbOa5fvXzp0hUfUj/Y3HgAZvSfOT66IjeM+ZVBvq3A+zVqPqRGHEJKwx555vwyczPBj7w+TeOjFy+UjbQtKtxaX9AbpF50FmeoqCFgAgGNSqnlOl0Z7SG3WlvB1Y6gVb75YAc79gD5cuDeroyQJhpV0PKJAwOb3b2dLV1hQRQNOA0dQKTl1ZbHmxub21tvvn3np2/87PnnX+S08sc+8XGujuHYPguytBzOVq3a6qllgYwlDrY2397ceufNt248c4stliuXVre2tOotdShNJNc793zD4Oc//dlPf/IWGv6NT/0mZ6IH3pqcW+GjAPQLJxt7+1fXBhJt57E6DPfix8fbx0e8aqncMVjgG8pzh1tLfMXiYJ8/OhvNnDnqTfO+v0MzS5N/vK8rtkjxZHsHJfK1G976QE8IwiePpDCLJfvTRgi5ICfWg+splFsPHx4fUV9BsZ0YE48MwVacXa0Tz3UAAQAASURBVC4VrN86y+thI2FpY4JqBOOUB8RGGa5J5GGOD5PcvHR1SZnmPmeMQpJDKjXLWBxQn6qw2xEgCmcOKJXTrjinjKZQDeM/yeWxk7Bwbk3JJpaZcOqduUwZlQWOTwbF3Uzy2KISsA6KGrB6/pRckU/hMfxf7WMsYkqayHVa0fWaqZSyEDaEtNCOG81TykWKQl3qKIoj70UvFcKvISofw0xRYzUHOM2JzKmGqa2HNkoNDclPJtPzcRmMiwLJ1LIJ//g9E8IiGQPVxwhAmynA7Bz337EAnUhFuY5V3OFvayWHoFNC4Q8nhoO8icGBtcOde0zzfvvVm5wdnj9Rw5QGvWcAkGLLyY4xeDIotjVLY9pIij1VH76g5DBRCZNKZxY9n4RJGkcPR8c8t7+bccYk2geBwP+DkDc5E0BOPkdIvtKgAHQOFDk0/6A+iX+2zuFEck/CbxyXfU7kTCr4CsSvUgtyARe0D0WlZ6eWhJpUCWR6n8lSHWaITU6TkRWME0wjT6nj6DBKBK0Gh1JQspkKDkHTQsk7fSb4hGEViFojNyxuoIb8k9DU5iNi4MMqScHKwuupD08TZBwWQnxMk7j2GDweP4RqMJ7mOc9KVFMdn9CJpXWVMRK2VkiPHiRBQlunhV1y4cnbOclcIDpp4TOSe/DWO5MUlAKjIxdWGRQW8YzayBuhyt0LVfo4aTuVNooMVaoZhdEITwuQa6LEloDDBHoZiBXDCVYhDFvbHgBcpsSyKwbEuCxOwRIIaGh4b2P7L//yL3fv35/hI2BmDln45LGFExirMpGkyTNGCzIFn9g+Kqwa1VgSH8Wj0nK2CESSFvgokvtweY4pauyxpUW+UnViaVK+jEcFD7yhPXUAs0zpR4HxY9OEZbRzC1gU6840ZcvLul7w8lWGJ8ssaEL4kLegDw+Zp4HMSB+nz+JUx2MNDn9bTolNmFRky0/rGkMYEEYYfHiarSo+AebqqkF+rT5wcIhidKc6Xjt9olibho9i6Ajq2oQxtQTsGf1QUOF7GXEIqqGWd6jiasz5v+CDhI/OE56kgT+pt7ZfSbhnCSaEyoI7zbbMIZzkWssKbAumnIe8idWD1nPUWDF4W15Zo+gZvHGAq+gE1pZK5c7Fmbzra0VaZ7BVuxTGiE84kz3EEWePpTO10Ky+ZpMICggzUxmx1OPXlZABIcDhJ0mLg9WC5QNHVg7xLc3PsBzDsJnJPx9TwTIBIlVZvuksELYulPjqhTOvg6XgrPR5BqtkaVeqcZIvxdo51XGlIVvWKThpQRIIAyTLKyHkkcwCIRZHHrWmXHtPFkuUUxsSsUqHtXxWZ7SI7Q8S0l0e6FV3HsUwZRSBqp+EpBLFazwwFRMgfPiQFlra3tgMtTRpS4vY+roejnUFlaxqN/yIAgefGAKHvktXZNU5nuSlHGDmPKXutxQTGPMrYRkSt8eG1iCTgeA41w29SJKxZaBSqR0corQ8Jkxjx2NwsA5z44mAy8vL0xm3tEKQJKdmNLwv6idTTar2iNqRIQLHH+M4FTiG83SP4nweJTigROwmSWRueQkPYdZqlSh8+APHYVo9PhywPTZLuE6Hy91Zhn7/3dssm6p1qdpPANOMfkiFyhQg3DgSav46vasqrbaomDE48J90HVytEB2A7V/ItA84FlUbVZZTszAtBJzYsxg63YCJAiW+kO3aI4E++4jMKiI+lldxZzinTGs8uHGLm4IXrtygVeEmHQjV3FEr3Tn5xpFGMQzAioXmP/vzr3BEg7cf4PD53/5tOPDuCN+4YhbJEv+3v/1t+LOqHKmQR2K4K8EnTBMNExq6x3OztLfvLS+y/H39Bm9uL+grdpzz8aXAtMaI5H1DSlwLv71rzJXfCZ205EJiZGmA1PGrirTUaOoTFo4bc2Jx4Y+fQKiQUFu2itZ6C+TJGrMG8o603CUdfHKB/MBZmTYPlRpmgK6IAjNrGqurugyKRSEs88b1WwCxVQhJAXhYVQHEAQEiCT5H+9m2R3MsXkMo/rriiAtcVAVICMLjmSU6sq997Wvf/e53P/4q2n3x6s1LyLz/aINUTvb1Bgav+UCLzeOTKM6L8uST/OnjDfiM0uie2H8AnRtUkpEoE/ysB5McPFeOLsOnd8DlbIGUARxgCIJh7D2eICFMeOypEpY0qRSTcReAiOfQ8M8nQDYyFYcCKW2OP2PkQCAmNpbWMyIJJGQlTbGjzvgCJWug9Xkk3JMMk+iYjOF0MSV4BgJRqQVOR0mRRJ/iJLdfGQTZniKtZPaJaINMrnuqPtzEaMCmogQCV607w9ElgM23XjABnWKdPaad0G6j7j6mEqpZ12nTU5xTcofkIfUpWAKDSdfEDxMk2Ztf4qCJxPy0/SEDHacmjhEVX87GDKlKu7vbd2+/RZPhQSIVfhz/Is8h8mFQvn9NmkV7h1wntnaJ69x/fnfnweO3Pv7xj7+8rlgtQLNZWM9Bg51pJz/Eoi6k7iei8AfCLpPziN5OVd00aadnacTkJpoFxBOraK/TYerM5tzK7BXunNPVkSfHWxmnTkt6BOZirZCR5Cvwo/mdOzrMbE0XQGOIODdNAWbvLgotmvLJhYzRMq2alGsS7kIcIo4gdAocYjiUiVzOPockEKyIsqaaojQaLtlTp7FiXmO8Tn+UbDKd893paCN5iAANOY/NTxflx5IomBzGYQFQmne+aIz1UpVUzS4s9if+hE8RsTN4z0HhRypMljX0KM6ll2wmWaeVwo6o6VQKOm1LbV4icOWjjqGN0InqqRKuGY/Mw8a0pFIlKpmvfIlFJ5E3utKzWxzBPbKDJDiiHaEXU/orfEzBDYH49nbb6QikkSehnuqGmFiaUx0278lRpA0DmjeAnAumETuZ03vcs0trzMXZ9FRuLFG+LFx2xU9N96wIzj7Dbflg//H7dxkhgqpWGx3M7uslPKmBeXQ0lGEELT79SlpxcS4yu4j1dW02/hcXWH0+GujSWOtQOJQvvhyHEAC7GyEXwLn3mc5iz58v2/Uple0FvbOyy3s9aID3V0gyhwCVIKSWpJR+tDqUxyiUm23A5Rjdpgw7M1ZVQBwxw/bU5que0Ij95K+++fCHr+sm14MpXwiRAFFUXXwsKVqeaEZsazugHFdHf81fFCHVNu11gYo7/E1ehs8fXqjUEQ0vi8AtcOFEpvd6FyY/FbHX26lIjjgNU0MWSirbb2UrC92TWU8DmA1TRlV8lxcPKqxxhrX5AkdvgIGRwjMmFRN7kLUBtnnRkNlCeGI2NctJqZdffpWZ6cL8PrMaTmLt7+zy+R/YzB8fLnG2ZG1ZM5MTXc3BkSIkzp9HKRJIfy1FZzmeUkweO+CTBjXlpxFr/F1VydQCFyeyH7OkdyfpCpiW2PBjvCdHnNxlicINGBfyqlbremPNY4TPfJ4JIbfw8nmjHZ8d1nW8ZUNRI1KNvORmFxY5VMTpIoV1dJd/VDx9+AiHVQJW3aYdYEVDjYvPRLdiE4/Oda1xxEjrUArZiQKHQCUFd+Wc+xmLMIK4TCk+nArTXMAvU5pI57aXskVCvV3tdTcYck4QX62m674WK2eW5+dW33v84O7mI27FZRmZhUna0/3dTe7d4J5JViq1NsM6PtqTGJpCk6w5SHafoSsZLMbqkkou8BGM1d+s9TDtXxqsUBRar9bJT+deS9DJ1gn3dIi/T5l5Tftkc/cRd2+SLNcNXbmyysH8zZ1t3ukutDMenZb0+YGPapDFo0QUcIG77KJSJcBqL3CZCkfXhtQOiVZzQjPD0n3Wkpj9/V2iIEHzGA+2J052LAFImY4l6mCg7ZAB1sL051Ano+m01JRx1A1N5jZqliNm9eUKrrnl+JUOn+kklLoknI5HSs0pdzUReZQVIpcXMhASYbjX1TAQSET2Ob+4wD3Xh3uXkXNrdwtdab3ZB67BZymkrD6zcoFla8jFsFIDS4Xb2WdnM5nFr+9BpuVRDVKJqVjG26KMWiNS88Wg9koAecRX50tA3TUBqUd5UISCWQjWI+Eoxdo49gv4yTt5JY900WEIpulhKbtCY/h6Y0gPXmL28gpw9JwRJofGrT5pOyOljFtoLiHiclnRNhfx2uNTBRA1gkEdsSmyyXPQPVqfjkiwldq/j0U5F2mNrNU++mnDvbQJnybb1BTcQMqU40TrUsJwaQAYuy+sLK3fuD5/aX12ceHeD3/I90M0ANGwSihQQQoR9qNWgrEl99BzUvXokG0w5Dni54hlJiWgVtsO/cQBULNsYw6E0RQQrinmEbUL3Tc7o1OCLBMKnlbLkZWl8CWMXLIin+YgQD3YbnuT8SiaCFtjsuHl9eBknLo10JtGx5evsqC2/uyL+hLVIhf1zuyppdGis9opmScGKz78Z5F0wfMjCaR6pF+aR1Yz3/nlL5foB5dnX3rxuVvP3OBm0b/+zrchXVlaZiV6c+MRelIJSLHKHpMnZmKsw0gVR8eX1tY3tzcfPn54vH/AgTkarrV371xZ18Lo6uVlJLx+dZ2LJm7cWKGVoz3Ef/xom9TVG9MnUpy6yEtNjOdUpKK2wr4LUNsLZEOtHK2N3uNQDsgiDZsySUuHY/xNEUXh7ZIlWlTE8OLygFHK8mARnwvDWjusxtNbaLTGWEjKEe76xOD2No9gsu/GwjrdEI+MTGReGmWjb86VIyFv0vqINLds++7jyP9o8xFf4r20fuXlV1999OAB/OOkQJk2ZcJJfM/NXRZaOz88ev/Ou48e3OMLgbz1fuX6tZXlxV3GVdzjdHRATuZnFskxrzCxU/id7//gjV++ef3WFU5D31wfbO/xctNjVq4XmaNQNlpPR1VejLadozyMZ30RHvPb1JL9zdmZxYP9kwN2P0/4ONdlSUV7hhQ+PcBG8vziYHl1ZWFxwKl/WTG2Sj0sPaBbP9uEzkjjVP946/po79HjuWxGznAHlyMosVSgQz52iEgeo6YYRamy1u+H56xhsaN8cZT7gKvF52evXFpbWRjwujAvWKF1Th+U/qEmDSH4ACkmLzcYj1hlnZGtQidzuroEo+VcT2ltSpmKCxzwU1ubrYjMdgNbI8k7zRUcpZQ+FEqpj+rhaqhMMY7AT1rwaYHKU4LGNW4V8CH8qqQ7h7byNLUUn0gAqph0KyNR9vGTtYQ5BUYg4YaQpJvfYhukBVqURl1A23PDaAGidBDe5WqBrG7VXFGeQdg4CLGoqaRl0havjIV/8xPHI04GV8iHJC0EAprS8sHMDG+v8EV4htg0VSKs7gzyinLqb/gMeemzqrs/+clPZm7M0dyoHbRrySWQwmtMAYKF3/gE0hASiJwNZyxW2bQLG5DLuG4Mb+xxxD6HcXDDMSKADztROh6yuy3Op+APKT+MUKeJU9lFD63kIaGkwWb/I+UbdZ1K7wjn6OmzhIrO5j81tuaupJvHChxSTEKIOy1Fql4ooTot46fBh0l2oSQUtpakmGhLJQhEJVbwUxRJi0yJ0BggAMiniZG05NcheBPHSbSnYSCs3H6MpI1sTSFDbHdy/WMf7vHH5EwqkXxMktPyEs5jyHnEV6BWY8JnM+mF/HDDEuN010tFmKEhq8898HTSi8ZY59IGbDl8QaN8Ef4S26Utyi4L0DKSY1TNUBTOtLyRo8cxQahGDKZJHOSepEVdPDCZi0lIuMlQR4d077777n/6T/+pxSbQy5N6B7wFCA/F1qhfauxJeiZURjBCPhUtyL96f0zg5OKjE+M0LT1pimNihxzFygJHe39jajSSKAamDXnSFsM2ZtPCevRoB0jKLgiqR11PBFBJ+HAAyTHV4YJd3jB9/Ohdpmr7+4+Y4rI8hZ9p3uKCPs58dKjP5sAZkvhNFWGYx8Q2yVujD0lcMthoxwIRmPyCnCgCkDBUJxB4+NBlsOi8vr6mAX1RkFYGQcbH0WIwxKFdEqGzTK7Z7AzD+CDL4JEy+nEnSZQgJiEVAu6eBMHlh3nDmOQ8in1tVVrsJISoJNF8FDqGr5Rwbsj0K1dweGyECngxXRPoWsQEkIQoXI+MTnL8CiAW4BeKhfDOxm0wowoCoKET+Z5qan4uTsPiQCoeIUSm+PwoLCGLw1SsjWhJtPBn+s0cnzBrB0jSOIDJeQB80sUPCziAcO/eIx4vr13hNNaLLz7D2d6H27JP75QMjSR5N61G1BEv/OGpgLd5AoEhATRkuK7jSIr4LV+pfTACCHPgOBIlLMF0L4uUHPOAA0B6FnCUC1cTTIpYliXgUDIV6+IS6gVd8S/O8/q24eHODPkCWaUT2/DSahOsBSCJI6FJIFECzs1xTHtncYdUSBdM0sIhMC6rzynfbJQSBqf5MAENSAMSKIuzxQaUIwGbRRKqLvD6VH6VsIOFqqMdw+dxWBhjXLrHUFWuJSIKkaq7GR9AwX0OjmoMaoEUa01qwzSLPENAl+pTBZU6TGt5Ff4pKTNMFHkpRlJTaSQApoYbSVqJHq2k0hl25fpBf3tJzuXVcuR1M9mVtDHhuLWWZU3WXomBBBeslgsCwwExMzhdNLzPogL1haOl8Mx7OW0BGvTYs5Y53cLjA5Tvazfo+oTgBWgtm2KesXlv1Sg5ysuVAMlDCwTxIn/CykcsOVF6PseJbVWB2M3McE6Whd0F7r26fHlXW20SBzT2kuTbdNCagu7kYsMmHfG++tWvcvaZCi/MmROWnrXIoKs5BuSUrvD111/nDRJ6ea6T5jHEICvvdqidvhJHW7G1sQEhjj7iwfv3CMwsHKuMLq9STNe8DL2+rg/JXr50naaPYQNOFzbl067KgmQOcwI4llWRBAi+7t9wNkkZAP7KypoKfkFtINdjt3b16o3r0KIz8xBDlxWtsDopMoK0Yuj2jQA8dOTZ14wQAMI6By0hQhqZZdNhflUW5kw7DgfaS0yRJeM//MM/hPaNN9545513SVcKwW3qtAdyQKKAhwfEOoli1cSIox2SEMvCCGfP169cZhn61rM3YY6aOOkf/YADK0qZg+cPHr/P0ekrSzNczfHqczfR9uEeaDbamqhWomV0ygLjEvK1Mq/NAO7/cJra+FwdLMOWgkAwofmlSWIpLHwgcegh5qX0nR0wE0Wi4WA5eRpZ1YGJCqEWX2EnqWLRDXBq4OKYjQWJIlLygs9V29SXALXWXl0yiN+SSDiPQ7yKz28ygq604YVza00pNg4gkDSPYR7SFpYS7Rokj6f58HGKioctVHHBJzxGOAkZQ/iAj2NlRnIt45Ocx2J7zOSLHEFFBkPbAnkcwwcYCGwb5x4nVEFrCA2YQDkipz0IsetjixCAaMrRNK+cSLuaAB9zJwWPh/5+qWrJxRw2AGL68+xU8/YeEISOfPgRIq03cYrS9zwZfrInp187m5pPqRzPcDpbxc6uz/zJ/rtv/uDmx35LZ268xxjsdH5iDgN5p7rwTXRO/6XrbQTIsze7tHcw8523tn7x+J2PPUf3c33lZAd47t+Z53Ye9obZwfPHXp0vNWr17kilwL694GDN6uBhY54AOzbKTpWzKWekcGCCc7szRi7+lZZwfz59WKIqRnWTfMAYnPcWlo+vLq6fzHA3/uH+phhaqJL3QmZQdth8uAUZQaTkhB7djhqQ+HwAl9RTdiXvEpneXPshaVZyf2Kf3yQIQgmotR1pes6WyEo9FYVSK5xdz4NcrMvNX2xm4L305cNZhgtrHDjjBR9rjBNMIPvIiLaZpbg0mn2Co01AHzMZRoBUYIYDFE9/XrXPSPYPK/nQ3tAwRwmA2xrV96NcHjmdJPVaQJb7sed6p2EGdRqbsF8OVXjCEYjO+YSVqnNi4tuAgAgYY9LMqrVuhDnv5o1KxbJcIDxz6NkkuZQrJHmU5BaaclbYI5xivk485w4clBezKQXnflpADwhMXhoiEHjENcI+IMuMHErJRununDBoOodkbJjEDEs9MnCo/Z5jDZ8dW9hZyZVi+Js2M60xV3FSalVG4cx6mJiNuiO/YnzAxiLGtrBEVk8WVw8XuIrZmteeMZ+mFlVuUVRIOT5bumCN+wcDGcvs0WBxbXX+wQYqXc7RON3ZJcPCz3eZCVhjOg5dc+oU00dgb9R7VsnL2WcmLYqNTBQrJD7DoPIQT3eikp878DgxNzvH2WeAO/PLxBLG5+o15Mlpqbzrk9aychr2xCCDWaVq4gEurkXFZrAAIKwRMh1Rz4VhUKH4cNbOwVf/zf/rhDdVMS3bWGVQfiM2Dz3DhBukjx0jzyMHgfRp8Wz9T8UYBcJZuRsFfnRPSQ7+LdDSipKjwwa8YKDkorb84X8ubW/TSZ3SglVP2ONQkI5S+0MrJsMj5NYHw8sMAHJz4EkxhPkRiDIHVJdXjCNmxDafusAjLyY0iDDA6RpMHRbBtChgjFwfamcoPHPv3t3tR+/pcNDRFkN87tVD7J1tDtcQP7s4WHy8vb25scFIXTJXJ86dYTupIg/hJmHgzYeDmo5RV1pst3397RFJKubloY3u8F27dIm7m5kVnjzW5BNuLLohCDareyP8bXstFOoqet+B5ps0lC71WwMcklG2JYQXE3XLvAtNMlvDRKKH+cEJt0rPHaqfNBVq47QNJ29UdvRr4lFOcXpQQYQYeNGTlkeNT0rcGUuWUW1ToFt72hFiWOjFT7XLJ+0QBMSqKRUsHCWhHe0MItB/4WvlxEyRhx4QDDJJl6rDwsklc+yj/e2dzaur19ABqfB3PLd4/8HmBp+G4jDiob6SxKnZvYMdPpStexi4/5HPUdIaYHpkqvZ3ZF6iRFpnK32ulFqdxhNoDOHw5hjq88Un7pLmc08rHGGMblnxUAPMSW3vCApoW8K8NKt3NuHM29IzR49ZmHjlledZ/njv9h2WCbQmIifdNoVoyu3WHqAEhHs1M51ed80ACJjYnDxSYaEiBO1sklhWKzRjh7VrH8srJErqSoKWf0bv0mEaUPlaZgbi82iPMMft+SSCFrroO7iSlNz6emhGypJY5UftO+QVUL1ow0Wdh8f0K+iYr39y1p0lGKrqoQ/mZsyPMBICG1NbkA9hOWvkEeDQGChzuIPGKg7fEFve2HD3hIjakeE8u2yShQme9Vk2qU165jEBfMKS0U7ROL07gS0VkyM2LjZNfHk0YY3Ur6JsuVhAxQIcE5FP4saLx4xLARCQhp/CP+eXHZEZn42OJsl8XLiSmSQ0GKGnVMnixKjgaJ+Dk6SyBI9bwrtY7FBCa9gJOdMO2St8nmr0gtoQpeku6sKHZ++3xIJQU2zgmhcDtL4u4toqDLFG0DrwhxaMzLAjgNpR6bionrmkwSJKOD6xHg1QRNCGMEphWMUj+eF+dRpwahy38gnH0TnpzwReuzj1MiWS5rAp37LTHg1NOG0YYngEPlNP24mnbZvhU0RVtS2L0QBonjXWo1VyGHSNr+wrVi0wCWKKiOZP5yXLmgeheD/gyTm/rbcSceATfs5n8rInCI8Gi8h8dEmLkpc/9Ukg1PQHlcT9oDouAE7MdSHGzNadP3fA2xOquW7RuN/hT//0T++89w5HQyU8KSnvHLNV3qmD5Pvb3/5rljjXV1aZKO1sblA05airVCizIe87u1scml4ezO5uPWKODG3WqdnFnT+gj5zd3Nh9/96j+fk7HD9m0/ryZV0Y8sIzNxS+sg4t7QyNJKuW+OqaYc4iBa08jZpa27JYJKlKyyz7YRWV2Ky3YNUuB0uuU8mHB9taRKa4aLhoYLPATUCf1XPbBb4eDQAfbvjwhzMJ2amUOTKPHx2rKcBRziczy0taQoEcPrxO9d7795Lrq1ev/9ZvffZTn/o0nw188xe/wDwwTjjQz+oFC5s4ScBGwtuJpScDgPV3rEv+VByzs5ubj19//eHbb6+yuMy3B1985uaDDZ0f392xqBzpXeR+5wFd7n0+u7Fz+9H9R888swEaq/GP7z1g2Xp5ZgduSwPNQTAOHJS+xfWxFLi4JjFsiLuy25n9w3xiV5IxEEB4Rm8sanPfsbKPTLWlZTgFDrOhY90LLc1gMRg6B8d3tzbUegMia3YE+F4tfwdUNvUMKBrrNYoqB/ZWlBwS/N41JgCjPVUzSqpHcpg62BAQlsvCLq9funrlKnfXAr+yur6sY9ALfNFL7aGd+FAA5MzrjeKsWhzZtO3BlxpwmckuyChODn3IY4evOHDFynPPoEnNXOtyc6kaFlUj87IHJLbKqgpfwxd8aanLeJFc8SMOHAQQqSwEx0J/zWZTyAjFR/7gmlB72y4L6X+TfHD6DAJ3LlTFmoipVrLPCgsOCGS3x2wktY4qBdmgXFJz0F4I8XFRLIEWXdqUPEujXVxDIiBiGl+zEBdvaGShKhbSI18kHFY9Zku9BUq6Ng7VN3WH49kLB7U+3v/54Q9/+MVbr9CwhjaxEvhpXWgnOVBatNE/+tH77H9+8nm8a3SlklDNohwJ6jFtJQosFUlyJJZvjRAIDoHJJIIJ/LTYxmoqrVI6z4UzPnLSD4H+eHhl06nELVNgIH/Di6jt8cMKwHasABG4pFuHUA2HKBxVpikWWzXkfHHG0PqsnU88DUMyGA7nPg9jCU2SRsMRoNf2GGZTTIw/dR9djfPPkt9E1dYqRrVGmmO1rR7/j5HrsatAPCIYrU1LHSaCVFMHDg4iRf5e5qIEtz/JF2Mnug2xGivjnmwiDGfIIbGo8h0oeITDDRwnNaQviqqAUEUSeIYhPk4o1lgJm6RPBQBR6FB5r7oVE2M2T+RjQrS4Dy8QIfEjePnpyqVBSJMwAnt5hrUYXpte4CvCKKHVZCOPCG2SIm7P6uwcNLVAQio8XoS2URV8aHASUJ/2k19NBQSkjP4bFbgt3MQDB8dj71NfzEE5LSS1HBtmOMCTwCTnyYQCSWsv5s6yZD7Q2PrrX//6waNHMIpptVRAS0LNDyGPYtHFnhYOoXHlKZt+SH4b2z6AVP3jrzKMaEkugagXSINfUBjw+0J5UvKxVJ6OvAnf5A+fyAZQCBmOx27d206WS88nmQok3AjLimzDGn5VgyRAu6250O4uR3UIDxa0nruzq4kftwpCwolOYh8/zhfkNd3qUw//MVXkEeYUlP1x+5yKPwmEOY7k9IpldQiGhDimo9t8kq5+xJybB4ICBBIca7r4YRI+ROGYp4CZMHknii/BVfaljiM2jkpHQgSIRW89jhJwX8nrhI1bMBvaZIC0wkXkreU3fxKif2GZA7eyollmtNfaf2WkPrBeM1jWWafZGR3LnTlaJ3L73XeYS5cOI8Ucm3HDRU6J7UWCRDKYZ7TB0j1OKnIVi69M2eoUkB4kolKW85TPocQ6WI4UZAUFJjCksAgszOvw3SDzP56dCrkASJiiErlLjHEwDAFCuDCrG05Z1+AQHz4nxRgwC3OifQMAMIQEpJk4FtxrOTYE5cIJas56csJ+PyeRme0DJ6OoC8unQLABxurpgFJkRLugPD7X4JebMEoYBKRlfdw6o5dUtxURMjFL8bMmDJAoaTtD/+NjTlhzLTQJZVE79hby8MAnaSCSz+ThPOKD5JrOLIb6y3oQhQ4JgkEVa/egg4Ivq88FWGtKSh8SOdeUkoGSjJ56yFDJRoAoiAnkoQGJivB9FlpsAjDMMAhdxTbCsPmhtSVqAYjHLMhmWbnGWleZaXphusncEHoJR5k33AYuYg+fHYJD4zYWdcZjqJovTBfoGInEGxVEOY16U/lGCRI1KU8SGsV9sqeeQx+Gy9jjuXzHCpTH2A9rZHqV9v495MfBFlYEGkMgMgxvp2DS3NErWl9HkwVoFqxBjvUGU8eJbdX6ydlnL92yOAiASg9ywjRAStEzGnoP+PCIkxxRuBsTCRZMi9WEazhN2gQsf8NSdmgZVpdXWYW89PIrZPnRGMGZj5AnOyzXEsaBzudAuKoYcakITQwCdFr4JMcLc7w+QsuGT2tAojBhn4taTyytK6d94UNDBD5osB0rI+BoESq+0UfUweE8JJzZJcXbby7Tal2+tEKDfOOG1jFeffXVNJVw5lwyqeAI7+mCYjV68MGheZWAHaX54JH2trkSCJ/dPoV1W9CRrvySU3Hwgw8H4F4/5GlYPaKNJAGO4rpYRE0zSBTicd02u4n0oSxhuq2WrtisFeGhlrm5+pl8kZ1Pf/rTt27c4Dg5/Q6xpGJpCMrxiBOtsxaIY+QJM4Xvdgyl/eIXv4AzSb/yiU+zHHxpfYWrUWjjkC1XqcCNMPefcKfKW28svfbaa6uXV1HI6rq6IQRD/1wdlkaPMEkrX7PS0skc2xNlrJK5eTTGAA6F0YEiJO+0IZgbLbZqXcWsc7bupC736VQjdEKXpzeWpjnp3xux0yKfEkbGER5iAmJBy+fiyyNROMIstrPQRB9N6bSUhFMNAaqxcRpoVH5UgT3hlfU14wvZyeETJlOlFLI/4wRIt5ekJTo1EEyiwnYqToAgUIjJ1Blov8qoKGFqilFUctfy2AKNBPW2MIHGkAAOSCOpARVDwhVhyKDiCJJwDwneyAI0ILjUlewgDH0aeixIxwE40IZt6TOm2hH0WQfkmJzNBuIslR0nNUA5VcfpHYfFP50HLQNysqUBnJqEKGxIEWt5iJQKaEUpcgwSOLpyWPjeFdGaCpAffffbX/7ylxn+Q5qdLiFYfUrsCV2Gm2mFelIuITb75bsbe+8/fOvmzd2PPXdF9Wp+heRot0HmNDSSLpwwIWSAZfm1dUX90lZXq4GqVLWSQAUCHEAQpkuuQZoAiWqPYxlsOxgNYUqAElEi6giY05DSg9XLx8vri4ND+rk1X04a40C5kFeeCk86TAMg59kj6iTCB4EgYdunUipYEFt8GKPGGwfM+bL7OplEqlMxklMMIDYaWtRxrrsQjhSL0xABHbow5TN2qlPg8YHp1HQjeR81Vu5EHTgDGe772JzQVa6Gly7Zc/ODdE6SozhwYLhfzE3d7RLfuJ+duexNiCVrTHpW7+CRR+kmGHFC6MGEzvxhOzZUpnCaS7hbVPXmoK3HpraN5ALFkKJ2oAkVOMavj7/7GKyFjr0ldW84EWwZRycp1tQascUOOsMzQlIrkcOHMuU23PwJSZLOJSEZGNVQ2Squ2L9xQ5LhMnNAhMYShSezLPieGPc9qfYQxRN5nTRMEigE5/2MyXkaOuWlKOuQFqihJTnKBv2wWz7PAJZ7kGl2uPeZUlvgsjLyIFqsAuT+7HPaq5Ry2t7G9tyAeFoN83P6eDgJ5CZo7nPFFb2NcpGWXCheXdAhfxkSZwm4fGNBGcvZ5xCp8FWZYMe4y7c/62hg5t28HSMN7M3p7NpO5/PVDyB9b8tYmt3N3MgJQ6yrKbwvJsYcJhxpBhsmUXESqdoVYV5ZvLly6Qc/+MFPvv1tJgrz3LbHrTUa3Ho0XKnGfhuHCleiI5xLKdf4/GJXaIIWgZMRtO71vbw+F0FMPUojQ1rNMkfZfQhPyIzra9MY05bTCNkehVbraSNJbDOSEeSG9AECqqc9ebVGYKSl1rC5jLndGjJ1lvC1+kcq/A5bZMIBK+ZdOSfX4criF4ER8zIVfERF6+eZQ7KPn4nEysrSlSuX3r9NkeqbNjgOSeJ4XVQ4JztMgTa28lL/yDU7TU4l2tcH+tna0kYwfJBxvbQtamogyLAlAAIBZw7jRzjNacVOi2ia/RFLznnQmTiviorKNw74lPLM0f7MLu/tzpfVVZNyBGn74FBrsjo1iqK0GFt4ohlCrCUyONFbaRSW1Uob6AVsn0HTyfJUKcaxzJDH8wGVZQeuciEF1OBA8a5dviINOzLUwdvd3ONbdtw+3SosZGDiTKlG12NUyQuQrjMLl4wcY2NAYSh6nerhtofSKh4c7XMJ0ezRGocTj+ZnmJuiOxR1yOrCHp+T3uEDeYNjHRZyF60uSfw1Ap1h6I6eGSU7xSQ9XApJU0CUNFiWCLQkpBrBgcWDRS6U3J/fRXLKjnUQ1vbdxXhK6FxxtErk0plWFAlyIyxycR6fdZKtrZ0bN3h/fOe9926zpsG9MaxYQUdyok5r5mB0YpbFY6WKRt2RQDhfKO780eYL4/jkZz/7Ge9c37x58zOf+Qyy0VBrBQTF6Ljy4ePNbb0T7XGL8u5E0b/mkF6A9rwCwfXLf06jUxzEcnc2gZKKzlep50ClcDg65nj0vOYBx3vHBzrXyWFpbt7kPspZrpiGnMGf67skzlwmdd+FSrEB58UeMqt8ybIGLpIjlqd2DnaWVpYuXV6jE6R6qEpz4SjjPZ/ohIICYhzMhEgBT6ZQENUl1U0sfUJQbIdVm8wNt5kVMYzKUykLeg9xKLZaC6igVMwseVh+jco1ZlB+lVPN0fhTDrFNT9TKja1pAigCcfGJZmZIPAaS87YlAfTB35wOk0MFQhapzVUoQI58Ai60oZLY0qX5B2S/x+nANdiN91Th4GC/Rk/5JSGJZF/Rmc6Wg6mKmkIzAaKwgKl2Xgx/jIGozpOzkYwLrKKRtU8mHR1asAvlIkmQ58tXrz56eF8SYQvKlWYHOkaLoy2nFTnR4f3DYxpGtWn7vGYQTJ+bZtxC0rg0PojXOxipTUqr7kU37mcHWY0N9cuLzkqZPtoQjcaVvH1DhCyMoXGovxCgd0OFqBa4rUH83Tk1Kbtra5yBfeHlj7H0uTc3x15cOvg28xIjj8ADV+l2DgGAcOVEDsFevXT5m9/85g9/8F2W5A4OOL/q3Mn2tEBJhSFPP/nhD+688zZdwsEeb58Mvve97+zv7tC+XbnKYsOl55+5SRuwuqRrNxj1sve5vEKLAqvhtpnaAXKi6qiMIwMfTtBBWLuHDx+zEMEB1cV3Hq4s/hLOK6uMJRhNXGaRl6aP5dGNrU32th8/2qQRosmEA+eVCavl8XjDy68a0x7oImY5cPBV7q4j7RF7WxjMLXH4VVNFl4YXf+FDFkDLRjVcCMu6q2MrEfUwX8nyJY05Gjo62KOxZeVdLQ/NiY2ZNn6O9y359sPx8e3bt/mE48ajR6wdEwszVmabeCAAYXiAjzz4iuK/rFUS8BfjNW96HM+CDw7v3bn7/vsPUdLzL7zCV8f2jk9Ycd7a2WONm7ua0ecJJ6IHmMf8j395Fx2yK/Dw8hx3p9xcVdczy1v09D3MeF0BEYwXjJD+cE8L95t7Wmrf4tVU7rlaWcc/5mpn721LQl4ck1CS00VqFfG1Q/oJDQOQW2pnt4YLoGcZ/rHBI5g0ql/XetLFJb8B4o89NvhFAinKSczA8eNQxeJgdn1t5dLK6oAT9Sf+OE9qpQUATUyKpGqg0iaQHdwCBxdUu4VSW2kNyEMlZArOayx9XlAL2hNXQ4PcwsGsCYpzXOBBrjArtrTYxBSXWB4aWmjLYwdvCB9ioNS30eJDAORpYtgoaAnPWp2LPnnNATX24oVPY5Uo8x8aTPLuto34Ybo9nz6ctjGQ8QVoMehE78kIS45a1QlT31PqjhrDvdCjczIsuZZ0AvGVaNMv0+qhglyBnQ44kmROuyt8BZajEJeuv9io4HOuNBfBmcqEdOlm2e/auvcW5ztuXrmirbll7VOpSUN4n+JRF+BBsyB2adx5JGmYpJIQE8hYQI+mwotaxgRubIM1FltJz/qNDJc9Jdi/oz3Vp3MR70lpx+Qfkk8UXeRU5yBne/Bn09BLqCzAkMEFQ+LVOVLpnkqw4bTAJE4PmcqkR7h4+IIFimAkiuMVG8IM+PCZEuDvMnmqnTQBGDIE/Of//J9fe+6ZO3fuMH/D7TzcJEq7uRrCqG3PRaYE4BAqKAnj6BIwWqKA4wLkJ8j4iBF4cHgEHzTaEOOUuqzBlnsIsznHg4k42IV/wg14Dn2NBj9yVoCqZ2MYgVtUAkmi+WS7hcmRwlWwRhiEVF00BTyJ4uMa2pMGYHtBcjDJFBpOEqGKwmm+aYKIw2c4VESdEAWSqLum+GRiN63qsEbXyk2kUwBKrisaGYYGq7r7OQLIt0DJGsIRACBvVLRAFOtyGfOTkFPtzMnB8GwShjCpBxhzRbEdk4Y+DEDYEBib8hHzY86qsLpRTs8VYxgSTIQauWOKnGNYEa8HsiaBhKWWnmKTcMZBRS6SnZ7Drz0c2STehPyRbTLXvxqZU6YtrciJjw5pD4GDEJX29a7hJxD82E8fpYJIj+bJBWh9NpVWqfiljoQWHKowIx8CmiLOaGa46AtnmMmAs3eg89EEqEd9JQnz+AgDAgIkQLgFCI9JAuRcZxJhwV+Th0oQVsiDQ+zB/ApHdUBgishkww6pdYgJQnRJuKTuPoKpOYIRIX5afMvounKvv8BBw88lVcFvvgPikDy2ulKpyy/pjkH6R5hAzvwQIG+z4rMWLM6WLbHBL/L7AXhji7YVldkzBePkWqxYWXvCqS0JAfYS8Fd95yYCxKEuPo+E43ZOYnEtaZZRCfvCZC10YQMIK55sNVqlhFugpQ5aOJsP4NJWsE7AhH9udo/2HE6ez+tVax7AZCU0mBKgyg+fFBNtIFNxOCMnR/m+8Y1vsJrDejHILd0meeRv/hgchpDIXD2qITNIBWccwuArqqabs4GBJC1OxcMZDjxihPg48CEEnjCPkCzwYjXLIoNmv2XqyzaG+s1qfiTBaArMBdbX+Cr5IooZsGkADq9Fk1DSSnZAc+p6SjjwPJItbdEcHLAYhHUxm3j44DE8cQB1s4od4rGMBTCPNnUFAYcPSp/gr9jJcS1UTYAEGqQFeniAbRrpx3EOjSE6rEslgqGHoe9S0L5vHUaWdbJGjEV1+A0cJo1Pgz9dAPkbw7M5nIbZwxUeG4icyVT6OVV5Z1I6spd8ohhPJUdI4hqtHy+qhKlMYYWtc+D0zu0lajc5AuKiG1o4qcT6MFoCvJ8AK64LU+mzgoY8dQGa2NhzH/ACNHNp2GhjEp8FaKgwL4WrDhWd3KVyRSnxC2SYg7H23RKPwQoyNZEFxPVXX2XpeXewjGCkDX4Yl6wOGZ8aQjaQyTF8OJHwrW99K80IQFiaoXKCipYXl/heCDhplOgur1xeYabGLhO079x+i0XY/+Zf/DN8skXvCQKaRE4x1GqttC3rmnAtlcRAuLejz/pxBQjkLECzfsJnD2l/IIfP3gF3c27v7ugODfUkWjIGvfTCBOBT1ze0rIxI0NLU47uDUDrBh5Z9O3z6SvIY12LB4c6LyNzwQSYMMI0tPHHi6AuyE4gfhJo7NSusESMzS+kAUREdEJnd2tpG/xESW93VcnyKkqSqGcVU7EOLgzmrc1EI+DyyKP/w0RZv81x/9jndW8Ix59VV2IKzuqK3AVixxocniNwEwhHmvRurL730ErQA/Y3h0g3BP1lwUpJ8fXUd1c0vqbtEKUHAbxK2QBE+9FVsNEZmUW9PEhQIo9tK8VH9Npl9Lk3voqGclcEJb/ZgG+QxtwVMJt9njbqNumgOOnsQRdrYJIGKEgBH253YqD8v3zgLDNC9TM/c4en1PbQghHNjRQAIrEgUP/Aep+ffU32QcHiSSgs0bi25FpuoBm+P2NAYsDFpgSiqPRJoJOfy76n6cDiMkTeE4biqgTQ81WmEYVXUU3UYDUH23jVU48AZZxy0DI1NC78ccanI47/QUsMLNzVkDDgAcKoeTDgjJTWdMFaHx9qDIiRPUUStp2KccTtdUbMD3Wh7OHtlZfD2Gz/6jevP8/HQBd/4CXn+oJrSHovZOa4pEbyEyx1PnGCQUxO2M7vy9oOd2+9vXrmy+dJzt3hJh1yAPD+nWHbvecge2sJgkWZxjw5Yp0R0KiO7puJ0uksXi8qkNxsTyiEwlSI4U6N6oE9y6BJFmOqU0Mnx5uxg6cZzyKNz0HxdpTpOstTgqb8XPwedXaxSglWlU/mWXPtkTaECn61hDp6gB+66Zabj8wsMTeCQYTFbwYxr4d9qFJUPXZ2fh6lCnAnsd+SSekNHwhZ+0sBpJWs+xYrhn3PNmQBTF3DbjOaOD7eWtbw4/9w17ilbf/lZOrO9ZU0+573vChq0TJle++f/5Hgwe3129rfmB3wg+M1vf/8rX/nKo+/+nC7zmR3w5y77qo2FE03Djme1nIFtyObIqljJApsNStu+6406S7i0z9gq6NZ/6h/vK0BDZagNuF6qcr6GXspd9XbUwQeX+tLZv/kZk9jWIAjacWCQbJQycgIzoqv+1ZJKaSbNxMaueimwJQjUnKu/UwpiBYZrZTiEYclFV09bQj3DDyUc205+47MnDucTzmwxVeVoJWM47nzmisv5RXR0vLjKag5nn7UZT+tLrXJ5UGsIq5StPVtw32omfE5NatmUNlTSJ4tLC9u8C6JaOUkbSEmlForPFXLjM6sFg8Ghj0lJTpcUvm4vZxfdh54z/0gUZ/UwG05J87g7OyAsn2U4LyXs+1S4GgytJMQ44ltXzi0Cx4VhHwZSrKKgDH/6qJzEhI9uzmM8urz8zf/b/+Pwzr2F5RXWb/gIPKMhUqX789BITMx2RDPSfzVLB4Zy1lSroO3Z+LOHvOjHq4i7HHnktb4I3FgFl0ccUThXVSk/kMrsQ/5NXYhlnssaSc7F+YAIvU23egrP0xIWDtUihdClTbVCWobAFKG7IBmWGg1+0uuofVTJMuUQHVjD3I1YPpFqQo1TBXGqpletpIgYdQ3JZ5YHmpiRPpNSeoGdw/3BPHWCuTysWPta0dIYVYXLaec4L+haowRU1j2f1mY6cujFQsAkEGgfHuLVEGvgxlSNwwnZmSr5MjATMPoiArTIKIUZ8Obh/vZjJUG/pETTjGbbHm2SH1urNSE9irPn28cnuhSCHgQiGi10Q4y0qMZZygQTUipbG28A1NhAwy6NWTi6apyZfp0RnNOckFUEkopvD6lT1QyIvmbYsxhBRa2GVQkjgGPV+nk2KwrlN1Mm5pgcc0NZ+vMY2y0wJ2wl+s7h3qGbTUjYWj7e311aoQ1f3prZP54/Xjg42Xu8ecR9lKxaHutFbNVo9hugJjE77n3GMdx0oyRD0kKnHW2ybNcOAPnQf1C0ry/NID6voCxwb7Laq6MTdgfmN/cPZgYHKkEnVZbgtbCb1FUp/E4JdqglB91iyU4DZkYAKkTjnmU+G87ntn7vS1/k5DIyRGmRijAS4ccQ+qiGADB3s4L5zM2bl3Qsbp6D2ZBZMCmcAKsDyR1hIAobrvENAnvBmohUARAI4Ji9k9AizHQ7Gcd0uTNAN9yiDYafi7O6q5TkNHY+olOZP9nltlUvjc0fznMqn53dxQGr1ExVNffVRxl0kTU8XcSMk0oYCG/DyHHjtHxZAFehbz7Y42Ae2R8MuKRlkzJFfr68hoQH3mYAQpi1IHwo+CVfFGDg4qTHOPXmqQw8Ew40CDLPUcfbrYkylZgUVh0migRu0yYgWzKAX43lCs855azMlTz6StK8K0egnHfGzGRzkqqIG/uHi/iIcxpOen/DlG7SqgQGO5bJaB6e1FcGq1pOo5WEtp8g9GEg4pDZqApQWUlmG7f2qIDHZqzaTrIaYzuJ0Bg+aWAq58Zkqswt9owA1k0sJbW0ukY3xNxWkwaKVS1kLrYt1NizzkHz4Sa/mEDzwctgGEg2u6hI4ONk6t5YkU1bqwHjQ4yDMRzZVFNsStymQ2XBOATEqSOoJun0e2tJqwIYblROBdwIQkDpxNL2XTr7q+ssI1792CcuXb7MgHlTM2USkRE2e6mWmWTkn2GFqIvtuvfeevuvvvG17S0OIDO34pgqFOiqaICGiQn4j370o+ODvYVZ7rLnxuyjjcf3dndura++9O67t1///g9Q9f1/+Eda8YTy6GhpYYm+nv0q+POGkUUpHnpAI838in4SmdkcLRP7cDPHXJix/3BrZmYL5B6fMA0xnDnaVMhTF1kFXlRd/uSnXkOTlDytou/qONzZ33q89ZCVa9IRuQtFZVcd3JiRNTj8gKg0pVo6H1pdbIpkgfA2n8LAacyzLUePT377vDSBQWO4QGOJZkmR5mVhMLjqGzlgA5xPKcBnjwuauFDFp7zho+X1WJTSb46OTDaK4y1R0PJR8SXuRqcvmJvZuP/w4cYWqbz86ossrYKA+ezsHj3/wtXnn3uRtDh/zYcZKJ933318sr21tHx1fU6nAdhgoE/xtSU0WroaZZF9Az42uLJEt3Iwy37qIReBg0lPi1UOjlcGqAGbc8+NdTNCINYtLYKow7W5q6jJ9M7m4znMyfpUFUl1cEEgFWeKVR00C3kapwI1Xa/z1KkxjihEV1pxNnRp+fLa+tLxLj5nn33fv1/2Sm0Nt8rUenbdp7FglMg2FXyYvjLlc92iKJQ0OeY9Czuw4UH5qkXQSwCYpep17IpmgzAdNOKlb0oGRhoIyzDmuUCdJ4/fZKI21JbxFhgjzCPkCTS0HtLCU2nHgA05gVQkMgmaICp+6SSxkrJzCNHIO7CCfZVM1UvrGrSIDW3I4/ccgNgkSTdJuFxqcg2/BaCN5mMtpf/rOV4wjGRJmB9KxYcsi64vyAE0Sy+ViVd5FHV9dMAZSwZoTmIBQqoODUoAl7QGhXPzjHQ5ksxeEy9oAk8U6GFb6crvVOAYzuQjPMcIWyp8OPWNnU3e/njmxjWOeFxZ1a1MrJMgWyaotJuhxU+gz1Qgkyk+KWRSwnM5QMJWAnLSjYG8f3e4AH0u7dMhNKWdSz6WHQqdZgcqyRwDMAtMsFdgr9gzkri4GGcw+bCievlbeKqExAaB2kOpHfqarpnVRW6e+uT/7Asvvvji2seeX7h+fWZFV/2eDHI6SV8PLKK6lWIuQOzM3MKVW7eu/OEffu5zn7v7zR/9yZ/8yd1vvI5im9pJqNUykbulI9au6JwwvOnecqumu37SUgWnEkOUkiIgQGntjO9mXWyrM4LwAPC/PDpgZoJPdchpohEzCCaEUddUwh7YzAaBezgcxMSgFgUkWVHATjhWclV04RFgQyvQ03/E5/TYc2OaGA1TexJaLpFjMM2jNvok0Ig+LefTpyx+nUOZ7Gyx6Y1hdOApwb50RKXvOekzFU1vY3JK8rjIn4kfwxKVWumCie9LR4/VnCo3QVqdmBQLklbWSNLcVExieziPHIvg7POPv/c9xteShLqW+bTxGmebXNFbmERUsFqA8Ch7sSAW/IbjgAbfaIGwH4U2JphAdiAQhRj5gEkF/9p+I8+vLflpCffqTfykkFFvMKNzfLRKRcvjJGNKvJU+sXmM1UISqvjEtkDj0yAkiuOFWaoYp2zykj4nzGAInCToGULFwGNusAQhwEaeKDAb5xYAByhRBBR2/cpjL3nDnxoIbaKgikJoea5cWeGE19KiFgEBWiRhsWTHdqnXTrn7R9uovsbZExZJqcGeJhl2xBEkCepVS51HuFEFDBeYAFQ4wtkzhR7aDLUhhpkeT68m4jLNMdRkPjy/rC/UZwGatJprFIK4jYof7SEPgRKG2kUgP7lzs4VmgMAb8ejQdXSI7ypyqOqeSpaVGM5w7WxsMXnmdCyAlGJy6rUUESNGTpZFb7nhBKDi7MDnNxBN0Gp7EtlgGoYVQV02Drsi1SxAuFkVHBwcAdLC53SxH2Vv6AorJYDMcIYnDuDXvva1z372s9zIQR6brZrNiCcVTjj0ibTwefZZ7bJzzA3OYObMFOgwRAzSBYcwUcqpUtaUAZ+wSGwAMXSoEkVsSPIeWCwkIhwcq0RA64FZEEEMruxQXvjgVTsqNa97OXmZGqdpt7WdhOxLIgbfhLnClBkEJ/U4BDA7r09RIR4OVcs/lM/yXVStCEuLJASRv+M5VBepgcBz0nUQxOlOsT2f0bFBowkrDMcQ1awyWMnyscFoFbiUrNi0bZbBQ3fsU7TGaQoR5NfkopYiVZUBILKdobEgVHT9NkgLJLY9tgBFFuX05Akr0Q56Ef0EB+Yd3XgwsS1HEWAq88InxTvORoU4mRB7S9wLwdHLhi7Oo+Jgt7pWBT/v1esCSF1WCDetJ9khlXZ6UE6UaTCPRLKhIUxZaJpSiEtqJh2GW7rCbwJNBJr+EVXSVlTCVFUmwusvvcKbzduLy+Y/zIyQn9xJmFl9nf7P//zPqeBojIorGSpjYnGgff/73weNbdW0AGCurK8B57aH733veyzsAqdNQASUmWaKJWl28lguv7u7AWayht7AmVRBYkkrmQAfSXgMvoSoUS2XiWqPEBAmdRTF0Wxk0K3RtLedBTYmBIIfP/CEwzCQJhVRjQQEHhvkPa5wun2b5pRWPSa3dvkSjyAgTHSCTxSLXDCED8v0bE8i290798gFXTaxWewHEwS6JOx24/HWHlu9s3z3uLxw2TLbZHAjSpej7EBF3gmQOgfkabQJg8kqCnurP3/jl0i4tbFBXwACcPpqSLaPtxEAYZTlbgkYWhxZQGYbuFiBIzR3ZIhKFKyAkAt2joHjjGA0t/NAYM4KO9wcP/ScwpTh3xDjSUIkSZ/l1MfJAoyPzGhAw6T5ee5PR/ONZKqEjZekJZ/kNHInOzZYOISWAMxx+XhZEGPThIMGh8bzKQJJCELzgWVxk6wa5tSosdixx0mSi0B6JojVP4YcCPBJVtLq6AQEnJD32GMMw6oH9ok6XJgkxZZ0T9ILc+oCdHq/yWYrxDlbB3cNeRjP+tU7orKqXUbBJZ1SSfyUJTDVWN3ja3zC1EJ1IzCTprSmzy5joa4/5dQkDTXDrPo9VkVqc0fU9tUxHe6pFXj7rZ++8Px19pMccarXFARGH24EpylO19ekdxRZpi4sMrMppdaBhdvdveON2w9+cXeDBejr169fuXZ9cXGVsac5Hy/zzXedjeNx2P1IFdVW0CThs+uNtA1N14Q1sRPoGY5F8djSYu0BzMXs8+klwpmNuYWlGy8wGWQb9tJBbrJnX/RCrp2DhufZBOcimFw6yM22GXHEMulV0kgT4FwKDSFmASZv8GALskAQPKDvrTHaP1sqYp+6wYps5/JXErHYCVQKJVpL6TQNeu1K2GNK09dsDw+3uC7s+HjvM8/8wR/8wYt/+EW+IzG7ojvLtNoFQ2cbFTm1qoNY2snJ5qOH68tL+97Hnr08OHnm2avPXf3j//x3b3/t+//23/7b/a/+kC7zGkdXdX6YElXNNR+v/rPDmtmFBEU0NtFB1CYztu0YwRNnn3o9jLCe9Q35hgoOLPELle9ST8kaqEQQpdWLaCO6Kggq+aJCIKNRwBXVgL0ydW7WdE0Y2LAEqm1ThgX8p3qqsQLA6LmcwmCWjU5ysWIrUvhDKwLLVDQOzK6l3gSeGqjUUyNHgGiDpKM27Mr8lXK5pz76p1rwLsZglebmeLBKIXDJWM4+g0kBCN8ZyFsRpUWty7gj6emhWlEX0QncNFHOdumwE+s7Op6EBsfb9sqjlCr61+kwkOkKdPQup6iMZYVyvsZTEvRJm6XjnbGZQ85b8c4+5j47d+DzznuzmjbscuJb7wqolT4p76wwpCEsOQlJY/JT/rERYiQPhNQvDLXlDvGoWTrSZgdOnNWuoDD5H1OZnX3007e+9//89yoijh2c7Erz4Uy7pd4svV7aHPcFii0KNI/CPz9iPnSlvxgFIgH2iY64A5pjGwUbnJwWTCmXnpqUZJbykYSsuXoNi2+Y1IcUStUsVfwCPKfY2QWoLo5SmopTCCalBYJ2+jIAgsHRNFCwrL/JDEr5C0uHm6qGk0grLLd+w/zFxlK8DaeXC2DFIVhEcPuDHWlasjBY2+cMpSRh2ZJaQxfIqSrMzisCvBnDkZ859RSit0M2XJ+KbKDep6l8dpFQjCH3hKeFpcNUVyWEtTNvQonHl5dX71FR5/gYBh+yF3X6OO6IJC9YCHKmrhFB0j7jo64MeKlfMeKicHPQkSJOk2HpIOrPB2F84yT7bb6qC81xIIcpIZg6Poo23GkteK17ai4mcg3n6E9TUM0tM2lMs+Q5JHygSiHJPCCInu1DDIJ6Unqd7gLRJCRCmLhFp7Z6jY8u9eTymu6XPDpeJNGFy2t6dXr3gHd/dfyZXB9qnsYZZUSDA85L8qpqaq80ulbLSPoMCxTbTV+FbRex8dOloX/CVqO1fHzEISy0eXCyf3y0u8cRwcEMbwmrvHwCxTo3Bdl35tOC0YQyeUZIlmhB5hGRpPm5w6VVtgGOX3/9B3zNiWVoVtihR5bCRVpSMEWt9r5yFo7a8FoSXg6IYlU21iG/pChyL0PwSKIQciyF9k6m7sICUsJu2znqrLGTHbcJcJKTO6rQ48lB+VAYVCw8M07WseijQ76lgJg6Hc3JNfQAZEHfy8UhBsszMnpeP1o45OA0GywzXIGqhKgIcwc+VyhlqeIq5/cf3GM1Z+PuYzaTsFRkIzVkQXLysufPMXE0kBwcedSHZiSPcaQbBYoPlbSE77zEo68McAgTe1nbkElhGbqeWgzLoUnbV85+9iO0LDSn1HSOEc6qhmQOHXBoVTYIlSElXf1UUR2K1SkYzqkiVbyIa0lsw72FyCCexBW7chZdCKPKmsYKMcYVCJEXYsSGxpNj69NwembnIvTILTyWblVIi58SuAjOGFnRg4popOjH0PpHZ0cFzAI064MlKjNTxh0wol0yN8yYhmqGK4993pNvGoCscRv10f1EXYWGpUxZcFu4ayCPwhezmryxZGS4WAJN3WgpNpsoI0xj2gJdTGoiZf+aBfN2IMLur+pdivVXXl69dm1h/cojOIOB3dregl9OEaijGU1Nz6c6Wl5WkL/+ja/df/C+BwwHy0t8FeVY/9004UP83e985503f6H64ozqcpKjg2euXb119crd9966//5trsqgh3jrlz9/9eUX11fXHp08pF7xDQDuIF6Y15XQyA8fc0ttUh+cAk2AKPF3kwhmgDSTKB04PagKzY4SEyv+G82/xQs5ybFn1sFb4RQYuQazFEM1KsSqc9K8GSJk+mL8YoGmTuNJbyH9GxKp4McaK0m/8867FDgXfrBbgGP9nU6ELpIMHuzqiDSdJmbJWjB9EFM6tjxZa2bNHgtAu3z5D/iV69cuX7uKuqEils6Ib2ZYhxq/pZUo59xtXnzYAHguntrZ2GUQc+fd91jn0U3dMhKs9IQOmi5vMFix6atfo+WcG1zi1c59+oqTHe6wXljkBLZe5GQqsMCbkdSFw+2TnZPFlUvMmOZ0kpfX1lYlw/KAEj2ht2GFf8FraLnIm2KU5fsHBCbo6O/ocH9n+/hgP5fS8+wikf7IIC6lgZ4llbwRV2qcYYgMfqJV90YdguEaB8J9PKlQWGSERWcudQHvMp8rwciO6UmReuiqJOJErH2NZ47RM92x38jhbaOxtMgzb1rM8UEjJmQQyXZL10XSyGJfIpGYtOQES2la0iL6iNRGGvWiLvKCaFgFgdF4JTQGySO1ssGDcRpmQ7tgYGzZD7bYcxMiOVVlllbcuk4sN2vQ6Ip5wRTDCn+Mqj5KJ638k038xEbnfUIYlWyjBz1pGNaw0MjWAZtphj7TOUWUFsdjs32JOywp5dBoI9ySJeAl0OGHJ3AcZz3wWTnlfM3166+05CYDNZXJmKeBkCgMcRgobQq1DnMASNNMk/fm2++gq7XlGe7Jog7iiKL1PBloN5hmkCRj1uHDIwHMB4Zj0kxCxhD6x8atB06GxdPGG3yKEtloysE8vHN7Ev9XBjlDfqKKszQoO5oBGFviUeFfkyPpJkALRBbJOSGYgNX14Qorv44q+SIJOlr8a7eu/e7v/u5L/+WXr3384zO3VrXuTGcFQ/OMEqAxi04hUtQsfQNlzbvQ2G0wub6L2fknfvd3/7evvvqzlX/z7//9v9/b2uAgJ8sFrStqUolEbIrw4YCPVdPNadmxVPIiM3Biwcd3B68esZE3tlMDYT4ZFYbAR/lIpC63ohvjkMeiF+sndVCodshGzeypCNOcC2L8vtWqRPrtSXqp+nCPPxbuyceiLvJIKo0DYeXCyyss7ZBBttGFECGHRncRxk+Mk/w6QYKSqgjWpYtIza4I4xhPyDa0TiJXSKpWvZyF+J3J2fwaE+E7shGeJjfMVVQyyCGKH5WoYp0onLFbwqkjhNN1Npwh8WgIDlx1yoEErFwxsXzC3QgGJo4hOdUafDRQ2UiAli8D1W3X2BEzCxBkEOTDt2ah12GjTSA4+FSe0IpsvCqNEf0tfZyqRoAoPEvPDaFpkqbC5WUbVjnLbK1gdfegNVUGyKP0b5NIOAgN0hCMJY4kisf8ilmWak1M2dNIlpJxwcwsSs92YRu/iR1uEbNPsUd+0nCTE0KyHLaspPFiGKOg4+NdUieA7xcedH0faJweDSbrldrrU6OrnKLHJgBAAHlMAB8NsP2SAD6OPmhBXY0WoJ11P7izY51e9Vr9pAjxR5m3p3MCsIVWHaEdYbIAQ+o9AZYmERVHpI9qlENtPJI6mRCRZVCgul4Y+NNHU6fB50LEcAPIk878+pCvVhDtSDG0ZilJAAORyy8nyzX/E36QG4kZyCbTIBaZ1VyVGURh4zNWJzP7qBYZPJpV8xh8M+FBiSUMnEk+AsOH8QYz/GgMcgLgwITLNAl86UtfKuTFsNvT9ACcSQUmsMWoyFcYAocnPo/JXdDgAjB+cIBDRRgnS2Ei57PwBIBTkiCDwyOvqTch9GhWkPDOAZhZbAVIiqzYAAeIzzPkOMljTc4uKmp2oAMoJ0uFJwwpHYSjFrNExRq0zkHv+2DBEXerHGyxoABjCXLC7X34XIGmB3sRjCcSws8j4R5O2FFDhMQWnPoAThwtR4XZjKsVFSOK/Vg/ejHaDkKwCCbl0EeMlEJoldNWEGabxyawmf0qPAQeS3QSEjkC75H78KSsp/EJZmjRyRjh2TynIA+LqETCgaTHMMcee9lqBSkoLSpMaK8SMSnqJE+snaOm+Kl6NBkw73MEzzjgHOmHQ12AVio0lOJpqyNemHrGaG239otBG362F+rKo+B2wsRKAcQY1XaTRz5qR9289uyznHqevXEdMl87eHZSF4pFEiU2O8v7cKzRq8VwZaBqA4ycAMk49wW//vrrUSM+CLo76/AQOA5k2jrgtA9f//rXUfgie7Y2JOAsesIBZNCaWDBP3xQ01mdffvllZvcs4KbZpBPhSDU+Dr1DTgWNVHALn0iYcKLGkmjJ9e0GQAhhgR/8nqqRjAWSKBQNntRjD3Cw9hSZABd0EOZEOY48gsBaMFnjM4+Mi7IwHZ4gkGu0BCHdFlRoJkqGKpJzkwalv3dwRD8FGnre2dRnKuBQxFCWZNikRRAcfD6hxFhlYemyE9L+I48ghIplUqbqfKpWRcO6MGMDL+vzOpMqSwYMw+yq4sCTJHAS0jup+Ave3FKv5n6NKKKBlwa3ThawGYoyhPgJaAjlrpYUCYSz4j6ACxN8mVbX6jThiepVQaGogLJF2uV3TATIcQDJJn7CJQBH6osJCMAN/sGUDHbgQwiWMKvhJWrMb2ocg08+RoaWRENosjXI2QFEmkR4UiaTHICESWGVjBsvOozfCEGLchrk3EATsgUaSWM1GTUJCRWaRKSF7OpwdgfovM8dZxeR7ADRmQlcfIWK61+n0legQebFYu4ByCHgruGo3EJI0yA12RdEF7bQ7nvCQCqqRx7ocgeZ1anY7J7JDKuK+SXD2SeBh2OKJ/AMp8xmlg8Gv/ze927+vRdcQXUOmij8hs0j3AMMsRIbxgcGRg1II0q3PnvALWklvYE6bEbPQIPEmJctSB6lfVainRjhx7szb755b+744MvXXuAWOXepkoGTHeY8PhypaZVf4bT0x+JOf4wFmL+QWhVS3XNDkDLiq77ELnkpHInI3oO5wcy1m8ezizTZV7aUCy7MA+fIO3U5BZ/zsJS8ONfBCuFyDppQdVIHtF0rVeY0FWHqL7JZfmm7kGbrx9tjKIP4DP0ZvqAdNsiVQZ9HEK2kEmnyqNC4K5Y1Cm5mMgoeCi+mslecB0wOMZxS8fCtAvzto/3Va2ye68jPCidoTmaWffTzoByX9VS87OmKDXYgTyvHQ9dKLbPvPRFRGzVE2OZq3+Pjt67M/f7v//6n/5d//9Of/vTR1TWZUZqeLoAG4ajTVtJD6dichprvvX1OCW1fXr4M2zQKB5xVXRwcs9V69dan/of/ZvAPPvcn/+f/6w9+/vMXfKP6Erc/Uiuz+6obumCiCo+y8ZnziXOmLrw+to9EmgFSMkhEDPLrBASOsjpmFnaZIPv2HOY6Xi0DKUWmTnliY7m9wQdQuRd1cwWzPZ8VUPsTx/yUQKUVR8LpyWxvBY0fTEHVno6QhgvlESB3yaYyMWzsSq8Y0paSOQA7p24PE5SKKg/ELabY8XNkva0sWYCEN7KIQBqEk6Hx5C9PcYcydrJ69Rlk35/3jcD+0EdqKFvwZjc0+ForJe+UWpO+oLPbJq1SrI4UCVLuadm0ljwzy54brzQv8x3n2dltvUBS+HPSmas7Z73w5F5At5O7woNywt354mpLYFwM55gAOOiJG1AplQxAj2aUl8O5RTTAV8sRLLc/788JfsxXl61YUscB0YlggVz6UhzWDDh1pGgSPJBZbsY6iAqhGkKVp5iEpcjsuM0NkA43z5xcm1n6s//wZ3d//gYfJ5g5uad4vgNPxuh1cZSSq6rCpZTTvwz1qJWMM1zKokdwXaNBRKrjnS2I5xc0IWwndyT/JFU4pM301Q3SA3qJRUX94/bXUr24XTcSeh2FhzVnGPOrC43U1i7ZSbggFhisYgAMVDoSgtwUQyQGlME9ECwLR4smWhkX2c08U4PjWJp6heYcZi9aBmnudYRTMKDSORmbl1iGVC2wjA330kuv/PjHP6WFZ/ohG8BiOdCErXK7IGuU+7kZknXMlqQC8LEdFyDrWSPRIw9MtBA565tu1THcyi45QhMeo5TWVbnWOU0EVH0paaVl4HDn0vzCIeap+kAvKWFsZviDroXRPZDiI8Ho3hcW9foqematnc+0qbNXnyJ59OE3TpO5tLhkV/fsnhytr3L4yFZK/zYzzwgHRgdH++iZ09jcxAiJ1vT8GjjCOMfCd6IjReQovChcbRFvPTOP1eWEuFqdkQQ++LSf/CVTpd65ZCGUnfDX5rQuQXDoTymPQkJ+JTIHjZdm5xb3B8snOrh9wv0PXLb5eHNj5+HDo41t8kn6R7O+2E1a0kErxPHFERzIUJJ0wOYKY52pdiJKRXUQk9PZPjl6QMFqRpQpnMcMtEUoZGHmPpeNLs2vLnJHJ9a4wzGuBVZAZk/08THO6ZE1NhLIHQ0myplFVhs2Po4k4KdQef+D8CzrKwDR4Rt3b+/9+Z9/4QtfWFldl6BpIJy6JcL4JI67DgllKvgrI3t7OzBvDaYywar3nj7ax8ewvJAsSVQpquMpQcwpXX8CnI+GFTwBsrQs+/Qtz0pO3SyS0qMsYHPklIEEiw3c8Aw36ibZh5Yr4TFILAA9cNwZDnDjxZR5PlCAhvlgoR2sVV2aQzl8MGB28IkrN+Y/85k33njjnXfvso6w+egxSyGPdpGOmzvVq9oo5l0RVW5SlSqIJk88ZT7i+list9qU0mq5VrK56aYKQJTH57IUakfG9hSCZJ7XWewZ36qJCGSzzBzdtxJjp4OsZW7oUjukgomVCp2c4xMGUmYBtlJFmd6YZmMv/POccid5Cy9uOB4T0JdVqwTppgOf6o9kXxjUZU0a4YCWxFUqUkTnxN6EJcVEedjrIPgqAsJg+o5wgt6ugApI/FD1fqwlZKfhJLan6sNKbqxM+2gLZMlHofXpjEQrSvlVgbvdbvAmfCCprCxrsvbHDQkSrJsPqgSxLr2pSpuhpbGcfl10W62dGRRWuxLHuqHIuIjS0fhPJRVbimWWHsfjmVJi6bySrssKK7L+XXy2t6IxX6EQbgdcskwDwps4zNReeP7azZuXr99ATq3Cql0iyORJ/UJpkKxzHptTG3SeS8P3//mT//c7tzmLhqFQizSbINekripmttwdyu0NhLlMApZ8K4ow7JGhr+n7B9tXrq7tbG/+p29/kw5ILczRLIvIdP17WzpNROs8vzigTZZx4yw6b2ew9Hzj1s2svbISzUX8S8uDpcWFq1cugUVaLM5yLz/NKZWUzc4kKg4uqeD4ccTj/Zb04JJTRS2zB/LK89e5hvTxxhY3ipzMaPYRTdFyVn5DPtBGw4BstDKa9LZoDAitrWq9ytGZ4sc2w9tLkLj+FkXdu3MXyN2527SZnBDnneBbl67SGv/yFz8jR0srK2zXs3GIo22HCaJKyRbuiMHBoQqF/gsm3K0x88wNUgcN5eRAIXeNUGS5YEqWeXxyabDy3htvv/g53fWksYfGPfpqwuwMgzE+EKAXcdYvrRwd71HknNfen9G5Zi5FZ5laXy2g+ZHOZHL43AdNp3V8uEHJ0okCOT7k606zO/OXjmc2FpauMLA8Psmpao21XEWkAUqTTG3cfbS5dZ8XQdnhJxadRY/CRNiiPI2EVFIpL+FNd+QoejZ+mZpFVJG739GbHxRSdRQzbPnTNxB8ZJiRGKf4FzlgQP7cDcCNyukiLmSRB2VSMtQ/Es3QClUiA7GkJY/O39ZDD7i4sswQki6Y5MkXkhKjbkxvS0kzopOQ9D4UlPgkMV5dUix/TTWOUN2hLZIlQKfxDH0uZ54zxnOuNQgmOhowUfEg6B8Ja2G0usnYGnPO73RCJ9Xqi1i4pqMd8KV8/p/ihKBWZVzaoPc8S3V1RPJr3oWvVBfjUZ8u17PtU0ndBCGGRABayiKjbVE+tUuJ1sojiXohInTPvI/t4WNh0KLAMS1Za0llSEGuIobzXOA0vt/97ne/+MUvckAh2QYnVa/yHnK4YCg6DbIkrONUIOGJQSdgOYVYZHMXwiPwFqXoD9U1zpHhbN7SGEuAUvO4IcIHeCRnCxE+K3wNQZ/PKiwVdbp9n53uB4xNHmlk4EOfgJwl16MVDmAVtiQIYFItE1gfULohOeqVhqsbSwhJcGPAiqtfYtujcdWMBl/rC3KzDCD+2T/7X33p7/ydmd94gVkCaKSoCLvGhKewIqoHioevr6Jk6bSatMHnkc6P27A+9vnP/3f/3cK/+lf/6tGP3uAcNLEhbGzhCUSdnPnbLw1ckLEWGrXGP8LEJy9sipIQtRUImOD1CH9jw8nssJA+PEGjNPiRRK/kPoWkHhxspUc2iQg94dbRswwN0X/jJj1/GKI3MZps4lydwypN0JqBAZy0BA1S9IYZqMb3olIIBbRTWjWnhOOIIVN5TQwIjzVGv2N5BJOxS1zjKZjIAjB52hZzhgnSipVdkCM/wEJbSIc/RJGhd9969w2ufma6lSSG8dNDLZVeh5OoLXZSjZPIWVJpemhJNMw+C4qtzT4IPLbYRtggjcPfwsCkEgLBV6Czw1h7tEcVkK4yIbR6J1UHZpAVVa2OYCt0g43UVTQh00d7MXRzZ9MyCAIVztgyYMK03QRAGKYixOICjF2FMBFieIrAlfTU3/DED5PwQRVUEJr9g/0domgyCMdFNqYPjQRIeodA8GGCeNR64CzwNYedS3hXclq7U2VyhLNZ+hrx9OyTmTnvq3WKF6rTLQGTljBNK0xIHZQGTwBg1NhkK/KksXF7qAXoqtWMZ4JcgUONxXIQA0fGcToe68NuQQZOQJKYRcIRoMDHBQQ84sIhoD4MJFJ5E07Sw5ZzgkrLwiOGOvEF3qxlvVXGxlo+PgjA2bIIT2QubZGuwtRrW0Dw7YxizbD+wpXQv/N5fZZwQqnhNMWHCanjJ44AvJMiEKIQxgmBIcEaZuNV6qZzxLgHhDAEjlPvxOaG79VAxTzSuQLng59hS1osUSlHnm/rl0R9Mk43IVVr1/mayhlk4F4fK5pEGGTj4ixW6CBnLW+wtAr+9s4eB6J/+Z6u5tjb10IGOQBzzmeoFbAjgGywLZM9T/AEM1v4tMy2wBhQrKwcMXHzopyyg1HLWltgctCpzrZ0Gd0NWXmZL28hhBACMFvywnRZm5WynMBF/CdCvghDcJBnki3ZR+YLcujRGrdwgHMgvT810TEZwEc1YI7B+7RaOKmc9tjgCTQJG1yiuuVokBaYRG5MpuoHIAt2b775ppY421VOaa6tT+XrI3NN26TQEiJgXRIpl8RZoXJYkIV5vZHAbRu0OSxA0w1xW5DQjJxybIPaxiF8nsjn7DPXOsMBeZp4YUjbyDyICv7Nb36T3SbjDFszJAEfSUhOVYmjJAd7fF7+hRde4HgvwgM53NVeLnzAIQAy4V5ayuU3f/M3lO6cWmailNPDI9Kl7wMZzlDhiMLnMeSQ9HySZSBi1TlI8gQcxyNbEZ/61KfoIK7ODwi/f3+TVjSbDZNte8ghbCz7MLHtsW8vEBL80NLu8RhIfKIIML3lc44/V7Zm9xe0nUZrThSqUy7YzfCmSPjj44BASKwWPXDa1JNj/suRcwBQobGdzR1yx841YwZW/zk6zcsrnJ6GQFSdC1sUTgBq9B+eXIsGJC05MSTRiJxlr/wO1zBlNvQOizO6jIt1UPCB9FQht1QqUHQDBJyoiEBcS+WMQJifgUBUS7opPPgNLm17AZp0AWJsirIQKMlA+95c5DHkSKuM1ccwbzyB01HhhzlFBVUTADSpGFcNEgix9LYEUrLRBsAEkuiYbwbE69gHtCdeSgdnRIxaXiCPkX+Ijy3FMZ4kmqiknsdeEoRvamm0k2iJCmGfljDLpm+jJjA0UR6G3EppjseGcqqeAXK5WXrZoY3nNGs5r9GnW8LCH1kXZ99FuxsqdPb8VWBdWeScAjsVPScySaUTH2eAANQZGvZoLQx+6Atj7bKjGcfX6RwDzqprRSzMcK5i/tHd2zuPXlm+9IzrYXLauBZLqrzVxGDGXfSwyWtA40gKAqAzFHfAD0ai0WrIBKJ3fDRGUdH28xdyPdmlCBMeg2jURnSnvWi+36PIyK7tMIxxaGwnUyEqVGVV3qgHQwXogw9UvG2ydGmNl0t2Hj/e37oHZKBbvXTMhXwxEocumep3vJNu6ZHyYF9as2vZb5GTEKLILyR97qIB7TrKZrg97Gh2gC3QkSAVyD5VxMZbtYaetqX11IHkaKSMJ3iVFOlsFhcJ86dzbRRiis2lWcq+aqPxwCiiooLgCGXUVWR3MEOP+Op/9b9gMPFnf/WX//CP//iT/8UfcP/UPqdy1F9yWhR6cQj5cNQPH5uRal5ttoRWG3G+C3x16Wqi4EFA3S/avLJKiS/+o8//3Wf/11//P/2rn//8568e+5bGE7bWpQYwaeejbAO0b58oyz70Uhd4zvkFFR/XeM0cPHP9mjYw+aMtUnMUExzXcWQLu6n8hymdGpq0x4Iqhr3GOw5IoxhJi09FTl32o1uwDnd6cFilpsdPgSIP+Z0SUUF9TnKgXMbFbruNjDDt4f68XidcWL7MoGlvwR9R0QSTJlm148inmSq/oYwXqi9uvUU7KeSo1Owbo1qOxiEcZy2wgd0jdhqOSZzB6NzSQGMytyGcSLSFqNXVCwM4a5ozKEpHZi0EwEoU+5Sgs9zRCWtGc5jgvvdTd8rtzwOQ9mkakhh+sehhTolSuXvhqdYU9FoyYD1AQwzNW3bcTaC8aw4g2hEn2dTfcevc0dzR3uG//7//G8/BKBNsWhMDpJCXLxzUrlGAkqiS1hoFvnfOldlR1yeKNmojV5Fqr5pnlfX+5gJv+c2uwAo7EbnFJg9iXh8rvfQqKnOWTkDqeh/QepHAVFmN66ExOycw2Zclu6OZljznMHqq6L4PLc3lKXzAVImMFIXLujtNCSlyYsj0i6V9Y3MXIp0GxVRQWzm/mV4Am1Y2PbEpKiWVLucmGAoEL0E6BEFgwoBLdV03byzygZfl1ePtHUmiIyEz+wd8mJbTsvSSbk/m9KGeiDpkXcu0MS91wYhZcOqRp4ZlMGUkOSVecmNHnicQTZg+hbrA6hbf62AiByRzCanQ+fIqWrFVgHOLurJMVJ5UkGEtbki/cqFaZB1hllkC981rNEtV5OQB78ROEUiFpYv8ICTWp9cL1srq0tyjmSMOLUlO1VYwSaJE1x8IWSJn2k67wIyaaOH4As26SFcWoMWHHLn4FDYHCI/48L1kL6dpoIejbIzrEYWjvPNfC3kIOKBFmd/dyWftOAw2t/HoMcu4ZN9ZYOwqElMAUXdMifAo/ioXfhTPsA3GDmuBXzDoONdce3CVo64sFjjOYUF8PLqoAgnMTw3F9sbm9srq8rVVdgK0+uGVWdJl1sk0e3lBuYQVmBQ0AXzgPAL3QSXFtxT3lwZ3drZYheFKMa1By6kFpk7BhbPehkzxkgoRCeCTEAaDKjCzanoiDEJLsREiErHgQ4gP3Lku8gvNS8wcagOTU2xYLEC9AMoT1Y3bsfFJTbcA0g7MY69YM/eEsiDPVQPz3KDtd49A41A/zMW/9qQw4c0w/P3jPfTDO/F800snnnXT6MIzN28dLSzTV3L3KO8jvv/o4QEb9nt6kRxREXt5dQVMWT9WTeKuR/QlBJxnNKw+rDwKFCdaLCyO7FAqyMAjp+51ekvjBOO46aAOC/9E96gsLWocWG8BURUmIv2fcOb4tgdFJnzOj7nYulOKgdtkUyPAwjqF/Gtykr8z+0kpetWVFrLK26LEpDq9RIrZupGheoFDbPMrVvlNFA8tMIaQx7Njp5KcBmysWqDHBNi0obBLqkeYErYlU+g6BH3lMstknH/HYdFBJoqaSJhvDuHbMmgWxDzhoBHfIEXPNYLf0V5YETatwiGtEm2YIlw6KSKNYdRi45W5JPe3g7I30IfRDpfWmE9dfekF1hYPeIEaQW2wfF9HbPhsggkrS36HLqkPn6eFGG1zVIh7n3/4+ve5jolxL1/rQ5I5n8HkLiQ0zD1Q1Hr23rj5HS3VTqS2ZmabvJ94JPnKSy+/+LwF5uOzXMbvCz3gg8LRP0zIBJ9DwaM35Ou1n/nMb7os9NFatIAM9F/ogiOk9EloRvjHx2+//fbjxw9ZTWW+P5GVVkrV7j0AcS/ZICKKweBzmxBf5OLLBQ8ebzxz68a99x/x0qELHKxec2r3QlhHwiopXFg1ngQAIgd/1CUecc615Je1dHseRDEK0kXKdkmXlwEZD23df7j94BFTafbsrz9767nnnlvVqjEyaat7Z5/y2eeCZnYI6zsiPhErk2Lhw+nOczRrkcN5tMlqBdW76T7Mu9tbB/s61CzJSh5T8bmP64D5DismC3oFUod6KTVWiuksOIJFfdPVyHxywCN/Z13fsjWXA1Kh3yGrS3xFhpLbpqldWVhxezKzTP4OMkpXZaKOHB/ubNMaYzDgGiSvlEsdRAl0ipN64WO/oUCuPlstQ4GRLnjyJxxo1jolMK/l9cECX4dEdXrNNUKKikLHFyIDOnhIYFI5ZGBzjIJoJvDFm45EdOKZXoy2FVI+/kjVYmgU2xYH91bB5JFyiSQeA5Q+PcYmfdIup/GxSEoIDtgQVCbEACQPPRi5YGeYEjp9NhTyX4GPSC2VEvYMmjauRaWtkD41DSmO2PTICtTOqEbqV1QdcwhhYITYc4+rcEpBWG18a/IwT0GjTEonR8QaPQhhrA0ZCrVFPF1AFmRHQnBwYyJLAlb8Cb6JmgAXqsDBUaBrhhq+M1D4G2WYBUXJjGx8syc/+clPfvO3rxdWpk+498c4GEteoyKAS+4IjCH0kJbrWHnDBC5XijMvnLpqNQwL3JIQrh3MCSeJBkSyjm56MMi9bNPxRqEVv5gyTHAAcXRs+wuHXK5NS0qFVC2tQo7y+JCfJEFXFnAPJAElFjFGVSLxPBMTwq/WoatoBi0lQPoAx6QIpCEkdhKtp6K7+vKXv/yl//a/5VjljZdf+NTv/z6WRPY1666Jns2h59ZSZDd1c0ufa4BVI+eRcAySraXPfe5zn/nv//t/+S//5d7WnvbMXQpBBhNtj67GjKQjzhPZBwNypojsG6sjmoYwwqV7EMPqLkLY41e64W/jMBUNoHLHj0N4tr+hAI2RokZd4zwKvtDTBWnH0PKIJAQYXTFCOl5YJb3IlthSpk+o83OFRkun4TB+ImkKmiHamsdyvI2MGBBAxT6XCLthxCSfMCdbfRSZSr4AJizf/V6DJ6qpRR3DkzuJ6twRKNdSmUmfCiI0xuT3L//yL1lFQKwGnBpAsFEmysgksNEmtj0iVSvNBuwDINBozK/46hVHNA7RSY/chxvniDcVOVHxe9onCjcFTU0CVhfkfxr5EwnzQZARANdLm7CBw6rRI/S5C3ws9lx5YN5waJZxQDRcZrHOK7ZZhms4CYRqalqKstE2mx+j5RHCLtnJ+HEI+CYpjSj0SMX0DxvDJxwEfCTPdTphQf/TMph85ZG5Osi8Pese1m2Id6fYcGNqrQUv9f5y8Ik/JlMPhFViCbBSAAf4j+HzSOzYGlkI8ZmyoC6vqksYkOkxGoc+rQDBwbFAqUfj95/kgSGOtUkIYUsYeThgtXmo+7JpW6BlfYe8Qy0NuHcCU5DaDVszIy2JeFohBCDUQXE7CFOasBVCrsyy3oIfP8jNB4iD1f7eHsex19euSvN2AGGF4FKIF6ADwQcSB0IcFEjTuIUDB8r++q//+nd+53fqGrSUn6iz/aCZ83BjgwwqFUvFRJZwJG+s8ghVIDwGP3DIMVH0yW0i4DBxjW4Js8iMz7IvPk9kjYVbfC5lNcAlYtuAFVRaj8xCPg/MiWv5JjnupoKKVCjc6CpVgx0VMHGgQc6yzuLaKjpnqQirCENWOGUYs1reUu13HuFGbAilGc8S8whCHPBotvx6zKzElJzKlyooyf3RXbJOBeHoNbHEiISdFOlNX7KBf8jM2TaW+WAUXlNsv06iPf3NCpC1aG9SLIldq9JkLJBo8kPJ3cWZgNlXET+WKtOHm8AB4jfIBw/YEnT89pVXXuEW4w/O8OIcWkZksV2mZMR+7HwjaEai2sjJ3Osvf5y57Y6/B8AlogBJV6XPop9aJzULY2wvLhiYrGv/4Ac/+M53vhMqKnUY4pMWjaJkO5lhxMj7H9TfqDE4saVSQ1OPnMFPfOITagcWF8FnUQ8OrHuCzJifg71vvfVWBCZzv/nZz9CKkifG3gCZ6+0daPmbt1UoqfmBGjf6F7a1fPMG1+jTkqhn+YAuakQYUuQqe9o0jg+TNZJOphr/5BegXLdJVgANrxZlB1AwDEmu6a0hhAMIQNLf5TovHolCe7RafCqMK7mPZnUl9NLKKlpdv8KHuq5x5wUMOdHcxIAPrklLSwgHbbdcv4lu3333Ljq8zCfTaz8SMSBPcvgoHDk5lRAIUTBEBoogvSeP3TTCRmhrDKvmQ4g+B3oFermuKQ0rMgIQ24vRwhASbo+N4UcXoO9Qln2WHHWl1xvKWhMGAdnigBEAZ6qcUpEdgegzllY56bdwy3jG3FgshWfDDEJjFdokhy8xKBVdA6Iw+2YQKg816T6tjyJMok/KFpIx8Zr2ws3ilz4ikKdIpUk1Rssj/HugIMZGc02SRt4CC1eu3aLV4x14gUyRdeucO2t45wZoq2mnD9nC0QRd7QiDrYgFbVkLL5Yzwkx68Tcuc9yNPXfkyJm4Qu5Sh5THwkcH0Bgna0Sls2mKks2l4ut2ITvwlw4PDh89fHTvDq9FHLNTxE6oWYAMyqgfIgO9n1AVKHgwg9EyVQhGf4gdAnRnUXnkN0LK51YZbfhIBuVWuzpP4MDv0jiHcFJarCH1sJlFmStJHISx7+1CPngiGbWQO/PInfHCOtOMlaOH93nzhOoJnwX2bqEqJ1O8X2cOcznlJ2YfyEV+Ck2CmHNTKXylSbTHp344ZsKpC+2SydV9P3UGySwlD2FPK/InbFDAl+WJv1zkKRrrisT3eOoWIVrbS1dvMknIQZEj756xBQotcxHzmJCh4xOE+Nz+rDez/ssvful/+N8cX7s0N7j50mde4u2pnMxU5wqhLR9F4Ert71kkrCM7Qs0TP5zGokzn5jbpBfmAMnAZmHBUjzBNquTjVeaWg8v/89/+z/8P/7s//T/+XyB65kiq9mkgsdCch5N/2uHUZKnYc/2FD8ji5p9SPwGenGytLsw/c32bbUc48E15EIpoJriwN1ayppMUZbnQ6Y7pBDm1g3e6axXEfNC0t5Rbhx9SS9un3nInxm6XT0uh6vm0+DPhsSWn7lPGeh0LAmxP/vyyRnurN3iV93Aui4+Sy28Gl7PPdKtAkEEEF3Y2//Oxm0IOZ9ibZ9THOInrydZnZvdO5nwihn4dNl6DZgcbVQfA/J4k1O2rB7G9uFlPaxz7US9qBPwd9zIHnCKYneHsM37ufd7zQoyu7lSuySSk6iM5VDCRZWkgnBWQyY9YRYrJzYh0GCuCBhEmWOl+UhqJN7713V9+67t86h38nNIuZ/fq2WfBfZKFc6tw4ky0MqSJrcSFBT/GGfciTIPy2FtpgyeAzPN8a16nbvYZ4KZnlspJxfpJuxpkGiTliAeEiHW58axNqjJbG61hOiXWgMnYId7poWiehKfq83S68ZiQPx2TUqYu9jENKxn3gGPpBQ2/h/OIQ+0UirShSGxZKAjmX6sXL3QlYZd1gfT8Svi0IiYtnX2mWVLt5wT02tLK+mDgT+twVS2O1wKUsBajGUSrCkxhfyqIjHBv31g0rEhrDNg/CsE5b0AgyNIeddzlYH93a5PuW50jOuK1O2ZGLH4N9DY06bLuJZ9eGy2qHYCH6kVsjHVmvi+gmko2M8oCg1OpGg7qEly97jPLiWi6rfTYSrzV8fKAlN15FluypjSKRSJ1gg52Xsal8LRIvAI2s3uwT2rUKCCUooTUvEnvZelkNHrCHowflWn6WZ2uKrSNCMBZV7VTwoKhcm9x5cGThhvxXWcP9ucePdrg3khqsZVEC69kdOJbZ19V1m6nxDtZ6OxHLRK88eNyafyxum0O6fpeSx3clCTq032zH0yUX7tQEfRMUqpm2LCztbG7vbV4+bKOaXFMiGudkSVjs0znIkdUYwj2ABNb6LAJUfZPZjjwy3cy3t/d/Mu/+uZnPvOZj33sFUxj3k1plXr4OySWnqBXe2hdaFZP+apnkN4yCxqfC0UVSFI0774GoJjYEUVi+DiGkv5sgNYR6LGChs/Rex9w9hIw69EsBHvsRw+LDej4tR3rGbqxhZvKdbzrUFQUhBeLkx8dp7dOOE7Hwi48Kad0haKam+UPGwCHPF1aXWPBhJX6rZ1Nlj9YQkJmxuOcreNzClpCsi1pHwP5Y+e2i8Bje24PSrVgvoYYmJAcd7Bjz9ypzivzPhK/xBHrlZWMolEmKDkTuL+1o8PpMk8WqclB6Y9OuK90fo5KjeQ+dVVqrs+cqQLBQZqtjuSGj5EzUR5DdnGV4CP4dQWsujqFP3ISEz8ofbgnoph4dKFQzOpzOScCUDmtPs0kCJmxBghJC/TcWvjs2IZ2bqDnQ5hKSUn1eSktT+zhXHZGEIeTBUwSPuSL5km5cxQtIbGjZZ6YYcWO7dV3Arrxj22gmovgfa0fE01ZqDJL1TVJwnwZlJwe+jZn3ghkkfH5F19ihfRksLhBvVMDynlhNXkHsn1tAIIfaw+rpCXrp2TzMM23PmX5lC5HQH7y0x9981vfyBxZBc6ZRLf8aJ02SttQ+jLhX/N2KYu/Zpx0p7Gutve1v/iq6eSREBWeAASsnjJPRADG/7/5m7/JNR3cdMyFzkQFTQud/koEzeziQB9K4UgZi7C44NQUzsjfdMHGoKw2RhLuY7l8+SoX2ZNZcNRR12a2kURjevQ4UGhdw8sjwweZj/vZINMB5TmYhOPQHQH9x/caBS0VyqltlyM6j5bcOuQzf0e7e5s7D9677bHKmj9geP3mM+gN24CC3oosHHk0RRN++erNf/yP/zF3qtx+5z32wvmIETWcXLOnDgmNM9kkkKSwPTI/v7DknlxjDFpIOktWLg5Ynd/lbaqFxRXd8hA7UV3AkJkv0BeTGC1GhgcuFt41IoeH26y3rXAfNKo4PlqHZ8Yz3DLy+O59Rif6WMKc61eZX0CM8hkS8vaVcnSGizmdgZCooLVsDvE9fLFi2bpUH8BAYZUDy/RgLNS4E1KJoBNk0ggVX/WaZSWdfSbslQeu02480QkNTB7pGtXYsFvDvIZer1lUej03sKQOZxwBblRPl05N0aqVgHilJaeQVPd1kIABDTWUxSS6bvXHpEcso1Jywd9wFNvE+jACEuWpnMaeOKj1J2mjClTe+DU9AHH14m6DYWzp8Rv2MDDEGcJcWOJj1Va4MJMFK7aUUXkTlDg+EJgCci4LprEW2BxjDnC4r2nAB3ExNawLx8hV841ausk/vpWkRCKB4SXNsVhbUTEOR4lmqngdK9IWSsmqmyoeOf/F90lZgG6VJAwnfIneJ8FjmDdgOPMIq4ZMoIWJUk/eRK2BIURmcr5rmWqoPUThUVEb2tkBCCNqywj44dwIS0IdPCTAoWIAxTB0af4G7/gcbjwao21MLhKYSnsaEBkUdXqulSMP71Iz2hCkSXIa54bwRIHI0/tj5FKaS5/db+oXqctmXNcVpQo8NLapsk0yhNV/8S/+Bfvp+tYSH+7zMWT4iJU3DMZITnsUfufySH/JiZrr1262mMicR71AQU8wGHz5j/5o5qs/+tM//VNiIYwPjrJgiMY3dNXOe2ydoiHv6e/oeITcpU8u2HPeoI3i7FsSO91XchOxStquBUZRLlTjRkn0hNiklXzh4wRxoEcG0D9OhkEYU3jDOZe2YZ4RgHnKU1UAsfmy19LS+iUNSnSYoUWbxVgbeAbbDxLV54swgnFLAEnT9zNVlpCYhCfeSOzYYWrg44bPE6EWSwDaDCtFg/O8BEMU0aihEFlaBi3KnNWSiNQKxROmXSD1KTD5DcEgIYNJPeKyPz4fQ7T+RiUx5jme2Z6lhNCT1qgA42ytH00CGTir6HOiZ6Lm1twNU4RtgNA2m1Fao03HeHof4Dl5aek+NaeI/dTkH4TQWRhhEPvsQcpgzOYUTUYPPclpYcoF/olNroFwIgafR4Zf+DgQSBRMLSd2iao07aLz9tgYjkF65IQv6Df+pE44FsX4ME0B/YUMjLUqn8MCSPMlOSdqTTIbqUCAkEUDZNA0zWzhyTIc3MpsvMvsaaJGtj4WDhq72sGKRJNij9PCRDEQogtjsiJkR7B6KLaepvYKJxJ4tM4P+WUaJKALsUQ01jGSwlCMac9ZhJTeDpTNAdctaI6RybPk5LElRwCGEZ5Awo03rIaYPtXLzJVYSY3jLWA7ghOFIBPCwRk/fDixS6nR6DE+sUgaHhOVZjhJe89d6Tf1UnxaPN3a0hkurqzIaoISl6NQSWL/aJ/3F1dXl32rZhN/ekBUXv00A50ZJ4AlRMiUKTIbrXBIRnhA4AYn3ZRj9BmSIISz1gOsbZgjJVG8s4vPxJZUchqajxCCw6PgfrMHPoQRRvhURp+nFnOGVrXg6MwgiQ4IwEF5cheJD7LITcvdHBEY7XGGkcxSBPhkJX7SEvF5Drapa/lBFii4f4O05pdWqYx895Ig68ukGIGSfdCSKatFyUQ/CWi4iLNdKdA55bp7TBBaSVJdwgAr4KP6HUt3LJmW0zH4kz6encqTcrsgvvRcFdjrNuSTEOCRs/cvmFZDw25Zcn3jxz9EdVna4BeGSx57NLQPN5C89Dnqwy2t5Itqi1W/8MIrXLtxvKK3A7nEFz8bMEHG4EHGNdonCpC6qvnMDKvALL9+4xvfoKGjMGDLknNYSUIttx1duXqVw9E0dCgq6U5NK/h9VMTDhzBru8S+f/c+86mPfexjL730EtxYAl69tM6rhyREGIjeruCF1gW9qMHMlFPPOv/bfQEobLu62Kf5BGEEJkU+qMhnDzlHzDI3K/4ESstgTkjOL16MRLC6wjimfIoCNPUqow2F2aghGsMPHJ8sLw/8kUkOI1r50qTbrkqiR8K4tEyQAEF4OrV33r3D48Ki7lymNLFtjjzT6m5u7tD1f+UrX3nttddQIzpk3RnOJBFWbodhM3TA4cBoQSdydHeT9gwYmJN3aOkQqTCYDb04HghDSq99Z0CHDQnulhIcSpP7oHEs9jlfiqA0x05AN1aQEGvmZtEinjYAK9IlCzH4MTYA0R5Ol5yc8OlF395pywcTWlFpVqE+NbRi6FBso2coNJzLDrj634weDUFvkKA80OLSWxEWz9B63EUQkVxBxR6F4IPjHvtIC9A0Wb4SJITQCu+pnOUtlH34qZhNIZrkiUqVgU6fPVly1EMI9yoai2qPIWzJtQAICTcmjWGjJRCpAglmwgtXbt78jUuXvvutvxKX3ApqbesrG6N1oGc3GWb3ADoGmtQd7jLjphxWoUDDrChZBICbmmQ19P6xx5jKqRTjA8aj5FNtYkQ0hDNkhl1ul87+J3YqWhtHofLLLLFfnVWRzalROHh05/23f/bcxz/HKG22nvEcCtGHuhQBSwjLo3pS7b6hS0IcMpB81VULJMoMSlOCTWD3wBmy85cTxKGDEX+8iq7WWbUyqhKPC7roZOr+DCIVUat6G88RaSsUoHcP6rNzyTdZVbyczKCKL66u3FzhDRYaaG7pN54ELqcbsifjUxIu/8qn+51MF8gkEAqA0SE+JimI5cmS6zDsaah06OYsSZEPESHWk+uzE/YJgrHtlEVs9/LVa+/eff+I6jQ7Q89j+e33Ha2srJzu7xNr3Ih9Z/X4j//4j69+9hN0N2zm8RUg7kkidzrzWS1Tp6qlr57H9HCtd4qVWtlXWF57+OAxj2KAMiXSkHZZhjV3vDJ3tDj3xf/9f/3vNn6x//U3NHU6yavKOcrUEQxJi/JhCCyTnHSfQVm8ee1gZfHgeJcyyjS+WHImKd6pix7SUfW0E1k9J+dh2fRDTqm6EayTV0G2QuElFaRcxnLGXrQJxMChs6urVTpFNit5jPWYIFMetYWOnTtJHZFCxJzz9URxcPnGgFfeFrQOxS3P6ohKuwuAM+omYKtmCuPzQcpvXwAdxRkdHliHxwcDPjLIbc8I5QGWx5w+4sUIxLohL+avhQvGMw4jbSaypCrJeSeDWKb1xGq6PnOy47spcwPaDgfUaF19u9+RTha3Fkm0pNjKGnLCsQc6G2LrwFeipF1nXcF5lapiaSGoZ+rFs++hYAFg5eDoz//8L/ce3FMkN9yymnOkFnKOplMgp5Vw2klKR8KwGEXTpVh8y5b2Q0SnueTCckxHITNMTaim83s7WizRgVAlIK1yLbAOOktjAiChf+h+imYcDZySxXKMco5XOEyx9HMISVFCpOlWnTwHfzJahSnT+EAu6WIVPbcz2EpsOwKqXBr+ammPUS7g9D50mk2msOI62J5nYxI0yeC89JkB2Nouwek71DLJKoWvR5Ua6a6trcwv6qLnWU6+AyCKNSusjjMITsAvqumcYm+64tnrPCoQV6d8YcUmL1AlL+LqvChAX6wDJiVKzZJlZrbMoIiKzOurzASYv81xkzMyMwTU2R9Pxmi14KMSJtvirY5OHahOsqjL16TDB/tpRHjPKO0ALxbvHTJvFME0J3ZhqrkHyWlixnkFpo6wk2PVTYdYve7mTLWyBBMS3MHRId9fkG494gdOyKn5ewwO8QwTgkpPw1IpxbkRsFc8NYg/jkWpLrjOpYhTyieHc8yK9/dQHR2/+tKcFoQf5gdTJeGOi0NVkkRfZikTTkSNAFl6IIwG8cigxZk/WdBISQJiIrIcpYIlEStkFyVhTmuyogrxzBEvTi2x7MpAn7tBdjcfH+xcXr90dV/zuZOBzqAgT0md906QBBlQL5NtNMxrl4Qpbk7A/H+p+7Mny44rvReMOEPMETkjMc8ACZAgQRZZrGKRulKpJNm91qY2a5OsZfrb+r3f+qlNuqa+Ut8qSsVisUgQBEDMYwKJRM6ZMZ8hon/f+tz9+NlnyMgEqKr2jNzH9/I1+fLl4/bt+9kXX2BaToGCo2I/Ovrw409u3LgxWO5sPnJh72BwRk16fShK3RzZ2taROH9S2IVIZsmg2FYZMSpXgIksjFNaOWag5NQWY5k/7CPlQRCOarq6IDI4aMd+Zx2VzB4zDe85PZZhDCfJdo7xY+H73GfFaIf9oIIl6bwbWiUog4WUMACvq9NN85ghnX4Zc2apejzkLM29fe90FrfIFXNpFoi79FVQYXsqDutRqu5smGYMEL2Sx11pHMXsTL7CBUPJkj7lmdIm3uUQdJ4AdVfIC3vRMYTY2kxhXIRiA5TQOjrDJDZr43TkB4kyIxN3RMtBCFRkOUG4Ztn7nIyeefo3lEmgEldEdWUqxTh9viu05N0leBJycAphHc9c9eveTTUk1winzsKvaWfF3aqPNQGzUO8TjpK1YsQbDOpUJ9nD74mWOo40qmygLyx1uswKX3vtdZbnnv/Wi25eVBYx8vFYlLZPZOGCdU2ueblWT6ba+KlJqgjsYwZE10CV9D5Twfoxp+535NVLZ86du3hx9cw5lgaYzJJr9lXZW3SN9zjdXKf6EkxB5NftrXUL8PQLtrXm2zt3/u5Xf8tKJrmlvtCNUTdV66gxVKs4EIMV6tdee00IQeOCkyzzDmfTxh3XWDX1yr1r9KT4b7/04ne+8x2Y0BTsbO9xUsePfvKn5OuTTz/iYP1enxOHIW/TCH/88ceXL18WK1r1aCSxxKSf1CImfaZObcRBphF+5513OA/k4qOPPfH0Ux9/8jkG0Dg3hFqc7B8GzT9io44oKpr9B8j6eoxqPF6tnBkvtzPYv6EC2cRsVubUfBZ2gdAREKH3NKkQqoocTpUIQY5bHfjLO1TcqoHs7/X6e4fbN298mb5w9tDFx9ldzvD6w/ffvXbtJt0ZJgW1tbkh8liqYOxVpNCKdldWaVFXNtePetqQvhT9UmthIxaLh/3B0eGAF6ro9HR2oq2kukaJM6Sh/OlVcNXlOP506EOZZLhjziw/Xj7qLkcfCmCBjwj0tncRrRtsSZD3AsDjQI5Xx2bUXyGfIEQeww9j7GEK281xZxzHU2Cncqf1yKmzmzwuVYOu/+5XUQ/fRqlYk+fNHpQOD4k90UURqgYVRxu8XQ81tm4vdpfYhUeni7kRp6EqmyyULyRE3qPukAIfXyGnXtBzhlm4U12IpPDCQR9GdOAIE4twLR6O0+XzAWIGedRYnhoUrf7nR5yLMblZnVR9chpZlP7qBTIo/aol0Q7zsIkskJu5CebhOUGVWSWIbyu+gjcEwTiaTGGhG16gFqwokyMan/FUh8dlNBMwqZieKBpFmDBRCzEIM5BnDECKXcIWJ+I/kT21CAARUyJFuUlkJznPlI4Jyd3mQ8+gXifm4NbQmI77KjNVwXkBaHgtHbbmDLojJacgg0mIypDYGYcrcK4g0xIIJyqPgaqX4fSkwaQgk/PATIIScjBuxGu0Kh8idFLRtk7NwOmlkwizf4XywqSVpFnvfXmnwcq3thgZmZraAMKzAfFtDa+zUCMDJwBhw0n0o8lWNY7UiB2XNfB/QhzT8cwTQWhoryBue1I9DT+JGpBcvHjx2z/6Ea/IMq3udFfgCJBw/1W2KRAmqOer0+p4jW2f7J4//8//+T9/41cfFkLjYGQgnvilUvegLdcpUtUGcVXWU2ABgpjtE6f/Z4fJVBlRvy5oRRK0YpQg9/iBA7TSIQfzNKS4K8nAjSQSggdnlaMCyzwe8Pfrc0AwmqMHRcMDesZY/dg/CGc6Wq31Rk6jMpYB7SjvD6j3bLJGjnRLmetHLR4tml2IuCckDU7KywmsKobRhDrCFTPENfGLfI8gEj3NnRrSG7c23Sx1UmqDZmGBHR9XPviAUlHK/TiI8p65kZdvpJBkn+j+KQPcwS2khUhErgV1XFrnpBJ5MAPm3Ez5VWYnLFv0mUJwL5BVLQrfC30svdakoUOdNEYz+wYODSazce8jZaomeHV+FpZY0d0ImLvde7q9LKYmRO5WRBT9FSlO6aamKrWTe4U5F/4mhDnqsdpFr+1zn0HAS7kS3G6gVSBr4RdkUp1PaBl7CI0Qi60mBJkOiCtgnuiCxiqnSSavIUcTEiKQsAANDuRmCy2WSSKCWPjxeKawEsRhwjjgiEEO4paN7IjyZZyYKpiNkxq0viW/LCujHlMmShllMQjTDPFNnCPXznvkiEaX3CVrhABYHR72Vlf1MViEcwuC6MOQ8SSaJlo83QJBZOlAkMbVt0DRhA3Z9DiMA+G2t9tjyWltfUt6sjCpwYA8ytJjVVTKMEv3Xl3QRLW3Bx98AD5egGbSzqo078WzZ5B1WfbunTl9ht1zF7e0eWoyhKpJSRel8wVzeCIFkmJbZ8eQSVaQkEenwtbB+sfAUlJ0K7unqQ23BNaAIzuxlyre/uR9XZTRC9Ws7CxqXBpEcmDivGPMbQEiyBtuWO8IdpJSqypgBJNIw4G2aZUsgMzqg9UjThI8S2odbwCdBFts1e5qPM+sHt10/K0Wr8N02edBTkzDK5Ci7EQ8CS3cp0VMLSNOhMKYFMXHkYAgtxhkgnoEKHxQvkABzqcltRAWqjpyEgTwpzKBFkOhDxGClZmKWUsscZOU2weOWG5NXusQ8WT0rymRzDIE4gBo6i/HEzFtefj0KT0FHBVIrcXMeFIvvMtIGTKFpOTFEXJSOwAEuDdJyxsbLBquXbgIhFU7MYozeWkISaUWAIAWC7jIhJDDfZkFbiys3L59++9//Xc8WF1bUwvZ57twEWBlfwCNlo390Wq4wkuBkJplpl+AqKVrDsTLJEqc+32qPxuNn3rqKQ8AaGbJDttO2Z/L8g7LozzPg4gKTuv64YcfcnAEbW/h6SxPikYgwIKW5c/8JV/oA74ZErHleSZBTrVfTbkYZcT8ae3M0QqINjcBcENn8kIXEchqV+lBgBeSqeoVVuSXAQJqYBm6qoODHiVCaqEypn0gKrHUK+QFDSDx8CshoAYfbLx64yYQHjKiIcxB6EXD7m8AkInCh36Y/b8ggInsUF65puAoLDpqMInIE7o6x4NlWCH7oWxYQ11CrOlLkzhEAvbEaYUh1PFjWW3sA4TbEiAkHgzk246U1K8ZgSEmQNuaD7cEMovlScXmi6fPkC+ANZrjAZSGZB9uDQzIySXXUYcXZRG9leo1ZuBKIF9cJ/kDcZJV5Vog7ih4dw4EfFdouQI6U1zJheOk5sSmkFlym3j3eT9TXubjHOW7+/u1zrXmjk8KBQ7QV2TUtyYpggtOQStJjYgxO7vtNWIPPfetW4OFvSuxqBSfhGLrieqkNn/IM6aGSdk82ZOqFDeDX41qOfaK4ZRa9IrNWF8EEzuWocnJyC0DbftSpHsHnB5dIMCnZ8bXwjnhUsLiqSDfDy3aap8af3EiLSi7u9s3PrvE80C+uo5EQsmR48LGGcfzylNKPS/KFdvMi4jCoY7wLZBgYjcesaMyqvWP2nV8xKuUnCejp1w1t8gcRq/tkXiDVutcS5yMG7PmPIlTQxKmn4lV0j29sU04lwc0TjKCcC82Au21NxaWF9afWlbjome8C93jeAYYOwrZBgJk8ciPEFOphtA6LsBUPWug43OyL6+T1ShTnINDlGJfmHjjU9p67Jh+IjQ1yPDyOwehLlHKWFrFFZlNchRhYvDQuV2+1Kq3QbGWfJz/tBr4N6kjLwRY3xReWLvLpPHwwr96dfjS+UM+Wry0pLOm6PTILy2vSy3wNaOlbxopMoUnDyOFC6EuMZ9X5dHdmTPnGKbw2YpgpjGcLB/s2NhD/Gixj3cPTnWe+ec//vy//OK999672JfFVyJVudHGO20fM4d0XdTeJfbA4v9qU+hmFjvk4m5/QJfceebhT4Y7PGPkVFDVeNgFdVQ7+KkobBliqqHpJQhlVTmI0JRo6P1cyV3VQCWfDCAFS6fIJjfVVVgCdPaswf0I+bq43s1EVwwjXjUn11iS60FX59kNOJwd68WrjmzAAicXA+dCYklR6Zm8tJBV7Tleepjle8I9QXCP3kBMBuVVM0r8sDc4Plw+PKBDYSgJ/l6cj2xTlvIFU+qRpXChVpwM5q2A/XicyQe8YduL820H7CdbbOncZ1lDeT2MXc9M7kUOI67KKG7DJWK6U4iuKRW4exZgwOtlcVtG/q0KziUsJ0tKQfNrRx/Ujv5oeXjMkPS3/+n/hUIcaQs3DmYTbepPdfZIComT1NqLXcnsjwazw2fLGQYFraXZDpms+Qt9LuJmUrmXDHRgvQqNenu8fTpoaamC8SqZ4CB3RBjHSrn1SC5euGAZjzLz7pUqpRk1h6/jUcnywXhSk6a8fD8qoLDdfNOZSKWDFaoAxHYYg1YI41E5FfkVVbhP+K8Y0FLFwiZlHxYOpsgS+xisWI4gdAU22TjrhByWJyXZIfMRJLkIO9zZexhdsiYj7JzUB+uYAu0fsLeGb9H03Ioe9nYZ37OwpClQhD77oags4Q8NOxRdQHDttj5FKxjMIim0daTRREAOBE3Yk8VeUdzTq59oiN1k1MhdOKl6TONLOmM0GZv8okBIoMLw3ivHIR6n5WO6SzjT3XhOCJKnZFoV8CpDrHQDL9khwghNUmg5aD+itWC/ntWQxMpP6DfjFr1kev4KLXD17dQpnwJcmaBYgFRC4UzrSDWOgbZKFzg+ww8sQOsdDugrN7fOcUzkzWt3GGRp3qViC4fDcOympZmBJI6tIE8IYrsQdgyTwlK5oNihgCFroxw3KeQYoYkQsbqhF9e2Z95XpgTY8iMzxwgqmhllNv6kJPNi+sSFYa+zyFewVnmzZbB3a+fW9b1Tp3iivBznWlKLMQVKgI9V+gwAljuU0/beXSiXu0vffvFbh30+JTK8/PlnULGZmtIh72i/ttp+/rknsGGrv3/t+mdk9+5ui5WUM2sblCkL8TYU1y4Fxj+UiQAHHmbwore+J7W2xgyYoicFDjLSjODy5YpoMI2VgCorTmxmr6TeUuFa0MAkTiALrBxDR9EEA52aTCqW5ikJPTVncTIYBB3vw2M6egWYQ541inOQDIpHQ2xKX4ss1oH5Au+NUxw8PGRmwR8HYqAMLhpeqtlWeDH21WZ/FOCJSzuOdjUHWwqf5jYaHpp+jbLay3rz4Litxz/6ggpXXlCissv/OTdU+FJJ1UycHMfQxI49GmJ/vEhGS+GUHcjJytmMEFKMYjERohbDOirbRKoBpS8QnxklaN2ShlHWpsW2FOgsqoZA0AqHRtIJby2ooad52q+Yp6i2xfsZFkdpg0ALgqpTpMQp6uzraySdUFWLtlY1B8NrCHHzLP6f1cMmsRioKYL9INONlZrWHL1ktne4+/Z7f6ADgtUnH3700LnzDPLx54O+VqMYxIFJA8RFb5ipJZVrqQ7jCOFqiXGUtT2W6kKqVynprtBtzGNi7CdyGDOYC0tSfzwO5AsocoPV9UcffXTzwkVU2HdGwqhyV8pALqs3GPiBWj4j5dIqqD0QJmpkJzxQ9hkPNAdUQ/o1Vp8///wztUIL/YP9XfZ+slJMEyM9fejH0dG7b7/NczsY4Nwyga+ZoQyTQykR1uiApbd8Yrb1zDPP8L1WjoZgJk4SmDQF2Hz/YPcffvP3v/rNr+DDSIAk5neMUTkvInMVMvk1VQHqFssgPP7It/VIEY1R1cvU1qBDoPiwlQY0scpxNNCMj6Zqe/dwew+rp2fG2Af+7F/lSrOGRWPVsaw7RksVRnf2kUL/TgC/QIjPCiKlxQnNWYU46B0uLbaxDAESz0DD2s6Teg/h5+IWceRbV5VuOAdKRNHjBl4k4pEpD1d2dnS6JqYGPKDHZrOy3tTiQXHUCDmRegIk7S8c3b557eD1nR/+8IenttZopVudVfVNu7fwSY4f0yJyp3X37k5vl150/9lnHgmJMjNdIsrYt1sDffeWLexI7CymVWb0XKTz6Xdby7wavbQHq4PbPF2BigAL+zB9BhutNXihs0q5Ty6d78amSc6yORB34FbiVNRRHJguahypACUvBUYoLV6nYbzB5wqo8rwPxOsRi8cxD0IFDf9G+Oqe4aAhl/o/UgsfxaO2atih0ZJKRAfnL/N9gpjb0rBqQUUjHJiQKvWIjNgnZsBlDafClp493uFuxcyLbh6KNBh3uxSPFsggAXcPLhNMA2q5SUz1gx7lzpSzMAtaHZkUVllGiCrHaBCm9R81p7E4dube5cVgTPFwiNSyTkoVtS0rC0MYpqYQyJ/eqCqZwmzRTIYz5NaMvgGWbAGlpkDphU9jykchpiHg2KB3rn5cGEliBAgmgaRIhYlgIDVKkpxqtWcwmWAwDxDMpUwSPSrZZJRRUsVGwDDul19+yeIaz0CBVKQjVIoWPxvdR9VtODGktQcYv+RatFE6NRPHLRJMAtKLCkSATOJnqnmpDSpYwXkWt1mCgmjESepNmAdgrn4jTMfoY9iuwthY/eiEWVVvR01Jk5b7SVmTkEKWkiY0NJyreoL8AV/ZoTLsZOEWtn/UCGqwOHh85cuvI4VJ16uvvkq14nEi2Wx46dfhDC0MVeixpZ3vqtuYYzwxI4154ABH+kMPPfSjH/2It6uMbH8hHmXdrERIsBRSNezUvUoKfHLEkptKKgcxHN1laP5VIU6bzEBVM8no6Vc8x4MhU0lSkj2TGzg7HhycStTwca4z76YKmol9soTCk2n55tYZiDSpVfc8PST8piWmI58QWqwxid9I4tYQq3G/Dky5U8Bw0MCExZXYFwBIPONUOJq9GTpowJGS4h0ISIrpJkkmIVYbuKkat/iBho+HB1RMth3wkmPhAGZBLsDJiHGoEXUSQBovIIpUtaPGua84fLADlU7jeN52D7ZcZ4koco1jHeDggjNwvgJ1fkoWGmwLh6kMi8ELecGfEymsHLknk4I/yXNOUo1coyGOW1+Nk1ObvWpRrKAZwm0mGQmZhJBmKZTIyMNjqxfIlPLuHt8JPthnLsJemHh4jAbygexRpuKq1etYkh7Jq2JFqwo2PQon+DgNHWpCbgm4H4spRKw5t9QalBQktg6QF+LeEuulBy+Swtkk5il8zVrVg1gccTybuK0BHADI5J2uEykYRBllrJ2pzKowFB/Iom+Vk8eUz3y4OlE4ORgTckcoDaORbp5EnJ1yOxnJzCxZUozDtXCDCeMHrWhwfBzrhrF6aMxAy41bDFajMLUukLKZ7VPYmgNy4ABPS+FKryzaCCxvhj+k6SswbmWTsJBLhHhIl86Yl0kvnsYeN4IWoJeXtdTigs69vzVnE5xpIWc0rhkn2sbSbcxAU+9vcWkqGLqRiqsMl/laMg4PUSp6uJkh+keS5usU9/q69AeIIG6vXtVRnhz3wS3wQu5c1FfboeTX+FwJ6CBaBjFR6biWAENKBlqUAJO4bBgf1wUHsI/jwObcsuBLAREJrqlMieMy8PECNAgRotHW/kipzVXkETGtmQOxeYlQHHie8bklRFzoWrNGt9jjzC47adjWfmf0iVRxNv9MJXICqY5MvSp1rDRGWCb0tUZJDGtQECV4LXECZ8T9ZDF4luI+GcU9sOBW9CyoBVjLKnHj1ziTHAqrOiIO4XUN/MKqIBdZBeIIhE5ypMGnRjbPcGE5im/L1Zik1iTFW0CjdyHprbfeYoMtroX3Ut1+8pOfnF5f5QN3tAwg+5GNOVgTj3NqnveMi7AqAhzErKTMuJ8Cpy1ifnr+qWfUhPodmvhen8yKytEnpiwFbSOD91RmEoEeBya/+MUvOOCCOKbY399FOr0wGvb7+hoHWqEbXwrhw4OsUNOyTfIBgoYJHvOvpFssefO8FQ68uc6XBpmOkcSWc/BtClOx6AznnX0eB+zRfbDrmUk6+shQEWSB8QJN4tzUR1JBdpLxNV2MUCTyBoUh4M/iaQQ0LFQoU5DJaxTsWBbAxJEaOgCEla/myRW2SXSVCg78Ne2c0iQW0ntH4EOw5sZmNQme585dwLYkoSRrp1wXl5aNUPCdQZD5RgKbAz799NPHHzuLPwz2dTwsa88kQQI+ZURXxS0Fx5ngFO7qmub7ALlytJrRJMUNbv3wI3C6HY3tqWv0v3Sq1qToQwRlIK/hc+J1fifRSAVo5Yn41miOU7gEd3lcCcBLfQ/C7EZBhuZRKTXjbgTolOpC17qOOk0YUrKGw41lWOLGNDDhB6/CwZyFEAx5WUuQGO8hNwgTT0hIgTOBuG9N/nWu8Cnq3S8f+9LJqYogIpNyU2qYu2BOZe5UcyhXMA2vaYmXW7TFdIUht9AKwsldYVuuyUfZSdda6r78wz+jTVwe7kDDiIkrp89w9aP5wsgRxJhLAw5QZyvpKTm77XgultsF6kw8h9f6N+1CLEWZFoKA6M6nLCEfCAM+kkY5UDr8yHbYLKpfzYf9a+ALS/WBp/h6WAUkPHbx4PD2Z5fe+87DF8HQnpMcEEQU5UCcLGCaWro1VxthwpBLkIgqznQb8QpGY7dZip9jY3tScXk1WIvaCVgjk1OlRq5L4SHL/Sr1y7kDkvSRKZqlUHQrkktkTlLBKZFAlnYWWuCNyB7F224tn72wtnXm4MZVesEVFojohGLfq3LbXugcafqn5//3CifRcBYOcxmX9CwhKtyqPsxCmwNviMYyQBjWCx5xT5lKC4pj7XdbG49evHH9K752vDmQL/OsVQjR9hKJ33Ql1V174QBkr7Xw1FNPnH76mcNVnqVHcfBAJfv5pLZjLjWZXC3gSgtprnJxXrjimUxMVOiqu/I5sKiEYCJV+DHbefRPXzn439cXL2/TOCsp9JE/U/On9XAwYAGQbhRyTsSmDbiz3Dr3xMMHMklA0SF0S8+cwyT2GO99sJK0CfQ9xL9+QG1Pp+Wu+GfsrTBbZzOJ0EpnGCqXDnDyi10IMXmP2IxLWDLSquFCqgnRHppuvJUb4WszhgsAu4X3ahc6rxC2ltlasnb2HBbXh7fIQnyEM1ksKTeFq8XF1bj3YUzyklu+ZBD4qFpFKEYrEYFj1ktXQIPHIV54Eu0eCBRijKsCxeS+hoCwKhN+iWOTPe51yFarweBAr6QsHET1INdQ7McOYio2cdsHieYhfticRl0xikx+xPfO8Ky096qqC9ZZ7XsO5kJVyAD/qjqQef6U8aMebwcypmNG8cFvf7f70SfdON/Zn7Dgw89Bk+wzziffHWvISEblJ4u8owdjuduCdwrIwaRSdjfdEi/l6lsQymJN5jv2y/octZYBMntE2VXIFOQo9lVRD8HTfpHMs+gquWrVIvsSo7iqycmaUOvjtqgoOabTvW4ejKrBVbkI601yM8QINVWq7Kl+O6VYJSHmUUoqnTHy6AiAYAGYR3PaVj9I6xb2nJRYk98zTilkzVlj1XCBZtXtGA3Y0nLneHh4NDhgbyJ/SzQV6okpN3yVd1OYe8ivo1iJR+nHF9JTQU8Rfx/twxTqDMIOynj2JsDuJgZ9fYGHuQSQ7pI+HE81B8J0C08jY2IQbz8UEjmhTJs+NEe1hhX5Ax9arpSg7E0F1XeHYpTlUah4pSIr+SVCCJMqFe9m9bDHpjjVQ0mX2jmA6WgQKS49FGzJhC/NweQF2sBHYYvIbGT34lKJHiTQYrSZRWhZcOP06eWN9X6vv7u716KtUzsvueynQ5j5w8GaiEVMRK07VRY4OA40uQz4iaNeCQCZ9x4e6KMkbCPGsQlHQ17R04oq+6qWcal4q4bNUyoONuFq65I2zbLxeaE/ZBmbLzRdOHP6cHfn7o3r+6dPbZ2+wHn/R4NDFIOZdGPTdafDTrTe7j4TC8x1eLgbR/rLgoxvKak0X0ZlNmjZ5FJfhkIlas9Bf+/Kjd72wc65M+eBCE8mU1uorcGsr+pM4oWD7Z1ui5n4EoQwQE9WYbwBkFbaR36Jb4RiOsziuOEwdiQEqZSFEKWphyUx1KSfYcaBIqQwJIGCBpI6Jjuz8a+ljZAssCzpTBLeqwbMeeLKC++dKEeLemAjrrHRFf7kA6HqIvks1YAvfcixCVGaeLONgmLSTRw0QpObccvUR0qy3wwt2AumV1wQ4a3YTMX0kviwpTNwh5xCiLpc5aUgEKKOR8Oe2vmwvC3gDtBzEJeLKgaFiKrGkM5YJFksc9Mt/LmmhIwcOY2bTMJNAsZoZ4SQ4S4a4HUZmZ+RC4nLK4uSleQjUXCTtAWtEZElg8Tc1G43MKbdFqrJRJJQA25ECDDnCpojIWqSaAxiqho0CalTiZdcNyIFzfByOxkJEdIUd6IO4FC6qcozVRPVAoykT599+vEnezu7/SGT98U//fGffe+VV7vHg62Nrdf/8BaNM7UDXov+ckwM43mDAVqP0agR6OAlIaqF9InNycn68uxwO+Dh80KI4GKK/kx1wePhhZVNCv7xb32bKt9rL+xTj1TDjEsNliztegYY+rsUcr034+Z1cm5V947IRkPC22+//cbrv2N83qInoUfiXRDWubSkkQpdS8J7e+zgoUUysClp4r5GI37+woVXXnnlscceIz+sNtq7iLtEuEbjs8gbKcfXrn7yyUfs/+XR1MbGmh8NBnt544QcAYDH4EWpZkimBMz6myrdRvHQphg4ccUqFqMChU9CcN13KWeaWp0RZtaB3FliRm/+QhI4KtkkhtFRlghtzbNJPO1e4oK8IZfb/mD/+o0rGIeHr+p/eNQRvaT2mU8L0ejSSy1zYDTTX54ZrHSO8YH1Y31SmFYbVoe85cQ+6sWFfTrkg+HRpa8efeSc3g1qUW+YnirQR4PpPHFrUeizd/cmmnRXNzvtzu3r1/TOzsI+CKzBaUCbVaIOqu3WILDActq0X/I1B1MKBJtJw6qmtNmjrT8+V8Hnr1Y6RIoqKqMYJKn2EYcD+eLDChpKaSDE268uw5hxxJIJL1EhUYsRNANLKwvLq0KR7MgOB3ZDrAe4tBSCmN7ZKrmoVQVo94Ajca1X0sjF21QQU21JVqPH54XFa2Rt86yvNdsafpI4tEW9+filxEdo4Tm+1YhQQQ2N9MlFrFvAM3oxZtRF+XuqYQTwfSKCCZO4aDeAjJiwOiqVWNHR5o9kPkVSlDSN+wlmRIvJQUJXP3nbQMOLyBHfnFwEZ8DoF0vRhRQSbnGvbJARmmMFrZkQWZI/5eDsjWUyJzV+4SmHHpEqnTdQOJiJHSXEneVaNPH61ggWnuOqJA1Bvi0IXnCZxImOTmLtQw0+yDUHYVT5NZ+SWiKT/P9RIKhKu4hoRsnEMSyPfA9v3mBKUxYJnR1yrW54hvX+GMpLoBdqqir6xxA0hyd5p8h4An+zrpZzCGYk6VloPEuXl0SYgfiAYJTUhChOrKZT5KVaMRJQUFIJZu1b4utPP/3kk08OPns9OmCjG2fkySYpV5V+biNxGBofDim7E8nwL5zr+l5oHQk1tEQOsiFo2MCZvC3IJJWM1GhT3VIq1UhuXsch97yrRd8T+b4Qzp07x4CSTXFYgFkltFOzdl88/0jIGIGAetiZiKWgM4OhhkRnob4awbSUOy+4wSQ2hI1et6yZqChjalGA0NovtToR5rIygZC8eoQ8GqoRi9Ts+QUnRWJtkYEjqxunNzf5ostvfvMbXvXXKIY2vCqOJL/hTJkdTSVTBZ7hoZUnXegYuVCBAjNiI17XkTopc53yCxoBE7KhAom0jbaDRZBUBBHhpoYXNUiSbhFOUvWMybXwL5CTREQVnlOrehLCBg58zAF4na8GGreSmIo9GWQSx5A5OarNZZ4FUmsyi/MceMkFOI6bs+OUiPY/RlDj0GPLFwcb6Hv3pdRQgLjLruZWsmOGc3SYk+TcNRAsJcsdJao2xxmR9DiohMK0CUAGsR5X9IEAIAEICGQKZM0U8pu58VwJnFjqIo3csUrKcmcsYZNT4cdqNS9vMuFHUEzDUuUiCeaWUmQREZyfCMYJNHlFjZ9Xz5HDuHqMj7MDbeKSWQFxAECEq3SOiMvF+EjhlvknhoqvJ3GQpV6w9avifu/SmoAPjm1SeJKENlmU8gg3QI6YM1YCn2UjHgNYE1KJoA/9M1cWrJG4sqJDe/Z397AbCNK2mz7uRJxATn1q85fXbrL9anX9tPSMYAVYFsfs0LIFD3y4MevmSgBYIpH9GHWkxdaUChMUR1vOiUDbm8c3ed+cohQ5C5+susfZypCTiq0syxkByNYzXsDHRMAhEbcs1KINFLfIfn0LleFcQz3adrCkM3IVC8MSZ6WdOKYIuERACwnP89Gcx8NcuVU8zoA2MgKJOJAo0dH+s1PSAW4x+0ZccpWiTyLjvtIcBZiDALH7yWhAutpx2enoOIIWZ6wQ4vtaaYQcWXOzTkrwa14wWhOU7+ckZZTR71T+U4Ejmv/psZPr08j7pGsZYRbDgi//uddQtiAXe4h5lMss/mCSVHRwxOQ1ieBR7ESAWxkjGFJfi3QiIlRIvsHuTrY/4/bAOaCAtzaJcKsPjT77LG8hbN++xW0cQaNHL0HbvNgOPA4jwTpM98gmXbqHnPrO2xVrZy+yuXiwrPPu6QALOrfEWeUShPUlzH5fAgqjaRG4scjw+uuvs5ZCG9jv69xnHhYKlzVvzsPq6az829eucsU41PFpbEYwGGIxtzlEer3h409c/MEPfnD21GlaMyCMP2GbDMVPvPABPbWe3WB84fCDjz+iVTSC4UUoHOi/RsJGBTqCuYi5RhDcTRa3BUk2/CZCtD2yVdGWOIJ864K7L1k1n6+jIHZGLsG55oqDXbhwYWV5g5V9ytpKIm6qRAgpIRVTnK2Ph9D9PfnoBR3Psqd+kCQ4ICX4KPvuqsCE9vTWmvPOVfMbOugY0bmFt0TaefgwlKJwWeMCZ6pXI+Lr2GEq7dQsA0RzNHQHRJwcSavxELQCkuRADj0WMiKEREAj1XFuidCZ+Ra4knO3ZUzTKqnynxpI3JW+4BAhIJ1yUqpnZF4WycwDxWy+yStsH4ydvCE/xIUJrUSwGtmztkYRMSkOSA10fKpJYZIws23N1kCVXYy3i6zwt9Q6FRFpihXFmhagvbp/eNy58NjTt776ggarGydRap8U2Rrq3JapmSmS6gib/5HR5oRStkMqAdFR3Ompl1srdwmpShdyn8IZC+dBFp5HKvU3OKWJeqPEwLc/cU1PKdl9ht/wzAlaFQxLv4ztFvs7OxefeebL23eEWWVKz2aSlqCFqGj4MCght7nYQPUKQxdrhm6COxIsg148Ru0yOlAb6W5gQDvEp66VPi1IjeDm4pyGMhNmu00+p51JcB8Jo7wUIqzKzhb05Gg/gEN26Kx3V9ZX92/dOtjZAb4cpbbUVtOj3XZsLxrIlxiCYwiApRwVVTkV3vcRsbkSAZNAlUYpAoFdZFM5uqwbSVOBwoFtnhpxpyJEUpSXMigfbWZgu7PYPrPFmIiRx2HsVe0y9mFHQRNR7BsB/rC92z66+O1n+eK8MsV/rmnHcKCfgE+DLbc2PK5IlCIUhGn/QpvpLq/LnTplCurmuB1rRhvtx3/83Uu/eCNmO1EdUq3hJeuCl5w8lOb5JEM9eRFbqPCNxccfvrXGNiWReSJExsBEqCBj7jZ2U7hH/UpJRajsMyXMrG4FF5VK3BGsZEM14LU2M8SNU0QtGOPu7AXcuynHUoNam+EIYTHeUCF62FlibN1Ziw89MTAFpF1oet+EaJqmBn42nzhk2xKdEpzBUYlNQWmAonSKuRuJiBtPIl/UvvZip6cddByegdpR013ugWzpvtJG4JHyCibwGrSxE1gfKWepnUWSfSpYfOgDsYO29kgexldl2f2GXJ3jhgI+hsScgTKwi7pJEm03h2pGkYVtoxT06eQIdgC1Ffag4FA7U8ILZFa0+Fy1Udp37v7+//zro90diYjlb/b4iJC1MXmz2sZJ7wo2Cxurj9My9A/2kEuRhv8fYsTW4hW1NhHsG5buuNjmVLX53Bs1ro1WFExOo0Yx9sRqt+wQC+Bc2i9Az8ZV55JmP/GARj4Jz/z8NnJhzJE9ZuWIbBRdJvujcU0L4pRIzmDoFtlD0wycgj8fVAyUmYhjASZaV6EZjJyXnIhWxeRVfvPYAD1pT8GIp/1yTiD4FVdsr0tTdmZ8gl+Tcw3cYez2kD68X7XU1bfB+Tta4AyEHm8LMHpjuMFL/+oYaYCP4kXIGMZwTEKViyQYxWrVqIz3q60Uq1mYMdPvAQdkOe8a5LMoySIE68G2BUNV5irQdheO9RZzPDZmxhduRk5ZztO5xjRzbBkaHOswGQK88dUwBYlHq7wWsrTMmBUnhBtXMg384LCvTb6sRBwOWRdgJAYhVBzHGtpyZYFQy9/gQVWbIJirNjjClcwVIA1ybGiIcSlQQnwnXS9G8Fr03VvUbmQVhqT7llQ4UgD8r1LRIenGJ/RijVUf8GF1eWVltbO0QjGTI+U6vjCBUmpfeLSvtkbuCzOvg4OjUWpkkzgWieqsHUZ9uBwNV4aLq+vrHNfc5zWOpWUNyKSR3HN41OY4xCG7mFegai+vqKUlQasVA523T+HwJ3vxchvjmuHq2sbaQ+fP3d3Z275ze/fUHdajbUa+10JOB8f99tI6DfnNOze8GWYw7OnzKppUcs6iXICy1DXMBWOChIa5uOq0feVM85GD/V67tdfhLGW0pQOEXJvWtaa9vX/AWGuFl53j3URI6DHweq8RwJ9g+5sz8To41RAxH3fj2J6GwdXxSjeMGmMVMihuwxjhx8nO3JLOW9kYiHeh23w7paWvgPCMpUN1xKicB62vwMgFQLbHsoU7RKs94ZsJfc7t1+NCShEU3gei0kj/COhgZCsrjYCHaygB9WjhJWn1FGtRvL+teGcZRYMZ19RqwVkvDygrgpBeOOaIBEVGk0ij6KrWIZKCKNyPWyloN655mQM5CCluuyQhdtcq0ghJkUBESeXuXkG5/icc8BO5Dcd5a4wxUpURChmk0ObqHqWQ2rpG0Y8aEHMIbopKUDhnw3oFYY5EtCLVllfTIOeKLs00MXZNfI45vbbDUQ+v//Y3+3fvsibKs8If/OBPHn/0MZpr7bJfWOQLeVtbp7/44hIfxNPTQK31aJWNmWBkJh5HjWVLI97YeGiXSXNDo2RLxS95ZDTYUhvV2kTI6dVz5zkA/rjb5iULfYtIWKZAG+5VWz3Gg4K05JnO14Ne4UkRsqn59ddfY3FZg8rFo+9///ts03n9t6+x+D7o9WmFWHOkbXzm8cfIO8cx/83f/E0pmhJJVg1NAHLLGzDY/8KF8y+99NITTz5GFzbs0dzJaKTS04FGsO5xO+T4aT7SoxabM951+qp7UrlExtQrRNziIdMyLbRISFUvs5cbxFgIuNLJdmY4hY3bhLD/qMqDBwmaR6ujyBTKCmTkAmgoTBZGzxDU/ym4dUJP1au0vUPtle6nhdTaTCZFn4j7a9ZKhxhW4MISGf1yp72CcMzo9WLlqMpLjlOPJDhuORcbbdscRXWltciDma140qs5CwZhA/BAYzICg5UlWsbu0q07d/v9IT5D/ycWNOb069G101sykkEBwB3ec6OD3lnY7t2+duVLvZzDBxFxe/p16Bjiq/fgjU1VSCkTtfsBPB9Zzoz1nDRYgWj1ucW5z3yOhKmanp2TFBUOtaI+0oagSzBi7sYfrEtBuNBRmwzKPgw4gjWHMrCMz1d3yTwA9EmnC2C+CMKK/kJJzRAeGxItyO1AlC8fshiIY8hh/IhcBs20uMydgk20gLOyPSnKoqOdzPNxg+ZejT8bpTi/bIXxPGajB2ExIOmmPGIJ8fAlV12A1JSUlHHMUKwymo67pm5Hjig47OC4lTJ+QR7JwuowUUGpUIQQraxKYdxo7u9k87ID2qyRRPvIc0s+zErjVMovsUt8jZuutR5VgljLb1AiZJtV4VNhjkUDIVwq2yKyIUsQMZMxglAJSNiqkaLb5L4VLWf3UJm3Lj5MC9IgGPEfZwecah7cpBuhJiy3kUISI72oXUIqEfTXPX0DV+YVxY0EPUEodpiDK5wI6ScbZw7J10+KRxyprLVthtBa4AUTdrzQQB8d6AmwncCGsnE0WaoKxfECKRm5L/XM36VQLNDgYH8AaOTQrYHyR7nlVamPPvrogVmzU5ghKuTzc/cA/GFor8HmUWHlojxUZwmgcAucZFHHuVLkbGr4PDf084uMVHxAIboTfIYon2wuIrgVz/sPpUAL6XxW0r/RHBbKaZEH02oapyYsNBFQkVxzm0j53mqw7YshbK+z5EpHB6/55L1oM49/nF8Xh/NI3O0eVzolRiR8e2OWWuSRt8VIZZAiQs9rkxs167c5x1KzBIpnxkSWBy7uj9CEkITSRTIVGd8UIPxI91Ky3/Awfi2VEaCbMoae//2//p/bly8vr60dxnlwIFsAqhBP8kddQRLuH47wZqMKy2lowoIKQJ2WEcHkRVtlrGg+u0NMxNWP+ISfKGfk1yH2DJpng3MhreHzMQuJI0iwcRrw+7qdJbHW6r4YzkcW29kYsnxV0UCejTuWAmZYQ0CYBKHk5PgY8qybGrmOm09NRZtAAAe3JAwOe0gEQnHwSqPajaFGIKV00I0GkduxHJ0gd+AjpRbt+Bw4smp8bkGmo2E45EdE6EmbQDEAOejryGNrVa6aMEdrAASdQQOfXbFMswngE8QwHkqlrQytNlti9W2fOEMZoZilVgMSWAWpLjSwkp5OplaSR+IkQeUrERQgYE8U0D4mnmLlUZ/latdaVUlNCAkRApGigxhFKBAicLNlyBdSUImGItYiM2G0XdzYjJCYLdcRnypq4yMWzpQDaEDgHF9E0y3wTj4zJFgplYUirtzKaCtaYB0e6rOBiEBldjdBRYQnAXDb2tpiXxgDbNZfMCNJ1gQEIsbkHFJuGWOAz3attpeNjhYpHR5HkGQ0UglmiziJ6GuX92p3hVd/WIBmbaW3f6ClnBeeYRoPQ3Tb3d3BUES49QQ7MgK1FHDcV+tP3BpOvZqqEApHnNRMQGgjaBIYTJTZ+DQuERQASCDOlVvywmIywbuhgQhNK9EgyCVYKuYaq4sYgShzYR1dHWbQkoFSoyUZv44pDrIfphLBApgC/uz515UFaJax0hJzyjUQ6EnlMsbofm7QZz66pMxGmZ86n/M/euo98140BNPWpmhUHBhtmusVtEL49SOIQ6j5oMM9dbYOoaeI5quUuSl3nP7MicZUZOr1k89+i2UEk5NT0HABdrdwYDHbMz/98F19JlSPCNUOhAdKVq2egdQvgh/9Oq57QqKS3yKbK08B4b96/gJuP4ijnHQqEyHbOVRIjqh4VE8hfHOB1We+AnJwoBd61lZXf/7zn7/yystk6sP33mcBWo1SqFoEojMzZXb8oE8jqeAQIQk0vtFF0AsxR3SD9IMpmNDNEQ0LUL4xyJkbvIkCW8w44Oi7VM1VEGAKGCc1zRGa2YuEuK/02MFKOhlitJPwKQzvGYEbg/fCs0SKnxApLm1uKCOqPL8AaCoKn54Ly0OiI6bGh/r31KSBAB8/TgYOQxaRD/YHTGMPD9PbPA38cisnzy/fSIfofHn8QOe+8dCmLBl1BHxlIfKC1hrbRFfCQgpzhPMXTtOruuFwZp3fYhbxWVBPinsQp4cpCjgC0BYI44yVYAOTW4so8GCe7mzbOqmGOM4VxQg2PhHyYiZ1la+ZKDWcrQAbkVTo7ehh44UqI8CciHcGItdSrEZRu8BNotQsCxyCmRMhBYYgSOE8holUtSfMDS3OfE5+hfPJkQtmynK5n4jU+coikiDflqtZ2UoFCL9MpYjjXhpOi87xfJB4LaimmtCoAsxdTHerntpEq4wUlmXXNs6/8P2fvPfb/04Lzi5m+A1bcWrnUXrgZi0rOdOjaKwQiRifYqutafExWE/2Mm66qo8e2dFAILk3YSKNveKuziTr+6OAu5ChHgvCLPliP2ofkN27199+87ff3fhf4ImA4Jy65xHpjBjeWGehYMEkAu6qX/WJEyciUQGhxauVL14zZWPf8SYjMxwCLWHFVbs7Ip6viKvdPRmkyK0jxVwAs23r9FFcZoxiKZFRWhUTTgg0mklIrwXFLSn2WLbSqvLzFjrT3u6p0yubW/u379J0Dg73qcwrQ00Rj+MEWBWdSlDvjeamI2JWINSrdFF0auazVqNyzxBT1yVbx0f4DSkPcGu+KkLylJs/4obvcYxia/Hsow8fXbk8ONzl8ETO3gvc0QVMcmeondq0POujyz9YaS+c2zJzBIzI7iPWlGjSSQfAP8+cOXfjxi1mvy5oeWsl1CRK6ixsPfoQfBQPBGQ4dapeml3pC7cdpmJ3u0csqbfOndrlVDg5I74vDTEOV0tzNzzJKu3cqUajrpJ1rzCphq2auE31pElJuR5Nprh0ptt0Ejt7QpYedK4xMeNkel2IUumHHbwz/ZDTNHmdfOsc1/bG5g4fOGZLSXcZOzCu1CpHrn1pT01qAyUl2crcA546lYAgy6bnDpurHL9GqMnrOA/dtKmxxbnNHJ3BJF0LHyog/H1CInCd/rVwzCm2DBcPh2ot+1HWvQXeHGj3Y0fkIFrXAQcsHS8exFnz6YwufWItbYAmp8TVwmQpx8dL5BMfI9e2Q7yKgLxUIvJD+TDtZ9Qy+1i828EbXyS5XSVCFnQWNQtDR4NLH3zw4Wuv85h/oX/IEko59xm57E2MNt/+Mt3Ct+9cYQFrbU1vU7LKogFi62DAqXB59Cj9XSWifqH9SJPckgOZGdw/BpU2QasMlAE6djLF2aA0PezIkHLacZ5qot4nJ7/VEVa2mEwyTagNNkuHYrdZCLPgZBWBURgJRXHswaWGzqKfAS8uQboLWYjT3L9oLomZW02eYeEb8hzhpfIuaYpgdLl+WHgsgZupDJtI4/fwSX6tQQcv9bPNVn0pcEYpS0ts1emtLLV6HRoa7eGIfnbAy1cs+cLJI2ztTJV3xQ4Stnkkjhpk1wGeBEMKTo1QrFRwxhkkXOoCwaIBwYrAnIpJNfUESByPgU+yLNlZiAVoGj1Eew6jRWHOkR+wMM1hFJwLsaKGcWmJHPqEiphUM8Y7amvipkVAeFKhyCa1cnmlDQnTUcShBkzQhFAwUQb8frfDfA+GEGIYIASzimvxAsEhEVrMtZw1gCWPphJeYMoDSA0p4Fgc12wrLMBojyZN8jg/UcW2wD6QXnd5aTNeuGbvMnlUyxkoZgsHguNc2U8M85SkCBnVEiQfMEafGGGKvey2sHywr+I4GqqtsZGVF3wFhqxX0sIeHB8sDXqHg6UuO8s5XnKxd0Qbxesd2rFFW7q8xPFBC7zB0T/cx7Dnz5zdvn3n7u0762xB5PhR0CLvytrCETN2FqfQAr22d3fe//ADpsrkiKNFZBDGSdnNuFVpxkcjbav9PS27bGxssYbFFv/hQe/y5Sufffb5px9/xtP9s+fPsPzdWdEHsSll5fQw+TN7HbC7TURm43FCsRi5zOa3nvQMFQTlrX80morqf8zhjAajiDBQkxsQ2MXcjmeTyMKSQOJ6xE4iKmGfD5ENhnTpJMaqiLKMB/ZacXY5IyCpIymDI51F0z8acqVUcUX2xOkQ7ggg1MFAeNJFEqdseDWqu7YBzuESm0xxquhO4hpPRjCJ9kvhbGODhJrptLhrOlrQbNqTp2Ep18qDnG9k3oJZgJolGa+kzSAxK7ImzuHtpjCrwrBi808uauUrtWS/yA5WUP97xC5erXeEB0QSyEKI/sQGjYO2p5g06lfFeyIagmTrKbaqZ9CJUA1I9Fnp3j881CnkCcN7ZBeOLl/67PXf/EN/nxMAaFI6f/Inf8KhN2QKQjVqygWOtnjY219eXXvupe+dvXXr+heX9HQNlOERA0RqgfxRnaSourHa4FUlywozkCJPXuzoXRB2PVPZl8+eYvx0+uJFSem0WP2VU2HJyBdo4TDiQc2w/qEPAPn/nJCceA5GTvITp9u3b/IV9/U1dFqlOeVYNpYQab689ygKNxGQTxTi7Rk28bAiT3cDfmbW/P3e97734vMv0LrSANIJki+Qjwd6YQhU2kmu9GvwB+Fv//a/08yyCkkLL28iMNLTwoRaBiMDI85DMcMD6UQX2IA3sqEKSsEQR+znYfNIO9FFpZMDcfiNBGX42G9tTCdEjsRHxU/LoiiN7yJ22Ojq7HsaUsyCywlnbrmbYeOKRIIVsyzs3+vv3bj51ebGmRgGaKZG99AgtMfSeEOOb1ME/UP1Ze1jzQ78AifjkejmNHpBbdmwRQlz2piOmWImyGacWzd3L17ccEMeaDTC2jTMplMNQPQeFPedQW//eLBHnxwuBjucJJUTokNPu8WEmuMAl2CxqrSoGl5uyY4pSun7Vsoz5eONsrQE3VruLjFUKPglYp5+7acwF3noy5VS0ltBVWFBq1Fsm09TdKm8LkdcWdxjgVHWp5TChkTRhFmeIjJC6j6SwtE+YGtJ0ccjUgaxqVph98Tadq1ygoM3FsCdkkIWkBOGyJF73aDwfHM2sTyhyvIsRPoCODMrDv6KM7rjqk3IkRfMIlrG/zGiQ2OlOghd8QJBaC0TOF+1KAjFOKYuVNYz2dNpMTpN0bk/oVw+AxpMcQn1WINmGMexs5999hmSzL2WQbxoMFdENHw5q4VPMcJ82lpKUaMmuSeCkbEs25fFIe6p+TT3ZI3HiYLO1aboXMslbl+vgWB6l3QNnBOv5RK3IPAFD/MWyD3dvWbVkFhzTswDYz5Jg0lN2EhCyTJJMxqcPYDw137ZrsLgYHDzOk7ljBTRkxExm1scDelTb2FbTFcjTBZZnXryOMyL5jXVLDg44DNOok4d39SxJDXVSeJq7E6dgr+ycBKCE+DU2jqOkgT13HnqXtgIXgVuIQFAjmrkBlpFkaIgaBgau4eofZexBdaI56LBb4Rm/pMcZkFkogiFsEQmtSpJUNT7W2mmXTqFhAgQLdtJ8TEjzNLkvuBoUmTNIsTIDC4HcXoj30jG4FNGOLOIM1xZrtRPFqg7nIz5NX/nZ4fUUQiFyBoNMkDk8l+p9PeMk2JPXP9IG2SOOnp1i81dRN2IJ3xhK4g2ulhWg7j16nIammSEYmp9aQv8eLHIJoEUSMjXjzELvlMTldrmZDUYM9w83N/99a9/bWS5Sk4Vo5MFqHhE127pJT5WR8g7+zTwZ2XkBO5xMiEJC1nmrGFoLOXHbG7EA6H1aG+UMB6zbuOw/0l30jD8hJL4BkWabWFYMlgiJYnIrHIprZCRizPAxJavmcyJF/6FQ0PoGEIYpJZIKprg5lzNgUZDJZ7ezdJEFDgBBwYOLQhMTYpKJJU42RW3Gd3WLDjktenMEFkAncQVoHHYs8YSKXEvELMATRLVwVd0Q0/iavqkuThAwew9ZXB4xBiDVQ/Nujl8JBadQQaHOBNOIhtbm+BzCwdmpLFL6BAcIHAmEHHQikbsorVlADYQ4AAMOBECKwKI0HJuyAWZFiPM0uySzN9X09YQ4lP1AciLL1ijNjXk7KG1hsQltHIDWBX+7A5+8vHn2UBHAQL0AiJtI3n0ksQbb/6aNgcSmNjOebqiXk+T5cND7AoCqZiOw5uxnie3kADRK/YcbBQfwmKVhEfXt+7eYKGZBWgKiK+RQeuSYlMe3OCjVi7aPZIgZC+78sLDwVwQiJa3xLZh5wV8UrEDHLZ37vIAu7u88sEHH2Afdvz94Z23EHH6/GmWvUjiutFi/WVsrmhzFRGINucaYiBXh1lJJjQO8YIMPqtnQIigmHNNnAhr8b4FrrNotAlRcAIGYRFfsXgoAjdIsAdZJqC2rULvZ4ilh9wo9+y8UEELDgzgiSCzsnpcrZjR6PQQeJKJbiE3B9+G9GCYzWitauQSTxJzfTdcwGS5gniiSOJ2ItwHRLKhHoD45LpNFUHZUSjIncNnKmGt6iQtPMMraqx7x01ife4p9I033mCTLy0DYzfaq5/85Ce0DOzhr8XgyW5D4Eb78/iFc7wqceurr5gbHmxzzgBfm1Yb6G0oEEpoRY8mZI0HbDDhJXwa6qWNU7Tqy+dOk8S7BeDykKaiUBQmhpRIA+Fr3poty+e0eCjD2sLKspbFUeny5cufffYxNlmJU9dR2y2tJbqsaal4mYMnnrUa+/s6cOP8+VPsruW4ZxXEkT5YB44KNyZQeAn2xFruKBlDst7NSjcPaG0oFGPcAT44QDjGgXv7BqZGGRSLEpHdHjgEw7FqbBENhg1g47aBPOvWFpuVCpxs2mnJKabi6rzjikT06djjY7qPORxmJQVD8TcC+otf8Of5ASv5+DzWxaQLPDvPoegDAGR083cUhn21/13v5I1GG4YEK1+oKCPHLR0mVDEetbrEARpOXiMWl0UNeMwna5F+4U9xm6SRNPUW/JMgg1bIS5wIARNxJQtkxHFuC3KJAESQdZ6qecGEleNmW8NFGKcUIIg4dyAn/bN7WrquGVI4FLYlApqDIbDyTNA5QEBRpsHkhLdSMYeaVQ1P+s/tDoyP+UD251tHC9CqBXa8UD/EGV+D1EjNKqTfAozISMMCN165tSF9mzVPZdTg7Ns6p4boBDVirPpz7bVWjcHpY8+89P07h8fXP3uXxjT1A7zESSWL9Y9JR0qbysw1Nj7AEXdj6MrQl0GoaKOB0Mo9LhDPAZzF3M+E6uGi8RBUa+lQ+cnDWDMZD+JIgQtn14HDWC4EIId6rI6K/YBYGP9HI74DD0TqMIQd9Bh6f/XhH1545Cz9oCsGODxpCSploKolWNgGDT4qZhKb/gshdrMs0ROaKEDYNoKmStTcn2F8iwNw2S6h0z9FoZ0LKCgsriGIW+FTtOAYkrUTTszFhNAItqcnG84LU44kexwVoCRWgV1v1R1UoRIgVefQXrolFH7j08Kx80uWUil52wvP4ti8v3vADvR26/wFJn/me+uTj8FZDZl+BsgT3aKbxuColPmH2CSLHy8qWWHaN1GF8oZzlUmSkqJK8MIAO6dqj8QMHct9ArpEMsbol4KQbYsUpwQrPDmxLPYMGWx9wf93ljoXvvXClStX6QNWelpHPvBxUuHoHR5eRkD5ohdRfcJcJw4yF9HBi7HLJnzbyOHbue6Ywf1cQ0+L01UntVFG7I87Xl5Z2907WFvbkJlKdgrv8DseKDO5ZCFPCKqI8Ag3jpZE/EQobYmTl9aROuYDTpDo9bovvsCnNLZZe1xg0BlFFl43iJeGTCIO4iFylyMRgiGxT8wA7WIYGUHowkEbe9Higl64dtxoUc+yL5lHPAsllYNASWXOJ52UIwzA2WmKB2NlJCmAYlFWbsHG6kzwzM9LLaC6hizfh4ikvPcvcxADSf04Wax76hwjp52WXp7VkZ+a3OKYnOQY1MHH0t2WZhlRCt5DbUTtr0n5VR7DOBl5zLYF2Ig4ywam/Ti5PVGvE3Gk2krlavzu0cHy8ZABCF5CW4H9+u5HdEbt8R5DIp1Vpjds+i0dLXJjoEEkI3VeleDJoY6lCAtEe66ycR0E3yderXpBWZx5Ui0/pF+Q6HCaiKE+VpVhlRYWjmcNJMqetMUkybDxdIQrXhuFqx6BW3HzVj5FzG2hSzdzPPjlf/mvgyvXurTcZKS1rXRO3SWIKAjV2UkL7VZQbQqNhKE2E33Wj98XIuCYrJXTAVAfMDs4wMQpg0IXCtA/hpAwOv/OIEGqEKQwQFa7dahu+lDDQdmBLYyLirOpkqDMqu2NBSOvTKNtmIvU8Cpkq4WXbrkiOEfBQEwIdG7ccsWkCVInG3TCq6Qr81GCmSZGDqlGFzVK4gPLKhyCp3vPkd1zKr/kPd/Vli4li2PQ5Kuyxojfu66klvxJv2pDzL/4w1gGM/d7/UaZxA59yNEKnusrq3xwnN7iuL/QaXcP293+4GBh2KfNULHIF/coRY00QjbNFP+iutmikjmKZQ3M33dIIRN4Ant0c7p+oZKTV6HYSbmLvb2YFlfizjZksrS8sY5DqlJr1yffp+r6AI27B4daddXmbHbd6hhlz94ZjgLX6ga+G28zcMogyxmbm3icTnXgiGFOHOxwMkeU1P5hr9XpLi9z0Ife6QEHUVRWCoN2HReNoZv330l12h7+pLAC2Yn2QD+qF/R4hnKFEWh6eUFv12pXWmQtjKyBYnEStS14AiQErCZjIP5YLzhByLmPvBxC60gDweI7c9PO8frq6jr2ZNfUmdPnOF8VZD4p31nRsmwowq7BQwxycMAmKXSjTBC3TP54dicpx0fPPPMMm0jQEAPu727zzjtr5U89+Qz7427dvkGtv3D+Igsc/+pf/K/s+P7k849YLrlx/RbwM7Fw3GPrLVs3jhjFtW/ePuguD8+fxczLm+2lznHroK+TspkLcGQ272oQ51Fcf3X57IX1F59/bHfh8M7BzZWddWyiE/CP2circxSv3byBDzKEYrFZw3qekSCg0zocaHzewRpkwrWbiUOne6h35iiyIYvaa6sbcOOEV/Zff3njyk5v99vfenl1Y/Xt9y6R3+WNR+Dw0PpDNy/dvfPRu+8cv/Otl14kLK1r2Dr0CDMeuenD7LKX6osbt7y7GeGpyLTtCUVUklH6UbmAOKh1G4dQiCkt9IczyviKZfBS8oqNDMGqZB94CbFpNEZKHJWRl633D3VkOUVMiQ95fZyN/Dya1WYs3lpQHPWYPogzb5VFJwhDP6FdWV7j/PB+7HkfHOtBjtttOjZl2aMIvC3nyK376G48RkcnQOq5FKfHJC/teLtIe3L5w06Vwws9zQXcIPgazfioiXAVE2+CdBwPairQrD1aPK0UHrVRDcJcEqLFgPrJweSFiXVqkKuM5BdqoaSRxxK5I8ucmpkF3uBTMGUcZ42rI3gfLQ8WChEQDv3ukcYdWoSiHEfkcC4a53jTUhm71sFxc7MdgDTKKJdpptevLOaSNAf1U9JT2uJfFLT18WyXFoxV1z+8+9alzz5eWmzv3dnudNf/9b/5X7fWNzj9iecqIpRbMqrw1IkfiWDcQVu/8dCFjXMXUG/7Ds3S3cHdbRy+d6iv6rUW48NuoYpeW8MmcUDQuccu0BpwlDKQTryX1ouxLifWjHJHA5NDMWVknzsbj2uokXyiYGWTJG/PXKpfGxOALKMf0aI2XdKZU2c/+ejTjz/8iLcxnnv+Ga6nNvS2DZVXfoX9WayIXkMbXPGGIZsYlmilOZiIdhoOLA3TSnOI1AsvvPD9V77Hsmavd0CTQNdOSKLDi3Z3drADTSLzKQ5z4NuP169fpfWACfrQqOailD+zNEVDjBGlsIyvbR/0+8DUOgZDLqGkroElZP4nCwXIl+J+JgQoQhUvHiIU7cSUoTVjwZuRTxw43pKZC62E4KPRi0oluCcfCwyRE0IPWr5SO4p0YQUVzElNmRy6R9ZhzeudZZ5KMjb48ovLDC9AT2UX/H2x3ApQouovVMz6Qiy8zV6fHpBSnH7ebt++cxO/4Yws8NbD9WjuRd/SGogXRnev3kK948EBO9XXOIkZD19aWD8+x8cdInPyCE5x4g8sUjEdHQc8ROVvw8SY8fJXV/j44dpKbPBHPgh8WEcfFRcq86PD/UPMrOaF1xjHG0ANe6SYxi3JZNluFqRrWJvInAA55ogCGWudUECcY86igtDnE089dP5Cq3f46MWHGgxDB/NQCuXrv+R9GVuPo/TwVrXVc2Ryunlqq7fIG0VhM+ms4kE2V14ZUt7U1AEIz1Rm5XlSO7GNQpI1ZCaCxh4wSeogMSHa65hpohWgYKr6omFM2BaJZslnKBLviR85f2gykZIklmKCv8vFmjBUSSSqX9LZszMUEKYqE6ZRuxe1j73Zwldba1WVqnwJgZ+IMJ/WrTlARjwLCZSxC6m+N4laf+mgvPM5zaK2cXDcYBu2rdnUbSmD3BywIWxVGx3ClCkZ1jzK4/WQnauf0nrSrhkHAaFDpjnBrzyDbKi5hThcJEouZ+0ELGajRIan2E8SbfKKVsDwVJr4o+4iDwyfev67BgKXdjMCOBiEK6FG8a3tUxwRBMGFmzoM3cI/nAm1jFk/iIM5wKkKJNrZutX6TI3DYSrnqcjzgeZjleZjzkmlV6bLHOzeFrfo/BoMgdclUWpO4ZnxU1kIH8cixCucYyVUaHKkLqYM++P+ohcegt8xTUU6I5VGvZ0vPjKr1k5oY4aZT3d/qSiJ3ZDl7ocxZRzjuAZ8JiPX65nJYwnm44Ljypc0uDJ4wiC9WFLUfDwCZLVQYL6tgWOs73UDh4IyihXQ3IhrfW0C1CiapEjFfy6zEyViE8Lq6hZT/cO2PjvpGSldbdCPmu8TsbMx7zfbJ2Sd0WzhKL0kqbY5WErSwNubufC0AKSruOg+B/JLlKva1XggEXU7md09EcNa7lniS0UQmkAVv2OXBoKoAi2jxoJOUCCOVLcPJdWMK47yBTiAycLT3//933956RL3BSGSyt3MSNJhRDcT8yQJ1mc+ZpYokbZ/NEr0RjH9iIkiabJPdPCzeNbwOj5Luts6n0w6C2cOXGpH9SuyFAmCMcicZmoO92lJkhhFjDHqdMNzUp0yPY4lPVJSJBhiCnuXjCwpY/ynczkxFJ4wZ4hB/0JHo504sbeUYQaySEE6OCqOLLbWgXjlxSeVCo1zd1KCZNiEDjlOyJVKTaNHhGvNCm0BkiOmcGBK+XBR5ZTpICEWoE1SEEBjdg0CJzgo+wvapWv+YAIHQbcx/AWCCCAE54XCmZUpMBuOBoTBJFNcSKJpUvvMCjFwsoUa1q1xJc1yA00X3xY0WLG4wFySXgBV0Zn5rZHBQRbaOiMUtCsycAwFkHbpiSeeePQhZqkXuP3d737HejPTb95FY+2eZcFHHnmEdRHwd3f22UG8u6ujM5586kmtg1y9wQnO23du4ULZGpLFGZfM3vubrOPrNA/s6VVwHI5bAgqTBiYb8eDMGI+8EyeCerDiCho7oEl1AEhw3jOs+QsVASgSeZCMnizpsFxOrvmU2RcbXzhfrL/wSAXdyDvIuzduIIvNgKy5b55d4qDV0+fPsDoQjxpYhNauf8wJ5nzpRZsTotX4LiwyiE3wQOxDJHKsC8Upq8WYnyupAmrxIU0EuOX8E/SkTGGFG3ENgFwLVl7FI16Eim+0OXDDZwgYTghR34nAH2xJKbPrkTfnRkHsRjwLcyIwtziuDkLNCkijHE/INfFEXKpWoXFbpSiKzvAnUnRoIEy9NcnUpHsCyUutEqxktwg1/J58piLU3JLRwrYqvSzXcOd3UmJBm8q/AaztYFZJaANvxq21tcRJuZgFL/3DH/7A8jFTBhBYNOD8DZgVKSUyVQLuSuDseBZbl/jmJp1CX20aazzEbXSvkbBggf79tmo6NUp50dp+cqT5UiwaZNB8Lf3gVK0mgbUZ69QaTqNNLi5d+pSm7/MvPqN1eubJx3kHBYUhsSVdytxK8+j7eE7GJuj9/UNaNhq65557jlOG+HShPtod3R9XAvgiYT0zRsi8GUkz+Pnnn7P0zCZc4kih1tf61HrOj9u9obUgrsYvkQY5mhgz4NNbDJKE5pw26E2WM4V0qrjaKvIXfTqERSVKSmpkITbjNH56mYaei24OM144e45bL7NsLq3CEIsxse0N7nsHtI1gfeBTLAycQBNN54//oxL9HVcKiFVYkmLSnjR1Sw4x9yRxhQ/l5WRb3pydKtPlUgCHW2Nypbj7q216NHoCbqkX5JSnVyFxkQFJ4U8qcYK5ESlM5kSENi69Rhar+r6KW0nIsRVZ44pBqNp8NZjbOgsmKvi+lZ4VN0eTtVPrKxhUcNMj61CSqzHr3BUgSaysiXOgZf4jSDEJOITgb37fzNU8G7xSpiqo0bICqejHaLkhF9H5Q6ekGBLqVUdsEvH0PddIMO9qBTsVGqJtN3HIc71KkVFU4io3UJscD4Nd/I0sBLfk2LDwbeZV+Usxt2XzqMz1ikoeNSORABwun+L64nd/+Pvf/7411GsdODpBwycFoc/0xMBImZe/LnZi1gsDFNDzcmZKgSMmQpCK5o/DEC9PE3Ab6yzCwOQKgvQZH/QAdEhaxfKEPRpM8COLPM5nxWvYGe5d/fzDhx97Sp92wVqSkZ0T5mGxrE+yZmBVpgxhKCPOuQ4AMxqDx6Z6sWgS58nQkR5xGJa19VMdN7t6cuhs6ur0dFV5xysGY9B8E/rLPgbYAqU2S6WMOflLYiGcTAWSqCt9Cmfh17aKOCNqSGKEz0M4lWbs9KFayGdaZy6y3XFx7xRPOAY7t7GFzueWBvIIPhHNlSBIlCBVjlsebwW4uuTMIsu548wkxvLpqRFPzKCoifKwu2LxzURHqlXVVazj3QJyhYPd5fvj3e5jP/gBE8K1vjZYLR/HTslQIXGIbDiuKpHLpcOTut19Wh5cTXb6YwYL5eAs/ugy6chLx98Uy76YAa1AbBeNNG9/046YaWGX7VvHx5+0j77z41d3L164zaP+ZY2W1NZyHahRIMKlPJKLzJYsj2V8XIYqB9UQsfGgVN5D3JocLzYHHLFpaFxF+21c47HoKFVsw5UtnquLQMoGllvDEUGOGV7j6LF0Dl5Q9tYiFksAD5cYQC4vrGyyXtLvrGjO2dZeBp68BpMRbcp7aJtbj8xXuU6Y6EkLFlhKLV4qC8cz6hHN/cfgAVFp4rgtxeOkwpKC5eU/viXARrhDzsFU44BWtDnqSw9Ck36cCz9Y0GqonuZ3Wpwzyo4/bRUDr/J5bhXYfSUFuCiErCK/SFbEbYgi0WNlZJlFbOJZcSGQYmoL09g3ECRF5PaokMWUi2Hup2++9e6v/p6Tm6VBS3WZb1CPNTjBF/+Rd1ah6FDB7hEda8/Hl91NCU9rOI3RmHQhMAoGf4Bjc3Dpbovj2lob8vNkQg2daYdpmalPtkiRojqVRwACRkHqd5oCJePasXf/wTUaJVPExs+lpgG3eMrba5OG2vcnTPkNXspFIR3FRuOcMQTkJpxm7rLnMNSrVSus7y9iF1JuQ8lEHLfizkMalm7ZWcDW9v27jEHPbZ2lFxwMdTTE4OBwf/su+oV76NBeVbzcdhHljyaY1HoHR86XRCFXqUFV9JYd6lqZFaPEU2EV1BKh9WMnT7yFTTdvCvgw5uG4QHNTOQw5Sp0THmRZswLnuE/joTLiXWaA3XaXU1O1K0wuquwQqGTUxXi8iw60qaxoq1PBW71TDBwyAdtATjuFLcIHMVEpJBScaJSkQ0ByDlzKI7cwH9YavL4QOqu3YpsqbOcsQJshpsBcBMwCK24NhwNf5qOT4r3t02eYZNIj6VBfgrxU8w4tLTLPZATFzBM4GWOj7PIq8+v1559+ioUP2r3LX3719ttvw/nV730fuMdgXnx8+KGHWJa9cPYhOHAA6X/+z//57XfeYNn61e//8NsvvPjOR+++//77bU7nw2Isdkhma3vncOtUf2Ud1ZY2NJ0/ZsmDF5vZhMiNptyxygwQa188fWbnxs3t7dubm6wIY1dlk/UplrzxNmeTXZ86Rz8cCXMFh8hkboZAc5nSH6ysrHEINci8SL63d8AZ4aw1w5Bp/35rZXcYmyDbSwfLrYcee/oSn1eRWxzdHRx+dXn3ztuf8EaX1nReeAQqju3UPIotT/hqetPJclVJ8DJo3RpEj1FmJdb6Pq5oS4CAVoCzm7EPwYbC51XtAoYjL+p4UFb82f6uakWAigVoAoTyk5GTC1iUwHgojbaSE6MIOk9u+DonD3N6bZ8fKnTz1K5knC1KAF0Kn3tGRA6ZxEi6zYTP0oFgr8goTOXG0j70N8+RrpWMGkHcKvwKS9GonlN5NBD//+pWfagqtDOuCN9aovj5ooc8MDxTpanCjYzpattW+XRSBbjPqGSNkTTtrGZHIfwk+v2EHnuNW0M15t31pddf+/WlDz4+Pujv3t09tXHqz37ys1Nb59SakRPeUlXrJAehmYvcqH4lwVEB0onkaNPu9OIseCYf4Ng+GqhIUelmf2WRWvMgeXK4pOqX9BrPS5greq7c3gjH9drjf03Cq6qk5Dp4rE6uZwdz9giTDILIzuXNzXXOyo9FyUUe5l398gvaXs7Z4PEejQ/+7LxbNHlDAM/Vnn/++V/96rerq0s/+9nPOJwQnJvXb5DtH//4xzxHfP3113k9BVqR63Rd7WS6/tVVlp5Z7IYVrS+nRpHERxR4GjpVZVKBlyscXJcBmq0jXJNZVGIqQiAEE9ZxIC6XiDgFPDVqarhV3Ng4z0ZyOr+Iqy0PPhAaeYChlVD1Zm0EGszwpdAl1JAvVZhG85XpKql0Cnjfres3eOZKN8d6tJ7f8nmWTqs3PuCvae8ZRxOXsjFdjjwxBKjenuM17t7lTaitc+cwbWyOAZGR9rDTiu+7hIc4g/HWuN5M0tc7bCaGMxrCqHmVJ8GCTgpD+mMLIPKuUCzNs+OVTnYw6PSHh1ur8WADRNVlldTg8Ki3pwUt8YgqEd4uHflPAF4HxBEMbabVeDPiKutMpngEqR4tN/O4i+fPnT9zur/PVoHU4Y2VXcxlKB0rpmvWXHHlKfE0Z7ogpHTW1znKR5oLRwiZp2D2Ll+5Bd95VJui+8DR61iKE5iY4mpWwGWKEoEKhf5IcipyIrvRnAXnQEuXjFPDRnHKAlncywFgn41mKvmn18uUI8qcKiEpHdxIBokrF2nLDIxxk95AAkMZD1a01RjC+iNFqbhE0EpolP4IYhKNdYwz0tOxIBRdUZTXwGXAwZGrJQiMWgr/wExMAGZBqWUumQ0Ml4aivN5IGNtyIkAOMCJ7HB7HE7nPP9BAFr458f5+MbrXI7EslHYO4pEBsSoa13wBgl3np9bBxWmVzEfXMPp8Ld03gMz0jOeHbMakUVcbNzsgy4IKSn1Lw0QgCTSYE5EmUeQFPyKp2ImjIY2Is2DlictKmQD+hpcMEjE+aLX0TKFfSAq+biNtFnIkft2LJD4QDzpjJiqD1S5PfRcO9ektWmA4aVARbmbODf6NvJTb4piYqAAfSK97ENl1QSpKljiRYv+sgxov4FC5f+VrOW+++eZwqGMQnUTqSQLHlrPBCaoH3kt4TynobJW4ojCls7+/q51QeQgyyYGxjv12MmkWhKfQvFz2WXU2nP25HQeqwC1CrkSzuEzAYTIB+wYAmILgBegoyXDQiOUifnApwSG5h55mr6zSjAw6K3CUUJako1EqNaJIyk24fumspuZ9vnrmnxlK1vwwjp/Uo6QMn7zCrSYBk+bV2VHcz2yjA0u0sT/a+UJzcuQXheJ0pdEohCQ1rBrEK1hnIrMyW3CsD7Qlm47VCCSJf7BFSW6zYUdUoFgc6z5UZDYeQAKy+ykqSpAL39qZIXzMsEg/ecTiGvgnB0KYjQNR6h9doaV2KE+58PyHtoUMW5DUVlYwrAARSc28mIx4plT95BLP4gz7Wlc0zEUw4lMUQA8pNkpJMSNMgO8NeGDCWawnTUGOPFSYJFFeSjc2kYwdGov4Bbl4tZmz3+rVV1+FQX+wzxolK4lsIGAmjMeyD5j2vDdoDnWc8UlLNrRAYlIyfGCWwlNLzawgIRS2vsUgJS8kEfetup48EjBVpOhiwmJMwxl6aR9QBAZX/MKZgJuASYB5iAJdnoNxLK7ok50NTuqATh7gZq2QBRW31KlpU+8RS3AIaMVVBOUa5KiNMh4kFD7gJPzA0drr/r6mCNAu6ntWjz32BGsZN69f490+3n8ng2QEEo5nZR7ulze/unINJZjVsjXpW89/+7vf/S6ncHD967/9b5cuXWKRl92LDPgZnv3hjbdVBH5BW0uNRwzVkLK4uknqcUsPCVgEQlUyL1VjAEwRgEMvz07A7Ss3pGFb9qREWC65ceOGtUUHGJJH4FwJtpvKpgogEwCyxL/UXSZHLPezvM5HEeFvJp32KnlZWz+F0OefeBJNBtvboK0sacf0wXDIOvXVazd++9vfvvn+4re+9a2nLjwEPicAILSsLyGlEvsNR3EGZ5CMkGtdY080YojnpWllUyrlwGZQkFmARjcvQHNLQFNdw2ngIAOFqxMVN/WcWuYmEAGz+A/IVsNyuc4PkBuhLhJkwbN4LAghP13mMJRuaWxf8yvd0xTSoi1pxCXXFvt6hSVNTsChxrElrQ/wYpkpSs8FmWfhVrNSUqUYmbU4azspsaadKnMOwiS3wkFq5JDLP5ZO2LhFJa1GOGBR13jJgG9gQMWuTKo5C6nf+c53ap2Js/FIbMeKPckAnvqvLBYprC6X7IPnchdiBBw7Ec/+KchEGpmVbrmVbiSZX6GN26xWOF4RWOOQLd2GekyaaHNoA42J5p32Im3RW2+9xQsZvHrC4jKHEtCukgtS2TKF0XhLhxXqU6fe4yEZ+8cFD7V/8INXX3nlFYzMY0ICfFAYWprBd95557OPP4GW1GheUjWctfpsfRr5rW9L3FnjtkRKrokUNMcxJcGYimUEytClZuDkVb5UIbNYwq2BhqeNNcEcOHk0E1uGK/xrJk41bcBlw2vXruOWWOzCufMsRgNX78BOmPsMFgq5M2UjjJmIm6gtdMpLHErORiK2XBL4emDM9ElFjeIY1pODkeFp5iAIPzu58bm1M2AqSLgFTogq0MINWsM+fTEH1JAKJkmI8KZv4uoqQpKuMf8q/EvESfe8NvDDwmPOUDgglzgIRLg6GFhwSsTwwpyIiCNEUqp9MOnHJxydhHknGapo8gZN8KHkajR4SkR0HygNE7dsiXvlwEmTUGJShKU/wFW6FWHT6IsFJDSUy+UeLafKlgcZuuqTGqyV6SQu715DexVEoxU1w7hKcHU7Jr7INZRbsQq3Ef/s8EoNm9T4dXyMaRY3FWirmtbumReg/cSvIhpoB8TC7sLyw8++fOPaVYZxS3FSFWvJFda9o212cMixZKOh7MlDKBmMbQGYmkPPYNGnOQIWmcRzgIRyGh9CpbNxqIoxWtRzzxyKgciVnxXwPJ44pR35hBprqngSTz3RlX2BECC/dvmjxy+e2rr4LNuFaAlgrCe2CE2aKO6SIAKJ7scDbQFNjJPoD0pDOXXZDtHOGK0BRzvBCeayRYSglVZFAmxFkgMxIJA0+uAax7jWx07p1BGXaRmpOWSLZanlNzwkMJMdHK93vKaTjzRgpjT1wDDvfY5M8RRMEGmwHzxapy6sbZ3v7dFu320P4lw5ffj1SAdY6dmImlSP0D1XGPc9pRbtiPDUVNkMmDwLR6qzbb8aozD1/flzXUDQo23iEu0tuUarYWwLGi5qCr3c0jd6eDEVzBtHC4/8+CfX/4//QQmeP5r+pNrc6mt3yJGKOxs8E2beETWHR6eB4Ov96V9zzvHgA+9cO4Dj1TyepdaF2RJijBPyc63h0fWr11gijO1icMDWMUqIEg4C2zpZfLerfQEP/bM/u7y1cidOjezx+JcPOrNll5ZOVVwV0/bkejxIw7jkM1lX/4JYSl9PV3MqR8cRdcHb1wDAIafP+9U6IkG4xodB+LDJ4+G5W4ZcZYWefD52Buke4iidjCPbqn0jBH9ziI3Ax8drnPW52lrZZMMeX/QGBc9B2+P0XNBkajPqHgw9I+9KZUzFNWkV1cGZyHkRTt2acUuoy9FmrE1EvNjW+OVqNJeR40L2w1sEjds5odHsHx8dLLQ5tIsvdPBs/DCe5B+FbYdHnDm+QE/AcZac3QUHuRxBeWE2re6W1gRAas3UQrPqoQIyfyUhd1y0+EQZglMqrI4eFXbYRDERktOEGhDfuvgLfzgYZziIF72Pj37xi1/c+ewLLW7pEHOe4+9H5dG9y8xX9XUlRG82Lou7mM8XnBxB4sinlRNZIHuUkCCcLKNZQBHYZyJjzh1nzgrI1lL6IJ0yzUKSHoG4EQj7095oT5b8JcQRVy/JjcpHKk0GY07VZBJ5GuRErZn4a6MqGwKm2GEa25kw21m7vUqG7CHOMlkluzMyq9o/I8g+tB8aw8lg9gSA0nwGt/GkEeeweiq0KdI4RU0FotYSzrSx7A4m3m6tLq0s//iVH55e2XzrjdeYstKPcBg0nWvNK5SpAVMkACIjZWwzHeNeUHI3QsHC4flUsXiFYGQWbWJifTQmnFzJDhrSy9RGY/Mdjqo6EoEkwvbuHrMvDAyMdVfWKvlQOrufOKJxZ+9geUVzfu0dk8+o0ebwZOkTZZ12B0f/NRjuceIEbTh/2n40CqlOF4Doc6Zy7qo8FrzxSB7PkP9BnAkaxR6r5GSW1o8/skD/u7GhN2AwEbnL/OVLHKhMTrGJWkoEav23u7GxRWaZfN64dZPRFFUXKk7TZn355z//OUuu7JAD/tf/7W/4kNFf/PnP4P/G7968ePb8s4+/8PLzL12+8gWb7D79/NKdne3vvfqDxx97+tTWBVau7969CR9eZDsYLOzsLewdtA6WhhzCvbq8praN5gPfYBM++9R0fjXtOLvQ+6dWV584d/7mre39ne3Oivp9THrz5m1YEdjZg9oM9zrtld4Cu5UZ1vH9CXZBUzg0MKPMrixv9OPrrDvbjJZZTB6urW512kusyyCXiSiLzp0zDwFfXduU0aL0n118Don93jamW184PsXR1adWOl+2DodHl6/eHN66BdVDTzxBz3vsD0bFW1hYjEDd4Fp9sX6s1R0vyel3k70tmwaYPAo7vAM91ary1C/agRjIUOJKl3UiEGerkxRyNYmBWbxrA0BwxkvWFyK/C4IXQBrvD3WOl1YZTug7s+ycc2um/Xap3dGDA/yqNHeSrFDahYxo8NhVfZBGoWQnBv7E0YO8xMg05d3nOib+ypgZxvqlbZB4zhGElcYEVzemmlC/wohaM3Y/fgMHbDgOm3cno8fQdI5W8+jH02pujmv/mgTE7HV+xsZZNe7MrQC5JQ7jAnmAiHXD3SgP26wdHtVe6C0vLf/6b3/He2q8bLN/d5tTm//Zz/+KJ6A8qbKh8DFWgXAQNaWVvXMd8eJDtKOho0c4UMk5o8o4C6WZlZFSHkxVOUnF3yjZG9WG2wi1zz+wWZJKVX1J4sI6PB6jXaIdpr61O7zrq+2oPPnljRvOhv7ogw9pkHnXhA3RmIgqSxvFssGZM+dYu9dhBVGh0I0Wm6eGHHzE0nMc0LFPe0WzTz/+1h/eoMVb51uHvAhUBeeoqFeljHzAOFwdAafgFwiKlU6H3gicQLcXJSuTuygU3dJkkuZC83tUbg2UwfFCqWcxbg3gjHuAV3ZKR8elR5uhv9okl/iodYoEJBqSfSmgNJt5qTrdxw/nNbFcS5eHMXkMX5IKTyB1vCAQQTqqrqyubK6v8WRTOcqZsllgi1CZMVqJnatXBysrmxcv9Ae8t6XP9jKVhQ/FR6807IuYsYt6Os7o767weihfrJD5VPY6KQD81ZXVDm/8IP2YT0XJ8u1FHamET7WW+NJD0mCfbyjt9laXF3noy5tuON7RYOFgjzOgXVujdpieOsv2WesZJQnXnA+VLzx9RUgjFHFE0FudfGA0CreiomuL8bk29g7u3r093NdhXOo1lG1NkdiPHnaUCbWuQY7IPe9EVVxKFG9kmRCFNUjg9TA+5sHikmfZE40baGIaxOhZ4nSxxJ1l+k8Etml0o2MFGT+PbKacQU1bMcFbTK0hubAIIqZh6BYymxfnt0DDP6O1j0K0iNAz1UTeK5ed4/0nfWSLcjnqaVTEc7x4NKU4n44rHJEvW6r4kEVI45fQMJeRdRtZV2wzB+I5mnSw/ywgNyfQwxMtJBlcfsXffIwDd24ppJp5wa4jo9pYQ0ucPBN/+eWX2UFw1NP5bg8WcqMmakpWbVO4nXyiyv8kcxDISuU6yVhArJsQIvOBOcZAnMMQXEFGqDEDLnLafvZlvHLuSQbQzAfGiKubQmtZVYomZgBhSOMCWkmalinloiCAD06gpZpvPkVWwTSVUQ0kHlZJhAWzjohz5L0WWsiJTMILeWg1DwFM4xSSqRGJGPn2FJTIiFpPAgvQPL8d7u/QVWgxikCTNFvPWoGSF4qDOiuqKdK+GVCRZXZ0PxKHsrGs5gVoPgUBRI9wCDwVyYNp/I1N0Pvr6wwsTH7CK0O6Zc63iu9LYPo/UvbQGasSiFAj3LOmc3gnFT064uvzIE+mzILo9MlzOpmL/UcQ4uoIwkPMBIkQFlvRS4NgNLeDdf2iDbEU4dfLfJVsMSdgLUdyUuM2g9MvFboexFg3rvByHDz3W5nwvn0NBRiX6PCfOFKzz/A0Nx1FRGauXwHHsyB9xiE1/n3FZ0ocr3oFzar6tgAbEoGXHJEke+bmmqTakiYkL5SjugTsEE7o3PkaXjByMwPtLbWUiKdOIRQQSa1Jmn4wAModgRGsg69SLxu2xIsUVl6oFJ9+8P6l999nHCAEX4OYaM2qEQ/EEQJSsINz0cDkFolW0kkSlLUqyCcHBm0SXfgQQQrLdfChSsKWxX6AHqghrcHft6QTcdyaGFK0Ep+wQ41Tp95XXJbnE17j1igcQpD0QYcCfICI+ExjIM4PwC6TWCvcWrkIATAkDtxJXO0yUmC8umUeY7+BnzClW5SFMYjjS+zcZVxR4ERo/Zjxdloc43vIUIf9WbvD9GCvEBKxAmPCvukbRJRQigtreLcyEQW6TI4OGOhDTMh3EhEWW62OchR9RKkdEAHhGvYg9YimFX8mvxvLaQEXBFixPktDpHEa7CIAL+ULgFsCCK4Ovm1cQbMOBQ4+bOuKXHAamIXEkVBBlwInF5AAUVsYgaTCBCmMiLj2+j1yR45IBRkEdlcRoTsGCC3D9UcuPoxihzt7rAdR6LJPNHpswcMffvjDH165cgXkX/7yl889+xLnjfLFQj6E1VrusGWMXfM/+tGPAMJwb++uLBytAUKZwPdWWlgSiUx348hW1pH16JRgnWXq5SMmnKCxbYWjNihTklh2Ycmclw45QwNkgLxeudg6ByuO1MCATLwBEifV5avJabAld2QcIF8YsmhuKSblMTYGHhxq9M5UlsAhG1yHcdZnS99Y1QmzrOYw9wd/7WAHTAZU8OzEW0e8Miwj2xVycZCKdQX/JoKzI0uGr3LNGZTNAqirbrKH+0wy1FBgahmTZuUr9Tmgp2AqmjB4suglS4b/5HSxLU5WxwvCfUWkThA4O9xy5+skH2enAZcOE/WoxrFxasg3ErdcsyJ+kqKdr+fX1AoDklOLkAFtyYqpgNn/iTSUKakVxf1FbQPzmaQs4oQwPoMW8uICJ/zwsgVRFviojH/5l/+G44yZ2sgPs3XNhNvIyqSQ+4MUlWaRSdVpoUFY1Cu4djlKZCwpHLXgTI2Ys+VCTrNJu8dMlhUteLorcfWzCF40+fTTT70G/cSjj8ETiSSxJM2LSmB6CRs4W8tpctXg93rwoWXmsHsgPtx/+/YtV8BgO672uKLO0Vi+AgGI1SZCMJEj8EQucedOybldSpFkZlGZCREnmU+5FstIT1rZCALmggJAkvMClY/DyuRJw3ybfk3ClfvMRklFE+ORDoSMcPKYRxSMDWiAnXpfVzag099ZBDwt2hzoU7hVqxvNOyVFGS2u6zwsxmPqkTvaiEbXxoPPvbt3rEmRbp1rhiThQgRojVZSAzlJN6E8hIVu8n7EbJJFgrQDuvB3BGTUM0kjafLW4rgWfEeKGpMkNQQ0CpQrNqGfRaWjgz2sYRy5QZTUHG6II7VGMBWERDycqFMb0rkltTBJkUAyHCZYo3AAgUC6r3koMN2fa1n3FTfzWSTSIHRgnRmPZRkLDfketryXky/QNlbDMvmY1xuYGWSU8V9SXR0KOCCjIjYcQcXUhhitUM2KNLhxC6avs0gMby5Au1QK5eEi3y1ZXFs99cxL3//wjV/zJN/fi5zPdDTewWPQQ9gMH3nOwKozT3qUbXYZoCRft8QuPpas3ndwFN+3jecktFp69uO9KtzgXdSm4j1Kyg4XjYv8JyRq8IogMK3PYh/3Zb9AD+K+7NziFJKbV7/46vMPaP17LX1EhX42+OmSnycEM/YcaHeYg52bE+yO2H0JGlJ4mwYoCHlHZEKd+oMYPjKuPSwLx8ucw4mCeubXXGZq0CJI/PX8SaJEFcUMmrNZ40emQ/MaWsVNC1oFG4tOItTiRqjxPDz2RMST0GwB71a2NUbvOmrVw1rFomJ0SOz6pJoNmQasbS5t6hPsx329QNrfZy/SIT0hsnjpgCvthuKxp559NxSzH/rIFsqItg9p14fi8E8HAnu8fBSTmZHaGC2yTr9RA+fEgy1u6D3O6lSOdHwqAjVl4pAp0Y5LwWLHCzqX6nCBLSmt2zoTu/Xkqz/i82UPDbdHsmzDyvdGSXFWztH+4a2vPn/o4YePlzasRo0wNY7QE2LW5CmPQUt/pi6wE9PLsvOgYB+xA/oqq3nxRNN1xGXatKc1ub7c+t6Pvn9XDwwXe7FHb5mv91ITRaTjDylmjAOEICEd2U20tB0DHdKiY994qubKHF6Ej0Fl4/mEZR/kkttqeQ4DfHGrwySkSo3dPDRV4oE+kYIO2phGPFX2gFoRe5H26+fgr8b34ogjNpgCZocpmYqHKos8b9GKQGfpgOEpe8DV6OBC6GRZujovmR/0eJruLCtNy92Rj5D0zHYUwihZtxE4xywr3yExoph3BCLWuAUtKovRuJpL0g3kGPBBp0Y+x2mkgAwA6dhhLQlRQYWj5+HUB6XCIfGUmUOXQAog+4LUDbuVpQTADwehycRb6E38lEIPiyPKNRaksjlAtkdla6hrV09gNxO7JJGoQ/FgFNW75aDEtbPY37+z8+v/8t8WDg45hws+x+1d+UQcKtKJU+wRnrjkH+2AIIPhlPR74ibR9Byx8JHRGr8wV3UIuSTZPiOdAxtgA2JM9amj4FIqEBZ0bUPR8khMo0W2JPbjEDFeSFhaBWCPdE5iqxvsNFWGxtorHuU46SRFMiglPjfStJiR66zZ8RpMpKetPVeO+XgRk7Kq2SoXKgk7aWJfbA7maJ7UkB23BZM7RoxcZUoFuOovykETUTVfTsBsSqG6pxJBBfORTwgnrBHuPlJ1XI9iWBDwLAmKN8loZ9gdq90cjNPCJ1vDhaX20tOPPNl/+bB3mwN/by8sS4iEhnLopkokkC7fSKgLS0VkcQiITJHaQYV0hyKMf445nPHg8GCfldWjwUFPC52sYJIN7AY+ASYoHNr6GkuTDCC9qSf0psOSdTEG55qvcBznJnOVwwMtuNP4cKguX+6Gn+ZsFEfY3x5q7klPtq3lAyijLKbYJezlwiIfPF1jDBd+Ze+SkwUzOhE2NlAYzHcYjAQnzOHiK4VIxBksEDqE7hIZlBzsZOeBD68Pa4Z5oEkmCz0sxXIKMzPVpdUVUFk5R8ZKp8OD3heefpb9zhQ3u+Dv3LyF6fYWjnhffn15ide9O0ft55987i9//r/wRYre3u7DLzz/0z//Zx9/dOn63avY8Pqtm//7/+e/PPPMc4jQ2EZtMkeaMGQd7uwf7PWWV46GS5zF3Wkr6zxM9Xdk5IasjuLLaHmIdfmqx5AzyHfaTD7BZ80F5Xd2b+7tUhdViOz7W1nVYV8uVsQQUfG5VMh86hMVc8upWIRudLi9/t7+dswFoi9ixKBETK6+iQuWFWRlSe8VLTH25NWZbpxgvXB0d/dO7/pNbLi2eZYXmRlXqkZoGSENHxaPY2WBgX0EOITkdMm9SQ27R5zcgeGy9jXyK7aug/itzEbgXdHov+w0qQ6oqoOc6q/1sW4igTl1ud1hgt7rrizixtE02OsiHYRRi6VWqBns1yPoCFv2DwJehdYwO3rdAFLZcN/QFt0TB1my7Gkc8VNMSfk6nqI7m2gSXkNqrWr4SeJIJyMqhjkhcgFOUbU5HptDOzsJuWYISh0f3SLX5S8TqX17gNDgDAcghU9RoEDmR4JW5PCQ6YY63Z5qCqS/v/3m7/6h2+689cab23eGLzz/8p/99Gd8ipRHijwD6fH8hLAYm29UH3X3DYZG7XMe65xOyiJ1TvZdHyepCqTBnNucKXIq20RoXbx4Ue+TaQTGDrNomkiOlpz3b1is5G0VeHKAxv7O7qMXHyaONZHO0zgWKDn6+bPPPqMqs3hnAXRhRBDAhpVXf/A92mfXkd/97rc8RIQ8hmGqE9qVPDc0sm/+vs6ig8QBoTkqKWOth5pb5LuMVe5M1NS7qlGQXbDJ3d09ZvR0KOhftsPD0D0sv9E0VlpEHVULph4dAbTJMgL5hIZmDmnSg5B+fKMGBGOKcxiNXoMev9Nd4nklrHhvKg/EEv4JfzgCCszIpijgnxV2riUmgAwdVHDbN29STm2+1os81p00vT1aX1/DM+i7Q2n1dGQl8sZURjvmqS0hYpEOvbu2wliIrGirrwzES0J+S09dq0YobBzpaRcjB4Bv7/KF4IXj3lGfTdETGynDbPIxdulGr6L6y1kOykmE3LcKHiUixSLevMjXm7Ap95SCvg7RbelTZ3rLmfkfaomarkIObLMFJNFrxu0MuutIHuWafhTdPaMsnlBrrSAZSjNHlTXqem2o0sW5xj6K5GtM7DUC5zlHuO0xZsV5G/6Mm2EduXIEp/q20fJg1YQ23t9ZYlBLDpGiQwBTJ5Y+IQgC5eije2SGIeN4ec6xPrHgjeFmogrQDEXNNEvNmNEkVci5lAWCrcYSpRIFGllhKKdtIL7NOpMBoVcBEYyWLAiwV/m4JSRr18jhMSQBc2E5bpRq3SQK0lBzKQTcsoOAfQQMYUfFYtR7XWth8HGAyC2FqQHWaA2WTm3ggA9kErNApiKQmgTFoN+LBez4YMg+o8YVfomwCCWiihQM5SzRsNFtUCNGUjK1qLK2UiCeJxvN+rh5dRw/gDMk3JarOQEBE1kIAuLUkiQpEQq8hjjpfq9W6X6p7olfqq34e8AXxgROIG9MCdqrK+g/PDhgGXRwsEvDzQQGzhQd9oHQ2SyZdSUpWb6nDveLYFMgGuPHR2N4NVhbexiQSWi02GSmthhwJeHtuXkilf5YZ/4eXHVqjd9QqSRhE7YOMUYBYoYNzKm3Jj85foMJOcXgq8srDXi6ZTq6s2MRXBWZ0E3AHNh4xYrArVgvQKU6KaPot8BdBZxEpcLmuD2p6aEU0yCqSbD3PCvXu2iOk1j9gGYmXOFZ4rMiiCBIDzcRIYW4W/4qQ7MYJDhjTWWBDV1sFlta5boQR7/1Yss8OqHYUN1c9AP3YJYabnf+J8nFvfhNSSePNbS2W4EXnDrieIGYcAQMtvSnARm12PaBckVE4UDceYQVERgQyZT5N0ZCagYxY7S9kGsirODSS1oDR4oEBYBISgiJ9S3wxq0xpQNvh+nV/h5HP/d4fSGYTEUuzB2RNjGM5rbOILT3JIeWvRs1Q+dlPsSC7sm8MEEKVhNVbOVgSVpbDGbUlFqBOl64fYMR858jBc3tJ/OFwkEIudjTbaaZY6gGZqYY+1X5OgT/cuvSLcpzS9y39XWMV74BwdEcadwmvJw6uqU6KDuS7bU8CLW9Ha3Y+vrl55dYkbR0aEoE5AarxDH/gEDU1wx7kN9JKYaYM/37zu4OBdqPZdaqUmsETxbInfGJgwYVIWV5YSHertUsESCNLVeGEDTCBCa9XsjDsUHAIGbuPMAT9nV+1KpEgIklCiUmwDWa4/BXoUcPBT4BOOgoyJVbkU4L7s5Kqgl1Fa0WMpbbR8zY9/cPEQEaUiBBeQzFiEgdClP9dpsFXBpHI5DBjXXWUhf+8Ic/sF2O0zaIn9nYgi1NGIGdNUBOb7DWcfqV73z3P/7H/3jx4mPQsjWM05//0//x/ybV/sPeRr81bMUEj43kjnBFSZcI5HDuLPlBtfb7ozCpwE1C/Oy5DTSnIBgDsP/YTMhCl88tRr5gpTfSZa7a03KllVJjLWFRo1bPElUcFEE1ZUI94UepklMQCEE45GTVu7s9Zjerq6ySs56vikODCKtQJvUdRdzJI5OKmRb+k0ysuZyseIsepaCE+j4meIqHX6W2OnKHCEvhSqpu4B6hIcJoACOSrBosG4i18ceSIGzg+9bXydQx4rixDvKJiYDKE7CZgFoN8wS1waFxC4KsGAGSkVcZFGaBrTlPSc5oMIEzaNN4ZKS5vw1aM4TCcCkQdkaHRpK5NshNyLXWZxLHtLOu0EpuhDpe8K1VuXXktddeQ8Pr129ysPvayvmf/vSnvNxJxWcepGtMh6h3tWINDt/UrZUvWZjDFhzrUyKTyON8pnprk0jcsgvxOJCXToDs3L3hdhtsGm4Zln/Z1PRN9MXJOGF8b3elIeK0Yq8BmRx/A83+7O1WmB2IBmnhqeMKN3Wr72fhm0Od6rhpiVsHIg7AnV8ioo3qNLXWgM+WcL76w5NL9nfTp8CKQBbuV21YSe5UMVY0X8GBeQMTCAHRqS/IyPf8hQocypSrzubKNQVWU2lBQDRlR7048/DD9HAcOg2EDg4gXQxzf7bWQUvcSppn4QyQJIJEhAMazRLhQ9BT3hioY8mUo66yxir/VNvC3O40VedJIBKLPpOpQKxSSapviaMhFYG8w2SqPjV+YUIEfJJSamVqhrB19osCuAPwvHmr5jSKw1Mh1dDRSA/FCGmoZ0+e4TYkit247wXLph0MtGzHuZZI0akAaeWJU3BctTmCTirmsGy9EFU8wYrqVQSFJoWR1U5Zq4c81UCiQh4pNp4X4IjGklIgBxSgIKRGhMnbjJh+JxFMXmtcuBVaWrFSBAUoJfZjPNmNUyAPhjrp/PGX/vz40qU7n70Gl3bsC6ifG9RiCiMiaZHI+4MW2ZGjjGIePdbhQQ7PPWL4j0HS44+KmCSQtPagn4iTqkWoZF/AAHwNWVBoB4XQAxOT4jxgCSd237YXlrRWFTwxO0ZfPNz+7L03n3z5T9leQs1pFIN9L56IIJRP1m4xZrVHxhwD55GvkCcivBRoe1olWKEFQbIdS1fWMvRmjfT0E6HIkangZm/gVtnJ15qBEWpIHYeqvv1m4ydkzrKsMNO6XbIDELpTlHeliodPqCptjejPkw7ZEq/tADQufMN+nb/lrTPxwVfNxA73dzEdszDi7D0SbTwVwU7EV1tCxPAqxxgG5UE8YCaRurJRJ250oSNQKVgh9VBSFcjq+kak+jLqbChsRBbOI5wwuZTWn/zTOGih0ognuodRwz/Z2njy//K/ffL//H/gbI/uag2X4QRXuOKk2ioVtSb2CssBsCTqffnJpRsPP3b6OfZnaZM3OEfDU8Js4Y3NIKoYpkx1niZ2unfGXbOUC+zJNjRYEWJHJKUpa7MSzPXzt9/d/epaHEBo4yiTUPH+J6neD66Di3jav77Js6utn/70HSb/POjl4LNoDfY4+YomICQhlbgtFpWTMlK7BJCw0CbXHNqk+ZlXnWGhoKEdLqMbVqQDABGlLA19vjw7qcUhAt4Tv+N10WngR0RFQAY5lVIP8FUYatdDT9L9lkZiGGVqz7HmHOVMUj+mymceeVr4wZXNzgcqXQnA9dAzqySZ3jHNXtsABiTpK3we9urHIT6zMF17+IQ+du3wwUyVfis+4ynoY7sAxm2cWCkpQIWiW/MSYZD6atoaIlSKRWubDI/SoTR+F0ESlc2oMUk8PzzvV9kZwPNfuEmjdofmlaKh9pHq2oSZSV08ZsunuJACQkhXrZE9AhmrOmKeIodBrGelUgi7EfdtiYCm5kBsj/HZ1ZXld3752qevv0lLH/ptS005CaL00HgQrVDdJ/LYX87DPzTJOw7MP9oZTSSsG+QlkB1rAiT1DiUNkSPnSdBJCAlTgYZTO+CvnRp6SzTKIPZBk4rClCzvI7APGhxqvYD8h+GoB8utZSyLFFUDa6Zcp37jV7ucG+35zOtymcRMbUXYbTLVxuTaSEoOP740Bho+wZUAvu3DflGQWQTEGYjQ8puVuqwRV1WVhj2NBom5cUtkEkcnbSqFGkOEEjvqxgc38J/YLES7dPzi839y/erhrRvvgKg9RKohfGNWVcjfBbWsOVfkWrqV8TVncGzwWpgo11ULpr3CjK3ZDyS/0wiBk5uZqBzwzUROTlhY3NnbZ5mSE43dxsgbY8XHs3d1DZhINTIY5GVfeDF158q0nLk9KhFhIfuwd8jTv3aXzboKfO2Yt5mIan8+AAEAAElEQVTZnNfm9Zr81Eqbq2jn0dOlz5Cj0+YFNQpR7Yksq0c0UZhgAtPIxqaAJy9i0PivLq3CASAlS87I16mtU+z83b57M3Y8hfi40D7bYuAbKs/kdIjFFTQ4PBoub248/tQzjz7xlPeYwJIB59WrV3jPHauxiak32ONLkqjAHLWzuhwdhPLAw05e2Tx99uxX165d+fQSc9GXX/oWR4s+dPYhTWf1bCI+R8Hw9eiQBehHH36ExY7jTpeddF9+9dnV65+w4qzHFYua+h7qiERyRr7hrXyr7I+HdznPcfEMB3TR85JZckEgEUJsvqwDMQcU8VK3s8RuXPZNx+EnnMHNEJr1dJBhhjpyPCTFcvDigh4YEMiUbKGQjBNxXypItEXgk7DGd2oIiqYl5rR5NNh4pMTp0iRCz7YsvjTQG7J8r7eV6c15RYnBCOdTHw1v8277xplTqDQ41vcb2cJI2W12OGuVuVD0SnaCkMWoI0ROXqxKykZKDlUx3yT21HZJlqZrQUTUal8gFjzVQT2MCZsxSAlP5d0LHtKzLIXnt7vhFfJo40Ar86rfzupNU6ZWT+YNEQCRhZ5UAyNwSzlyJcBPw3jVSyK820C3oVbNo52a4ax43T7MwjEcvpJYBW6l5wkCUtBZfjAeeIItQHBNY6fcCBdZfoMzC5rkMc7xZHdFc2lVtZCGl9STMWti3S+5fCPb1dmEg5mqaDV7VQHzdIYt9ktHBzzfunLpC1qUj9+7dNxrv/rTP3/5u39C+xO1W7MY+RohfDUZF5fILZ45O9d1LYrBiBKRabjZpHIJMlwgF0Rghp5F20D5x7zQBrJYyWlX6jL0FR3NdrU6wPgw3oTTm7sYJrYQuuidHVsshhKaOoMT7+HwMFVzQ2fQV6gI588/9NVX1/iYlIYUsTvHNc4WmzQBtDTX2JwIC6lq32aEcAYZ2eLAIl5CulX50CfjFkw71ACwT5me0iNGAYM5P5vMowU/2lpfO7u5yXs5WAQlYmw/0gD+3ITEKHm38IjWlAtDqv0haLeXjBk3dtFA1zZhHIwXYOHMIh6NIiqlpXslOdBnTW1vc/rYb27B9ED3+6/+GZsv777xplyTvGsSPkK28qolhOMhvY02nHGm6O3bfCy3s8QWLmGrP20vbJ3ZxATqLldW9WrkQoszqXAbVoTCB8hVa/+wt3TIw3Im/kftpS7nfVsE14N2R4sgnGzFeVS85RLFBOAua7SHR1/eubmnrsriVILoiQxI8Bmyb6WBC5pbaWdFmLnQEUAqdR8qlaXGlIkncIZdcScODjC0htzazej9T29uUfRykhiXtmNWTqbAUQY0v1Cpq3WlmAOTVUFxjrU+ChrpbiV8ZdzMNx6tpPtfNlpLw6r9lLiwYyjmotf4JLKbPZkDusL/pTay0Fk9S+RJljGuGJCGOok2ur+0602lKbA4hCQ3oOkm4Pi8EByP/lD1ghKJxWW5KUzipK3l46iPVBMgHNmKDlko9En3WOkKUbqQa8dTalZDQPuhk/MVuUpx+WqtVQKSddAoekbzNCbpLifnLmWzkpLEB397C9EQIpsEON063riKYSijLSFTQ9I10syRiQFHVbAATRJBSo9PvabyaQBFGcXQgH+zt1I4LOd81sxRmxpiSInzAZYnX1bx4JekSseJoPzWjq6cKIA4KQWgkY0wwUwOxAyBET9JxCmLOawa5EVciRQEICWe6k3cj8FHGFNiZjBL7RPyOSHapHgIa9FYxiMRNYJRJUjlESJxTZpZk9KFt5+1C8nVwM2oK6H9k0aBskjds17b515vjdaCPFHxwpa1quxoQLqaCh/WfXaTRn7LrSIZB3RlJ1/5IDvDFPEIF5K31HWahPEAK/D/5LnHC/Px9Cl3xoQ/kTqzU1AnQEWKaWOWntovGm+e3jMfDr+tspeZ1LIgp4BeffXVj3KqGMbusAwY/cIQOySJ2ZN9C5JMhLmi5ycKnLPCw27yAdKkT3RaSYGKw0jGvWIwkcSMBiviklWFohIw71xYbOsUS+auLH901zfQam+8wyjUZpUZim2RVXDqiHNtyFwHSUoWRetSqBk24lmT5JzlFrQ6XlMJHgYxgq/YzSSFKkVi6EPB1vCGbtySmoGKw0oMK9NMxQFY2weqQKuvMBNn/eS6VueFJN8ScdxXA9GBgVEsQLBIcZN5l+HBrGYzLy7R2aNCSBI0hwZ8UmtNauTJVPHPGakx58RNgq+bUG4fJchDVKjYxKh+sOrvzGpSipWZI+jrJIl5lF1h0lAABPRE+YYbFPx7RpL+YfA5yJPZlH9GmEwyHFVL0qTahvha0IoChlhEyRrAwscIca0qSaHPEfAdwGR97bnnnnv3zgdBFfUxRrc4DjhmmOnu77doOIuskZGChlC6Eu20jeUs1KA04UaN48ot88gaucSdKa4JLXoHuBnOSJVITBNb3Y6OVoMQzuySpqeQY+fKQoRbCJWFQKNToRlnQuiGHUKSoCqiGxFUZZLJ8rfZwpDxPAxZ+eckUIAN/HILSokTQUkg4MONYTaL19rOoL3PC8TZR4aIG9euc4SoMUFGFnpGXuQD6Ak+OxNJ0pccFxY++ugjJvxba1uQsNAGAu8F8zD4sUfOIwhktkj/+nevEy5fvw4+n00GhzMciGM/6VO1AEz+IUEitGBhpfhcgqb00kEwFRmpXDG1TvA4PvYoFwUwlHaOICXC6tpoUj2aZaUGfMwykDjArUQRUW4lfUYwjp4oI9fkzAuZqh8ektSJY76OhvoGA0fiYQ2W17H/6qkVIBq4sCkpSpOcFXEzRM0DQztHyUKZtE1+mDt0QZVxp5brVIYAHQrPOkJSuS1xMyzwP1KkiKv54zD17R87Lgf1uKKSZB0wgu1Q9HTEQK7c+lqRfmNRnA01/nj8T6KoM1gwlf3Isi1gOH5Irb/80Ue8XUE1/81vfrO7u88mlZ/97Ge0TgfjXxcorMhdid8zEsijjnXkr5nS5cJdKDaqFzn9Hr/flJFRw7qN2qSQTPOY5qeNhFAYomIN4jQvdj9IuS260a0BqT2TpEJInMCrJJzUPzjcY1Z46+4dyJ3zEvFt40rPCO3du+oHaaqJ12PsBjK35lbzrOOT+JMQOi+AyOWK80AuoTPCZCoQt73uZWwZ1CoMUjR+sBj5qvkDscIGcuW+0M6PGFEW0rmJ6/RoU/HNvyT5lpeDkOUBwMpm2zMIIE7VqVlLSwYyzEBJkihfIlypWdzy1Jm48bmFP/ES1EnH2SzgqJPKDYieqmou3GxXjWY+RdU5EQTdF7JZQWU9yQghbpUvPUmNIIgeZLJyaIBqcRHkqJmQDFpCyj8kMb/u5cqCoejXEVT8ybRiSEw3KReWIYCrYVyL3Mx+7Be2Y/df46ZokkqKOSVZi0Giz3oeHmkzJYvvukrXVNyWmW6zApOpJV8FJUeav6Y1VKUQ/pYfK6cBRk0DPsxrKlLTbTZmjT9KbUDHb83B1+YCdGRG5UQlUEFziQjYPK5ZXlt/9af/6le/+tVmZ187ePcPqEjjzCfvRm7EU4vYH8Kyv55x0J8xasU9eZ5C6HDeJvUwuaapKA60CFfwQDAKSds/aD6xHC6W3A8ITxlUj2O05ovzoTy4kLjXcn+a17ATjPsFmkbOcX/n97/58Y9/vL3HB7sP3TR4V6N3U4afay4hz0EQjlLKLTcNuZVRgRVxk+YA4tS9/Z31jdVjvrhO4IkQWYtz6CAPQfIPpWQniMKQpQy0Axm58KzFlSSA4jgR4DMBSwDTNhBqhg1C7+I0QrVXDiyVXTrX1WroYZqeEIJMUUmx2GeXqjsaKXvcSTc1LuGh1j+w2StESvDsgJCMyaQnziYZpi/1CWUUkJWeR5nFKEWlSbDYpG1441Ec7Z8QQ8OKCH+KfAXI+6m9ZqmH14gInkhE7Ujl8UJMgSKT7AIGZev7P9ztDW/HGvSmjoU6Xux6l9xIju2pxfTFo/Xj9p0vvrry4QePPfMMe7mU7UXt/ZlassBlxcgsV+JmWoAjGROxguyU7Tt3tQ9aO3GpaTqKtz04+viNtzr7vS5bhEI3RBDILdVZh2tKnOrvna6+/Pv4z36+fe48uSaoxkUAJ2aj4GkDDzCuKgv+sDa5ClshTjrnq9DC0ECFycFYVMgWM9t4/o4OAy2fxdwZbdTGBxuVaIjVJe+uKIC0yiF/pN0jl0w9WwM9utWZkVCqQOFQLMMzauJa815Y2Dj/MEMBTsqmO+Q8a4r5ALEqeWU4eYV0TUXl01oNdyaVF+ylZyCBJtR7h8Sh9sxx6pJlfDLYjfQv3MEhz7Ih2mUT4TfcCoStsufEnXDEKxCM7ysc6lvHKQVw8Rh+2bjHTg3qu0ozmmyZV0H8JCUsLGsrgTPDWHnx1nZAaJSb20p0kKv9V51T9RIfSgGZqBPHpImd0SJVCCnE8MJxIxA3h4JPBJ/vHA3YJrp0vPCLv/lF//Yt0FoLvXAJBkIj5iOq+vlz5CdsKcwkWkNq4uLh3I5ox2NKrWYsKFOnO7VAGrfAJyFGluGVhsm0Dzrqmkrb50ErEvugW7wEwFmihNA8Hs+jtni4y03PBlMLL7iD5XLlFur6NqOUX3tZuR2LhGRpkyJOrAoUAPWQJ5EoxTXXqTEmvmmYbgpGBXJ1MICzSEqKewrfZpxRa1/QiHhXRUDkDTaC4GGNhCkXTXXAkFmGyoeyyfS2asEnEqVpQLrGuyAMkWxbrKeCwALD4/1nnnv4icuP87m5A22hpdqrjZIa2kYFb9qgmSUCh5xrCbK2is0OkzhwIOATmEJ6qc/Q0jD1mwOKKEHaUsZRHlWC4kH5LAlikqttVHxZGD058giSw35vqctcb4VZGXDkAhRBkCDIXsFtjsgCtP8osH3nNmddBC6tUDj9LCUCbp7mjwzBgq05B0q6SAnZhQY2mkD6lvhj/xMDYd5cPX3hwtaZ87wm0tO8LbV7jASXVzcvXOzwYhbvMrNUyv4qJmN87Vgtb3ByG6vzoGn++rSfxze379zZ21mCVa+3GrvAOewdlTbWVtgBvbasnc6Xrt0Af9hSTae1A6I3VSJkxmojlBpHd3eOh0uLx+vLGqv0F7p0t5QmxmSfFzge+3WWlu7u7uwPegPOUR7G7vbewf7+Lgh6wWWR0Q7nQx9zPnR3f5lZ2QJvmMhoCKe61R7o2ocoDYHQx4rVOFKyCmD6znaBtWZ94dvapEDqnk5yW2ytIGvr/CMsNx93GG+geSwZcMrS7dvrfEOKg00WFvcGDAQONzjalm5Fu7Skm/v97ujl3axU/KoujQf0AeDreIruinOhm/sTt2N+5E8tLbTqMBTkXVQaj5ewG0eMEmdvKiXEtjqqUKCFXwVymD21Y2HnMWWApO7TZNXVyAkQIz3iykiYOFby9YQWB84FQ3uMIGno/oJSI24+7AcVeVQOIicM0XoJt0ROSFjQUs7zPeUuU+fw67/75bPPPsvpdmipIUp8kkeJqWEZUY9ZI5M/wC/GM6sSeQAm9yR5AOZoBdWIc+zK4aRZ4DZa97h/uH3rnd//A8+pPv7y5tVrdxc7p3/yF//i6eeep5KnCpF6zGTh2tQjzjNi0PBni49pUuHX8DpeoZwo+mC0NVVtKtQmiVc6aQjVpERAD7xdDh9GBVYsDIC4lxqAh5XCYvQFyfFGZQGCGskcgrdIaKN4XYa3N/jWK/xSS1KrlUn8S7tHb0hREgyRLHqiak9e0RAEa1J4+Nbz6AIkoglT9hpXUjQsCBiEOSBhmYOY+/7ij7KW53FC9Pvl1j9yn6iRxaRTqz3sHwvL5IVL6a8ZEwP+sLxbIAaB9JdgavyvxQH8NtjTysb8QpW6njElOdN/PMrlOz1I4eWsnk6voo/QxnQNm8tIKTQO46hp0+5grmirf63eAR+cuM0MemGZ12iwlXTurupYy+W1VR6P2w4UATZhDENBM+bhaw9eo5UOLE/SkUbrxMiE04EpPjfBvJmJbdpHsQTX2tNimtTKLWzqiXQLf4sg7hDOSFJ4Xdgw2X9UdAVzAuS8j1syLCCfoTvtthf547RMD/LMKGyiamLMxD3czA4TcMkiUlzUspNv85ZPi4/yUKgs4c3sSiA3ldkWQbKwcirX4U8TB4afYUo8UgmhW7JIIRuPmLXqdXZyQ1yl7DO8Qw1R6VXBVCVj6wOLQSz70qLSATFhwZko7uAjnPBRXCccSgxQMmwiDaRhhFy6vtNXlNJQv5EQ6amehwPYpLiBKUlynDG/pMtj82gkzfIlnSTjE7Fm5T5pUP0EyojEKbZPYRLZHNE0F6BDXpXteEyHoaBAMNVjbXWV3vru5Q+YJFCEDG1PsAY9kkdM9cfmzq7GCNGGLtdQY4yKG6vONdukiVBwJhOKxDopWMFMJmVYz6bO7soWD+tKIdXIxBN+Fl+UdITUEkxYEBp8fMsjNTYlEQcNifKPaT40ldbARBU3k7KcL2OSQ3RrsIJkEljj1Ag1/zqe8JNX1tQ5ReYaJc+y7RTKqCHqv3MolR+AddDkImfNC4s45BT11PRPb1hS05ZEhJ+HxEpsFj/tN2SNcKeKnkbHgcBdNkFf/+AD+wyEBE3F+TjlRHAq4Pfee4/XZpdPn59fcI1UyKFtACeETAdYNB9XJ+Kyu/nVV1SWJ9pbjCoaNOBEK6oSJzDiYevWxmOPQRhJI0M1CMutXDqGsA18bsEhC47UqQAJ1B2APGvmmvrn6ID1ZCK0oe0iiYAz6prb30jlQhMfHhI7n9gQBY5n/+ZP0RAYTlFPeQ1RjVhHgwk/psIQ8Gcfn7ilIbh4/pMKznWtEtZ2rXCSr3UhJUg2O7Q1puMu3Mmkwgc0G78WPSeu0oxg/g3MooCXA7hVyxlnm4IJLcUkSDy5kjvFtgglxeiVW3DclRIhmH+JiHY8IOKDDz649emngIkTxtPvcWf8wr+BPT+1RnZeagi0NdvGbY1Z4sa30MBPKcQJKsrwCKYKuuWzkZgrXmCn0kALEIKGUCBAJ4GCB76vSdKJfzLV2ICm6F8i5sct+LUODTlkDchk4TbQym3CjKI2rXsK4L4FM2uYiKxAA+g0FBPD6K2KCGGG91n5Aj9JxFJ0DQvPJ7FZuLKRhzMZ2Mu/yOAbz8+O7ExN1dycH0DDolIxV4nUSQy39B4YX+Fptw96HGKk7c/EwUEoi2puOhR3vR43O3CCkWmcwfEVIOt5SATiNWh/AUmlEC2/94IVTYKDWhLWaVHg5upq2fFk/jVmHcdotpvRkMi8AQQDLa6B32AoEq3hdlmPYPGCo1RL9iEklU3EvL4qO2gWQ7OnYJ7wJ25ZXEscI3Drg1ZQh8zq5JNwFTjwbXoCy8oI6m6eggk5L/qbNXNjiwBO5womExWicMY+WvtmD3VvhTEAPSxTAD1Ils01euEoBmQ5MAYAh43YSEQQeTFbigPgYBiY7EtRIbrhTVkLtBiPeSI9YwHa3BpXRHsM0I9cYzgg7A2T3fYZIfY4TIxcbEUxMbYh+KkaZ5igJKpSRw73Yg/axmn8MzoZUrwXPiptQ+TsW7KG9FnpuSjF02hGNUUmDA7jYmGrGkyjp3WWsaCE4BY442TT9AAtC5qWHLBJhEmI5U5lgX6ltZmK8D8TKGVy+MUvfsGLTa+88srLL7+8wlfCYvshWSMEmkyYcR/wF1ZmQmQWC9wAcZKaMBQ1sNDO0QTkydSpwFkKGN5gMsnhr//6rznjmRXPd999l/bqlZde+Nf/+l9DKzf2jpL5Ak6WKrkVJrfcFTtUKTVWBZ4bnczUXPRRotUY3U+L0Tz6vGC0Lfh1HCDBefHbIeDRJuOS2JAZrrwuWlrQkAAwKLJfRL12Ek3osNNmwzXc1DvE+HaORaCiKaatBt/BfEo+AALhCgQ14OlIQZiMiEPx2dzm1Gj4yQ9/+EOed77zzjtIh62yiaAaaW4cZDXQMauiowqDJNvW+hNX37ug7lv8WbPO65janVApOVfalET4YAoXRJ0M3Ld1xCBDpHW8VU9Bn9ragEP/UAeOkR/6IHpGtjmvbWoTFeq5aGAICRB62siRekNuZbdo7VPTxQIvPXL0uUlKT0UGk1rDEoeVgxUjfr9rTYUVEcjNh6tYRTAQiAdgKEM8W6imTnHwpcU4B9KAeAWgQQPcrt6AT721YF+xLpFUVKE5ZSnZ08Is+DTceTBlLkSoYtKVaMFbS94Efw+T4YzoYyBXtIuIEA3xlVsQk/7zZI7S7DC+J96IjPAiZv5EFXFxZOOUJFJRZrrJKnbCCeVrwpI+NUmDL8smmaC4JibHXa+DMfZc5Gi5qAbyfUbXy89/98e/vX6Fw/vW40W8OrcWNures3CaA6LOAaZkzw3PMiiTGCbyzEipaK4rAnXWrOLeOcvSoUoiPXUBHANV6QoVujaLJkkPayWTiRY05dSBZ27kK55o4RWcnMbJVvufvPP6Sz/6F2KcTmwUbjG6+gZ98ooT7pJItCLIannoT2RJJ/6kQKpjgZK8ShD1FW3674IADDMyzSCf3othiKsx5BYk2miGFIlQ3AtMowEmosRxwxhYSyyYDaBoc0is8i2/BTLfHwtakKpMvASskiazybDAi4EjQXfFTQISvuFYPPtKFDgPwLTj2A1ZnGspzJzxpGF4TvKKipt51te8V1SwjO/08TtWG/MiIzXFGGkh25YPkDoMnnNGhnuB3w/F2LYL1pWj1tJjj53+2c/5Ev3zCzdRuqPzVzkNMfGrt40Phv3O0ZAZY//yVx//3a+f/+mPuxuc8yDWw/aGCWZdKQiXr6zhOjYN1T5ti+UqJX22zvAKFR24usAWX53f23vvzdfZFrRKh7i/12KzpJ5OoTTP0DR+2u+o02U6Kvf+zvfPvfrqF8vqSns+/CREJ1vluGsJV5dvtYArWyRfDiTihZaaIgZUQGJKTc9viNN02f24aiE5LLDITrCYqbnKQlrqjviIPorKRV8doAG5UqOktb96WrBW1FzlPfTyvrnakqbzgnX2Glk4ZUQeF5SZv/kYlPlHWjwOzByauTBm5iH+2nk8O9B+kYgUWSnQbK6gzGRVEiAbxFdsWN86rs7VMU795sBX7YCO3XwqqdSqR+XIFvcDztBE2iBCu6SRKs3ltmgYJww6Dm/XO5cgNg/vsBsEuVgZqlaRw1GAYhnjcYUvcN/i16IJggI0hCtTUp63vPbf/oalFZ4OCa4DvcE+gIWdZequimIZ4aJftFH4HgMzkYcCjjTi1qokzY80FJ68NXnh2UDQZmdCOhtTDlD2QVN1sAvDA/olvVHI9xrkJjFaCOdL3hJOa/4N5uW2RObnZWpq88EhJsee/A+fQS7M6U4lQsBRRznKcuzxEdlElQdS78tz62d/wP+m4pdGQ3JNEH0Fd+D7KsoczAf3xwMoe8hVA0jFJcJr0bMsDlp/EhnxwEolk4ePAXO6DS8sBCYPjDi7Y/hr1HaYMMSh6VMDfXT02IXHt1be/eruFZZlF4f6hB2HDDJB2r27DUftI9cEoMGDFPkwQbHIqSI5wNZGy4D0m/DHoeSLUJKWOl2OiVxdX+tv9Q/Z+BP5BcEPkkv24Y8rMk6DGamFpXSyGRfaLLMyu3MSyO5JIZEFwpjrqzpiwhYFXvMJKjVlWs1b1IAQyBFvuUDrd9Q8YbDvFfFYLCYYAMzNyvhWcbxUfyRTmWRVqRsKj7Kmlo50nWJx/uyFcxfO8xxV67Z8bYEjntjNzDAZw7Pge8Rb7upb6Wj4LgJ/mqppO40euvPDBxE7LZ3OHIIQGpNw3s/haOx4NMsAllR2WIFPfcZK7AGiemNd9DlqqX3rxhfn64ksCoO5pl3qHEmqE5Wh7bZbHIuvozVwoG6X/c5IkdV5M4tzpGmv2axEOx6BiTHvI2vPCnLZwyS7sXnlgGe5xBs7oG0fwX1Wob0u2pwAjl1sdoNc14hjLcPZqCbbx5UsEHp72n/dwhH4/iF7tDpcO6qYy6prKE6R824Hr7dc+uAT3hVY29z4y7/8y6XVNXqx2L9U+qwxNXSTao1nGWm5v4FUCr0Br3Ph+GgjW6BiE3mTPN8Te9VEvU9GQ4EPtNoDnKG7NOiuUhXdJthsejDAs9eGvNFtqulwt1yukhShALmzHRNcIz0Rgqzd5dr6of5adldHNyatcIsG2gzu+zrLbvMZjekxA3VvZ/dgb//a1Svvvfv2D773Cs/nOIGebGizGk/pGHOr41aZ4sjBI2aj4Y1pl/cMztkfZiXPheNLKgT70lzMP1qixsPooKrCyw+82z94742/37n++VJ7483X39vZO3rksef+9b/535aW14YxE3F+S3F/I3pFr0kBjEryfp5jTOnFHkyrulIUDtGLy+8dlHG5yiIPEVlvvXHzWsDVM9L/Gkd8olyJAGGKpDVZ1yN1PoJqD360McYBLRJGtdJw2NKasa61vr65srLGvIwqWkgsrnFFNxDcsZZiQn+G63rHJQSZRHVZFVytDQhegdH8jrFB1AJrSBLDf+snQnpPvSii5Z24U2ZvXr3yi//vf+22O+wCXu62+4e0taLQeqp7RH7c4ESTQrsJwCm2DD06UvhaGFC+t5D7WyHJ5NGseD4eRypjOz0JkdPgNlEiHneJ4P6DrMCBJ6fWr1xlg9FBvDgIU7pz3iYSO1kypOgGCB05kMiRbAuAMyv393vb2/SSgLX+uL9Pb723v3f7q69aT7Kj7BzdvcqOWSdPQCkNjnduaR/k8Ej9MLu5GcWELBirc6dmskeK9QCVyCLvZ6p1Ar8sQMNN9o1AnCQCWDCToAxXQUcGy7VQKRd6IqJxj0hKgokzB7oeEOwbwFADQjyTYQEFLSYA9eUmpPOneR3WQSNh+kyuwioijYtrGZlh2LGytjrKVQNvxi2aMycnMQ3VA41TmHUQc0xT0UeZUybDFsUalOq0LINPICeNYP5kE4kE+aU28zCk0kFkZBhmHGEm64f9JVwyzcl2UhJGF2aY2xp5vpioxnuEwB+pad2kWHh+lPh4Z4zmOVOizZ0+FMBDocieWEwJro9SryJs4kUSTmCFSXUOfVuAooqBeIcZCENSRocetuIWuSDMWQrZuWVNPk6i4wIWfvCDH/yP//E/dBxFNEnBN+fMdHOv4XyQKog/r1j4sVgYBQjwWlfigZjgjdQiytxAAj9IxlRqQLjFAUxCvaXO8GLjI7dv03lQFGjY2K/U0MqEXEskGEZdDf2tVUntD7S7xwpZExqdoB5pa7ivkBOxzUELYMmoIg3k5G3RPTg+csxwL+UoqMzFijleJPr2619h+PWZzOFQ868zModkVlJhRcSV0wzJwT05F1ozn387qQBDgaefeoqv5fbf+Moz3kmcBmfQ8NKlzz5jZ3F3nSnozBFqzoUKIinmKjRLxgw4bsNOpc2NNdqHQa/3+9//nraCRoN1Vp7f6lXnaYFWgm714e99DzXsjdOwpsDspSTUxq/jhYZMFXgjXnDMB562AJqEMpPdhylGcOpbzWR+HDWSaR/IwvOZT6ZGFibB9w1JXjFB58xMTTWwJDU0GaVm6wWCXLSB2ZA5KZEigwQ4PEuqgUU6TIAw/6ZSUFr4p26jCAJHzV0KWv1JXa8RxjqGjMWvUyuA2P76179mZEdsvPevsZrxOr9JIj/SLfmV8tUkSvfOoKkaKLAljw3gA9zCXEOksK3srHwrSHRUWDoe2LIrQxBeE6RtjKMSGFOBBo5SXcqRDceVlAOQxDD35nVqxrrHb7Ad8ZzEttwiq0gEswCt7SStIWMlVVf6Oj5BbLkF3Lg13AoUnFq3qQgFs47Y5wvkhDYErWBat7hViTC84b2rz7fvwPn0qbO490G/xwI0lYhREAhSO3yjCJ0VKbn2cLmgNTJuuIFcS3C5qlsJN0MfDjQmlRIBqE21OcCB4ZlDkUIEbQmO8CABkpILuDEDggEM6bDoakHjNiiiNQgPN62vdmfwC5BIQ1sgjQBP9Cd4dZVUSNCfFVqupBpfemaPsojCxyJYQWCCSqcJJpCsquLhA5qIGhNZRJzxOkIcHIQa03yY9nPgBvGt1VX4d2PyPzjcZ5Pv4b6mRrEp56jsd4YWZAJCzZBrhzeFFxeXl7RXXc8toolg+EpBSRYnF6BPPN3ltgSkgYM+DAY2NvUI0GxtE0oKiAhzo4FE4xRIYWV4dTtqE2rk0QQsOJsKBdL8ItNLdGy0tz7YVzkNluiAm7HOj8NwOrmH6PjPdmebTd9xCEnmMvtX3HJhFweYhR7Io8SiyQg0LYbmBEtBZ4V40JLgbsnD4FkRGT874DSOgdxQZhKvICgS/jaJMwlBu0ngPxEI7qFw1Ocs3a8uf8G4+sXvfOeZZ55Z3zwlT4i3KFBV1tOT7Jnj7QfLDjyjHB+M+sGpUomMqtHMERpZllMtLGKfy59/Tgvwq7/9B3ZALy+vs/eZbeNkoX5Y9eA6TaOUm/0RwgOY/eSacDozgQVo6n6a8EcWEocqQzQybudDn+Rj4BqzSCy3k64CRAu7y8vMxSAUwgyLkURAHG0aDGkDWckN6VIOiBF0E4FbfvGTgAfnlHLSH3PgpSsiq8srtJ8Ht2/BEKFikduoBjtTkSyUSOOq9uN4gaHLi888R2b5vgBvLezvH/LEU5+Sjd6KHDFuEEU89OQwDkUjF0ZAbjFpMD7pheaB3pl5ek2e9RyJMDvMT5IL2ThkGQ5UGbpOHm5h//7+AZt5V5eXPvnkk08/3eH8dDg7mIkVlq10VJqKhnhD3UxBD+4C1dKcxzkFUzhRuOQdHQr85JFg4HKYTgSCs+lk4gS09TgB80vzVOCJT5AkbuIfw81J7vAJ6dE0xfY1RhTBfp4+k3waEHjWoZH6dW5hax8jImvH21eMDrmV/9J9ZA8kFxZUIuWWlhZMQoG4RifM3HoUhKkKpyo2La0mdJwrzGv4NLoEM+Yc5MLTBNxmlROHopsduvMXf/EXOC5fFeBDfLx1SLIcpoybOzkeFqPJ4oW1/X5vbfXC09/5yZfv/B0KdbWfYoGz3SZCGnxPhfNEZ8gHQ+OBAS0Mr8DR10vbWCyI7dFMdq1kXCMjbAslPx5oap4UTynQwTVeV0AhD5h+47mZzl2ViugT+xN1gIz2S/IRbCGH3FZbuzM+ePO3P/3pTwcLo13MwQwx3sevgwgIMGfULfbRxqGP+2kgbEIxSSIMtSEZb0G4W2TWx1yCEY3Uj4DZpXYobikNVsYs1zrVcRcctLq1IaIPRMN6dE4iQsZEjLcj5na/Vz/VUYZgn/aDjHhkp1JpxvMW9IvHQEaxtqEYgLBBJNgfjFOuWCwwVJJkJMWV7EVkAaPaCxQhlcqIb4LzAJGYj9+Ls6cTHHWyRmhjP0xJ/klGriSWZM/gM5W82lsMfWUXLeXj1az3l4/O/PzP9o56fC/o6ZjOdY41VbYBXEAWxIdyAkwbN7zy1hubvYPzzz7f4gsP03ZA1x5S9DS3YD7lMnV2Agm7Vzc21xZ5ftLrffH2O7tXr2ELJtuLB0eHB3t5ARrdApcdr5x7xdamJ5/53p//+bXNVdqW3bbOHetrd1V6GlmLxzFzaaey8XqYja/qUBeEbjQkcokbJ/lQ3CTa7MxuDPBFKhik5ib3HA8uXe85wQ7BX9au6yxDAcEjJOrQxG1U5CI968NvwLIfNipdENa+5HhTn1o7b8U2N+e6Tp0dr6UkLLRyLNWFTAy8YBecnNikNcJYO+Oyt4Xj7RDEFATWtBgfcYWRX4tzvdJu+RzSOx+xj9WlI3tS6fQgt4TYI8DuOzghK/5IY98X5UL/ER/PS/kwjYs74kkSEAfKNgtPv2xMkJf24xW5GKnj5LTPv//vv9y/dJkzTw8PeWbu98djMUjnsKNJ7OCL0y1TzxV5hztyiwiExo6SKeNItT3Tg73C1xGGDVt7JnLEfxTqOFDlFGUKTh33KWM+D5pVPhkn+DBsQlcRqkfCxykbumu2NcYpsarH5EgIh23XC5Gac80/Nj7ARdv2aLlEVYfoI+zbNTjF7ZUT/QipElERqJZpHMuf+uUiJZXAuIFLasUgRZWfEioBScPQB9GgWLxd2McOFDpHshFGPgAc0RScRqnxIIFb9rFwSUiRU+JCEzZ9RKTYAinHJTOKBIcQ2OJhNhGAqliZYyQZTahYBn/tPPXYU1/t7OHbHJXQGzAKYQWhvbS2usKCy+E+LfbxQJ9om9wHLRaVVyfu8WO3LJCc/QJIefd9YkK+pBPjQJ1yKKPITeRX/JB96jqwwqoMsWCi3ETAnvwCIZU4+GVlEyTgdEbKUR6AMTGAwhy4EqxMSDGgeeU0JnRhxGdMknWSYx7fIBGeZSkBZXoLWr5cW9pgkbeQFKYhaLwfCR22Tp9m69yps6d4yqU5C0LgxV4uZq80n9yKV2tv/5D1ULg5v5FFnhCRyi47qiibesM4oHIW9vISyxNntk6Bf+7UFg3aSkc7nanLlP7N61dYY+VDFDIyWitfGK/jfWRFYSIc/kyprHfamytLp9fXlvXxQQ7WoBFWS8hSe2vQYwGUxYBWRx/02zvkfGeOJu4csm0yXgo+OOh1u8sogGVcTLTY+qSE63gs7eELI6HJ56MeOJNRF1zWkjktNKA2PuJ4x1GEsqDG4ajUVUYJy/z1vPsbQ9NrMw/Bj447nKzKOR2DVntl69ygtYzZsdHSmvZ6e1BRWvuRIqH+cWvfCNhBSQ2dRtgpxtShwKTwbHwph6uwBoov0g1qr5T2+/P0YKHdOVpapfS0+ZDCQpNsy2SxgBdBESktyQgMsnRA62ixFQmIgcJTnSVn0fiotdE4J6TRBss/LRxEN1ezcyNm/yTCUC0Gn6jg787O9ht/eOvTzz9lGfpbLzz31FNPnbvwCGea9ph2UkeYQOpIH/JEjuWZdQtJE1GyM8WyJe1eEeyIAP4cYReU+gwNjZJrkDSzx7wX85LuN5/KLRFk+NaFyy1Tf8EXBocHh7eufvre7351/tQZ3tf88updttz+xc/+6s//4i/jGzCoqyeXaiQwTTie7r5GELkZzWSStJ2Rfn8lUGs7cvXMehKSU5q/9nmWXGl1SZNJ0zm8NaZ1U3vuttFptRT04dZa1XBjFm09WGWQwJosfVCXJjVcsxZWx2HlNYd4ysbbH+yv1aoNDKnvqvL2AbW9JFKeWpPp8JKKINInOluRaMSlMKSPVpmjrUZ3WmKBidgFlGt7oE/X7u7v7d25RRfApwOoaZEptRi2RfoBm6nlIp2pWjoaM/bW0tJwKBYf7Xvxuef/9E//9LvffRkcNGE9953332M71GeffXbr1h7nWSwM+kvhdMhGk92uThoJZe7HFat+J9Shrh31hr033/z95cufa9ZO+0pvotKJk5cDCUFGtm5cket6VCAYm/OgyTgrsz31v72N6JFv39y+fnXn1KklhuIYCkJZM0qKdsmcebaLRQHC7ajVU748LYx3laJocLTOPn15nzUGjBd2jVKjw4BQzRc6U8pUrOgJFRsPuUwTtFDBP/400JIGs4PestPJlDoDmkcO6qNwCL4P5ke8Qei98xSR7phZwDMm8M5pzdv5jVep9MoXf3pXKVbeZAHap2lhkk+NpQzI7dg6yydu1MYgPpoaeXj4OeijtsX4KCvrTVis5tyJRwUsOhP48oUcl+8aUyvjbTMwEwcVYjUPsr8FI2SBQ3IRpEZfgAgeKQUfA9K6fk5PaKxAkiNqYuWBJSnYl7sUGYmjLIq4Jla6n4pgIHzq1BKve0n8MEbfI+4dmkue1+HrDK3eeust9Lb7mt7OYfQEiXaKtu+RRx5p7zz++eefAy95AFNoc4uqCIfKnZZGLTE0VKXNBq15mq0hukrCzMqQCDOfIi7rplkNeyKQaBRF4rxaLHDlyhVatLOPPx92SKSkur74XuTUqLhRkjwmzYKA+10JcMQ0noRwBYc5S80TiDahHB4uxQuJwWz6xeJIU77HNeFWdgj+04mzv4IGn4Ij/a1Vpq352IA1W0MKeSNSMJtNWoVXS6/Af6xoUemeAhLmHNXHWRjfpV9SGuIatwWtEQGNHYVUpe9+97skHbz+FrNK45iDi95FQ+dEJSVVj7sHh+xH2G13GSgvrFwEuRRZKSlTNSRyW5AnkyYhYkJzQBgMOMzr9uef030yn8N1h8drTCaLlChfGZHs8ET3yZ/8hNkm7g1Cwblf6bU+NZMaPhm36Xx1qrIcsQxslF5pcgLuS4yJIlPR1NA65RHApMT7gpCRCr+OV+BvOpozfg++NVodL2QG2iwNILekkh+uIBRyIiVeSBJyNsVUBAxFM1XJSraijoBfRNCQg+Z+nCU1m9dUxWcc4epQq0EcIFfYEnEVMzlXXsH+4O23ScWTCyYEUxUGgWDaghDMxd9SAkUX3ea2twDrCBwaJHVqI14j1/GC1mAl9eh0QwfiqT4kAyciMoKVsS2DZa5enaZDA50BjiBZw2CQiM2ssCwK1BHkFoRQrFkfC/Ks1NC8YI0isPUDRUWyd42SI1ZEczcLp0Hi25qwIAg4bjcnTeVsYMmtGfpKUoEX5nMiwo9yK5Ei9558Ll68uPTxh9ptsNBhNZNhMs7P1jbGgbu3b7rRniP6ayZZbZjYGnQiBI5Ak0dlSzoJnNIhFnySnMoVEkIAUlyj/1zuCMKH6TeNWatd4xQ4U2NLgQmEwvGUAEaYOteSgu9IJEoQwW3IUld5WepoAgyQK8GcQXbctHqFaHV1a5N3qFfWH34CIG9IiyT6K2PSIgHhe37SIYzmiFsq8+EKDmr7tiCwzYrRAoGRLXEvQBNnioiG585ssgB9+dpNJvN7B/thytGkrogzT5Qhd3AAjkn39nQYFR9sBE4BwNNLGz5yo3/UoUytFYoRQGNgQBPr8jLP+goCAeaO4ByRmh0ie0tNMituzWelmj9X5TcHSHjbWc6WTw7lDGir/fjjj6sI9Py9t3uo8lrf3JjFvIbDGw6GFJWQW+PcMw6+aQsHSMQk+ACUy+l95y6nxiCRJKXG/ChhlnWlLKxmlWGjX5FX1q6RlR3uZ1QEEQbtiFfFpwZ+g3FrS8YfmCfl+9VXX9HX2XrwoWF88803P/34Qxagn3/xZcbYFx6RD3AGSynQSXGYxcpMJj0YJOyceM6yec254NRqNLSqb8nLVLs1gHBjRvzB7/8e+GuvvcZ8n0P72fj8V3/1VzQp2/2DItcRSy/AWpla23/0eNFwviaTaJOQwoEk8kt7S08KkLhXNsYQyk0MlcVtotY0RBQbOlJSafBxS9olvZwRdR/eJbWSk6Ik6aSOhQWWfMBnsTIYpm6lcLA+JEVqonW8wHIrpl+PN6L2Z/B4xad1ghD+NFT0AvSuiem0H6y3uto6tbHCUdpnt84SLj50nq+OnTt9hiTWbemDoGPZ6sc//vGrr75KH8ruyU8+/eLSpUsffvLp9va+2dsOdnIrD4RAiUwTOw8GFQdYw4THt+CZmwkm47QktMnYBNHqUKJEyDtk9JscokUuoLp16xZPfkmlBFl02th4LLTTAdYsVVOy9Mn0NTrvSv1IY8Bs4elqHei/GMspg9mdiBvD45m4jfIKeMEsaGNMZ9+IcDy1hiCdHJFxAnBlfG6opYfmSedCZAS4wapkjdRG8zWVtjCpIxomwbQKdWqJN/gXeB2BR60St5QchKxwU9QsQAOp889tIXc8l1UC19wKZh0pHEK9UUrRVtKjeBSZFhBRmNTpc0RnVUeEQIxfsyrxklrzLxrWQOIc06Yh+9nNjYPNjccvXOAFfym+yDmXMZwde9iqnMXRtMessx4eHT30/KuXdxaGtz8DvtzR23/sIS058RotD4xIdXDcMy2ePPAsGSKG9qBYez1d1rYIuvoYCo9WicUgMhYRD1VT4UKjx1IkmPNSHDlvxY95/Y6xb7yOoWVnQviAnwAjB0BPhwEeL+kbYgt8f/2DD9/7yWNPMeGmqSNH3oVE+8lmD1pPoaKJciraxE5KuTsf8jKjKl+chFueikPUH/A5VL0MKNxYSqb9oTFdPNZQXs9hCPDUeDE8SLeUjToJVgKcqvo88mEpAGeuSg37ODJ2DfyxehDvaULYaB18a57mEHlNzIqUMeb5Jq3HV2WdU0a/eT/7CBLPmtBPucuOlvxnjrjx9i9V8KKqy3e8mxFOpgpL1ipEvH7Q4kTKYQIrANnaJTVEjxdBwpGsg46c2OVrHQ5Vvosrcny2HuqjeZ8wp/nJny8NF1hWPt7v04Iva/EnDtaLF0J1o+36Rx0dS3d8in1Gg+HOu2/eunuj+3z/1PnzR/ENeuypjId09Ncvj8JkEfKCnGj/ND2R5xT9SySA+BsKCxs4YrsHg+07d2598f7e55+vwWrQ3z/U483BNhu9eCq5Sj624d9euLMsqqU//9HqE09c3tSrRvv6UgKnE0oCSfCXRSqPVUIV5CGuILm8Uu2Du54+h9opf8n5g3pUUrmUR0ydU9oUY5aMkxXnEfiYT1SGyU+ZRJom98ElXaRJhV0n8bzJAseAvqlIolqOmGSSVBd8W6HbtyfzOC4kWaPkzqkljw34OK3uZiE04L4tV/Jrt5HfRnASjkyEdgqY30Q5jpNG8UngzoufilP6ICWI0nAYGtWoCOGzZusrfgWC4tFCsnistY8YINKZRKq+HAI//hgIeF0S9GyH/Kv8ig09AppaNU5aZYDbvn74+n/6ZTt6Ex1kTYNJSyzs6LOOV8ip20z0Vq7tUVFIVaGRa7Vp5ANkKgyyIp/65b9zMfIBQCcIRfQJcIUy02e0tSp2RsgyGglwlEEpQTajoLxOuxtqIyZWZUwNr3aMGfglRxxDa/7UBlmorhGYpW5Fqf2RKntXBpI1podRGU1PDygS3UokHBfnHIIqyaUJIKs9akmM5dJJCrr6RYLflUllV+k/085mp/ZP3a4sPIxl/dpcxslXVMJISQEylSr/SMOkc2sfNAoBntQCkrt9qgwjhlAr7MxeCeoH/Qubafvt4dpyd2tjg5PNt/d38PNznDlGaOmjFCNxYRNuLcVwsKyU8B8ojPMRC70hwQ9f3eAaZWecWm7KqdDxqGxjrS/jYWqYQWA1BP/sLKg/BULdZ0mUyTkzASAOrnEgO7jyEjcHtw25YcGQ2s4FoVb3aEkYitEbkkyPFrt6WcUF3j/sDvvLre45tjCffegMOFe+/Iyv2S+tLj325BN3b9/iYW1X7r641NN1Xwc2LiwNe63DvaXWyhOPPLW2dQYpqaYcraXmU22iVuS146nbZrzJPnVGj8PFFb2UsKAvK8LHOjO64NCIlV6MTiOrKMkncj0VVCVl0bl9uLrSWVmmleyz1xfKtfWVpeXzx4vd9ZXVK9e2WXfrxRYy1JRN4nN8/jpLZ9haZ9Pj1uYam6nbrFvw3kNsaPAofbEPWpc3IRfbfbwuSuWQn157eKidGnoxbsgpkAurS5p7E6GIuPKn3bxhHJ0dKqXiVpEIJUJCxN0zQuhSCyTVCDKdKBRRXPgIMkYotRQ90S4wagvtHgJbWPWI/eOYkTc4u62ONncPhlev3t69xVnRnQuPPvLw408xkul32baGvy0Nhu29m7z63WEzOEZmOC5NFrUT/CgcefFYu5JtYfZZSb7avTBClFa2gFVLLSQ5KsF5SWMAGYl/cY2WlkaXzPe0zU20vJIyWN5o8WBgcVlWIUvKutipnsRN4Wz5tk8FlDGLAWs4QJJUfFk96alWnX5QB1CrHVPxEVx98TWW8FUiCRKJWDwgcYkx4Oj2njGVXZjR1JWqqDZJXUmaTJwC+b/9+3/3/vvvv/G71/mwNoIoUwZlPG3Z3j948513P/z0s0cfffSlF55/4oknLj7y2AofqI8dhW6DGFXIbmkUl5wQraboEP6J7kmDyoEpIoDRFoUNlSnQYukqxrxuywPB+R2xsQVcdsUaxVgptco0kKRBKFAbtmCpCGO+ycZXOL//1m/f/sMf6LE+/ZSF6Os8RXrxpVf+7b/996fOndo52Il3NMh4TEvDiZMyEYdnznBijwKREd9K05GvRDxS1T9qyIj7NRmUmp6afYtL3PNPMUVwmOIkGVGeX+LfSEQSFxdZMI2agpWDq/v9qCPOscuIzLJ2zOPBoOJCrdLio8aiuUErWx9AxjlpiqynLck1At6C0WQctUazM8XnFsDhESk4R0vHMFcLnJtiERMAsFGX11505DKNXizIREGytGi97EaM9sCkH8RFaTwhVdWJMS5adGJ8SF4oQwa+g97h4GgHHPoAdUytI5bpwaZvWlte4qH40888SS1jfZZVET+qBI1Umk7CsL+z0N/BWwa8ecgxyQgYHi+3FlY3W2dffPilZx7u9V65eWuHByTvvv/xe++9t77H2zeqRtge3dWBU1OPF/rRivKernIa7y/6+2S6pc0alZcBC2tHd1Y4k0lvQS3yEQagaM+oaaqdKSP6aJUIXXyna+eFkPaSr/RCyg7q3Zt3eH8FQ+1t35USfOPhUKMURYn3++zy1c70o2PeMup0VV8IpDqQA7EP/clc2D+8abG1ffPaWncJDuSi1DvmNAwYdPA0m/HjTc0plaqqhagmG/hN1uyEIRopdC9aW1fbNApaoYKAkpfrqIKzb0Ar7Dw8iCShOoOFSLMhqeheTPqrQ86Nk9FyG6tU6Qz7mK2k1E595oI2UccUr2gnizHupcxrrmkowgc7h+VAjLRZCksq48qk2p7OAGMt9tWZjwXGWvZ5rtRbPBNJctAjbR3Q0dK42tEQOhCCUtWSiKwDiHh4JF4CkP08NhFX8klqsdhIavQvIowcZTjSFKWscQ+u6iYIzoNiJw3m7F6s0BuIMik18mjdDIn1mIIesvA0gksx5V1wmQWfoJqpzR55ctCkPfza4cVJr/guMsiCBtwUecXF2FzDbroDEb48C337l5ckOJ7Gs9+8YM6PWC1nqYEp4EnZiBTR1orIFFbjoEBWKaGAWl6ZRhjAyTWn3VFzPv300+eee05GCLiSI4AImkPWUMQeoNkj9OwjNt0A9wI0IsgRjSkQxoviFE3qYmuJZ1YrK1r/tttImTCsWREnSfg5oJLNngEP/gsfMy8MidTsSC1qFMVMkrSqVEuYqkQpJJx8yy8Vt7qrs5bgVmBMRCaouSVjpaQxno0sZOr6dwy/Tvhjx2WiXKcaeaTe0dFSldB//+0/6NbdWLwu2lmLJyRhPRCgJRDhiQnPTgft9Uf4xu7jT8aTYSU5IzR7lTVOlGsRqxXjTx6LwgSeLFMjtr/6Agj+gSeoxoRv4I30m9yjTkAW2DOy8vTTuDpfKAXCgAlliEilyLtVAuKIVfUVIPUIicEwZyN5aSR6sSz7bU07Jy6uVRjdjoErjIgarSZFYYANtUfcmgz+ce7Rx21RQ3zAG7CZt6WMZmLkBGe/XB0hUeKy7XASylTFnwNJDTMmktyk+rZcidj4RBwatxk8lhpCiyajsiuKNajKLQgrK6s44W9+85v+3bsebE1SmX+hIgJOhNQGoqQDy7SkaoUFnExAUm2TDB77hRv3gTkG941rypSE0GQWVY0P/4IWmiuRcQx5L7kjwnjYEHBow1Wpea6rxXRZlQ6JOFoSh5wrPH1tyor6K8LsG0V6jTk1XhiWSEETt5FzFXDSZHQ/N2aVJpnPJUpFY1owlZ3cws8nhIRA1XALaTvYbvMJ56fCcxIhtEqlQyr2p6fgGGgGOa3DIf2OpUMJueMU+kixiiW06Dwp4oQQeWxUJ0sxle0wGjLlQQiy5HuRoxpfC3A5FDgRRrDRA6Zc0Iey+kwoCsPN3RfIMOcqfaJuZn76bdjQt1wHMfOnzJCC0eAPOT0dXR7vAq6fOgUOgaWHRx4+F68SamrNoRocbSe/iAACIpy1b33rW6effJ5dThiUxFHTUGtDuxFP2mDljHCt1TbDyNqY8jB01sQ5AhENTNt6X9swdAGHhWx4bh3KQ+4O9kGhRrv0oYOKAJoh4ICMValyxOXuQkI+RaWlEy1Ak8H2shUrVEKLYAgMYwShuWvwl1sQSUgTP5EkzsaxXGcQacAlP7sKv4pXflvzC3FpmJ05yA58NMnMoWX7MySoyrIIt0ZzBEEMcin97b4eIK2v6ngxf1YxJsA4kKTBquBbetI82ofQ1OApV9M6ASZEfCWCLLxFt8GH2Yrqr9Zr+F6lrFcwiVuilefWoTB0KsASySgBCRsmTdyky8BJmVqKoNJH0hus4jasN6MsisT7ilirhqz74lAjs9GSxvC7L73MJsffv/E7FrCoAjJaLJCwY/SDDz64evkLDmd//Mmnn3322aeffYEq46VnshVqJL+VHaIIAAa8lqO4EZxEfCqOaaZimqQmNFpTzMnuaz6Fwt6CYubMlucP3/499f2LTz/Rt2r6x0xS/t3//T+wKH+g73QqRwWZGyBzMlWk3G+kzmbdRtTwmmcNL/GpipE6FV5zq+OFWw2Eg9uf0jN56c3IajEytiCVRJ580DvQePH0dHNznb5A3+NdXeXMQ64gE+hiaG1YnIEHngmkKEyEJBDoZWgB+jwL1Td40/gzyxz9ijBaM1QqzQKNNrRARnhRiCBbFphOBRMcSy854hauwa3p/OBL4djWwKPOhx9++NFHuTzMAz/6IPY1c6bz6uqy2q4eDypbp05vYgeo6Mo5xBGvI3fE9cSS1nhRSx9IskTFI6f9nj4zMOirC2BF+4UXXnj62Rf/5b/8l59f/pIHJp9+/gXvNxz2yC9TS3uoMhpMsEb0HVr6TA4fFpK4RgBBeVQ7p0Cqr0Qi7yP0Ah+BIlYXHADQoolV80g2ySzlCFCmZoNHVCWsh1pAyvcVzFysMndBzChO9zbnnJh+sY+YSOIomNXofloMHOkzHuYQUpSkkhECcegKMrsBiDtf4/zSXcEHx3OQBhreUquCYsXySUqUi8uozmmDj3IUmZqjDDjYHbsdx5YmMB0kqGINUJhx9eEbLLICSdtJk+BEABqlJg6hJ7ekJ809gKngJi2lbGRpPq12B7yRy5m3ZkVyiRRUIOhTw+t4ITFaoXJEwIjVJFaMh+RE9MwlI9RStB6Koe/euLV7+26rN1w64uk62wI4olZV3TT1VWMfTtZb1BRlb7CwtHH6me/8iFeW2i0+aXK01NYUJVqMRGSv9w5lg6ypcPxxdnEUFr9oT/OEurzcR8DeqKBjxnIAaK10jSK0U4IOivF43iOOQQI2v846bISUDUEMTfgz5iB2SbeP9pdXFt9/740nHr8wWN5SExCZAYc/Df4kVRrLcICkTtqBxUMIwawQMQU12b4sqL8GP55t+uyb4+GN67fOPH5BOY3HF2ScLoSnoPKOYBSIcUdbEBoiwUfaxWZq6c5KQLBuXtKeGoNjzwtRW2+EGosCyVHGJ2MA0cKYxKntvjWyFHRi6Jmda8S40BaQ7JaDVB5pncqngmQ8fvVwS8U0NVSu4fSRiKn4tdQZCAU8nVXTgKBPRyRB+WLKxpVzr8idbRJVdfEQc9Kp0WDJv5cOescfbW4t/+lPNi5scbLV1rUdyne5u3zcOd4d9nd16hR8ZAXsz3M8ioPA7eDLT7689vnel5cYFC4/+gSv9PB6AvB2S8/niBCYPuvqHTRRI6aoHM+vUmffP2Sws3j31s1r1758/yNol2Jr6FG7C0faBvxiXye4d/fYwc9XnrocDLKy/sMXTz333J22psoM4emy69KRw0TrJgNKVnZFPcvFzUNPrnEkqG4WtBMTNoDIfZrdkYvAlG0jI4GZBgH2TEPuec18ZiCGqmNVOWp9JXMG4QxwcRtbPt2GElPKIpjM0jC3qEIar2Wy5KR85aKIb5LIR2HS0GECMOJaqn8B0UCKQ/Ag1RGnEgdQMEv3GdgBTx2eAH6mamxGmhWVoiUHQqyDBpHk2v2Cso86IKtNBZNU4SeSkFITRzxaML5GgPJhvuPT7fZrv/nNp2+8LsdaZLUFctUgXD/ZN0BNRuITCUisHAW2qqotvTnI8TXWAY6RdWpCsIna0WQYGUsSEV5iGS9xTgxsgZSGlGIxQI1bIO5xSaCiJstLE7pDSmyRg/9gbv9nT6A2/DIKlll5vE9e9N53KyBRj7XrhTzGWdKYPB2TCTt2YXJ1cC9PwZR8ECklEkVmW2SC+OVTEfrNNNjXfW/d0dpXxmtrEOdGZjL7kTzlUpwLkkgWb9e4Mfu7L44JTClrCDLVdHe1zc3XmKznxag96R5J2s9N4SGW02XQYjJfRQoRHnJzHah3wMkY+bCXU72Fdi9FfRMnzyEZCaoZ1ZEUj54/f3pt7e7ODvM6PxrpsUMBWbTeLbbgqPX2GaBRQlY5mauUlNXgNiWM/0i3yST0jFNcRrjUOw4B4bEHHUG0FabiZdUiCIOMWOVvbBSII8zizNNyPaljXkdnBAK24KoIq8bRbxoNCFQoSg7rFgdnJqQKoMexfg6lN2ThcNCDX3t16zTLMecuPgoHfZC+32c1gcWs8xcePXeecy2+vHL1qz+88zauyxHOjGh7HUlvLfTZ8nb2ye88+vx3F1dPoSHcVdXsSyO75JiE854BW1wX+rsHTMltdq4EnlCTwYUBHyLi6GY29uSS4shEztDU4c3xYokMkKa1UKWWJ7xiqbPAbretLUYL3cO7g2GPo/BhI0JVclXs9lKXgzgpIPyS1WdaQo1dIkSrCGtmntIXE0oBTEGOyBq7v3M2UgkyNTUENcCBCXZGGAG4ryQVqlEk7IOlAkel4yQaJvA52wM/QusGraVwJZAmO4cUjY05zh4b6bsEbNnSQEwFsbDIm9G37m5TkTgW5tSpM5AEsRpAqwkOEEzO/93DbfK+dWpN6/gLsQdqEUsyi9F4TxNRvTEiVTnjXKKrcaDbE5/kmPsV5QlyjMKokEiYWo8Eles4aKrvXWP69G53uLZ1xPGvXT3DsP+6peJG+Lbi9NopgjqAH+JqWI7LeQhcMQWiYvOazCkBajYwbNQe7os0cieVg/Ibv4QPNLk+mCyUpwZxHuuPfvKnL3z7W7yD+PvXtAzNEpikRJnf3dvfObj85bXr73/4wcPnXuMThaxE83CCV64g74dn4kWguxbjiTJEajOsp23Y1HnyPufC9sN7ohXF1B5F2OZB1jCCi4MUPJXrWG+VxYDToMop+lWlwu04UYePABwe/vLvf8kKaX9/jyMObt/a4ZSgF19+4d/9h//w0BOP8BUolzRTkWDo3Elu2hlHVYwwKY4KVSqvcbjmXCcACEy9uXHdodkpmN5fj54FcsKI7TOpz3yb1MyLhWtg6E5mVQusJSK0A7oVu1k57agR1DIIH/DBwR5/4N+8fdN4KgHKKLjxpJMAKzzNh0pbVfCtia8YqruyTHHnptWcRvZxm5AyHnNARKsUtAtVaHFV78BYGTS4A+RztRq6+wWjeOONpbUuE8NoDbQWm0uN1z9o2+hHUn2nzVMir3frzRse71Bfnn6ak2ye7C4vA2dYQ5tJpSOzrJoOBvs80YPD9t2Dg/3b66tr2tY07HdRCyiVyBKVLdr5WIZW9UJP/nMwcF/rISiqt91Zrz/mS8QLywvdJ88+8+TZ/d4r6ob2NQxoLeqbLp98/Pnf/d3fRS/G0rh8bCB12vsyKm83pGZNwsIOiF1Z6tITyoDRqaEPTs5zfHC+fqDUZPLBkMeY9N54BsM0i+Y66OvwJ2Vzsc1HlqUD4zXVC9UvlOWKKSLOqzu8pYO+lCl/UCuVp8xSOEIgAhuFmLuNbiMGrbQgrnUGXEDsZWqu1lD3ORjuK60VIw6KFXsurfJlgtTasHK119uLcoShXM7FF5DMKEYRvBcY9/Gliqr91EcwlmMTuWtOZG1EGTHnN5RtpOhWhQatnlTwLpteZ3PPq/Yq0YTXBqq27Quu5RCyxpxH9gQNI0Rr3zrSaFOffwRO3w7C8FD+ShsAWuCAQNCXXMJuXMlFCRpNiams4eC3Km0fQ2x5VsJ9q5Xc45hFqvomwvSWVeKRfjAFatsgdYrgo3GgslbhRPbt86nHlw/VeTErjILavM0sbiMOaRW0Fsd2EykammrRyvjhtNnmOoJDHTBP2HQaYDT6sABYM5oaB3klFpt4qMVZmbcvvUtzqWGc5kEjs06lrYHkwVVIvxHggMnjVmoQmarPLHjNfDJuVkUW5qG2FDQ3iwygeYnjqe/9mFsaHqcWHRxJFqrsVJypMC+Rwl/uGgEmBB/JpFgMc8GXxHywP4jGJp04qoJA3Gy5lakzDpEizvhunoA7ONVJDUwjINpw2MI8MRkXBxxkJyW+1c8kfAxSTQ8qsxX6MVjRMEWqqlsIiBS0MUE1xr3iD0xYGN+TQ220QFYhOtRJ9JTUoMceewz7333tHeoU71LTPxGQBb6Q07fm1SSRdzBJJcKeHfYpL9+4zZF2q2fPh0t7aCg1bSXsW8wl6EQgNR77DQ/u3OEonluffYxKni8yv2JICkWooZLa3eWBk9yWOE+ef/CDH+w+eRYIzQJ8CMCdO5rI5Ki5wXJhgmw0ImgGPkHxCG52zYH5oYH11ZhFEJEipTCp8b9+3LK+Pp+vycG5S2Ya50WS4Q+samYwzjff1e6a1EDgqMhSsz9KyoTWp9YZHICTmJki/dYSDSpUBdNMym0dsdwZjccI0RzwH9bCqFC8jctXtkkGoslkBCmc/LPOR5OJ7pGaA3cw4c4i4EAzC5CaTq1hn4vwI4h/rgUZpt9ZcONgH7v9PTFrPlImZ8LwkqqkqLmyRhnl0PV4xceLZbEEQ57A0TBNCzeKY6DIadgtzGBuzpfjkxBn5IRXszohMmgWd3L8E2ImtpUPF8Jaw/nSScXWKsFkt1wkwWs+LSg1gljlMKZJlCYqRRiRsEWLk/ovXbsm6VUuKET6DsYB5g9V4TYrIrH36lkKrXUst0SA0GWksUdom1JjtECqdSiRQlsgjlBt7bEexMKQrJFU8EvEDLmCD5CI9fcoFBLgTiok3GKWAGu/Es/eeUPusaeepf66jrALy8ivv/46fSXIdKDExTyyQARb+j/Tci/RetwJAqHIakQggRsIZJCelXwRB+hmZHCkjzeyXCQOwYQkAiRczYqkEqDCPmxPIi9UbuIkgcmWNNTu7g+0ASJ2hDFDEB/lW8xsXpAhCc7ibysRgS3XEkFHvVq+zJDhMC/dSxcQ4FVr5VtbPhgkc4EaaCP3I9UsLNTcdBVXUQHnMUagCZP/AIlETpvzEdCAm5BBDGqYLcZkpydTIW6Z1/A4AQhoHuc4vsRBHdDGpJ7ZLGP469f3sFt3hW9CryPTrBBdBwO1ucQuF7rVCCVurSIHgokwlrCLiwBhcZwiG4anUR7OaeFw8giyCrLl1pDClgh5N6aMwH3or2tmEIT5JvII/lRTFInfSORrimCIixooT2CZ74c//OEzTz7Fm4Xvvvc2y6+3t28Db3WUY5yZMfmd6zc/+eQTqjYD9WeffZ4XIDZOn5M5VP9URMFJdnAXy63ipfC4iWB4vhv9wqQkOV6urmXkt0AK5oj+gWK2obhFLqgCLNLdZfvJtWu7d25zRjzOTYv3b/+v/55tLvv5Ud8DiKpzd0JykZwQ9QRoD6DALK6FFRFwqBeYkSsNHNvqJ90S8xrTV5cdV4aCmlsRyy2qx5y0tHDjiQh782l+WYbWceQXLtBYuftAKBEaHgiJB4PpTYERSkYaChfaqjGQM4uhR30opFv6i+H6xorGruur7Eg9d0bfzT21toZ6Z0+fQZnePkvqB6wo0ToxvmVHM/uaoWWRiby4XV1a1qKTledMZ+DW3I2wOxr6WLgFIfsZ3IwYrWRCEWjJPoSsY4PMnBEIK9FmzvHQre4KfXR3dYPi6HY02H7i8WdY0//ww48x7J3dA+Be82yUF3wkAKvG+hj2FWY8WEWs4LWxAjNdJmp6nTgZd96p2pFZpRfOthJX2ijyyEn/GMfaAixoRIwDE1amGyJIsiPVcJHU9zPiYyJCsQZVgw+3WAkl8RAcAJVhDBBjyismG8FKLmiECpAqlCFOgsM9mZR5DYRG5lo4o4lYZE0mI6YCjirCq2itCRDjRJHBj6kP0SFjIMWP9PYM06EicTxP4pGSwpQlX2bOVUxyUHGKPq1+SFhasB7hgGuVMpF+i5KQ16nEqSUFs04qQEdISnpWCRlfSW6jMqRCiiiaYoeSl4LmSLkFl20ug72dHboZTnfluRPHzzBF5mlWj8V/jMVzAvITAyBXvPQ8hMOAjo4OhwNcbX9h7fFvf/+ot8+Y+7Qm7Dyn0rdK7OOcWSM+YW7XDuKyZDib1/tjpw/Nhiohb2ly1Y4GGpeQi06UgpVuhT40BtzGPh+KE/YptONpQ73b2tp6kJT2GuRTRzKROGNQzuUhIJVRLA+bPv7kwzOPP0UbepD4KwNhkkRHrpSi/+xStVcFgOEZ4FArMp0dLoDIIpgFmaOJZMysybu2BQUzRqh67F0akqgJfnwTjqWyzxxcwLEVOmnlHyN4P5qPARlLjpuEU8XNWaaI2VRCyPUNuEotAkROJeLXDUZOHQxHl7BeQo58O2Mj8rr8RmQj/slY6UcYqUyNnJ46V5T3GZ0hfw6XSpWEZYgciNZI1/BwJ6bnbMq2Cy4skggX9sPbV+M4o6PjFXYXfcBTrkcfWl1b3fnDHxY/vkJ14kN+vbX1hWMt+Lrk+YAxT2PNo8NDUmrKgrrS40/e/eKz91dOn2GYsv7EC3QFw248X2UrlELso8nOmVVIpm7t3+1xaOaNK9evX9++fh2erCdxzKTqBgrEU9i2+jKed2sR7eb29qDd/uqhFTaDnH/pW1cXF+lhcJB+V8MLJkXwp4ZyRdHkqKq2qg5xEQK3IAuS3Cw1tUoKyQkezVmMAeoqCJaCcRyBFRGZIoKSHzTAaqyoxvnMTRxHHb97MMKSx3Fmo7wH3KsAIxsClBkqmvE7JZTUyaSKriEo3RaSyUihVY2YsGPgR6FP2DhcBv5pQUcaJt9ImhZZRQQRO5iRkQaORqXRl4s6pCQ+0XyMMYlXA7SIurCwoXcYF177u18e37iuwzY5MnfhQGzjKTRUyko83/Y+SpsPQWQGu4MgnFjaUEEHZs/fR17QoHxrbYtFiosXH2Eid+EcI+GH+BgtLB1EO2GQnDjW+9RtINJx+IJWIjU3WxX9nNookBoTBG71dF97RUfnQaseU5JhAL1iJD3jJy7METC4zraj6uksYgYOscjlxSyLjbdtnEEkNHKaVSvqz4mMfKNG0jn00wKCyBFdVy0x8hhtUSWYdksMtCm8WEhdnnddpaqS9xAJM/XLI+NXhDajsBS8pyDsQBMmmfyHtTjQbGrgbkRf0RhWw9CHdhhc7YuGTSqdES6HyJEKsZyQl8fZ6cMQBvJYjLMr0pTyx9BJVle7urCyuPDc4499ePmShlEd7dnUDlm5MY06hRkQdquo7obSI4HfQAxVpWEYWQ26Xgvgju1HmmSy01MyEkJCq6Smjs/kwB2hchFnqkbfBHOCk5xakAsfuAiozihxoN9wKmBGgBiVbaYqIDaxHPFcSgemDZfWVtbWn3nh2+zq2uvxog8VRMsEufh0osXSwvD3r/32k4/fRQe9Sc2SRIxjDxe1V5LeUXJ7uwd3b2xtnlGdYH2RyW3KVlEQbQTizFnMcdSCVoTKGEXMknBkkDiv7w4HWkTDg0ik7cF2IEbO4IBtMQjVWUdXcwScKqjyvdDb7/UP+0POn+z1ODiORbbVbosTkWHCKFiN1zFjZb1OxXuNHPXOsNTf1YEPmkSWo7yS1SSdZpK13F6PMUubA5+XV1q7O5wjT7shI9OgYgpZXIUj1YDwizoexLLeDZP/H3l/9iTXkaV5gu5ubuYrdhAASXBfgwwy9sjIzIjIrTqrqqe6nnpkZET6Yf66macWmYcqma6pkqzOzMiMyMiMlWRwDZIACYDEDt9tcff5fedTVVO7tsABklFVMgrHNb1Hz6ZHjy5XVa9eJaEfIdrPuItbYrEUbTuMwINnK3b35FY/GqaYLI16qlfNVNeCk14RZl83ujCr0j/Y3em2O/qQF1vc9Ek6vf/ZfuTso2yaW1A3wgNllEWY/2BxSQblZUYmA/YX9lsrnInNoZ3dHd4b523641i4I/M4cyPtNo2iskXWpaV4+tVE2YR46BaHEMsoBPoSIasmUlHVLjGlwjNXf/kY36QeLC7r4UqPR8k+IIgsB+kQlg/2GTp0+CHEsQY5QOSaI0/VMqCrlcbgeWG2yeMPeq8MfrEAB8rC+aLpoPU7dfbMidOnnnn+uZc//vjtN9/iCI5ub5fthTL+IQeoH/Z397avXvnk6hVWqRlpP/XEE4yBL158cm11VSXKTrcoDvxPnO3r+MJo0bjEQbZ5qaCs9gR12DxqOr2oWgfFC7l4ojMlHrKgHreAtRBveU8OVkA787MD2Hp6BtcZvhLB+ToczfSzf/wHtrPsbG4xE03j0e6sv/b69/7dv/t3p8+f2WXGUDtBS4snZpbgHNld5Daj+c1a6HeUvE4ZxiPXSXvPP/hJc4jx1cfsFZZTx4GUW+fUt+TLyCy/sb7LPoa9nV1mSCkhlWCYnaajMFT50mf192gf2S0M3PsZaSVkU7WOTE9rhprzk69/fvPO7XuvvfYaKx98RlZz1iDQMlD9FxZWltfYpq83nNLIWV7RsBgmDYEooxEU5UVrTJ97/NgaG7XJwlK8v86JGPRi3/3mN1hy4CsgzOoc9HtMJZ89c5xMITpmGBfpcNUp5AVFCWbeADeOQxi4KjsHB6zPwVnfVyGvDAzVm3Kss1LpijjjmC6L126oJbxUpmPH+5pxaUtRTKU9T1S6xXl2B8ezdTKe7Fz8Ht2ZgZFdeV8dxwycE0tLa7yj3+v3tjfVrrKI29HZ/fNz7a+/+MSrzz/d7//w3r1tFpne/d377L+8O+jygUYQmTjlzRgGEYjA8jzu0t8pa7T1+mbawmKnvb7EBzWawXK9g7fo1kAy3C4gAaEq9iHYYkQwEimcURy0UBxyAvUOc+XzHZ7zD+LTDy3t1aYIZYeYUdR8vdwv3rDX6ihUUSGpqcCVFO4XPCdfrJuI8rOMWp8crB5XNZgBNCTp4NEmIBBac0vLHGi10Na0g/wB4/VYio7uLflkahzSiB0cipMGkLYljGFroBGyxEFtI7nyYDS9dZE1y79iEkEcYiaQvBBVuWDeyArzbAzYmGIBESNBkNqrzIRf8EXCWBoHZvQDsk0jNod6A079Bbuiedewh9PDkLje0vBID3S3zcGT9prfEM7oJIkpqjq/CZp/kAI8gkbg/AffIGwpePQOLoXIgLChEloUmS6KW27iFQxpIEKfQAsIiKIqKhmoq5oXmq2EHwiRAY1GKJUkf4jvGK9EkeUYLZQkaDGQpKT910OVFqldfAsb93U1i5pWCEci7q7IAVDmoGh6UBBCXnmgeeJIO6auej01uLbCCPERbooJSsRE3DYYltsG5hGEyAyF7UR8EMgUSW+//fb3v/99IuFwojIpQm0Ks7IOdbzBtkhU1Q13BAEqAta2wQsOSYbIxqD5LJJwJOsNJoS1iLwqUsMUx4mROI5PksVxTbZokqb7gmaJBb1EptANwdMwG1kYEvzBYw+qSSNH5dYRl0VdNRsIvi1AZ5cSd0cOXE816gWXeL2331rXZwnpgNippJLkyZC/A77vEB5IlHcfNfUDiXkCoTdlYaN7Y5NauXTqpK5rTKzxGhE9voKFijhWJnloJ7L92VW2Uc9v3YEP3S84tPoyDv+pgKkW6FwkOnNSeRpnOPLksy9wAuadOR0iTx7AhJWU0TNw8lIYOk4S0uM2aWs4kOTbFZWYBBgcImYLkNbdVIpnnDoSYF1qtAwcqTgZOPV3HHsSz6nkJOScDXFGIDPrX7JJ4LibHHKRhScEmKdOp0ockRjwGjKRT0XdjEqEVcpXW2lYQGMiABS7mVa3frKyicxqVFRypEhyimlHsabeIcKuTsTdOeTwzGdgjBCCw4dZVA3n5n/+85/zziklh9aNnI7QjN2AXPAlWw//qcEH/qS+sPLEY2c19Xz27DnqDjvpxGNHW61LAFPGGQ1mC4ykiQjky5ltpMbtKK8pdyFiKNd8zNYF4XigwYJf6cmV2u6I4poWI0EQhm2QsH3FrZNoYthX5INonQtkRkRcwywlYuRyG5HJDEgqyA0MkkpqnTQR2GBifWoq4hDaGg24k+pKaxFci5FtDd+OkwMR51EbFjTzwYEJcCCUnadSKaY4FdFIPgb9Cy02eHKwKWM/CogkbwIgrp4izp0szOtI5HrYZphnjTA7Dj665fG8xifauRQFpN09XgCIgg5BynJhWMcNLP5DknMBPPhJCkB4MpYjYi/FhKBxq1BWicKwEFoS5JFYdJQouDEswzLnL1x4+umnV46d1I6e+UW6Tmo5+CLn1fV2G3v+4qc/2bpxY25h8Oq3vsUTLD3jres3vAMospo8mXbma99foQg0aZBdVMJGA2ydav5kmYgGqHrdeJGpAelg8qh3UINQLCOJYXOPadlVJoSYQWb2mQApHJhEh0ozHRw90NckPhvKIcQolkgqt8BtFzFVSLoSY9+ZfC86KtiiG1vkXn311euff8ItTMApxifuAnKpxbS4EKDiKqZ+vBmbgKZBJbHkjjhChV8FUlVeufWuUhSFP3khWDS3FBClNr+wRfZ3+cRyvOlFKreksqvP4kQYYw8GY2yVZYZCO+yiMtEIokavq1dIu90+Iy7OWScvEEpSpQF38AFgnjaCIRWWogZ6HAUV3Bzm28tEOAumsKLYG7S1zDreQJt9W/hbmVI7UMy6hZETD5ADf3iL5pFHldeXGGy3iQxRYCJ8NhCGBPLifFGOFD0Q5uBefPHFJx+/+L3vfe+9999hGprtwBqHR7eF/yKOE2YIlz76iEPzTp8+y/mzj118kt2vaydOksoB3YhWpdCupvSqAXCH2RP4SAHNdoaJ41yJo9vsHM1OrZnUIlCSYc8bv/4VTxx8OpV5w97uHkCOAeTtxr/+1/8Xptq7sdAFcKII61ySGrcF7ghMHjQjtob1b3B7oNtxxQqkRGAocdmhFK/C7FsQyRpuoGFkDqgNs8K/wYFEt2NESErC4lcrPXIGNY80ztgtGu3UicDe6GyV29reOIpJXXymoolb6+i8fqZYifN6DK3fcxcfY97j26+/Ck8+P41KS/r2IIuLeyjJgVlwYN2T4GOyQCAwGSTOsY6qIUTuL7IB5MOOCzmGHNwSd8aJozwtW0bjN0yRm+u6lcsGKrxFC08maoeg0Ri6EZBFXyPW0Vnj0ozJ//W/+rcsOl669TknaF27eotSY7bX1JD4eQp862xtsQZd7KiE7CxTVWigD29dFuRdU5mxq50rdQw4s/3E9STBQZe7vHvQX+Jbs7nQPdvA1KypxIf/ss5QiQDoQhaGUAhGA6kV0Wia2598babFPeQOLgjW3REGhEREYzS3nBNpDTQ5cSKQCJgdxkCuhqfUGJVNdHjJDV9jkCE+OYiQ/2aegfxOhOArMEdz9bYRjBZ+JFfiP5PU8pBYAQBFyswwsblUrIg20OFGWYYpjJ2qgERX22KKwiVibG4LpEQkpcqy/dn4D3odFk1QNm4LN+xgz0SuysIKxOiU55cG1SI9jSaeGICqGYoXGQ5jyMUnRhk/RS3zCmRUCkmRoaOE97UcMLfHUTr7c8dOPvHqd//ijZ/8B0ZtHHUJGt/O5upZ/zRuFXUJSs11gu5ci0uUZsfDcdpbzryL8qH8YcmkuuxYD0xTgzZ0shj9xsQtSqaydaqvkZkkHwj0grA6p5XDoGMGQlL4RvnCwt2b125cu7z62BMqM6/MBC1k2DftxRZEW67DJvKd5PoxlZZzF2QNP1D+eJ1zf7erwXo7nBgmMuiBdiGxnkJRpUcyLIrNo3En4iKVqLCAc+2SzisnSSLc1FC67KWdSWWaMGZC809xl3ILsYEQav/LpIBqgG298XQIJehIAT6RHyEPy/RIpBlpiqy63DNq/p2u3WQdxkVQO1Rq0+yTBGBJi8QgLO1qAEsbRoEA3o02blm+x/flo1Jh+k5n9Rsvnr945tqlS7cP5tbCmXA0DhxiX5ZqYQxN5jiNKB448QtVbkoKXxocHO7c6u3c2rt+aSOl4jjqpEFADxSgbXUI5VW7GC/HIjVrmPFcJX1BZPBByeiMLOpg2VOxf/7Ms+fPHCx1+HY4e5/nllp9P41FLqO+gy+Nwo5pPRdZqAHToobNIglZsWCge0dAJq6rBvOc5KVhPcH4jpdroIVvF9CUSKOmTMTKbUhKlESrG4CJChQ+1qTcTomQqekhEms9XdOnEzhFA9Yap3FbJ0V8du1IrOwkDVZpDXlMnNEKcthNorSLM1dxW0/DFFxPHqEpMHBojUMrXQqHEilJROAQVAHT0CJWvQtGWt+GGz4rzp6oKF8XEGI1wTE/OGC328bHVz76+a/ZA0J+YQ9KPgMa1flX2cpxe7d45cAgmEF6q80gbP2Y9ow8/ijn4lx86uITTFusrjA7sW7NU6Z2MmH+BW7jZMDw1yRCiGKJrdsp1TWLmwY5FR2g2u/JYWhtlWaerM989FBBhdUVECv86voJeizxXCgdlQKtDnUzsDxN04q3LnRaNCdIgw1+tA+Jm6dmwnPU9+bgDOa74W+0WbotEaeV24j4MwuTW4acf+k/Uo9EORpyywPU1vGbVT7nNLUA0pkyCGvUPhCuDGHJSAxn5J0QuD3RWCYYRzm6Rmhui80ZGEc1AvQYb9jW1kG0Kvehuq52A87KZ1in153UIXCGzPyAd8bYDR1ni7EEQHtLkmqH+C0catDMkYLHV5cefeTMfne3q5e+GAJp4HbAd+HnO5Qa/LQ/VYuNQw/h9kGD7UDpF0IgJaCJXrljGASIWTY2pUbgjl9Sa9l+5EOnmpvx5V0xheRrAQbXoWjg4lwpAwRagpKCLEYyeWkEC8jsc5328vzyErX4sYtP0Id2B5QUJNqmoCJjs8+CHuB//7tff/jhh1s3brV4mJxbuPz+77/34z/lLQfMzaPj73/zFvutqDsUkja0HB68/fO//cEPftA+/fiIimQ89Zmyv/yBf/ri+vz88goLWIO9bfYn9va2+qpajNhpP2PjcIxC6eQjP5xurD8WhfzH200oS+5QmCFE4LDVC+59gOw9Im15ifPe+doDmzx5kVz1WCdXa9NyBxzWwemMygAA3SiesIPUZ5M4tuW/trIcHvS6bOtbeOTcqXOPnCDX83qU0xMdQaIxeJgbY0LrSQEi2DCVeWUB4ITkEg1LqUFQ3il/44hzFApTJRISrR872jgjRZUwgthFuWsShQnm/WWoePYh/uGlj5mCITAmv3333vbu3traiqcGmP1ZWtFL5Z2llbOPPLp67CR7RT0ltM83tFoHKy3tvEH/wX5rGxfZ7Z1cZxMeU+YqEZIQqh2OjPtVYfEtGW/Aoot8zs2oFOtRLKju7dCxgMemO742uL/YYRakt9DhLd8+lZRspv1EonLG6wimVsJYKMqQghqF0IjuKVTc0phdvPRm6KY7gv3WhMqN4GMC/uCAiW3CEbWgNsop8fIoI/KjUatsq1Wc1WPra8ePPfnM09/89s1LH37Elmc2hLLDg34QfCjAoVA2t3c3Nz/6+OMP6fRZYH7qCa02n7vwKG878cIEDs7XjuBMvZB7y2bscwzDVV24NxYOx3u5aECu8wIHhAJB44DXbaQAai4IajeixdeNQsI+1IIcsmDSjlz09/a2traYQ3/jjTdu377Lssr25hY4LEtxUtOPf/SXf/Znf9Ze7Wzv6yVL8XFX2hQbMsYu5BVWBVzHZYoIZltwSgRkmSqC+JRIxbAgP1DEbGtlakFmFZARy1fwJA0claWUHGLSaPJ+xdnTZ6hD4Cl3KafkNy++emIl2JiSlgp9orUXVLrFbkcPjs1Ho4L4wzVpreRJNCAHWjJhzzU4aDK0tdgoID6cIb7MQQsdL/MB5/tqFMFy9B207XPd/cXD/jdee+V/+osf48MLg93FwSbvvILJmx3ibEI1XGr65QWK+TttRHAMHEvyGS/JCYnqswcyDzN1/ER/sdAa9OmTxFBtTZgoTsqe57DjQX9ldRU4taBNXrXgrgrBERtqI8U58heaMF8kiBp50Ph+2UFnoG2Ug9g4BZAeZ4ldqvPt/oG6nvkeDSdFopaWeynQYqNu93CufWat9fKzzw0GT7F8yITY5Uufvfvuu9eu3GJmbK9/2OVoALqZFtNRaiU4ZXpl0LqbTiSG2WiY5pyh7Siq7qR8XlTAoMR7biUClY4ZiTy/8FYaiwR87GF/W4uRvKpEOl2a9NGHmeh22S2uzoV4tMz0MnETdkBK8Jt8gUkjIeHzRBDzO4wg3R010Lh1M0ZJoCjW5I++ij9eA2Gbtmc9Bt0eAuhe6d9rDn7iKBDNTnjrg4ZdKh5Kn+Kn6DhJxZNRICtb/Hkq02PmYGFNZAvVSYHwlpJxagFDFD6MooGBPTmPGYJa9jSHMBueRzkw94Gl8VUdWR7TPLziHdVu0NWIU9pBNey75ZFVwLCpNbZWVZKjmquhHHNrgJfSMCspWgwr7y4iEEe6ApvBfEo2AZY9y04SQNCpIWpY5COcBJVdR4JPchsBZVV0I0NhK4pX7duQNdZQWxd2KcJqKuJkVB3skGhOmzjYnbEcboqVZdN4Zios6kgoJwCSxI4WhLUsvoBD8cQpRTvPPcewjG65pmrEEdGA+BZuBOJxFXPmhY3sBGXmSwpmVReMIS5hZ411vI8++uhVPuw2xXuKPuhlPVUAEVBYqVnfyFFUjFH9wWEJna0iZmUmak4JfuMyG7IYjQhSEv9RbtxlgcMEVnLKjdXgtuhZkszWahRgmQwKiQks3cYKwt1DIawjRXMDGz1krUnFdsRDGhxU6aeHiklBykYsgCNFRnQoFOP8vTxTw2tKd5DhQalwjFnwyy2Vr1gDoCsjj6/r0WrTsfppjSSaRDXW8dSNLxTd6khiq6ZMC7/jSTUEVnDhIp81ZSQH2JAAUyXDw7mhdsCWTVRKiHVafoNo3AcFzqnSBybltjRhQBBuiQW/KBCcdVFTE2qAToRQkkqkYl5gjgx1G8YaKNVt7eYIKmwttNxWFIpOgxttosINDke5nSaFfE1Lms32vlRGKFciDdMDIViKI1xn5Lc2KVSu5qMOmFQu3HwPoWn184D5DYrSAw5zgAhc6x/+4R8wH/6pKhNDWzfiVqAoSaRabUlK+qcVh989cpq9kheeeOIphvInj+uovJPHjgshD0Otxgjlg99Yq8LKNUtCxszewJwmSmhVhTIfs3Xp+Or6W2oQChiH0UNwBsDYUu/pMwoWsgCasNaVAUZse+FKEtfsMtOUSv4cmEXmBGQQDHVmxzHMoaCNIxwFYvJpekwTPZFzIItTsaojMlF+HXsiIcAiiEhQ8asIV1yX4B45jiFgQDnUlyTIsRSPMayI8EjQ7alR9TudJNGz0MIKp2rxuC0B+LSkguMIyjQgzmADKNXDY/EGIuO0+EkhQXSJ1xHGsdzSS3ItUqYhG8dswan5Y74GBGSmHZ957iWd8BsimK8qtMwwYjGmg3/5y19effdtkDEc+8N4INu8c4ddk7QDa8fWeZmdN7Ijd4c81q7ut9hfBu4//dM/Pf3a9zlJNn9bUQzqkAyixzDM0+Ik0MOdDeK3rmyxJWplSdYAh+DhAcrX+jsJs/B2NCZitgxaXqM2GiQH+9qejH258E0XTNfjAS0qsvj4o095wI8sIVfFYelcwVU8nqVxLeZweZcL/ujHztCPL30gnwxTQ45WQSLLW3PrA6FzwSMQcHa0lVAEEbF6VgMEJxmTuEO5RW7xB5KMb3FcgVCsbFdkwZ+zbt//8PcAgbz++uur6ydAZq8fQ/RPP71MjvABpuRQkkDFIZUI+C3m56lQ8aFatELioKfd0E6NQY0QFOy90RKiimGNKwxRuN3qYIpWW1d9LxGbx5DP9glOqbI4Fw0mNo6SQshEnAaJeY4DgaCPhBE8nzRF84m0XxxIXuoSHGd4xNyNEwIpzIk4RC5j7jAIuIU/HnL+7CPshmaFiVdjf//Bu9Ro1hqUylor/hCvF/IozXLLB++9D+TRxy+yxfLc4xfp/U+feQTPoeIRrMYhx11G34fQiYrVQOtQQx4iDhNRhSdaKBv/aaDeeetNdKZRwmNZpiIJxWgrOFTk3/7bf/vd7/wAou3ejkmOom3RzWp/QeXDSpNrShH0cJFkkIp4tqyC74iRJ1YFkni1hbJmabVif58obJ3PWg2Ki1vabz1wHeyzVABWQOR75kiEV2YBUk2mPA4KETRCrQSl6QJl9Y3+6I+/8/qPf/zjU8dWmStE+YIPZ6jc8iCCUCe55S9sQa4n3UyL8ngUVLSNqgP5FSvrAzvImeWEM82sVIp9IqDhk6QB11opWQgxtoml0GMA4ylUcqN/oUMm4u6GBosI4x9xcDMSaL7ADf7wIVAXWD1CT/p6Xj7++te/3u/Os9R0+cpnzAL9/tKnn332OaVA3qjAmCKGKlYndPoCFxSzPVHSbHyLVkS4Agz9NQ3Km8YAWeIQVUzCaqI1XkHmCjKYUIDjqs5txM34Aa5FGdNYjRn0YROw9B6YdIttJxQY0jEyhDgAXsotOFyVI6bW8y3aQuXhBXskiKM3mKDBuUyRFQVIIi6SCOJZ0nIE5kSLCAstwOxNGXv018joZw6FD/rItfjJhpUeUwJMCFMSBYaJrjG6K2gAnSsbyvA6DmQ229mpRVAdKSSKRJYKxOJCgSFFnWqoiozseOtAgCAJWt2A38hCoMwtbl/5bLW3v9fd6HU3Dg75uC0zn/JjHTWDrb3IZUZzK2oA5mlHdK+yZzWIEMfQ3hvojMtnX/ujGztzvZsfwyGIyIuMyYKC5XH1nlmklGBz45NUHMoWlj7NbZEdwBwK5If2aCdIKlSOeBYkV1zB3ATXZ5SQp4ALa2FB5wrNHcSY0fsjFtqsiqXTl8KIrCrJyQbbOxwg9NkdhhHbMSlPbXKu1PYx4M6u13cGyKYaY1U2TwsWDME96Awa1xUW1Tj76O7mxplzj7DIKK2iZvrsMO1kwVax8ERrTAFGeojE9XPFA+gJ2aRLbZ7ItdawFKSiXCtKBT+pGFDOJNFkC7MsydvUXCHBsVg9FBsFlRA/uQRl1Qm1X+Isut62lXf3iE+sZVXLRwGbdAmzlITh2LCAckS5s9sZkqd7cvr9fqvKY4uNE1ASQ2Dwp0MISPaGYXLEtJxABGMI2y2OEuTmQMkNR15AKyvxQd9W+P9GW0uFu7EyvHpq5eZc7zyv4+gQDfRibqEvl6ZoKFA3Vnr8CyYqYrH31wqQgU0kKYfmrKHh9P1wUM3UgMlT6iLkRpxRHSbg6BzJfmthr39wt68ld/vevu1M40PHD3r2B51mKDtWgzDm7WAaza7wtIorHDfB5McKKwFeRfO6TMOUqp4BpBVyxNc6p2IyGqztKOxId2Z+FNTZChyFQ8GptdUeBsKwGBOWxdWY4ziF4bRIrbP9M/UlFYFx6qsTkwK5MwYIxMGsGCtxa3jFT1HDicjP+FONUGy8IhlSvLh5G+NOXCI3k3BOPqcKMpFjpYpP8F/rtf7xH/9x8/pV/GllicaNg732dGQn564pKAvwjSohRfMZbdrZRE4ZJJ3lLdyzZ595Sh/85pRnHjjjS1xMEXmPnrRu5A2/giVwVzWJiADPB3K5ceSwpHjlJLXSmCVuVeNqTSJTqQ2PfA6pTBJFGRVbLNWIlXonVmoe+Invc3Mfs54YMItGCT0SUF/5XxQTH/4TGIdG5xI4ssZoSHJFf+Rg5LpeZPJhLiyX3ZEkJU2Cv0cObgPTANQvQgYWhxyCZVUOfCJbpZVm3IubhvEhAtkw7UUiHn+ajsdLOWyXIbsGCjS/RGnXg38UA7uVJMvWDSnWs4hQuVCQ2qhCy0s2tCOWHZh4pJ5OtBFfH9Dq6/NsOC26HbBsKE4tPb7yau1AO1V1QBP70zlpkRROFOb5Uy4d5UWZ6Tkn9EE9SorDFrCBIeDDh1u1wnadyHXRULICh/RhXF8hzw8b2CdbDKpCSE75s2PYJiZHF0e4DnnqSKj4EJ/SICozVthFj2f6T4jOwjbMe/mdkFiFdDwEXdmAxQ7iuZXlJSo1+xmpkXz4jV2BzGdiUuqA+uOF1vLC/McfffThO7+5e+sW3zAVuyhyjKY3fE+evLC29v7bHzDRfGfzHk/UtIcU/eadW8xSr506B/a7v/yn1cPe6Se+hnQ+tcDVOvdBPDzs9OKZTStX2KzDDqPzTz+7dPI0RwSwGbO3cZvCah20uPW4l86ZOfHllQ6Q9SX2k62uLDFZ2lmNQyFW2prhXfS7CLG94/j62lLncEtv9/LN4y5HQy+zc2mJFxcZ1+BPGpIvHLIFrTe/pNnr0E0mDFsNX6DeG2iXN7WGQul1ORmEjdTyFs9cHls/0e/dDtoFnvN5sIcD+Ew3EGGDGkDm6VygzN2gMDvRONMPz2RcqhNpd3Z4vvCgk9cPwaTuyNhSJAWclph100bkmHoGwmKYZ7RVX7I3Msv86JPPYI326jlGNSwA3L53F2dkr965C+defPlrfLQK9ZgBJvH25tbtTz9tra4//dLXbm/sLqyu7beXlk+e5uxQ+GsDLRtxNC/R4kho5Pbb68B7sZsPidgBIJHS5hXXXWP/0/z8vWuX2HjaWZxjlz1TId2dvcFqa7nT7nf0HHbY0oEehwuxfSyGv3qvhHqjsRnJumr8JsdV7UAc1xIErOtmoNlQ4BAxla/WUMUu28Z0RlT/OIM1hiExAsaSWlHE2tG0q8bkUOtAvAjK6Q/861KDrJEvQR6YWZOAtita9PAkMp1aarVJ2SAyBTl3a/zciy88/9KLW/c2mIn+8IMP+CAhc7jdXp8z1CkjJqZwBp4l9wZ9NkRfvvzx3C/+mSmtUycYDpxmYMCBdcdPnqJuEjqdtrY2quxUCrK5nnwVfOJ/Ml2UnapiNL+RPnJJaCOwOSbEw26qEewEJOCckaN5PO3TK5+yUezy5cscOHDv3iZ+TgYjqEazMPnHf/rD73z/e5wCzO4yGKvqYSGVOL2NJAUrRRRy/0mK7ZfAqTVzmyYPQcQIofHGruAQQC4p3KoYvuJQS2yIKkljEfcn4ZzRvOBQLEDRiNF20cSFkyacyNSwplhE9mG1nK59WbRSVC7kvHXY2+9yMD3NILUATFpIypeCo3WSZ9L6s1GU409jIdYc3D7kN5lQnBEAX4hgN74+lebdzYs7Kt9vvvjcX/3VX7303JPgdO98zpCXfbXgqAtUkP40KqTSslMwGuWEX+HzS94MrapzSOuJbvY3VYY4LQolDwf91dwotQa7S7S24UaeQ1DbQgiex1sdviiot27m5yFkRQcc3E8NbPbksCB3epeLegQmndXe3gFT3Op34kECfmrVBwB77IDmlprJVRO3GDBGBBGlX9WBIcv9dmtXa9jwnD/YBoLQzvHlw4uv7b7+fPeTHdaPf/reO+yM/pAjrPs7yzFYAp/gMSGnwxO3xVKTUrVP0cxQRrKcPttA3xQH1rUXNK28MHeSLLOKRa+E1dF5p7sn59G7CtQEGYeT0ShgpqB3d3nnOKZB9wf071iZUuDkLM8JyLCyCHVSPQtlEnZNlYd4ajWjzsYtWsr+CUMZmhA8uvM3DMaTsSwLcGSBb1Hi+bAljiYYd5ujtWMQ8Pm16+urx2IMIAUxPiIVCXap/+os0Yer6PAx9T+MwhYZ3GjQR14AqB2AIjKm+pKCMhK39ijnC52JCAl9GAGzoL2vl88AwAdmRss88oyWOfFOGK8F9LuMolXhVAvVls73uzSsImSEbNVj8Gu5IQr9w5/NN+wsaSVEf23Rhy0NuX2ICrWAQJaDa2INIbxA1gnY0UGATztc2GVPK9xTBEKLaCaM3bv0a3ClfRhQgvHbjBL61+dcOwEq9WYMm1UDYyQc7YTzzhhSHjbkkrjpCA4wqF10SyyCESfQkMFAMewBVSDrTs2QqhARpQYEVwJCu0Z7sb64yIlRP/1PH9JeBNGDXYY8gzOSgTCOhQuNLPEj2vTBpFbY8oDRwCdo2YVKdmjMnF/SUQNbNJSxevgpEfOxtW0xWxLawsRyWHlmh4jjZmgEOIgwEhqCgOGrXFWHpUYgjV2Kj46lCFDn1I2U0Qo3Cy2irU9NNZEtwEIyjFSoBSjMCv6lRGvmYlgyc1Tu99doRIT5Tzc0yNEGiq1NZ3KubhyFQN2JzphJLakcVgEY6idCu1bJRBDRTMrUhIZHGQ14I1LIjxgxB+tTSCRPCkcTnEsw4UT1ROxQ26xDIS8lUnSm9yXuZpRKlSpLYpWyhkQ4mIR4iQC0VXNFmGyKofQvI1Y0fzhmzkuiHbdPxTSVnyG57ynpNR8wx7UaIqS2VKRDYGZUyrf2z1K+GSsRFnJHfDVygRAh1NwsF6C5TVQ1mKg0h11rkT0lAp/Cs+Zs4HjqFDYyHc37xx9/xPMYOJDv7mo5EO60+R4I4KXAqbOhvNxM2+r0tp92MjI/xbMlH4dhf+K5s49o5NrRp6Ksia9FelEMOMH8iQTngiU1GpBh2lisINvsrg7GKknV7Rh9BWjgkwKEq52opEpQPAg5tUJTawjQIQgxZEwYRc9YMIk4+Okj343/+sFsHD4ZUhuNVqW+DYIhN+8yXl6KhbSKmanww0JLXkhP7itw0/FAyMCMFQzDCIm14oVHVWEB8zACUm7EJMDlGIMvkQef0CFXIkO44qJcGVXrkSZCSeIORoJnDsIM5QHClqcXAluOiPPkY0KSBisrjAYZZEj2pACToT0mIRQYPIkHfoJZSkEgUiBGrpMeIu4MFsKRulC1hEYAuUYAaFUBYhme65hExjKMbKnRPAxHKWm4CxqpXH/2s59yguT+5k0oi1AQmLZ++eWXOYGHZa1f/uLXPEwurvBwRU8vq4LAJOxgYYlpKUbKv/nNb549WGUftKWbP1frQ0RFGYE4zQttzpmXXmJceufaJ8zSDnZ3wfTjEJozekdncFYWO1yXYhftqWPaoskHlmjr9GhHTvUVb9qfXSDshMaRLA453HfjvFFaPLjhEgQGpKhhc1k3X6Ei4FCkehqLdXSYuCbwLI31YA4f02IKuDELDtD5AoF5BObpjEYrSlvKPCxbkqHqdFrs9SaIPfOADBg0Y6CAArFePxyQoEnRioiDpZBEvnj8l7o8MMcbAET4clU8wPd4CAITHL4wyRzczra27JFEYCYOaz/34osoduLMPuY99+gTlNfVq58yOX7zxjV2SbMDmgyymgQ+2aGY0BBtYUKEjCDLVuFWGkTguCcQYKItqL0dLMBEPClUQOB8SQtuHAgFHwY76MbjEHCCqDMbGBaeNrISQ0qBF4gIIzUx8f3MawgMoTlW0Gv+BehIJNkLGikPfFsy1aCcIb2BOeO2MLdBJvJ0EkwoIK64x7e//e3vfOtbnBv76aef4jkffvQBHsuSCgjMrqmkohnnim9sb+6AyQkejApW1tZpW5imIZw6/Qib+zjoA4iWsyJAMh/7OhWJIOVjfUkONr1YE3bUi5IX/IcZPaoSDsb+WWbMaTHu3LrJDm6cKs+QqppTxY4fP8kHFf/oj/6ItgutqA65w5EK8C920P3RAiTW2eiNW4BIKUXAbY1cS6zhR5OcsGqdH45J4VAi91WAYnWVx8jaSpbDNAWmwU2HfdSDtLRR120UnoIywAngcKXNoSGdNu1SNBdBfJvEWtFM4cl//ec/YmlkbrCLtkAQwUZDVEJxE3KlB5DzE4uQMySAlRd+btnqOD7PggoQItEX6CgGXgZAlg3juRFyhFzcFYaaRY1lD90O5KU2Jh8/UmbNPWRJVXwmuw1oTOBypf2EFocluxxEheZ0YA2doQ2FpRsIyIUEHBTzFbm37t3l9vgBXw4//6Pzj/BVg//Xm2+w5lRYPUREGcwBHbgloAMlGNtW1lFmY3uLqTnOhwKBPtRXaTvPB36xgmpih92U9K39LSCoCiRzjVIL46J8DS8IDxQJDhopyS5TgnSLHtbpkBhCXpxHriQVZbj1DmhV+Ow2+ADu191JQxGzwlsKlSLxLAwFcTp+uXOWZXyuMFRqZZAi3cC4Ds1VCOsIOJxdDQT2xDnrBiYsF5hVjXnfuIVaK9iN44vnlPG2aUONCYTjrAwpVA0E4MXajaQvfksuPFtbs5qmiXGoowu37t65/PEHqHX39k0qACe+tU+22e9Qc5HSyUs0y00lJRVattlwLCttAg9sbJu5u7/QXj/1yh/96F/+5V+OzWtBCbPiIq20qVwuWNYKlBhCUJs4/TWYnaiPveTq8i3tg5bbcUY70xNVFxgP71r2EbE4p9WAKKaAKoWQcxKNV6TG8j3wYXPtncv6EKvw5XZazWJD0O72u7978+Xv/vFWb4uhasB9GTZnCWjHEkpyFGNTS8gXLXeYEF1YzZBGNudW76CHIee0uyFWOXiWVKpyrk+KEyVzSXO7Dtf0aK1JAC1gAKkfmEVTbJviRVsxL1XAzoFPBpYujaSApOy4XS6YbozQUzgFqni+K7NmAXDpWFIlsKIcRou2Q9ADxXKOqgYzTfXMZpM1F9b9dUhOPZ2lSjxnVSWFcdwKi8SGA8gWo5DlS5zux3t9YNi39+c4AGs+XpRdWJnXEbNaxOPIxWh5XYJahGVsSr+EbwRj+16qERL3AMGLhCbwjmYygWlYUeevP7/YPeTjUUq3vVxbUBIIG0eHzlQ8QXVTGgWbyLq8xDqKD4GMAME8GIoYnOXVFkDMtSWAxvfV2S+eCRrkBbnGbMRzsTTAI7eVB9Stdh0fwZcyVeZH0vKNFS53OTLh11lPCRgk2WIEs9ia1LpdBakoQs9WaAqwQIi4QzX/hpT61nHtgiGEMjVhwECJEFsCiElpNWSQDCVXVk2Zgk8EUJm2qNMlakZpkqT9nxIk3zel8fGdcM+Q6zO/wuG04z6Uh7P9b3n/YG9759c/+Tt2XDA3AzeeBLnyfi3jUTbpKR7DxKheaSrh4mM+ZENTz4xNeSJl8KRRsheT2EnIUbwRqJb8ugzi/DGpGa2BdMtfRqYTpalo5h3Rzg6YxLnWQWbN9aLGRFu3Nkauk4DQr0iv3B4qWnmITtnLXly8S1Rx4i1zUxhDPRkhSYnchZ4IItiqHhqiidql2NHFE3mQihY0cYgwoyYqd7FHOOPO+lVGKnOBmnr3eHRvUIJMU8OV76sHpq7OL2+8E2dWiqusr0CUuK7kJiwQcV3krzIgQ690R+4iGqnI4Fd9eQ6Y239yW/1hCgyzryZfojw6UJRxj4iCne3FCEH49mFiFAHPdkDwHcnA+ohTldM5GhhEIwV2PMky7P1QwxpbqhhHw/jY8uqJ1fWtA+13ZowhHA4M5uvlfJhod2+fYxvQiKyNBrG1YhkOJEdHfhtoTgPogLIBIUf6Rg1xcy5XfNglUnOfJmtEcHUzgh96GjICD/wodc0ro15nafHkKZ3YzlQjiUDQkApOaXPG8XKrdXzt+Pa927yMf/W9N1TEhzwW4j/a+8XYmBp/+uQxXv35+c9/ppOCoY237+HTX1jc5+Veymp+fvvWtfnu5olHHuO0io9+87PVwebZZ19FHCcmMuXYDi+K2ioVQgv5I+7AMzUHf66dPcfWyuP37vEiPZNcfGmep9COzrfUbAVPrXyGni8KojZzW25ZOIgDHXi/MHJMARzubHZ3dgY7O9s8yrIvq8fEM9rEy/jg6BhpBvna80PbtAwTeXvM0taFAiZvA/QPevo+BSHe+UDSoDdgogvOWAy37PcOl5fazLUxlc9jaZdvR3DUxOLi9s7u5sb1M6cfRQwbnvj+2dISh+UfsC0aTcgyU3X9/YFeP98/5HEUDWgt0UQ6cKwjXu+j/cOjUE+5o2IzXNnvk+o3sQQjdGPBhvnvxTatMcZRzVto3759/fPPb/C0zsrBk08+jbaq62xG62uq+unnX8ATjj/yyMrJ42vLy+C0l9mqtnDumUdhy8wdk3p3YXH9+qZ28mx3DzcxCYsWCIzaimx5e24ZpDglyVVbntgBvbF1+869tbXFDz76kEcwHsIXV9fh0z9ssYbRXlpVBcF3cAPO+6COq+prXxn8ZYtGYUROYV4HMOtmiSRBUh2sEVM8NFecNofNdGCqW4lE74Z27QUgPgEvcdVceQpqlpSM8eC/cGsQIbEJamAc4bYxXmpQTLQqONgQ6Tu0mYOD1aXlc088/ujjjwPHB5hi/ohP+P3+959flxPwpgieQ9uGj+1Tp6Mt6/b3tnY24XDlyiXypUNPFxdZesHVvWjRWV5i7vLUqTOCrK6ygNSmzvBVt9i7yYG7UBFoAvQYWQo+Rg4YHT8hMB7euHuPycrNu1rY8FoOGhLcMoBAaUbXQRFphogN/d/85re/8e1vMcXGEoh01qOGclwsoxxIpFuPAFcjloZ3FSpHEDPUtpFGLRgdsTgdfPtPiYzRTQaMeN1QfSGXpBnKTGY6CYratEUlxT7pnfLHT55eP36SVUYkMhoDZ9Ej1YLdjAz55BRBeB4iB9CrRGIykYYd30B/Z0HOEG6Ak2imMtUM0dI0Ery/mIOC0aS9r2nKTnzo/tHDeUatf/Hdb/yrf/Wvltc7HO7QaR3e29qc62qpj0+o0rSkvclR/dGD2qwhBUNcMgkO34vocFSQllt4exdZ7ZgcZqkTlWyNve34CPBAS3Gdjo7i7/PRsLtbJ4+voy2bgXFXmg84sEOSvAw+uQ1n101Uhcojapg7+1yxOjjKGyH2ffNVJBYl4/t8gJTEe0Nc4UDoxtiVXpmw3I8ZnrAYScWGA7+DDg0+HmVqf15l7oowOAZyb6m9Mtg/c29zcbe7t6xZ0UFUhYGHftEKpqbQDfKI78kekIQEXRxP19jpHL0DlZhup7O6dmx5ZY3+FOFIYzJ6Z09LuZ1DPm7KJ8PoxdjRiymYfqMsvJIkts6RIhoLUljq75QwFhpwa47lkSiKigqek1nIu8SX8ubhiC3djKYZovLXCkdk8LDP1mxGl3xSZKDRqNtPMomFhmZTzmNsQ+PCSGlpGa+AhPLiDIbFpRU6arSN4pIqymNc0REFfGt9k57oQA7cKgZIz608ljLXgntEaoweUu1gwDViHnj2YzmZYT2rNazVaEQNsjySVglkhM7zlpeq9gitS36EW7nxgQZxa+/C1rrD8eEyQil4+EN6SkUyEOViZgCNMahNURDhbNuJQ3i+k8yzPJmQVNCcZGTF5WPDh0rF6x44nvnIAkD6kmA+wdvhZoa1fov0T7yGg2fTEWIEhNHOqNpXwWQFwC1msIoUAHPQKoYYlbLSRA/HsXdsIrj++7ejeOTQfqgrHGZExDAWpuCf5Y4YXdJHADOY3SfJIpyRqlxGqQ4OWN9+zutL4SKz8Z1qFiq4KFQXXiZMxVwwGVjzuVndhqPgJJBza9qiTZ3rYgCXlDCz+9rmUNkRcL46tXAbjchdCJZY5BYNneqrgSWJdhD8GqGOFzSA1hlHVjxTTKEt+auZpfistCG6jDy8I9a4HUkrNzVJHS8IIxV4CH3YGErK261bXNOgz3GSA0Hs1W+mAJBYJOpiaNSaqPZxP8WwicPD/RSJHgrAxLKTzlGytO7FA2ubg1P8ytJNlTSJamL/JyMI4tGwylpqCpSpqFMN/QsmcHQDzTrMkDi5dJt8G/fpVuqN+Xytw0SyBgLZrO0zTlLqyHhSgYx0WlOyNIJTKKuIFavVmxUPf5Py4YH1FSpCyRfxIqQRt/UMdHHXCIXqiBFxi+JoFPc4eUjJTU8kQwLpT3/6UwZ4elwMoHPHkFiDY4YZ2vlI77rAB4V4Nns6wtlzjzDvzMwIExPOAphw2O/HmnkMdiEiqaQGE+1DVCSCZOcg3YZ3ggIBYbZlnGo+xs/8Zv0GZkIwlfkUmmmsjNZIrYGOFz6OYAFyXdAaqV/W7UT+swwYts1DHZW8+8qkT0zT4+XSXM9TqWzGMzgOmSU0uIubWIon5IygHEdWTomkaF9TvlJbK3ogBDTHsGmkFkNtw0OCcIiAwHjMzOs64lQmPpgNbO1uIZedey4jkPF8rsZp+KSZl2vCKfcPEklaRXZyKaTuVUlV6/EgXCfjJllhcOK+9bU4p55ro7i5YgEm/jiehVTyyLN9PPurPmqv4mDAqakff/Du1s2bkldUzfyB8ToFk5KbuwOeGhlYsUz1ytdfh+2NjQ1eqrt7/QolRyOxefdub67NhCbvUb/55ptPza+yAxFyl4WYjwYMxcMnfCh/p6DP2uOPS8/uniabrt2l1dKmy+3tVR5h19aYJqNduntTRxLzSR2ygEOLFrc7ONjZ3NLUc28PzvyHthdwIkhZ1U415pT1nD27rG1MqMBsIYephF6XmS92XCLluSef5MoUG1I0gxyeCQ5U2Jb3Ld977z1mVb7//e/v7G6xpXR3dxtWKIYOFs1MXOgZm1qsTFibVAJ8CI6bhDgKU3T8t3cRAYip9Vo328BDOsjx1vj8J598gsVogjm1n0YeZBii6o1bt5gOXj65Rj0iO9AuHz+OeY+fOgsCfQD2OXYMM6899cQT5HFwV88Lf/8P/0mj+mOxJJnG1amOW8+4Sh/UgIpIySluAzdIMRcqkXrsHHvdFphtUKEwsZ41J0mZSvmGh7jpZyxYKOASsTUMcWbHiBLA6vkGTCEn20r/P3woWfgDiy5yZYFoJHEJ4ky6ESis559//sXnn8dDbt2+wZb2Dz78AKfa4pSWu6qSaIurlLLjFsPSPOPVuJN5im0cktNua9JZe+nZIabzXfBXnU7L9DQ+SRIAeiWozEeTKzjDgFlE6nIPxfiQIJr097SnlUBqMZcwTRhvHLL1lTbna69+neuJ06fgD6HwY+9AofriEefxiHwwlHXGJtb2iIRHRD66MtMY1iYd1432ivHhZ1c/rd1mHO3oEAzCMIHyxZdsHBQTMLyRFoOCo82pGoP78Oa9vX//7//9915+Dl/a7WsBcjDXC4fT9B9Wh57ZveAvKY53d/eUIzkU/cQhVIbHKV8sh4MYy904GIvq9HExZZbnL+PJLiC3bnyO8v4Im1OZpITWGzLgWUKmTQBw0Ms34Rt64vNUO7KI094DJ8CfK5iDSNayMPWLTwlWIbjpvj6IoJ6AZr1ZRlZ2CRoXMeZnIaficaSoNQEViQ40xqaUVjHiiuqujeEFroWmhYX19rr6oG5fK0j3boJP/4U2RTDlZSo4FxFEwAy1E6ZTDS+0dUQ8s3mykikdNWrMRty540oWSIIWVpDQ9NGFLbG2EBDapaU4R6pB7ltwzAdPBuJjHGBC08pSNRCnEglMEw2vhVyRwMHAUoPJffKlofI+w6YhQcSUpJIYGpNbZ1YmjfrFbDk8qX0gEzOHkCJIwTfcV8MVr5boyFPBwdAlOwVYR+AvvZyROmEsLk2qRjIIm0g1QiPNahSqEgGNOBkpRV8yBaSGF4YFwZBa6Ei8ENAndndub29c57Q4VsNOHH+EK4NR6sNmINnp2DUm+vCAQ7brKk5TEqWLLhx6RYgTbehjWXDZZhj9+h/d+PzK7t6eTxpCX/C8DxpfEO+0UyqiURO1J0uCdLQKK1rirxLwyrHQ4MB+KCL1lIq2vg2D4ik1/KROIwmDpsmyqGfFTSNJzRZHwIlZnGOS42x6Gbz1y9/wosodLRXjjWpG0yJd5VIilIhUA+HmN0zEloxo34rU0aOnl37CnizOfXz181eef4pGZJHPo0sHVzDZ3LU+Tk9hXw2UEoGQ4JlqQoqnuzQBB5LtoG1/qjtBl6sWN9rpVoVgonsiJV6lpyQyTqqvTpW6k4IsHUG2MkpMHdbTavKDQKskDqvoJK6ClbxMQwjR1bjcnjZZzcxjpJnIwMm/2dDoXlrrwKxykSjRhF0GWAi3kVaxBS2WsdSxg4S3x34CKeedBbyWGxaL5y59i55D9uTz/QUd/kXewwe0nCcK1RL4QK4/eaWqQ3pmG9cnqfUgP37QpdiRxKImm4Z4NYtH6v2YEXdp5TeplAvWb9WbOwgwDOTLKsVW1ATXMJpoWIOVr4DKwbxpNWUh9jcFFhwS85I7GTaHEqfzKAgkFg/MiMMlkAIZjzSGPq6/RrOgIqLIHWeiFmM0DJErzUdRdOdcWUTt80PyghTEtl1ENXAsoZGLAs884TfEruMFk4jhXClS54g4oU7iNiV5Ic3XmkuOg0mrZFYUpxYc9U6KuFnb0krUFi7xzCZ+Uz+isTjS1djJ65gykQ3IYyipJFU53fgq2lZ/wEPdx7/61Y233+fA1X1G4BppH2rOSLNOfJd7wKvUa2eP8SY+U0jPP/sc0xPM+BA84ocJgyq1x1RAPvbdoxCSk0sASjH6SU1k02+FMBoaeR9NnHWHzg3jyBR5yNhIMiMP03N8SF5cAZ6lvxnKdjsZj6YMxim5LCRyF7ZPvhd7E2rXT/kf8kqxeB4ZgxogP5HzTUmeCh5mGZUy9RAYPM11f0+vmkbHyOYN/I+OWtbQ8xY/7GnVg5b2cXPCGfc+0d75sna5M6+V0QYPGVDtstxx1JL6ygO2449vwDGG9Xe0mRbUbgu9VyZNcNRo6lP21aZHgC1HHHBFK/1FtYdO4lSr+GM1G6ECLMRhCPsc4IvmbY0o2B+rShrjnNZc+/jaytKGdiHtc1i0TgzQt+x4AMB5GMgBEZfRQBJAoONJIE4EjjJIOMakdPjjXXBV1tjnYtvWosHktsHkiLcmTOTBhDhZqBmqcciByWVqdMLxWZmaA9X38KA7sdJhs/B7b/320ocfzvW31NNRYgQ1Z2hIe7K2sqpXmDsDvi+9wfmBWP2pF17ilfa5zsIj588tPXLx8ud371y7LE+jIJj637i901lYXzkF1ce//kfOA376m39CfH9Re6xaC5rk8mQTTsGDKluC4mlTT60L7dXt7r0DtunSms23j58+d/fqBxv37vEyNXlkYYGi5MRnHgs7Lb262wnfjq3wtFbRKrIpmnlnlTP7vnTViEX1AYacXIz0Ns9hbI3fZxyM9zAY1UzXSEGLap+nACg1+uX7gxsbd67fvM3M2t07G+Rl4dnWsbVVTtbm3OzlpXXk4aXx7iQQdNTZnf/yL7/kq61nz57m9Et2DjILhieqAkWgyeUQDODMr7FDCljdghmHDDooG6NhiNBaOHVyYXd/8fix05iP+sJkH3tXvf355Onjzz77tD1cx0GzM3SZneiH7EHTjqw9WXV5YxOLnTx9ijjnADNvfubso9o0usAsAbODcydOcMjv6sbG1uqqKmd7UefnEtDIEVzd+UJbSjM2qRGNz8qrwdvXBMe9u/OHxxeX1va2t/o3rsN/obMuP42FAZZGkM4EZXiCOE+sH+IfzZj7U93mQLzSJ6BuiKvmTC15qCxM/qu9U6uVeKQx87Bdr/mDA/5QXpb7oL9yntzHmHYo70F5ZfzUQ8XtBBVD6dJrZKL0W/JIJDaEz/OGlIYvvDvCygsvzqytPLb+9ONPPfP1b36bgy+Yf8G7rl35hBUpZqWBRFunCqsCjJPwW5zsnouGVNaAurs7ZLyIJhVc1fdkCtnAJIlQbpMcLAqKp02VHHExIa4aqgBTWnuGPcyjnX7k3EsvvfTiyy+xY2xpVZtqwcf38vgc1xE1YYI1qiemCTY02QNerUBNNA6pU8fjKb/jCV8GZCLz7OMqrLA1P7IaywZnz5+j4cXmeSKAMkoo4+q4sEd9e3jnVIqf1o85X8hpnP0SqllhKObppGE4Ut77rETe8ON6prWws7N7Ng43eO7E+je/+c3/9ft/rPfdb2/Bcflgd5kzjnqa3ebcW9p9f1NBGocL4Y32ATwcbrEhkunm0CsmfJGIDnQOeGk7pnrD6Xho1LSyaxxTRKItDQgv62geaThCI9WYROq4dAgPBxj1wvYIpKhHjlENCWiIJiDSVmpOiSF6SKQY0I0xeiLjJ1KpssrdUCs684o/u2WpfQOmQNObNM/NL2/d3l49sYZSm1FEfjFkMUZifmPY9i+lHfpIrKbjYRQmdU6Ji3+bI/551NdeBw+p9eVE4UkAL/jMdxaXO8v0MLubW70BemPutuonXNhjoA930PsyMgslYoqKLBFwP4sjboiVga+bhriNWS+hTw3T6rj4qHzV3uCT+rwEFkOzwf7O9g6DADiydErXSN70LYYOo0dgqSIo7+E5MLFiXLWjfonDymMZrN2KTctDxZBoTAQriqE0+NLbZ8RUcmE6flGJ56/4ZEnsJNcEtDxENlVZ6MpLI1xdFoZzx/pKxOcZ8iiVGcloNmkfi5JAUlCWK4exnXPLac4ZVbbSp4oilHIBmnIUxiQRFHlwamMTvolMW66yKulwyCCXcrEn4CIIKSWe0Yep40mylZ8XCrbYUTT8+OqEYfaxlEWbm7Wq4zUn4urVcA6Gfez4WFs9RQNHxtUMzQy2F3xR0YfPJQg1WZnU9U/+5E/+63/9r7AJ8WobjrgPGlr8Rg13theRmGxTOcGNK9wE+2LBrHydwQlZN65ePfjmN7EVyJS31MhVaCJh0c2RcmvkIjG4HTJGoZiJj2ZKbseAQbTeY56LFriUyPXQcXMGmVuLsyNSBcU5TAUcq5owZipNpGuhwvZGMNsCNw63DqJxiLJ21HJLHEziAOuCqm9cfoYUeImYz/i15tZItcQARgPi5Nx1NZBHbicwvb93jS5+iF9DeevD1eVL1RUkuknMTByxFDqPwyKOUo6Wh9ZHkIZlhDMagsOwxEmEG2+PN9QYJXr4O8SVkHUTNxtPhU2zNPq0ELCjXuz/iDBzIqbUg3oOyCKVYDQiOWX4a8JCLuSUmH8x9hB9emwUyS3SNOwirkaYCBwi5AwOIVUM4c4msFFFKqSIjkuZyTiRZ57pd5yJ8Qz3Vd6Ve3cgBtZXSAIsnoYnYdN/Ar+ZPF6s45AmzdHui1YwZCcR57dyWpRJ1Tb2uzGYOGTr2vLyItPNfFKQt3k47pPB+nJniVE+DxXOo9vSMYeXQ963ZTiasg+MhWJHN9QDIY+rYnK5RNR6IrUpiCtpQu0c5/TlQ9ANpg1TGFjgvqVvDIhaTj0DEPQpDZ7wyAETwQrYNPKiPiXVlmh/hDY9NKQXRODBWSVFAG5NJCIPOp2UZBXKwMTrKGRIwI94aMV9FQqFYTDWo1cV4E8SAJyZWkDc6/ygEXh13AqAYA1RsaJWtHAwXNyaKInCghKfyAKaE4pCxLFvwv7KfqRwyU6WYt24OqhGhya+rXNNxWei9Nq1Kxy7sXX9mhjEIS24BtGSdcZyO7vbQHhrlIJ67LGLNB0nzz3K3NPJ84/E1LaOBsZ8Q+ZxSmx354AROITMaW62jr322mscjIKfgMyVIicJX5Hh8iMlcOYuKT6ePtGcBOYmGMyjvDEBMpLnkAswewt7usYDGnPLcFvgFWpCDA39vDVehMUsaM4hAqDHA4J6+VKgOZJGXEyxXbt+Q1NvzNru7XEIBj6G9Zjzmg+Jbj9RG0LYAgeNOJz/5m/+5q//+l8B4XEPa/NQGU8iJKrsmIMmwi4/f1QKZQJHNQg4VwLm4YraXEUWh3uirdICwjwyfLpbOocUVwdI6meffca+Y2RxxDNrjWgIH9BQ8vjpFopt9fUSDHvhgPOuDHbe2dtl6rnVWQH59p0tWJ09c4ES/P1v3iY7lAJbIMk1cCag5Vc8IOcQuUk3fhxnYj2UFZAIV7KJrGMnF2GFROKd1UNWPV36LbdUTFnEVjjnN3Gc8mO2daIh8pZsPSL2ZEVylS8RaB1HmZpPI177RiPpIW5L2T0E7VdBgmWGKrkUwiwYDuNoU6hspJpLYNmAwCji8FvfwNU5OpaTl+/evU1rwN5Ahds38S4cD5PKSSJAyFQOAVlFHHWBOOlRBCEke7iIqlFZ8NCDPWyjwYlZqjhRF5c+88gjTDejEtdHLz6BD7eXvNw4fOqHmWWZ1Zd4Re9pnLGA6+NEcaKya05MfijgDGUKv7B2uRtGZlcB8GheKHoKsRTrkPgBY9YBCxBgSMtA2WlaLSopV/OjcKcxhgMdE6l8o+QHP/jBK+fO0pLgjR999BHjWZykHyc+a+GaTax8TVobcsPfohEgs7JVDOacce8RtmiUCrmgJP+X/0qrUExgFj/cS+rKIy+5UEStTbNQQQ5uulTRAkuR4JowiWdNUrsUTFKqFI5nbe6pEazUGxlGyXDRBRCfVqa1LKho/KdhNrXM96FhujE3OGgtNwLWcG/oJK6GFxtyL2A8+bJuRLMx4FOQEcDkl9RpKhVu5umrudWQRnwi1ThOaCgFyIIDvSFokONTtHIYHAhVgK4QOEDaoZpPCJL+8HBGlDqvcSnfHoSwzRfhqyCcfFvHgVlne5RZhZFT108cBAJJNLVoC4mMVpwgswUnokK2P5mn4VzN3Oh1PDMY8elcmE4suhfcFJGwUdg4ZDR96p01JNmRmnEdn0ofCXYnrORIA7mIaMDLbUPQRCsZWScg8oz92KNPQnNvk49psMNfq1gMoY0BMYG4r0mG9whrOJsK0Kn0ISB0D+f5qu6xY+cef/lb9z5+Y293l5EmCHxYlVT2cChXYW/P2w99iiciz8CxkqZ6pS6ZZQx081oZQ2mcUlKj1nHNZx5JL39NcqT846xqpZHKohI+lxojxYvvyaBpQVGfCsl7n2OAHkP2uYPeP/3D337rT/+CJ7Mdlp5EoCGpcy2SHPDzHNWveweu5NQToYaQLdN255dv7+zf2Op1OnzqVYNyk/dZRszNdPBRzXMqV62QZEwGK0bgarg1MMLCwlBPtA7msicxXarJWRRSagwueSoRjvFN5GssqaXCC16UKWjqSbhG3ilcE3J1hG2z4HqfrMvLr8Ok0g+5OTvxYKNVQGWKkOG+0zVNrA8Bk2JVvuQXzTAOSV1XINbxAOBl2drm5Ft/XxhII9U4+WpZlUTr1tqS9VkyZg0rPFMFDCudg0wZqB7xvhBXPzRyJkVsLhZXlbL60RLJTwvhFUjC3ORBxge1hBGbFOj9IvHYKX+ApzRl71yLk8bmF2PaIlzGIxBLSudZj3GlrLFSsRjprq3kK3BlcykcXMDVHIsqjYLXsX1klDItZxO3aB1SPaqLIGXa+UVm4qo6JylmWhlmmkfVxgtFIJ5a1kiZFHLRRFqtpABp6DaJLmDSfIISYahRIle0AkuPMnE/iUEkjLp0oa0jNVt5JbVYJogV9siwEbhiQxBUaBFqwprhjLj9Hway0mRjJmoX4AgreemQBg4xeEALBQZ7XLEahMoDjwcgHx6u9A//5v/8O47519I83sK4ZGmRd4CeePQc00ZPPPkoL1GePnGSwToDRPpE12FYiU9YQ2wRnHNtoNqu8L1SYUNmdRmtiaGtlbcE61jhHyEqrUYLFGVKKzqNwTiV+2LXkWhkVNgiDwVdU9ilERBpm7xr/4Cu2ae2WW6SLtOLluvUWiZeX0lw7sbzCAR5tjjzhMTRXJDINpOhWFJNPoMZGmfWhvUwhufwDQhNOSgxSjDRRlttz7LdXPoTslRZklT4YEHUYEQjJfUdDQTFFlP7WqhIiyvM6CPY5oKWNMRcGQhB5d066nidH5cIKMk/40UQTM+gjmZ0gY9n8pKrdl7DBo9njo1te2y/8UDJ5w73Fjv0PYd8dV0epT6oBCC2HpASl/KjvlfwJ0awJX/mg3lRFab8ee/tRJIvBYhxXSOkeeaIGgR0wKBYlfkgTWvGbmBKhA4aC4PfOex/8Nbbl99/l4/Q8/0+lUK4Pvt+glO0uHIOpchbBpx3PP/iM88hkbfwz507u3zmFC8ogV8PWUXLmRoch9zvbw7662cfQZnr7/zLTz5647FnXyTMLZ8Bgy2SsKb8KHC6YLTlWY6nOxRjCmmn32Was89R9rs7vf5CnxOnY3ipYSLc7QmRY/RWSUV5xUtUcI0nRthLk7imNjJG2gv6OlOPD6TgfvuLsR8odm7GYQJ+qjTNvc1dHozv3N1gtnSn2+PiQ/BhurLUWT12nCU9prwxb3txfX6Ow5EPmAU7duxEt8tx1fubmztk/LPPbvzud+9/73vfY/VHfQwieaAly7TMGg+31o4fY7MY54uQCzwXuLKTQylcZluYjiHj7BvlWH7DwUJEeD6DTE3cYwZa9XtbG598conPx3GSxssvv8ixNCDiICQhAr9gS3unta72f7FDlplfZu4G0YPNna7O/ua94z30XNxr797c+uTae5ranlvk66ZLnTVRaX4B5VVfokTQRxqrpvPxz8UFpoFQjMTos5REoOh6u3v35m5xIvDiyvpgb5tN6Avzvc7KCQ2dCNqAK39YaLXlvbEPUfDMGYa+PeIVHUSrwaN+GoGzjP1o0ICraVFllkpjRA3c/5Fu1ZI38hO3wPmrbUu83PK+gJyH0RCFFYfOyix4CKfSLLYff+rpJ555lk2CQFihiYqyyzQN6x8shANh9zHVTaG7R00hqGOKvmmBU6RzgebGSzaPRkgVGodUvyBQtHIHcWQHu8rW1k6dOXnx4kVmnTl/hkM2gFAtcEs8isByzgHfq6DHyfli8C0+obm9IgMMdloVf8Covf2+RGiIeqCRcatxX5IHRZiqiXvqmeyS5SucGBW5cqoU1o8f4x2Ofn+kblboI9Go0iMQ35BzkqLbx7fUKPX2+IQAUmg+8EX3RJTXIet2NHq9mGj2DtxlPofJPE6c+Pw6K5D9/r974eKr7N29evPg4Hp7Y4cP7G7s68sBe/HNLT7Wh6mZT9Dptmpb9PBhr+PqSFIx9kCCz61fYbVEmlT5f/QgPl7DIwpDbLHgwMSfraW78ZGhMe3nmSpkhYMHh+oioX7iEY66SbWwOnVBIzGlyrqef+jrPN9CKyorQi9Php2QT77WHW/06ydsuN7VLNbjSyu3Wu33w/BKigxzdQlajwA3L/Jhur0ISoupcCmnV1m0D6C12FFjHt7OOAF8DYyg0nAPxNYiX+c4Tqy/tanDWIBT8HREelEq1IyLkZN0CkrPZlWe1e9rCGDcppI5H5LrbTQ4AQycO2MjSw2Er4rJGyksHLJN40ZnoJdBBzrGYL6NNH1dVdtUW3yL9fRxvWnBWgBULl+uQPx+PwNgqaVebWFudYUBaLetl/asiT3QO53TZhEJF5/QQvH0bKKXA3EGBnUHzCgSnz/s84diZIsGkjaPJlrPLGEG5876YC/xjASN9GWoYK8hqrrpIguzKDXtOpd0Aun5uz661bB+aOoQljiLTd2uWjfREGrbBpFFwz2lZ568wEZ9LSICTZ7TQOMWI0S9UMSp970WzCRdJSMLQlhE3JfJbMxFxq+8hsN7cHSHH116i1OrcCXW7lrnGPhOCLa/r6EMtSTcJXIIvKhIN8qLh+/d+5SqYngxAYaIFmwC/xpkKstKA6Mo4Ko21egPHLdpsGgtaBqXDb52ffMmw8G0Q2oKnrWtEw1x2dfwUjBYns+gs7TVvXcXBOMPOpqKdTxdo6ISpxOiEsKQX93G2lGKizoFy+Ib2oUnEikCKlFc4wk2Wjhj2iv3ujvcqnVNyPI2OBpHUOCRaog/bkA9F1pULeNwWyLRD45MQB+20mOPOddX8aEuVkKJ19Yb764ayLpNj4VECXXjOQ1S18k6HnmPPqpW0vrg+MUy8K3j5ZbyibiuKcRgfa61xa01c+cdmaadVnn5VdalaNqOLx5QH+emLm9ntvk31bJcZBn88L/kC2awJVCmMOKaMht+YVF2kQQfl5b1KQjU6MJNOa4CcERVgiI1LugAHPuDYxp+C0/zKAlxG3yqBFMZkMD1TRVPfCrIjGhWZwaK7ObkhsKzaMbSChNS6nhBbACROVFcRhu3R+KUESTFZjeEa2oOisgqUqgq2Kxo4A/5jasKpOY5jjCL+2ia+GQ/xIV++9tf8Y2ehXjdh+aXvW+PPn6Ko/GYgOZR7dTpY1C7VkKokLkRrbWySgDrZirj3ue3ZKdE7kNwv2Trdj+sYXqNXzIyTJ4Zg7Z4Fx5C9n2dSDQjaSL+lwis8whbKU0ITyAu33abnBaEUjnrkT6iKmycMM6Se4giLhkRkxBNpPghkBRSy5ZaM4BCi2AE9OTBU1dPNxS+UyJQkWLaKSjaqEUgpyudFdnBkx0xQ2by4dC+YmH9QSgaVon3j1ol+k6jYlIg6MBb5kDMMzNPZXF/pvfDGFcVERZHJHyAXwUeyPkCk5J4emeH4EKLCc1f/fpXfGLuQLPPeuwYkaaMwJ7nlyEcGJ8vfeuttzg44rnXX2NvbDesiiCCznBkTwbvUsj3NKYnsLVtb26elgfnYEz429/+lg8Yrp99inZp2S/Ih1Z6rNOkp0ZQmI6BH/MODLbn+Kje4SHToxoKxriOyU8w8ycH4zEsnrFAC/2VR8etAooByS9+KkvAKReOuoVnV3s0tTkO0cgllQkyyHXOxt279za7mkHTvFl8vFLKuShlT5ChZcwPLSblevPWNewps3S7ZByGIMCNDeacsX/u3CkpJgUVEMpgAfVgAS0zaCCrRmYXAscZMST2UHuQoDO7/QBiHCbauIUJPAng89U4DkmAOV0A5yyhScKM1sA5NTJn65LElmTQpAyfetvaI/v9vr4Btbd7jbJDN5gvt1fQx0N1bglwRVbe9KaJHgzElW8gYre4hXdYLDJluaiKbdfmtQ+a7axgrh7YgKoysBXNnAoFfSLevBTOzYS4r1MdD7WT3YkTQPR1IocCFDnNE3W5gL6CSK3wV8D+i7K086jaYDQtaaUFEpq3YkNNl0T91Q7Z1gmOYH7llVdIpQqDTzlS97c37lGRcC2mpAGSxJsEBPyB66CnxSf8CymqGOFpTGBx5jvevnZMJ8bizOzBZ7qZJRNOV5I7hbdrIige1SUr16+JpVabmnjR/wvaKLS1zln80Th+iTocTeD9sYpNSgSa1LbOz7GVgYKgQS6MvngW4EDR4ySUO7UezpRjp71ECdKP0/h4ArpILBHc5i9+/OOnTp1RY7izAzKs2I9/a5M3bQ7KBDT4nobTQRp4qRwnPT6XCHCmdbl6mROHIsmfH6REgbPmxpUg6V9pcxDZC+NbjOsdGWJxTm2sa2JgTb1AHhzUfkZEVXeInRinbIBAheUtpblDTaFym7I5JDhSzITQYil3LorH+DlNa3KDGRe0yIpRJSvKmsld6jXzG5SjJRFRBzpFkyCfpVJokhAkkgMWpnQlE7mUjKA/gS4JCJhc8f+tjU3UgysQ2MY2/DM1H0jsIUSKJsRBXl5ewUs5Jtz4Ao7l0ZzFZCyQ5IzAlmA7EAEROKl0VEKYQDrkBb4UC0CRYibAgBdgoSmphtRjwoIj2rG8NAhr5NnxQuhIuR2nskHG4eMQmDhrhVuJjCOPQ2pyGTDMPo4GZHGus7TfWjxzsPj+m+/svvXRKV61G8x1P/v8wsJjdF23Viio+b0VFYFWTmKHMldPB6dVCCY007oPgyxV+wOao7m57bn29v7cE9/4yyu7P+t/fglGe4uqtHgU8XacQujSTyVMGhtHdFXXCp+2pjR56FI6nqLyZpmF58AoVa9oKaWEeIxEHQCGsg5NPG3vSQ2psMsaFxnRYeew1guVMpX0TztwxaO1r4nC/bmTXN994zc/+tGP+tFvD2JisQzWSYVQV5lqJIh3bsTrBHf/+8zgtlu35w5v37qpdc0cDvIwPgNgLX3y+E5xy0py00SndNAuckJowuQmUfM1rROsUqINfDEEc6g/iPFAaFDsBQNHTqx5SFpCcd2Ldt66pQGNmhSKDflYVnrGBsRYh1PRKrBiZ3FcKWyBQgq/gg914E4h+ZsSR0JqrIEFSeXokpt3okW8okOUBQnFTVJwzkDlGpp0SzbUpWqJ1DwEj3jKby41wT2RUclKTFLZWZOoLf2RV0tEEThsZKGt5MxG2fmwx/WJvQ3OxqGfovCDf0ihn1BBhvFkHrLEPwX5pFDCkgkmOBT6cRjRM+Urp8G0CclJwKUDpzS+c+MOwP6iHuH22ETH4+iiVt46epFOQCzmKx/wJiKQBjTiFM9ioV4AIyFL0K/0bHGYIpjqg+Ejr/F4i4hQouDoVZCoYFC+ppbBspzTGqOOB0n2eclNvhdlkfmN/MrWIbBo4uSk1gju9JsxHaahpvPjRpJr4qFYXCEU06Np9kyR1fHxnDJTUfNu2NI9VgE2IuVWHLAzPToqxPCNq1MXox1jqx4oXohivb+WqLh6De24IFr5aK6AsMaZa4/VUGREbVf/qEHqOxRkDtVMIdIa5bAa5+zzVhhLrZ9c/ejYqdUXv/MK+4NeePQ0I8vjK6d5kKMtUuF6GGo5QZ56OXGXkKKDa4sosla2ea6RJlCr4oBxchQ1I8d1uyutCYLDsOFmkZQuLs1SvgW5RCg+t/M11XicM4jl1Rg5igmxlVeR1+Fd9h/ziPqidiZlyN7iKxj1rTShOg/zPdSi6D8EfXmxXCDhBqNs7fj4nV4hchY5NJCGKxSy2ZnpPYwdwLRCTJ4B5PPwXA857I4BLqOY3GOmEk8ZrL1YUm1QK5NaJwYl8k8wD9oDzhFN/s2snvhHc3fQ0j41vy3EVXxSCE8T5LAXlYnzB0nRdzgWF/ptllQOVuh0te9DjTAXKubcIfv+IQndVFv1ktlKZ4kJ1p3BPl+RV44GmiLkE2hLSyu9Hb20y7qxOBQ7BkiXFFQnNGoadjRDP884+oUDfIjgCc4E8eCsVEYUDHhoGCSs8jd/NUSYQ/6BP/GSlUSMNt3IYFCGl5pvcGZgEpVCzyc8HfE8D5asF5UlNsZS8zWsWWotDfYGn3364RtvvLG/e3uogr3Fi9xZW5nosEP5Q8h1J6Tvbty7tnHvNx9+eOHRR/lGmV6dZo9Td3++f4JnrENeFQxylTJfXWH/9c7m1U+21k6sMUvb6e/ub2z399k2dOzg5Bl1qzHjwMyDROTuNQbMmoZA/xPt/tKpzla7t9dbl5FjxM7be+Dv9zyqkT8w/FJq5MIj09h1r2GDA/oQRHXY6vb1PQYk9xDbPzi5vNJqryy0linFzZ099m/euneXidGNzU1o1VVH7y7++Jwcea69tMRr/qTi5hQMccZTB4edd9+7tH7sNAccvfveR6oK+rRSa3N76//4//6n/+1/+7/jjpwdAlU5cIM41FFMXCggKQmQgKq+Fkhpwvr7Ay9seJcu9XkfLReX2qtrC+0OZ198/Mnlu5sbfGD2a6++QtbgJdu02L/VwrYoBU9cA2tqOoa9XfpqIYntVrtz+tT66VNnsbOyG2F17QKa0AEDkWYp4IRSFRTBow9abjMa0vLA3Vt3SYosRHaCjm/nyOep0r3+3ub2YW/QXlprdzobm7cP546tLB/DUdEE3TgXkKlOT0ZH3VZro0JOouufZK4KZAoBbLFxstGMqKY42Ft4j0gZjVoGh2ACz9KfSeI4z0qB+0SjhUw4YaJJ2Yp0+8A0duhckuoeR3v0RoPxwlK5PkRrYCz4UIjE7WkIpey52rKUOknWhLmlIWNcLt9kPfUUq9daQp7Kkc8PttWas9p0+uyZQFcrZM/BdxLbzCmeCCiEeDYJoeyEA8do2pialSFCMx1dgepMcNbzgks86RXJVkaVNumlGm2ExDZh8zNs5/H/klrbuY4XhMKgTnV7m0yRns6SnbMiLpZEXb9EMjqoGEEL7KGHF9F1pFYDuCcukx1SG5nQKw/KSimlyd/3aLh8nLfn1rfv3msdLIO339KMIUuOSNxWc5jqxbJb8Cg6jw2sw3I8VW11d6NdCX+jEY4zoNkj8f2vf5P4L976FdelU8fVke3u8SlXtwMnt9Uar/fZ+Tf35ObcYxdO/btvvHL6dOf07uZga9Da6XVpuA/1dYG5PXaGHi5HITMPQuArfbrGNyFggsMQaJlpEonAH572d7/Div1JpRaR2gGLM13VX9BbyGMP9uODCvia5nPU51L74N9OdrM3yhoOdd3MMP0y5OeaS7eyecDtXS4uezXfLQgyU5R1IL48oHqRWyRLlx1cc0WSy8V8enOaqT9cFIelgXZSvrQ1eOkb3/r0l/9Mpzc3dxzaw3XlaI93dYaczSiuceGRQpgw5bMfGm8TyIVGmzoVpM9EE9ZbaXFGcuze5bgnUsk1Kw1+7oCSp+25A20XWDt2khfU7l2/ARfMDt8QkjyKG9uZpSwn5Sd62U09NCGebpxHaUV9jiuWYTysb0Kkeo8SGm/4bc7QKdp65Eb5kgmaCDojODxy5iy9eJ/3n7a21w+XGYPhw/u8JDTglTt9Sm17jw+ubjOBLgUihHqShBeFU4WGC/oE6/xyh6E3+qiwQhnUUIgZLQ0vFJwXeZfvQYanFvZZpAmrMKglsL4NhB36WE8MNfU8UBMWOMEq+ZjzbbAW7/VUqxDtnRLhb+NI59H6b+8FHvkSVQyBzJI7acuEn0NuWbmLUUFAXTvMvx0tA2VhfAq5FIohXOEoWVGmWYwE1C2kbqPPKlT3jWDPkoUmcrJ0E+z7RIUqkT0uKqms2TiNTmejCbuzuc17QCykgMFncLiy8M4DOXqM04wDqQMJjQoy+tBLg/W1r33t8p1rMkE2/TjPI0JquzheQxpMsMUE7UeRZK+MJPxJ+S0UfFycV6VOP/E0Tsw8YbJ1JBfCoSkK2cyICVFBGXF1Mn6lVWIQupUBAUB7ZuIQQyDHabNI1XO1OjzFXTxRyblVMWQqpWYPJyp3jp8JFzuxch04Ofv21KDK1oO5Rs/IpXUN1w9xMu84dyGLcILxAz7UZNy2tNU1juNZMQilQyOA46lzw03iY+ZrTFMmhjGgL1NgAQyLJvbJ+QV3414xMgcrgv1nheCmSYMY4+JiPEJDTq49qhinNfNSIgmBtljNK54yIfvjTI4CsUnRBM6wRTc+VQ9hL57yd2NjTzc+Fdj2Vxiyg4FDv8wVGykvUV5kqwhljwBw3gPWNYU4Aih2bxkQE9qKQpXwwywJPX7cqxGNoU9KAd9+WMTNjIg//0Wsee+hAZ19ezI49kOwpnEDnx0H01JnwwtbC52EPNmNJkMn0RtWBHFbZDmP5bYRKbd1Fws5VEdp2m23ohFEhSFA64NNAda6EY+QaplIwvKFtkTgj2cW/uMRMOnsgDPvQAHxJRZmhZ549Dw7R04u6lywxUM9Kmgkkpu4+B3aZ5yn0LM+RIoyEzG/XGBSMjO1dCuTYQ/zW7LzMMQzaSjxhg/MRP9vkIhJk1Wj4O3VGES+GhVfrqlSDshievhH0dm1j1So6vxM81VLx//Ek/2R9AXh+L6WEUJoYYbC9C3dhC3sW5k6DK7Z56qOk8oDJZi0jQz0aVG5UjASHT0OfABujSo85BBiYQLEAcL6NoOP9GtC9OMhQdP5EaDkF7ZlcMbtbHYqkok40Xc7CYMQ8TCU7YY8tNMaqGTzuwu0HrYMlqNDYdbvnXfe+fS9dzBj1WVlRcbETVYg0D+7do0/ouunnkYiexoFDg7JEzwyUo51MjK68Uou+x939wZM7x5cuw5zMOndYuubPtxCMQHhykahUo4nzp9n3M6Uq5SJR0Q+rqoHUb9vr42TPBNp/5RmZuM79ehzMNBeZkIopac358VXmWVhgQ/sIXSlvYx6G9s72vW8s0VDyqEbykf2CpiorsApJhpgyCNG5qZMM7dOezs/z2vgB4wiOOYIha9du8aJtVJgfo78/uxnP+OsUjKLV3C1AiKOOuhcw5NUA7lyC9yCBMzSC0KJYCuesskLBFciQKWN6s89B44eVvPjDBG8yoS2jjRk5YZJ34DqlhCyyI4xfU1JoRj88y2/muoDxyOErW3tXzNCgwNAhiFgAndQ79ZewOSsNWmJohPbFGKZAbXxhKIAUiAvtzMiDTRopEwQwAS5NZdxyETOoAGH1ql+EpmI+QWBDeW/ILf7kv+Bxd1Xn3EEl7uuOGUOKtDwB64UCl7ta05XicuJpwdzmJ5+/xQrcF88KwZywawJS0acWqMV/K8oYlnFpYvoEmnIRVV2gPLhEN6uCPPayBooxO0wg4UQVkCNTF2++PhFaHmpgjdgrly5RivhfRTeUs1mUiD0X+CzUx5SvVITTaLbAfUU/bl2e/HVrz35jW984/iWDuTlXRaaHXai0v53ow3j3BYU2I/pvNiIx6hDbY7VIAJnmh1lPE6dhglJuBDB9ZoOyWYRibod+RKpDJGId+KdFe4khbnXaJR0nWAAUCYHdJjlnUEUxptMDrSklohR43ayKsZM+Kk34U6WQv/vfOc7dFINd418TeZWxI2wrfQFTntuVWscxcNoBhYPBJkhCj37jOdMIxcOlbSjRsnRtPwos2FYyhqvsPL03QDxQLpX5IJD3P0dyjCcob/9+te/Phd9PbM30BZVnEFIAGpE6kbgvgVf6KdH4Gzmvk5HTCkF2Xms8Y/CoYEzdlvzG5HIja2dSHxTjWSmlcUEjhWo+EwFS9GGbuMIBWLMcYMUhEbkiJwX5/r73a2dux9/cPfG5fm+jqCaW9S25627N9uPnVtgR01uMrzbl9EXVrDfDH2nCNfGjBFwd76zfvbRJ7/x3V/96len0qqFxvN1GG9dtCikvA40wRVTCrEFQZI1tmXxhTEzXSbz3WpAU7kcpD2eUiDwmO4KSbGT1xJJA1s08m81iPyUa72ua/xFDrzlgeFQ79HMd45/+vEH5x5/vMNBCXxQhQq2oOpf1yLrMjJqC0bKTYSkq29CE6IlNYOHv1VSLNhEU249U9vgAhIPibAyiockLaERIm7R9cq8m5fGUn1xndz2BOdKby/meVY7qZOmuIfTwcny1XQQNkc/lXVYQkxNrIdeVIz/JSalRwL5qlRISQApHV8LNoxLFgrQEZIiEl6RbQVk4tpIICeZuRcQNSBzCVbDSzSYTR3diubdlEmuaMbXkaJ2MFnA0yN9OwVJlPc8sRjPikMx02O2cK0bdshZzmRjU+Q5YcovO93JU3rgEg6r3bxm21rSGYvURETst1hZ5KopjIO5mMKrmOXpfuUd3crIWDUR1qpYhGIZtxEcUzWtY900ga917oiXwPDLvr1KM0Ev2NaDOl1hXLWHkVkXI+M8jmApxWMqnGdE4oggiSt6StWIGJk4a6Pg0FMSr0MQgjW2wz2Q3E/X+I5DYBG8h5lkudJOQA1LjjhasR71MJ7+nVr5GDwRwdeSLUj2YeFJmdIzCUIUl284v0lqZDqSkstDpPkaw8dVg1FIjlZW9qNcxTCiqehpEoYSx1gIM1VSpaEt1+iDcjVXf6wE+IQeWWZoH3mSChSgpgpwSP2TzuK2MM9LqRTB+pMXGb0dW11hnMRUHFLaoSI01o0rHi1bQaZM+TduykWqKfg3YzqvkVDXtYwcCQ9wQXMb4QFowjKF6ogc1LxWsjCYe7HcC0j+eE9dDDRRvXF8m3ci8lcBdC2uORcFch89HARjaDCjIuoYOwU20qgdUDfGFl2ZtE/VafGiB3FchCvTxIJ7p6RLOQ6yCPp0wQ7wY+On7kOK9lbjwtRQVVLglJKk6EvxTI/RALHxlN3XsOaFJojClAKH94uPONFMWWf05I7Di+X64Go3tOaTPYMGTnJLHhrZyoQkrW4e8oC6tLy0vrzEo+oxnjMHcTYf3U975Zj3XCMlJIa80QvyolYJWvmYmi+SAtdGVNbzn5EhRD/OY9R7PAIxmjtIE38Y3OTwSaMItK/HKMG6eYmdUE2gCBM381RBxQaLO/dugJwerYnFVCM484sLnKXZOVz4/NNrl9996/atWygHuOQ0iZDaKi+VmOIx9+fsJgwZ3EeBqRygj++mbLHdFY9aXT7/2GPPvPoyZ1C4jnz42zfZvza3+TmYve0tlgU465POZX9vEzGVSRN3/4RonaNCNqPLwxePqZ9r7zNBubTMKcbsq9TDIVvd9drhfIdU/htZGs4/yZif0bREcLpkv8/n9Zid6O3tkgo5fHivnwjppG5vbN++d/vzW7eZBoWM0ydkUrbzS8s5fQFCHqg4f7JH7I/DzqGqypoIuf7ww8uddufq1c9On77ER16ZZOELbaDRpzKr8sbv3jl55pFvfOs1dB7s76niVLaNKgknPeiitmLB1hH8X7cxxs/jOpWFq/TC4RI1rt1amV9Yurex9bu33+WpmK7g2eefP3biBJnQmZnx0U59zwavoJkgI6pXLk0NOJUdNnGr4wjpWTcrwzVmbNSrEjC1I2p5YlJGZ3DCLeo754Pv7Gxprzoa87oTNTrGQqzj03tosKQHKvYRdqnRB3xGZ38Vht4HfXi4locTyGKanqFjjL6sD8TyTKlhP3TcygioVGkyEsKebFjTL2Mx9Z77MQSED5sj6B9xWBU22xzhYO9l3R/0Ui4jDP/7uJGq2VWOohG5rU3jeLmSU5l1FKfB1pZJwKC0sxgy9n2I1D5nfLNPbVcAR9oxICo9K+AIfho8uJJRKkHygqQ0wrW6w59mG0k1PFhzSdkx0wDaYiWb5pbRR35HOY0kPdBN6ZprqvDSIcC3FGUDDsYDle+Q45SYudU8J6o3hXputcXbKCtrj5zutua829dzGut96g7tpMxJ717IPd5IV73kwkvCND2DtZX1Jx5/kmcupvY+/eQq+DSPvLx3vN2h3b5x9apWAA/0rHTQ7y3s99d6cwuDg/N7vFM198rK/OuvvfonrVPzN+4udtWet/b2B3xDj6VH1eUo2zgchp345JQen6vHRRy8hIXZR6nmC3VoriKBqWj4xPu3Up6KwHshYLIWCmZf7TdPbvJBPudNKu0Rq+h8bUVH6MdJv27K1RbK2WSHul64GuRWJdknKq49EfQJoS6mCcljoIyvskBivk146TaE8xEEoGr00Db0aOnokbkfrC2vnT218clnwLfiWbjfipKltCeFGLmFJLgHH2OxF5qj/Xkq448eh5XevjoCjdsyGw00Zal4NglqSgszL66dOL3QWelt3aFoGPTJmGrkCCoXPfgokD8bNfNLdb3cNiJGVl5Qg0yreEfnXoyRSo2iZ1M8HkU/0e9yUtDq0vIKB97H4rHEoymNEWeJHM5v7ewNruy++vLXnTdSkeKrIbwuxeBzodOe55sWMXKjRkSzJSWRKDvw39iCJa+AE7woC3SLqNJiaE3m2bwRTgtSGm9HanXBwuXO+vAYLSGhYWkU4S+7hCbxK4SSKkjwGfISpu7kPEcLtTWg4FYqZ9ppdaCW6GbKtTjTDX9T7oaA+8ceguT+TDOGPoHKCIwWjSW1ddU0nVzFVQtuYTswi/kUCefO5LN+QYaDh54cM82JbzuXP5xFMJom6WF41aXRHRmqFHgnjpY1HCV9gDtLiasHKFGUVPlqrxay0rPG3ByLjXwz5IUXXmDwpeHylFFXsdgDqHI01DrLde7RhCTkopWaDZWaGgoyIsbOVlw1rByWqeIGJ519I5pMFeVYclQrYKz6OkxVc40+GI9eTU2hOXAVhB+FIB3Gak7NOBkZtsk50dUs5TEskFP0O1Qm5zdDUo0uhBOqq9ROtsgmyb/iHA+lSdgQbpPljNW6HDUextAFAseJoElR5qiMggoOhTBnptbWzASpocMWXPBUcEUuPHlA9UdKQ6/CuOYh9JSL1H5GjjJQqYVj4JY74CLMyWbi1JKXIXLyoeHDDzgUK4MzT+h04hVLZoRo5agNqssRMExh4gj5VIQ3kTTwkg4gW/pEuUxAF93ANLKvsIGEVIIhjnP142i5FVmoxDYxknjO92wCHMCRPlXEt7bMmPrJ24eGC0qT1Pojzm5vQ9UINZrj9RVMEWq+TI+lJizX4jOhtjQ3xLnwla4Dhtaw5pxSc3UrPGtagKW21ghjRNluoSAcsCqjaAZJzKRg5HOnNAG9yuc8OAVPn2LjdS0Ff0oCEQTd5oY9u2GR2YwYuUCtc74d1r4M0e8oTp1y1HhDqMkAjnMmO5T4xKRamBlCXjCDlexQIDX+/yhxZ9/aEp+odsGZmFNRhc/TJsiSOAoTtT2ZmrVdTYERIx7v+rgfSU1MFgZb1xYaJpcFKcSh4jZjySuEady4Ou4RlKfAUmo4J14dt3HmUXSNLKIAkTbuZBn9Bx8aEhMqIWhjkoJHXLVvTC+yj4ZzCtAkq6djhYnXFiuii8IPEbF6XAk1Obdk09aQkrnpc2tZY06LY8qJSRiVRtV54QpnXgJl9y5dGK0BJIgOgdHiz88zF0nS7z/6iKOBD7fuUirCkA8E+3QdtgpF1YnSUzarzLLdl08LnnvicZa+OCyFQNHRW73++uuIvvL+bz67yiyD9qnxBjZX2nkh5Z7ICjeuKIwgBx+4cbignXF8uRLM1kKfto6DVsyEKzveaQwxCygclwyENhJP7uixscXGPSDH19mkrA3y8vA46Pn2bR218f477zMD0o0lFg015TNYCJXjT/LYRUwbF38yr9ptniZCB/VxzDvzCvmbb7yz1duiUefQZ2ZgOYyUnX2IwxRcIfnFL35x4TFa61M8vkJVjqRDGWUtPIQrt8h0cDxDhvCcnn7JHXUP237wgU5/Bp/pb4b0cOPpl9t8oqBK3uzhZVnRGsRx1TFJDStrgilEGC1MRFLcVOZCqg8rIDvACShE2ZHlNEWdFQWfwuMOHERwS2DXPNcDvlTFRw5bbSae0BMI/ZjpQGbcQ3llNg/za62ghLPzT4RgGY7nkn4Y/v8j0hSb3Ff5ccwGpHFrhhTxQ3CeRkIZkeTrNJwjwtH2S+HTEAfPcTtY0Dj8K9KhoVJ9e98sg3BfHJpK2ljmPX75y1/2N3fgn9ZqQpIzNS1rNCY8rfCyNbh8d4om4vjJE7w1AhwS2kn6po3Pr9OebG5vkHpnV63r0mLLm6Np2Th3+hvfeOHPn7kI/u61ba6raj8Gh920u8X5JRfwhA9TBorHAEQz00DU8LB3KD70Gsdx0ArDByDXkv1o96QVE42Ks9KVGzf2I8FHny49OGBnLyQ0rmBqHlG9hJjodnooUqajPEyK5FZ0tRTH0Zt0I5W6qaRUs5RKpl599dV3j53m811zcxoscYr77OyIZ66bxtQtsTACcQJsEcMVCeCTBJAIQUk5GMIVr7iTcUhsmJMS1wfXx0LhOZYiQBGE+IkIBRi6h0vEe9u4H2MYgnOEZxIUD7XDLff4aAKjzbXVxBlZ+sxzWMZU+I8+HDw9GG1iuuREQhgwoQDjFjuOk2T0NLgrt+OYhghhiklm085OhfkQIfgPb0Nwyc6EeapAmJA3a+zUSXmv0hVtSGykltsjohX8+0Z4aVO7Gm5duzTX2+ocLHGuob6rSENDQ7dxu7V8IfpGW93dJFdH7sM8ci1O4F3ZHnztj//yZ59+SgaWaaZwyqk7HMVWlY8NDjHNx9fn2QAR24AY5ap+8rAXV6aJ+bYlg8m0XJEU8+Ni7IH2iqK5eRmEN0nwIXRSPQ8pWtrTjLP2RXJIEbWOppI4C8U0K95D0ZrfAHd/bgX4pffevnjhkYXjp2hwWf8DDo5oI/gFFsdnXAu+h55oAsRdVGr4wnzjDpf3Moh3wow6gQuKZzRonh3y51ttgbRuY4Xsrckp1XyklaKAowRYaXorHsokR7kzGWnhrGFD6ZDKyqzjqsderAualllDO21KcU5ZO9UTi5yCRysZLZhX5F8gWli5nqQd1iHMXBNCZKV0LUpKpZdkg2bfyPdWcohkr85l3oQnqvgZrbHKdvLuZAKXTEBt9GBGIosbiz7pkccTTMZePJHwBwZex59LZETT4N+8pFxbTUupbOLM57wEbXWDCwgEvqSlkqMv6R+uUbRaM9fUHbWT876F6VOeXRPtG/UJcQxhQmnpLC+1SrB3VsLqthHiQu3YUQV2hIweHmhQQmPlVMOjhBc/9nxz5hrtkDJhtBIpJD4jmzoFAjvvcA/KJvEcYSwg5La7RnWSwd2wIEqhl0iRMh5JIoqsCoMk5ikKwDny0wpJBJTkmsZ8kXx8/Rj4KEiSU10LGA0CH/S6DFnYraCvq2eJcCgFYFmhtoY+tp4GqCqjuI8yqleMzcffa0MXYWIumGKVLCLVdDUkNNZqsX3eHOlCiqCkkMLV8TpSmzcQNYQFQa05+KGpspRnzY7FfNbK2irjIWbTeBhgjE7gDA6680WtSOzJm3Xshqj1CfnIrVREdw3moh2Dp0JkxtHRq5RJiRNwYDWKPu1u6DzjGKkG5TprPQuavWK8pygI5JdsltsZkci18y61zZkIN+bvMq05qKEn2FEiwVlOSjox9SM13R8u7uyXMVwRXNvNqbIS5c5fzq83tuJkBOHru03aEbOgydKFw0FvrkeH3GF9Y35f03ns89G1yq/tkGQxUND4QmMGIIcDfaS7Mil1GVNDQWslPO2AlAFddhpjELiP+qFxD0HyVNWi9YOkra1Tsd2GPYkoIvpEo/UfPF74B9pSqpzSdKwsrSx3lncP23QziGZUw9TnyprmJYmHOOlEJNkw942SK7BSiRtz4tXkTiJeh6QcluiPPL/NZjguZZp4VISzs7F/uMhE5927W2Rtda0DHANhAeoebSjLUyrTre333nzzs0u/twg1AthwvzcXH0iM9iaJup+GnLhNBxijnv359bNnn3nmOaaeD0+cRApCUYlyxcJYg2+Azx8/8eJ3Hn/04uP/8T/+x8N7l+b2+9gUHZgUxvCcYwiaVYoCVBJMuNK47e524cOAQY+oC7tgMqsQ+Mp3f+6AbzbYAwGKjSZPtbuZLDBfyRWVCLwnRIAtEH9Pb21tha4eo7F9hIl7Ojt2S8OBzgQs9FcW66YL5shEO/6sb+yABqXH3Cibj1tUnbnVE8e++yff+clPfkLOeNzY2Lmzcqyzr48aoryONuZE8O6d2//8z7/8N//m36CxZlqV4/DDiNj4JDniJOKRPWF6h6lbLdMWCGJW1k9vbnd/9+57t+9tLC2vPvv8c2fOP9bjbcHFto7nXNQjEDt8WWdlf5+YxwYz+Kub07YTVR6JyUFKRnCB8kiRU1TEEMqy9E3ppWN5HCjAd/pdbXaOonR/FM/yvE+mwqV751UBTtCMqkbFOzzo7WnPXEdbDjfu3eIea2qQsMibPHBdGOwftBc5l1kKogR5R4rsQcg9CJBiqKLntIgySoln9xObiIvZMJdN6tRWNMFf8n2tVWRzFn8j1CSzsMfSTF5LqeNj6BMA8sNJFrN/jhAEGvyHTpYJKYokt0Ac4Voi8MqpjpDrcIcheCgO554EBuGhbTVkXsXgdhSL4TnUFOhUX6JxSVSVB1ZcU/TLVbXB32o8kIiLJ47Tcv7oG1+/sPr/+PXf/M3777+/f3UTtn5+d+Fsd2B5qOrPNdoZf/OAjnCnf3hjc5d6zdTm1r2Nxx57TOdsxGcjOUt3b3tn6fhxtcT7g62Ne+oiBnydkpey98/fvntsfeV/eempZx59/Py9bTgf39Ohc3P7A70iQRHQFvCf1oVbhuJs6/J8AcykCDui1VZo12hkW8ZfYDeq7hVnjY6mnyGC2hUFsEUlGqabadcOewxceNsyGj3aPo0reP2H6WedbsS7hnKDXsyZqI2DJOYB3EMFYOJFLpFDtGb5pvxOqEclrURGt7HZICQS0TUei4zrusfsELctvl+DK8ZmFW9VObGppb4/f/xC68P3Nzf14V8mh+gdt1emTJ7m+oigKATqtqxHv+6OWD04r/lzDJVGYdoBaTXKNQi5qBDRhh8acWZrOf+SPqlGYxjgGgRmgT9cBA0h1HUKKyMgDmssLbZPrB/jCw+QoGevt0eesAwDzz0+TdwfbG3t7N699e7v3v72d78e+shfIoIXxbeiGIQuteO7JgecQk5SHOCp7lSKaDirXFsoqVjJOS2R4AZmWC9yb7ux2SiXQCprdAS5eF1tK+IgF4iNaE/AqaFSk1kF4YefFHDQCpP5xtC29t5EWfhXnIZCDSRfR/LqwK4ZYqia7Yx4TVWjTYMX49fIDx3X6JPxJcvpFCTuYkaWzSJe+9yFIq9EZggbx6HFkRGjmv3whz/8u7/7OzxV/I9qH3n/NFvM0IQkKTNFStIzVyrxVxXLWmXvQnO6waEU69Hv8RXUp775HTLC12SOotu4WQqkREJKCPYlar6rZy1iFD+I0N3IQ0WHMddP31fDmSFCI5ZkZbtxC3NfjWkEX5UUJq4VqBEgKU0DOASGrVz5L25xrTW0iGl6TqiKuaRMWK4quGFISEWQp66G6THQr2+J14zdQRaY9A9VnIlwtIJeIomfkHNQPyyXG0LqeMEEhxBtt4rB1QdIZjP5VzQ5pbDKgC/zV4JCVJHiiM2b1/mdR11LboUWKhrf8BFWYatECVJlOmfAyOOZMWad2qCtpRBvpI4zBGIXSpmyYmP6gEYjMJH8SwGiKmOawir7j3zb6tVZdqd768bNwK/8PzpjfyiWASt7ylZjtzWjW++DAz8bJDrXKFxzrqUY4mspu6HlA0RqZpV4ckvINTe15ED0YrFyIWXN0xGSBHqQIAGB76uLjKkTvjK/fvwYE+6M8BCBJVmQp+8BN1HErMFAEwoMKYaaF+FCG9aqAv5vEJFhjya2FEGJmK5xO87MCDUa2ecWTK78ZDOPk45ATDICqoq4Ab/v7RGFzuZjN56NQ2pCU4555hj2+/aowiSsEfNNfkhmRooQe0hT3sNu0zQ3DtyI+Noo2ZqQOAEFePhDKeKh3JAi0qlNgiy2dAa0pgZFonFX+K/zk2idF64e7IFJCyag3ptS8DMDjQO1hkfoEPclX1CMkGwVvIn7FgWIcAXsa5HduC1wRyBrQDJcfMxzb7enk4vvbdMm7HU3yODq+hoPSFgPCGg3bty48s7bG3fvJlvY/+GsZ6Hgn+w/UZSAzkUkC3/95El2+z7+5NMcMcwzGrLY5QscI4NpZOwPnIEmNmE/Mu3z7bsfwwgLqdzTSGbYngOxKVyCL7/8MueoEAcjIC0mWDnwGeackIEsH69x7/Ydb0eiGZQ5chgpYj+2hfY8FPDLMAQ1UAEbFAPjLMzoa8I7B7kZJQagOCa1B4W4PdAuLTabI5DscEU99H/66acvXbr06eVPgFAozlHmhyz5wO9+97unnnrq299+DYR2R4+1RsO5bRZuwQRufF8RamDh1ohgJYqeXYS890mc7c9sVDSrwqeQWCKCC6SO5NKR5YlDniEj+NYHVtTQIFdpAiTw5GWG+MBypx0lyIdO+UqunsUAJgUyPnAMSHeFSVlPZVqKEzIQypfrwPTGCYrYwxL4T9O8sFXdr0KZgpFyrqSRKfJTICN5q2hLNBtBAPEfekpBmRUZJZ+F+dWlFft8ERENJo1bODeMfxRZ40ww10PwOYqsgiNHmhJImlI5phAEOKhSszwLr0oj49PUmAavqB84ap61Kz4QCyomNZTw2muvff2xxz744IOf/O//b67OOKwoMkLwb9oWIIuDtMxkeX1t6cknn7x9+zYvXieVeAui1WKBkF3SrG/y5vreQRcSv7jxxMVHOfH5iZ6Oh2ZHKiKQBaHOcoygXNBMRKChcOEJAQ5hYRc1tyCqCVfpRo/BZlVNJkpbHuDhTFIJAAXJrTEM+9FPsaZJ3JN/dBrEdaoRs9LR6tBiEoeW0HCxAnfqV3FFmZCb7E+Wx1s22yGhVSoqg/3+0rHj7IP+9ZXLzJXxbg9oqG22R1eYsZY9gQ6ajmk+7+GqOdQ8iaMnivl9F37r1EIFcCK8IDxEZGKhAMQxGLrwwMUT5YFO5ZIRytACDdn1fNDTtyh4D4vvPXS7L5BrFAAtL8rKdHRbBBZM7LcTNSw6CCcKDP5gcjWVIpmyWCDhyKNHQkEo0HFISXJEuatAxk/XCl6ikVRTpJSSEe6HQqscjXLQ3QQugeRaWfCPGBkKrQgmAqt0qVprXic9RHyx0929+8ml1l5Xjxwx17GvrzzPLy/09m5dX3vuRVa+0rocxasSLoU7FDdDIS2ZMddwuL+7vbd0/tknvrdw5xc/weeGxJNi3o04iBX7dgymafHwIR28rH9R5fADzhHUEUM0DCo3HhTELKhcVGnGyyISaLhYxOAOUqviHYI8R8rAXhYJV8CvRR1D84W5LVre/bnVG59dOXf3eb4wsKmXTqg5WmO0EayJBQ6vuZGt0FJicikvncQkQxLrn2jyavPWcbOACCZWObRJnPN3YNOtfsxzUgmSmFZvAsfcCmURGpaO+qfNIFhG8zZirA1bibuloJLqfGQvUECQArHczpqqOrDCvxEBf8itSjOfDFC5JOuN5YgWMaPl31So4pyplJQEFeUzOoCGDv6uaFI7fMwW0xJvMwyzxkNCJEpbBLsdLBDZs4TwMVbMaLj1xSAUiF1Xi0sdPgRfEWoFW6m5MY0cVXwKw4g0cjGaePS7YY5iJBNbgbC/zv4biq6yo/x6V6z9IdSQ1SISrlI97sZ26DCQVpuVqmkBeciIbbGKNa4ECYAGB/EhxJJZIDat33tgaGQ0k/vaKPP03VhpRfsifLghUCUeazwyeRUaOlQpX05UeQmBRIr1AyAj2C6j9VTwNIaqbOuWjRZYExPaSsayGXS8n73PECAaMOQ4a8OrF9VtbreruaCly8BL7kanMqspFq4iqYhT6SsXJYRtEQ6ILeYyIMqpTOVCU+w5VImCqGsBeYWKNly0Fl21lsfay+dPnGbuQ8L1HiC7F0TONw10HXYCfIhNHNhpBtwne4pkNChfdVUdTfXdBByXQrQVkyhmwSwOa4ptskGUxhRu9nO2G8IUWpu9RMjajPa26FHwS6QkydPoK61JhlpKvvtKftHki/C9r4a5BpUalqXFXhgbzbmu3Q9fQTGaKE5nxStJ0pBk0ONLbYGmaSMe14JXlJoLJZVdjBYGbGvVDuhDjpb23qPY06ytRyBHewUTAs1P1Cp5grXtV4u4ehNJ3h8lDpwHO9TS4x0Q1S7sh++Kp4OmsgnoRoWmOOfbLTY/L3pDNR+gBkaV0miQz8jo2KKvJKALPlmx5k6bfGXJ0BYVSa39baTbrijvF5WZ9/Z4uNMnB4+zJnX8JCTbO/c0GX3zNqO4U2dO7+/sXf/kClOi/e2byj/WoZ+LCHG292gyN99aN18xIAjqL8iOy5dJxsPDc49d5OPbj194kmezgwUtgPV7vKO7352L3WSQUbDxEUsQyLXOnhgMrnz0Tm/jBuSS5ZHpQPgeD9sC6pw88kDw3NzlSx/BgYB4TUbs6auS8+3Y276ySO5W11dIZQB+eLgkND2eafnh+vWbo0VAezosEfcI+A0g/ojIh9Te6oomuBjNG7WEyQVNq0YDRGKUnvp+BYaFC/rqILZCFtKV01gmOTjsvvjSM3fv3WAG1i8R8cYembaN99miT67n+j/7+c9Pnz3F9L1OCZetpSHGhpUi4UVwJkIYxDAptgnyWsJqIKg2uWeXs9MVHAzmF5bvbW698977O7vd4ydOPffCS6fPntvjWULPKezt6/RggcL0lO20uuy8IyJ4qq5FTiUUCMaUDrF12RZUm00IMnCIOvvSIPQHONjXuzg7va2DhYEOWWUZaG2Zr0+yKZEGhadwcPB/hi4DbROa52xQMq3yY49hfw/rY9il5fbW9t05NtLN9dkLL6NQxGys2x9wylQIw/ewamRe980AO0xdoO5HWrIzZUuVpHixvBor9Orr3FocTfZh56a5zmBe2D50pKHeQ/MphEVbl12Bl0hBMCRubT17QUFMEeNjLyLGKNZ00TcJ8n1t9gz7Cn+t1eQ8PIjYRqYa5prNqaa1/X0dtmdx70IvV3hKSmpZmhL0lEE1+QIZsw7mm0e5TSnT721X1/sh1gpt0dbm2b0BlfTEZv/8/Mozz730Tv/wvauX7+1072m5sX9rRc3LRluLvnv6ZkOaiyCCb/AxGDqsGzs3mJS8cfsWcYDYgWOWd3vqy5iqo/ovr3R2t7fgwygChP/ppZdW5g9O7G7BZJkPDdJk7GmAvttS9+rPDIK8zxtbGqEP+nFEoZpvOESLKk6a4pTPC0oToLGEWjThcAy9HvajhUydhGwGAi2mukDGQ3EHMbA9XithkBMfE0MPmnXaEuB+z57FWDX5UW9S3cmNFTqEOAl1nAgBbOtFoyS40h8mQEvuCmdGYoVTAcKXxVZx5/sN0kcqMf+PDq2BurMTm7fOnFz9q+65X16+Mtc6SZ9+jw/cjoY0egwgZnU3FLkQCCeHD3Zh4zkLBohuzy8lixQ+8RyB4UilFOjmlnhuO+izqsCrQnuUPh/v4EE8ciPlyRkjBgYwOU/+tWapkSrMVd66acCbOlT4dZQhJhPmbHI/cew4u320BEFx4jMMePSlBL6mgUPsk7WtjU2EMPa4e/vulcvX+Pw7rxDCiprLn77+zHlfyyuAWHjh4Z9xT2gl3+MIaWXPvkLHLP+DmRTBgPqJUMczTEYh1IVb4uAom+FpBT9Zo9ox7bcTCoLwoy4kwoqdaYfCCs2kSEOuUcazMBFtEr+psCNyOCLaVDEPlbDI+I/DhngZz+Q0hYzDUIWCYTh7JjOVE0QokZwy/J2YxKgaONtp4El49tln33331wz9o20b0s6IQWXOeDL+RtztnoEQGgLaOBOSJsJNVSdZSrmKVZZbsxUJB9V1uyzj8P1uDl0zqxqnKDaeVKMdJV6zMv44BHidkaOwfVCcmj81JDdVUfMnNV5H5D+eFwsahx+R4UOj1RlMTMY8R8/r8SAhhMg1DQ0+WdOOa56XAWWr1DwlRxWkptXIHkhc4ROctZYIBz9ukXrfAD5hBlqSOFJZlJm68tSDMMdrPYkjglERkQMOMOAU3Xik9CQvfamli2RUEaimDROTyvFTcEpbHBVfXO9rBwmNMNsIxjn6tXBzxFKIF3FHZ/XfChNVbU+GrTYjTT2uxVDKmQoj116QNHUeS05LZDwj5uMrqSVSMGfQzkiq+aCkc9HABwdIjUmcUIAQqmuTEZTH4lHEWRWyhgAn5L9of4QIEo3VUG8G6X0xC0KKZA+fwZMkkK1MiWA3Mlg0nEZuKTZdwUmiuc8ZdNIQPlOrYtX7Si8Sv6xIyc44w6HyzWwNcVMTFNt1UjwnkhdN7oapScKLgHDOovKoM5OYCFZLyG2lQ4oLwhidU2KCfFQTuRB8xacK9W0dNwqQKFxNO6JHAVYMNFK3A5jcV0RL1SrAgQdVrprHzKyq9JEoCFbe18K5kaP6tqYXXBMLMgfqMVZE7n2F1hwa8aJPgcOb7WMw95kSHEZ8+9YGT++avFtaunntc3aWfXz5EvjduxuaRC5ZxixRviTxLFf6soZ6cA60eNpeaJ08efLJZxjkPru8ckyWZMqZEINWisi5gwNBcbXEmlFkpH3pvffYH7d9/fcH7G/NomWYseB+Fg5OKVto/djD8ywPewc9jUs3Nwfs6eb9V4qYVRJksduXa7ulnbbILfwLtyLNSVydPyvCFUdnk9axk+scbcS50kzr39q4q0GO0uLp2nrRphIRcouNUSwEIvHY6jEweQSloHd3t1mbpzNCBJk0f+Vb5lSuUZkZUI5A5RDVv/zLv1zi5VyxTLkuejoXsCXCt5qR4vNWOO0UHC+d1hPQ+4fa/nzts0+uXLmCdCboOX0biRIXZ2UwjdvbjAmdGOTQR1oZroiwXJvLetqSJMU5VbIArIxWrmASQGEAhHTDuQWIQ8KNAJxARqgL3IIT4GF+KTgTmhU+zC3asssSJsSBr62LuaaAmGqPWmw3Ick8gY8HNGnoDH4JRQMg0HItELMyHP7mw7WIaLAt8GmRmnYazpcFL0Y+OsMZNhxn0kBu3N7XMg38cf5Hh7iAjo7/h8SUO+WmDLmOA7Qn1P5QkozmTP33nDW6GL4hS6vefet37FY+Oz//yiuvXHz5eSC/uHqV3aDOe8m+ItHeO1Pckn0atUuXPqEt4+B3lwtw6j4NL3UfCI1YsRtNBGxxLRolgL7y2CQzBnVtTxDUpGgkElKjagMst8RLAAgSF3WFamHUr/GLMgQgJCkSWwPpI4jrw4XKgpjTIoDPOzPikKgC7uHHqA8kViGbePwOL3XqEHq0mGgD02wLc8xSwwszEFBZt4oRUTyiOj6NMt1b1XIvb/Ow4f39z67RgBfaaRHIbTFfQXNTgHnUjVIFOFbDhwRkFn5sDuNZuK7oLJMGVXBQudSBTra+PWIctjCvkWHegJRUsoDduDBfTDfkHOFU7o7RjfNBhBMegj72VcYnVAHGSFrvzQNIpGivXr4tIsYj5jYOH4fITFVeFHcxV6g1AmBuQSlXIxqnJp1IVXH90qISVAueybjWKvntTPw6saY1fBxiuItsWilMo6pl1fHFe59+2OluLrFBjJ04fPmUgSkz0HjDvo6fv3f1CscPdQ50OBw7B8bHfzWvifH5Jbkvu3GpnIN+j8NrnvzTH1/+yU8Gt/dwuPbh2BYbr/bEWqBbCxovHLgVr2+09cimPRc0BSoXBrgsDKmR011d4bxXItZJiKpGeVjksSH7NoJadIx44cDODCCs0LjkZEeda4Rkte9zh9pNwHdpdT3Y4Hr997+7c/7kiUefosXvxeCYbQjAo07d32Wa5Yci1L2oqGKCFP/ENbWOFaQRDdoEi8ajGgFXUXcYrFmBOspT0rz/JbKYUjFLETQcdcIw7IlxSI0vYms1wTkq/sdkqyFc5Td0RbrqL9mclcTYtVpEKBIWyBPcIynlJouIziBB3fiOZLXg54jP4lEfEiYSGK0cLxAjF3iijZ1NsSe0lKwo+O9F34SWf9i/kqO0ZSXKBHRYvraqk8PnjRenP7FiTImwcKgOvtc9WF05vnnn/bOnTy70dzAhFpDHWgi0ldvYOFAlbsG/3A5VeeCYpjVWV1YZ+lz96PrpU+fm7jJ3SU2kz8COffTlbHe4surKNbyF27pEgNsHsg2rYs5RJeEvcR0ajgwHhNQSAZCCCksIQ/xISGMNTAmCXwEzQfYflb68PoJNpL42JtDRIFqVGByApmTNItTItJMP2sqb/MjXJA6T2L+TlQJsZep8jbYZQyHGOVzo400H8zv3tnfOL58kmfELo1id5uYQ25pMBqBYiTw6XiDg2G2JgBnrURS4xrKMLQ0s10CWncQlyLSxUPWOG7lxgYPZDPbtbPb03kzMWOF15iZeIFBheAVunw8a9Ps72xrcP8HCPB/ibqOi/ZCOgqrjHXz7VY2Lo9XxW7feSQXxVDaHI6SGbiAMFQjMBkK5Naty+0CR1OrGBJYXqNLVXLzjsspLnUpppFzkCHZQjmKanQmpoSbhUcPbnPEaMjVuHaYmK8F+S15sz5m4X0miS2oWa88ehaG0EZIQk87aBUiIq1tv3eaWhklH3XHLcxQnHzLxxIGutCGL2tXCHh9d9SCWAuMg4jKCbELzwbbFPg+LtVmil9R7JUhR88+ZvPL13E4FJ23OpN6FtqQ6d0BcUfStH4jYFDMaKAX/wRUpGgDE617LrfYSe3GDPByNPdLK/tLqWnRDUniU0/BuUpJb1BESVyb0NSX8mG3mc3k6/5I9MgGl4jLgJLoQ2069eScRRD9SO+xQgxzzWIVMsGvVhuDRDPXuXOd9zy6njHASBtOvPCbROPzzP//z5l12obWeuvg432769NrVXnyilkbRoyNzrXOn4aue6Fo6RYFTtrXBR53E3IGm6efbyxwW8dwr32ICGqPxoPXKUy+eOXOGQyR41jp++gzlOOjAfWGJzoTRJLvUGe4Ndj/+4ONLv3+PsyAGO58VcfKr1K7Sw9IlhQ+gUzYHUcYJzibW45TgbIa59uEeT4OcBE5gRNvd665wrjE77OZZGtm/ffkqmGhNKnpjNCmfZVlAanMY2kTBDOLgcqj42B1GwIZclzs6ZZgIHv7s3EXO67hy83OOwmSXLrpSf7QHi9nfhTkMwvT65Y8uY3YiPKjv7fXQh6hyoVFiDCch5FZjUM31M+rR510Umfvd228dP7H6J3/8Y/Bjuhq1ks9DzhnN2nvMN7jYsN9eJkd6TmH3zPxp8FUlyW/YDaAgvXtMHNx4+0qXg0AWW1979dW1E6cZGrJLHbMN5hbYn3jASzGB3O0NvDcztOPhQMuf4kLWYoBOs8GfXizKQcWbQ9TzfGO6+Oxtr6uJhrk5fUhwiy/cnFj7ffveE0+c23/hIpP1t372W+x5fkMYy1GJ7y7p4fzacU08bRzsLK+uPnlXBXehO9/d3e2sclLsIhvM2OVO/lgYSI/6CwOZ9DA+R2lT5vGYa5zziBSKMvWtahIUgHB1cxEAQRy4Vctj6JSrzT4lkTJN1DCchvPfCbyoGpGkrXNXko6iagN5tn3EUL0qrjSs11OlBA7FF9/PHNq24M82Mv5Mc1GQ68hswoRpDR+kGM22Moi9acShnIqVUM+CRBVjyOFtrWuu4w+xg6CRzSKxsC8IRfMCAce2Y1KVeCe2o612VWmfm9u9c+3q7i/+ZXDlyvMbdECLq3Eg0tZc609ffvHfPvXYu++++/Mbd+9s7X00zyrd4rVFUW231cKxx5kW0u8W8zyCNRjLqr3Xxx1ohOY2793ZGHCgwf69DThsMJZD7UGv3+/2b/Zura8dW9SY4nCJwxDYLhujlx47pzWj4ZZKsxyshvGiLa0Di29qxlgQZ25DVTI6DzXgDGtk/xiwYH49E2mGBBxNEmkQT7EBAifQwmVlCzkEHzGAFKm82tHi8wvzfJ2A3bJMSSqho1dL5Xkw491Ei4XKbbVbIW7tCZL4xQM6BxNly5G41q2chcg6IRF8BnMAuTWt4jHPxK4rabfb2+/tn13o/PDi41dvXb11++7u4mmsdGtF80V78YQOf4I+dkBP15c9GXax9GwDs7v8xOlT+3PLvf3uzuYmixarc8ucGri4uEfeKQ8Rx2U/xqg6IunwsMsOd2ru4cHO5t1ed5vlADQkCDkCOhxyvApvrWjweKRArl2+YJsVChAmEgtuvvTT3d7ZMyeXl3T+G4HRabfX3bqr71LwzAUmAzLisqWsqOvVK5999OGl17/xCpPR/f0+FWDu5LFtGVT2YVCIoy3FmDfG2PJc+MSXTkhXnKqQKvto9uw/1jnKkYrDSDrNqjtfJtSbE0g0alxVUpmbFq3p9WM2Q5klVOZNojVGSo5Bxs0JiHXQK1XZeinNGPla9wKF3ApZz4yoX0OydnVKpBa9mykT7mvmRcMar0ao4Y6TCpWv46kPClnUdglNv6pZkfdQtDRxsYbG+JKxOxy5BU6Yxv3+SeHcoOGIDGG//e1vv/NfrsroU1mOiIrcCoImVpJ4ESr1cpMxQpZvwIRDvhv+1vBihMLWeLWhpTA6WOf5+ffee++7F54EPxkm/wwF5FjGyPdZeTQ3yAjerzFEym5XW8k61Dgz4oV/wSl2GFfJFgJunCLItzaf425SE88ga1hXaFEiRYogY/4D0AgFDZ4WURRuRGanNpAbt0VcgY9DnCQpY9qSNI4vSGGXI8V0AMaLAEhBKNbOpOkXtsJJ3kH73Pbz29pSKh0QCLXdGhzq26Nj1lQljhSMwX9U4hGICLUYnkZwJHKkNqQGEjdE9DluBEMcn3iF8L44JpyNWT8QzhbtkipCebQu+ADDAMOitlDAzuDELBRWE1MLsOZQxwvCxIgVmJg0EQg+GcSmPH4fHp7QGjtPOeGKznhhWDJVlHGk3BbvNYTuNnKqoo93/33b1AJkE9qtC7fxiCltvZJqoG/L1RHp786LD11EsKMS7bQ1FVhUCcyJg4GCMiFigxSzTMCI9ryhqtGUi1QnmnTGH7pUM/3Lv8csWKBYr1g4audQHAhOGoL+/yZG3qkXZN++mhtg5d92m2YJUmkVqQsiDPKY5eMdkREKOJsPlcEJ3OIDuk5xhUgaSXPp1FcP0GAjDx91eGckCQ1ZJgSTYTQ6U0EI0AHnDBq04t1f4t4BbcKSh7iNmh4g0AqC2RZMIoYUBEN8y9WBnJsETbxnJ8NrwwlnSjUyta4QIpFgJ098cvrly5cZ63IiMz0pgoxMIsc73Lp7h/My6ao1NVsFWJU74rSZKmIqUSxOLHY60F44/fiFCxf25xdpbdiShghOQ2DDL6PcF154gelXpg4//exzWZutvyxNLypffNMPfT5473dQHXR3sPhQUohEPUZ9XFlYKzqMR2oNS6qzRk5XmNfgvMUtbbXDelVuhGu0iRwqVrIKf3yqcHV1nel7jOBUyDEF5Lw5yyaV04+eYxr9o6tMo2tCmSsb6MFk0vlXv/oVW52Jh9n5beTV/HSVhhp5CSFUTvHf/OY3Tz7xLBvNOFqiwB3BdVGJenf8+PGlYych5GVeOHgCmluCHwgdH2zN88YnAfInLl5kH1YtDsVBw4OMzBU0IxhCCebRr1wFCxCKSjVm4ZCYxLQOOTOyU/EWR5h3prwWzz2C57QvXGACGlaSlZ+cL168+Pw3nkXWR7c+e+ONN+bmluCDNzIeYzGVciFg6i7H+Rwerp9c8hw0Vz5SVTSZ6ACkIgsqX7mThvyPQKozOK3MwGrkWo4+KSS0SoksMWEjaBLdEDYua5j2FcRKvkrk6EJmkEyzT828Qd64rTFL3MbhCuQo+KDd1+CF+cNFrMxEfeqirOO1oFo95WjoosljTVik1LRfaRxl0G2Gkek6r3/CIfO/P3PzprbZtUccmxE4tPQOK6+svvPOO59+eqXOAkk87uEkLOS5u5EgDq+gI5BYyQWuV1vm+JD2JrXe33dh8wV8+vN6F4SIg2gjcAucKJG4xK/TJl2dnJFiOOR6HXyKD4PgNtM8HM9XwdyWsECigKIEdoGwMO/p1NAOnKLnJF2+KMy5sCFyjoY8bZZQNXKjeqFK5CBr5VBoiSg38RoTbSzNOAbh6O1//Mefkr9inEzX/IUQHALIuAdjgP2FrmTz6hmfMdzeBn7smNwASqtHUtSAQJJOUosbRHMtAogHvmYYanhBmB2RSlXeZyM7Ff0d0cwA6xsULwfLxb5sFCAQpx8jX6ChFZ6AFFTms5xPPf241qe39Ib3cAk38lX8AQ4lXusDk/r2geLwFH7VpSVIgFP8CBwLZtjcNWuEDISJyhvJVCMEcVPYlqRxSEl60EiD1WwNpzF/OKqJ3BYHN+6s9Q6W0gA4juHjO6UMcHDEbq+/cafTf3TpcAXr7sWKBM3jREYGjptbexrmWQ1j2YLB9AKbDu4czJ948tnHX/8jXPDk/gaEC/Oa5uaX//okucKwyXZsMbYyaqWXBUPvsgRJC78xeIoWzGtKsXoMB3FLKxVpJVkQP2ItaLOA7vB21QaqLatNPFqAqdZQE+5qhrxRSP7Kdgy6QA++pdHiPIf1fXr1499zEMdh7I9O33YJt27aIXcDdbXRw2qGhzJuVbx7NAFcS+w0eeCrXIyHCfOg5F4jadtvSDWQrbQ2VjGJuDsDPCG08qhdon0bTZ7jIAbcVOKcV4TEUnrKghFPtHr0EprAaAoKpSbbExde1HxFgtQnVQ01doKK2xjWPB7MgjqnV79T9uWZgQo2Wz7yl7So6If+B2agj2oa/A3yC541bY4rB0nnAHmvZcAqvTX6r25jp78gOGYYiUinvcyZnHfv3j534RjGivEFFUsdeZalX/uJIXi0I8Xlko3l70TTXeAoPnpf3Vk1vIUybc3dvXe703lkZ6drlfc5M10eFsJizVDlL+11Da/QGmwE1Snhxn/BppSRTc+jutSM4IitLSZV8WWM9DvhZ8TPx9PhJ124aok2vLRADEcL/hhsjBCjRVZrBF7dBFXKfAUeiYpJRilxc7YaI9gJMyk8kjTppm79VH5sdm6v8A7KxvYOj+u0nGxXP2CySUMpBXhw9f59Iqjhq3nT2Dqi5jKUdvnQMIf3xB0nZwWfhjoGMjUR8DQ+buBw64wnuPhJs4LmVI1hwvI1ckIbaI5jEFtBur1t/paX1ilTWjuYgFOTFLbJP52ZAh1GrLOTh8oM02fGkGjpCavy+Wn6TOQXfJJ9hjYZ8+20NzarGVS6KRH1cW445RyUpGtlM/PgWw3LKqpqNwFkaQ220nRMkyotRZNSVYGO44xD0naC8YRJkKJ2nTg0Vw3Ncae6NXMPq53O2ryZjSjM8b4o0+s31QtiLY9MON+ZEG/0L8R7suavE7rVNgaNjm1lsK69R5zcAYhnTJK05whHDelAgNf6F8sDZCFHSVRjkGBMC6bOVfsuUMheTZrkCU8VW4LZCcvvvg4+XxQu+1jpRmjbKVmdLynO0SBwRgVwkdwvFLNDmx1HNPa6ACZ3KphEHHfbwU4TlNa2aHo1dXulVbTldc39yGRt1DohXSM2v+VAe87+IVki/haY8yW8+eabpuc0XZ7xMQ+PeXy9nRLHBmzXsrbWrZbUpbgoo6Vji+12Z2398ccfZ06Q/UqLiyd4xGrtD3bu3v304/c56GNxZa27u/lP//z3e72N55575sSppdsbLINpSxtt06X33uXQj9s3r+9sbbFz2aVAT6ndQiopLVugDGokb4z6YqexLayVDYqeIBY9iXc4pfdw7uLZR7/58ot8j/7CiZNv/eqNne5gj010cmMjDz220BJJrhkgnRYRqkS2yfhSZ22Fr9bqq1V4GsscPB6DwXtR8SHH4/MLj7525sKpT99+++07fY65UBZwLMjLnLX8LGZPinlrb5ECzlUoACZoBFx3d6f/y3/5+flHzqwsreImPpczClv2CvQ5pnUGCy2ehweLa1DttYafB+eoVCkTgxQedz+5/PnGzi5Tva++/o1TZx/h+ExzGL/CR6dWMpiJpTsQ1H6mR1895CNdB21WRVAzyWOGQMhZw1uMA3PtB9fe64X2qxf7z5z9/ERre/vWqYXu1srcyobeV4rF2sNby4dn1lvXv34G2vX2o2uPtD74r7+JJ39624Nz7NjmTdAlncXR6+3sbakxYUZ7MNDHDDlynCsLU1H0UqLWVhkMiK0txWKfoN4WUl5poMiifFIVm3ZB7yzwn7kwPc5BPByV4V7JMmJTh1piDZ8Rt2JCiCW9whn4NG6FZBrCDHFOegjCQlIisFKrmoUZXqpoyUhOn/A7ZOXRAl4wPUwziK0xZJU5FHw57/RQ0BooZtsA3vd2ojK1CMcb2haqFAkx4HBLMLIjXO+rw0SEBiE2qdtYSBoIBdJQNerL3HpXp+I8dW+TI0a3/uH/c7bfv9CVYutxtEI73rFbXFhq7e129vUt1uVO93uvvvz1555mie7vu7wUst1lhzADBz7LQDvbpd2Z7y9S1TQaiXyrihGjIb1+5w4Tl73dLfhTRYCz65n49l6PXba8korLUUXpNfuLKmiyRl7UmsajWeRLsuSrNOMkghRBO0Ozv/oFMDUKhHCYRY1SfEezJmvRSsHH7hR7mnF+SYyJBHFnEzbn+bJySEZIYMWMXNIXsbmb8RINCMTqKWLUoR4wh2jhR3olpyA+7I90tB7iZ7rhL5jcDGvjMCWVLGo7r8pSPLFyzVhDX4hE4EqyJRk/gLhEw8ph0Nt6++2Z9dXFJ164ce0SOO2DE5DcWBGHnZgww0jE6UiUqRyA7LfafW3b0QANy/O1p94BJ/xjkDUNLtp4FBnkTxvjxUENsqyGGpRuv8cENKZlXB6llsZOesbXiSeaApLSKnaCGIwEWyY9J0bW7B4jSPkmbJ5v4heI38Aj+wDicyZ8555xjhzG3sUKOioQj2cTsCh4bDZ39bObb773e2btF5bWD9ppFnuEe9yEwxZ/HKazwRuesYoxBI7HUMOBvopIPZAvuYaKjCg1guPe++y5HVvJb0aW9yDBx6h2eyK2sDHVL+bQa6WWKgPu81vUKHjjkJL0QJFpfKbB78vchMUrHo7PIq2eB4haqchlYH9CA3aLeBM0YpKkyrhFxZRU7quIk9xUAdYAsbtHc8MmaBbxDm7cBch4xgVZ5FYMRqJwq/NZ5MoXo30MhBES3wgz565ONgeRR75Uq6eMomoq4pgLWt6v5DmEyWm0Miu3LkZusEoImVFj1Bqp0U7EZTybAW48GGdeuQIM7xUbNqCjcFVFNVhR8RpJqFGLdqogFo+gRNqgG7kN/EwQKTZscM6GGqFIN4EwKaGC1TiTnDFQJ5V14UFjP8KEmxFlhVge9QNTDfoITvQikXSfB2NYlXLXkobChC7TrCJVolw6BUiEwwqpL+xu4SGWE19KUpA0L6SqxMLDiuWTrWZapsko38OEOssd52munX8qNhDFGzfxihZDkNBHEqKdD/FyFeU09fDBCucSTvaxYhl0jfThZXZNRMCQdkj0MDGzCv1VqR0xI0TcV0qNX4u/L2GNPDFeWo8xVqkkJ1LVwFzU6KhAknny5i97NFbiUDn2JABUPiOAg+VrJiVuDtwmXhHRbTWugUdBK4RDYBSyyGNh3Aglm0UuqZlWmkCeb0N0kPtS4KG7xmbUDgqRK1sD6Ln2V3Xi5wjaaN0vSUeJDDMyCXtaKqrWWSikVTYLbFbkQfFn8Yqys1ah3gRcq+0rydNyN4FyJujBczEs/RmMH5xtYlYTejLFCSPwI6kgOqhsKJyQNlEW9kl8MUssI2ZnNv9aSlJo+k9QWxVFaZC50isFvFQZ3yaNSRrWr1HOoopiBYER4HycGmxmRqTieGQ4SnefO7geJVOlstfsqLb17cPFC2dlMKxtfRwvPF3W5J2JAJ7kgQuBv9JvMsCrhoIc8cDhBmcvaL/z6omTWAZB0PJxHQqChyq2P7cX5umj108dsLyH9djddvnyx2QKTHrMDz/5mBONN+5d76fjPiQR3XAVS092k86pNI0QOgsyLGMRBHj0AgdIbIF79+5xsgpaoQzD+FHE+9/BKgLdgawCT6xETtfWjqWE8Hb6EWdwsa1DMF5++WXGw798762PP/4ENJK06YRsxpoGUgFicxpp5fJ+wdmHhMh773108uRP//zHf1GInMqVAGNa+54synePtLMMtIALHc1LnO9MErjFLDz9onM/JomQItQxrRK86hZtXnBJIpAdixN5DvB3CECqEUC4hcRY3Jbzu9ncbVaU2q0PPshs9AvaYNBfX19nEgfRzJu/9tpr2zf2meinP4cK6fLDQ53CyeMV8UEcCc3RItifE2O4Mk+AnoQiuhYBE2tr3ZCCxs6mrllhp9aER4kr1yOOexSiIY51szJF/2HyF45JvZmhIJTITPSRxAbJNP0baCMspt+4OCbS2miQlkjNZgZhjVbiE5mMpOYbc8539/9taDIuqOROkeyH5jsuC5wJZfngvtfgPFGrBk7JKstgNPUsMZ6LjwT6WFBzMAmVMZojfWQVKp6nFtbXqdH/+Ks3rD6urgrLCzdt1VyOrjBz5S7yx5Uqbz7mbLj5w5xU4vAh+KlNSdk0RisKl4iYjNoK8lh+kxeBNvFq0cKMMCKxsM5NJangg0uEi3iGVn5mpKlDhIswIkqT0ImlGk1WJWFC1ArXCQ2ItIgA3Em+AouIc50yDhD8gpZ2cEePQxJm54mDvu9iizO7LxU+wV6XQkicorfdiDNOIJ7xFWHVHG7usttzMTWfPZ9UFyOayH8OdIgiTJSR2IiZZxgEwQ0CTvpDhqLkOH1oEosl8fxIN6pREKqriOfog9yVoiSYuXxdmkNmnELz6KOP0vdBq6WbIwTJzf6g3MUclySUkFMLINCGhVjgdaRwcKTc1jiOT0wCiGcIoVKEG6tK6phS44wnQyQu19zJGPeDTlT4fkQPkP4F+S8eY78Iw36tcmt6TCYjsj/glDpKboG9B3fvtB+5AFj7Q/D1GMMUJ2hoOg63/fZ9ltDBYm//sM3Jz4eHt/Zb3/3X//M//e//T5y1Nb/LEZw0TeIW7XKgJ97+LBuns3HP3mcXCbqhIZ5NC53cj1U1JaKkML3Ycah9jSlQzQOe7/OvVjl02kuawhaOOYbXcExj2IS9SUW7UgABAABJREFUzzyfxANSrPu15nQab3/r1pWP3330ua/xoMd7NdB6K7nW9SwvSxHbKt6I1nbzKgoMGjie6BHblOFG+vA2T/ml5nWYMBYzq1A8peEHSNYKWwCyqCRU+OgVqnk/r2XVWVNiJmd9jLtUFrSRYZV4oiEfPDzrvV8LtiYUIG1zgkSkzqxlaeVPQQrWcgNYLuEncVfb1r7Bg3vBcyS16xU02a7asejZ48yt6q1DiXEOScNsQXi7ZN0gZlFjZaTnR1VGEDiiiyvDid3d7tlT529+fufOrZs02R2cLR5iMU4jKzaXbJvlErdEH48wKj1rga6VSUpe1MlRKzWLoXp5584duorVlfXtrd3ewQmM6e/Dag+RalC8H8CipLb2iXNqO8M+5ESmixy50KVkll8te6tchUnG2Vtr/EAzkKi0IhTiSJ10EZrO3poZQqvAiLKmTRkKYj05KwNGMSnxgpPNPCajUTBj6fcFVC9D5XGHacJYZYHkvnwwFHv9WIgFk6U+1hE27m0u3Nt68rFHGMVq8gRV408WlV+PWIxcO7jupPoe3OSdohiGkXg4oRa+S3rENB9DyELKQrQzGbJgmupv2NkduaRlXdLjMbcuCI6ZI3XAdN88+0wG/cP+1sad7u6puVMainkUnrVQA8Nje759oF90KLnR9F+D2BC0asCHt3b09BaCwDXyBIYVp1IrS66HbKuYGFaKFeSIRInxQYYwlzCpqZ4brTiUaKEVhNqR88VwyzhSuK66hXJ6xHW+5GU64pFSIgv3wZyB0zD4CGbVDrDnb4aMkiOTc2WfIPjalIx1uno9k92CyDrwxwnjUUFvevGciPWKAdlvyJNJ7N+FEipqDlRpJxExgndrxoEGavoDlNpDRFJAKn12YLiLlNagWDG10ToegX3B1HnqMd4rB6YCksSJPPDpKzPyOah4NGI+i5nCYhYQeKoR0zG/lWrRcpNUJAbi1AtsxTncT48SfFEgFvXVsmkPVfQm0SYfyVukgGRLfFg7VJpbW17p7e4NYm9abBo1Fnjai0Trt7lxhyc+hGPt9nL7IH+UhJMd2EONPRdWT3Ca88WnnuRUjQX2SqtjGgziGF91jPNBO7ew2Gmtnji3dOL4R7/9p7euX3/kzFl6aloejlNgSoKp5/4gTvmIR0q/hUD20cNb2Innx1tpSEB/As0X8Uk9XiomUoOPdywfLvK6q3rseXbXbnUPmDSHgTYqjbbqEjAlpJEAmuFN3kPONv1uf3CwubO3x4w2zJnwbLfXOE4kGtIDPUW32D6/396fP3fq5F9+949/t3ritx++R//CCF0lLEMyqknZkZNGcU1UwdlxErK4JeDyb7311tlzF37wgx/s3b0dqTg5nLQRhKpCjMMoe629zgpbB2mutF5uz2nFGc0rK8sff/zxux9dvnFv6+z5C3/yox/PLa7s0lWw950QdYECCc6ybQ5IETB0dqpKJJRKRSAFNDucKVT7EpUYR0n6SHbw4GOG/DCO4sVQbLx9apVJhzvri5/vb8+tS/O7W5LV2dUTx8GFE/zdPuzinzcGOyun1tb+8pv9hbuX3rqOXOozjcTi3i7zEUsrHAi91O2mfdCUPiexUjooR02jTlkfKxBx2CuEVopoa2LJXYaWzJIkKko0Pa2JZEaoRUxEA0ElNzOAYD7UBfJbbmcSfTmJo/rLMtHKzWI+SjLERPPhTY5NQ87p8as2EIMP3Wsk9b+DGzTDWSdkb1S32QU3MRUgPLASEcZt5RZgjV/M6LarDClH5d/nzi1tA8kSG8D6VkPO+fn1rV3ehvna7s7Pfvaz9qXLX+90zuzQJHU7fU0CLsZ4yVq1FjRjuMi3SRQYty6cOxhs7Gw91uncbbWuHugd9G5bffHCoKetwuHwoCKFADcQAO5sboVuav3i1SlQVAJbcwd7iwt9vciA6XQA9CH7qUvQszMJzfEMPKDFeljZgzviNiaklG/I0rSpOYFNhBy53ImbA0yEEMp68y2SSKQLoi3m3WviPb3AgnZ0WHrznbPL9ZCJ4DAQb7rDBAbiKfao1ez/fS8504NTZYPRYC8lFUs6lTjBpWN0SdZZ10NKm517MH128ML8sib69QYbpwWolA93dvgm718+8eTPr3329sYWS9Q7rXVIbMY9MpYVRq5EIxQp6+uHbMqMlo2BeYzc6BAO9+gYenvzC4usKq6wY1gNuMydeUg3dOl2NV5RBkJbNCfC/wPOqoriFN7RQtIqTH40CuaaNaDk5ajja+urS8uD/i4v8zAZv7/Dgdds3deIDlZL7BbvHTDilZaL6ihtXF4qY5mFxzN2QOT2w+Jl/wyZ2e65VWw6SFI/zILzRVWYlqXwN4SBLNcPO+o28Bkn85v3QY/FR7p8iDRiquVEqQQc5lHF6tT7xoN8FlZBiGFawgxR8fZUZAoooofSY0RdehPlelSxwnOW4C8vTaN8ibTdQxVaN9zdH2UiiTV5qpr3wljXhsZWZiKwoSc4eCT7RIAjgtH5n//5n/+X//Jf+j1/lGOk8Bq0vi3WQUlDikolqSZ0ap0EpL4FuUCUcdoTGiY9a2csKTshpOTDQ5Zxzj75PM82WRMhj5bpBPKHAJm/3WmcfKQOZp2tZM7JOJEg5JcLEZs0k44gz+Ywgjp2Y7VhTiTiqZThiehSjtCFJukK3EINTHG1v6owcSudS66NMCZ8BBDSBYkudZgkhlW2zUqPwYFa8CRvGJL7BUDxmsMQazSmXqcZxkHIT/URrqBjAZZDF88s8hh84/o7vFw531kKu5HCoTFy4JprMkX2c26NgLkDrda8ppsQl/HjQc7FxHI9G7HhxlfXD+aOQaCXRJV36Sm9kRXX6CkBjijWECDkUc0LAkmOl4j5c4XEvVpBnh4J682cPDLDxCEGdmH7JN36T1SyKFYiDTUmUjVwZt8WDiUyG39GKuXI0FWqRnnBkGmRx86dggRj1vOz4Fic8yUHCA+MW1VJD6c8OABVQjOJ62zR1hxKlyza4MZginhhZGBNpUR/ElDcSxjWPxAMdaQo7LIjR4Td3T4v96AS/OsMFnYPFyl6ziAvOEXPGch1UgO/8DFOI7ON1JpPIw6hkUsEm7hGg1kLLWjmwG0ttEgskYagP/BtrfnDiS4GmUg+O3UiSQMIB+bgZOqYetbmELVgqQUmlVCTcDurxQxUuTQFAKvg436VlGAmbqV0IjICqWUVEpyhqFEi0DLkYzKLZXVpVXnCiMYVR6NVgGa0gcCtIb5aH4RSXYHMLcRbmUeegEZDByo7JnIA0r8Z3Jq66F5S5ubYhcoeCDAhA0LTwZwpD0ZMKLDfmSM1jp25wLZT3o3FUJwxDyYtIoQR0aiJCO/KojlxpvyeeeYZ9kEzLCQUNOHEEBFDAtQTd2VV4jAXPII5EzUOkTrVODxl5iHwsJcthAzaGS2wbZaRA3H4JKrRn8J/FKw78oJQaws1W/HwZA64ZiYdy6ytaBra7SpoGLCzqp1cBDl8a/Fb3/rW+eee4huMH1++BLDwhwQLF80LvBEZVwwd+Nr33/7t3z7//POnVpaYroVE8oJ5yaAO4tjZQUP0B8HViXkbSofugF3DV65coTQp1ieffBIE8Rk9x6yhiZhk62WtZEzi+VZnjBDHESYUU2aHnuDkO/2yeU3SO4k/liE8/corm59s3Lp+3ZkjF9iWGWSKEg702hfPP8pWu5PLyz/4sz+7cuNvWdtgDgfOWBVMZnXYBx1z0HqlHymdVXk1HLgyzCUgpajBbYkTAccBKBH+l1TdjgZoDTQTG7xGaTCvkx46joXHBT00t9mEtf4lfnTphQQpDao6abYOpM5AdhKl0IhM5JlKcLTEJ2J+dcCi6kQRjVQyZZ3rqwkb+TWh0RqcC5MGfNrtRCbmX4uuyfFJ6hfnO+288VtvXHXrBI4bBPOM+sRxCZqS0+EYWhLUswydD+0VFfzGjZvtNU1rmrm6QjqFWNwyhCRrApwIwVUWAhzMFZTm10lcLV07ggiypaqwrlWltyXrmm5Z067wRBbXoWGrFjhxC2LJrJwtxEslzXFLidAqXQUgTBP6QPBpfAqciC0D2wKcJsLIXB0RScxCsJGdhhQ+GJyPRRNnrEe506q//vrrH/zireIDNWeY2CZEDKfc1TjErnCXY9jl0OSLsbzBNoAo30QiyzPjSBHE45s0U46cmtp2Uu0ttfSjx5OS9quKTKKz5sYhkSww7EFD9zK1Ay8udIAvz7Xokga7u8Xspn3llVc4ygzTFVaVqC8alZ5ZVeQO+7zM2BlJPZ39sHIMY6loSKLMK08GUjKi1CyF+ChaVc1IGw011UhKeNcIJG6M36ghFZO6XNLzhZlQHKXHx1cXWxpX17mpmIyL/cohvHrJq13sEtQXkxkCoyuvd7Ao5fWfzlxvb+Mmo2+ODBSEEopNrfk029DPqxBTVK2fqPZbahx3BtpZw1GUfA/75OnHzn/rjy//5D/ju52FrvZYxwDdQ1Sf2mbGWkULDYucOC2ak93kZt7pTNOoltmTJl6R9Xx/uEia+o/mN0+jmJkfDNWms1MQ3+LbsNiE0xHxoHhVhIc9nZxcnwGtvZnaOrS9v71x5aP3XnzxxcFhfBhk3NOLxhMjVqvuE0bRGv5Ru44RawuP5kvpOkUuKgJ8akx9TXU0kHFybQ6lgkWZ2+01kDUvUxrT6qU+Mxh6ShRvoTYC5yqqeFrWZGlsn1A5Oyep3bQqqcaCXz2OpSYADOlBIcTVw+6UB3xjrLJbMfP1tbQOcHTcOLhPME1rlWaFNyQ50cSADzJXS5IvDNudyH0qR4lqlIIwI9V1SmxLKZaI6CIctrWcZW5pYneeNcWD/YUzp8935268+dZvv/uNb7pNCc5xeFZhbR4khP0TS6uqDCkzBsbVTVWzea0QFIUXnSw7qni24eHts00e8fYO1+JxPc6A1qqwhlNRtVjcTCLSqnKyBvVJzCQRN1CUxdOsTLxZ4KofKfmiPWiNUKvfSGrehndwaKFC09sLbikBCoUBXvENEGJuYcyxMmVSPnYlZNjwl2mJ4c3DxbJxKIBae7eEcXxo09NmyInnXH1dW568sLi9t3Pz9p1Tp07R2nMQLYUG3CH5Xgw+cEVnM2qIyrdoIrg1JKcRYbcFZQyTWg3QgHClScGty6jIjZFTwU+igxKgA3dEymptYQs/kwhCo8TnvKO9QNn+/sCvIA4Oeju7W3t7Ozgt++esla73W5AoUpKI8DdnoU7KHmXvGk3JdxbquxLPOXoAP4YDVH5oYbYhs5/6qzV/3DLKwQ1DKZIScWvfYCHdogbY5hJKQCBwv5TkdizeQeHT6w3yP8ytlPzywjCPmactVvdupewyyuRfmcq6pU5NrSIDqejMMBlNGR8ux2rRpnFUHgOt4JTGNnTVmsRSLVMxRaXXFluVphpOlQUbBtyO5irDanmop7LwQEwPeBBxi7wo79HOwb5ECs0dCvOLL/DmADXDkiSIjLTai53lJb7FHjomoOOSFTghOtyjJHyBSBort7UrohUT0LEhqLRy96lrKIMBR55nYhp9XCMVU+TLT4yyKE1Hb29uwJasxVOPP3Xu8WeYwHUnSwu4tdenhRRnGY+gg30IKIw8RrMdpc5t37r0zgcfXPvkQya120s0O9qqhnXSvnXpgb2jRdW5jbUbazaZYmB6ELhta7XVagYhl7q+aRO7PgKRSsfIXHfYatSav3l3a/vt91bbSzphj/3B3uObkSj7qNPhaRYXbOjBo0BlZ6u3z8CaWGhqR7h+597K3u7qzs7J/ZMXTp0BkzxiCpCI9LW77eD40hLwUyvrf/HHP/yn9TU+TohB9IRcD0A9Eggnrw2RdQwvDZH2MRTEwrtbO3/3X//zn//Zv6ZVX9iP13bxdVUB2YYzzaX4Xk+D3hVlSbVJgWmC3ofvfcTf1u4ei+jf/O73zpy/sLVHDdDWfxBzaYzrMg6xzbmSpLhqXHRwGgLJlQ7abjhluiY5EOeIVXziFCPXzSXpv7GKMTsnf/jarbffvrn9Oc55odXZXegfri31l1pzb35y4403bq+v//Vf//W1U4P5Y52L//5Hn/z939968ya0j+zEbkeZ5EALBCsru7vbXe01XOBzonynkPks8ooPs5Iit4zz5cEmWJ8UZ6BJXhgVqGjwFPXpZEJ+mzdOqqFiPI/uY7kzky94HTdag2FBqDVv4Dzo7QxWo0luheq6eB9RWLvGhlvxCfO6D72To+eNqFrscZLSoWOcUYWHuLXoAtVpqnoNKo20i20LQomMJIUOI5CCF5EZSaOIw5oOHKpaeSmMj8V4ppFKklO5mqEj4Ne9Hk2OammxeEN2vs3NbL4f+3WbSWOBsNr6/prU43fvXP/Nr8988BEN4vlN9fseb3syOL02rrmJuf04UoPtsGjbjrZ9RUtph2cX27dQstddzm2phgp58wSR2jLE6YP4D6GNoNIPD9k4GNzb7+9yPrs+SCjL9OL1EFLDBpGBeLuIASWZMTmNBA05Q0aoDBE26448wWNAGzRKB0zutLvZF36rRgByNNETv+zEXcjUu7xw1zwAjQgvwTMcV8PEdlg1LsCQy2gkJrWlUYiMH10i1DZ3abozqeNFnEnqK0mua8ZJVKEeamQ1EwVTXIWVLc+tItrGE903O831GcV+h5fg9YjEAq0OxmcJ8Owd9YZ/8tJTfG1iM16T2m6rN8RrCi03hf/S6grfRaN/AhizcKwS8v0ErNI9GHAAuPY1zrNHutPuzMeHCtXpt3QWP936fv9w33s3ozdFBnbUVUPOIkLCK4m+9dU+YWvUcMeL8eskA2GOlfTm3ML8sbU1VAJCEoujTBSA31nBkdsri6u0fp3D+cXtTrvV1vJLeD5jkgvnH3vxpVfXj53a7e/iAHYxl7aeu3Obqdyrh9KfeiHFIiIHU1A2U3GqhJ1rMCMxbhulGwlGIyqPjF6Y0tEtuQjShECqRjjRY5qQflF1LW7i4tlD32tOMAfnyF5Xe29On/xbFCvJ4xAnJXj4NbZQG+4+PUYjdPaMTgG38xk+FASDpo3tDZwJTybo6NQsxq1HupvUv2TEL/lXS/c1S27Rj5lyeXkEXMplU6M9UFwiwlssi6uW6fa1l4Q4G0Z2332Et/vNU6Ubyw5HFAETMOOipkudvTxqJFPGqUEW3RARQI2nNbQqrhxk5t/ANw74vNn30ksvcQsHMBvSx6nGIUcnAVMyjhZCm4RKvFRLQLWRS/mWiGlqrep4ET4RWKcWVUuxmgSjFlpHCiYRteiTQoEOaRNaSinwSdSSaClT2A+JxCecEzWBciuIIzGT7tshgWJFuyoWJJFo8lGKUaqSpobDKtLN4M+xmMlJkrz9ujZ3+rPPPmMjD8cXMnWIGqSW1r9wcMRKFsNyW+LjmEPtScs+bHyuPECy9en5r32Ph6LeLfrZ3uHqCEVhaJW4RTEyodvwVr9IDrw4XvQshU4RN/0joLEbZ2oMPBFQN/t1fAS5solyRM2vICPxEbJ8M02fmknGnfpbmJRIA7XW3tNV5m/bNpAn3zJqVMuWONHIX7t2jW8Q7+7tAmfwxAWe+JIeWSNQgpZiho5TmChZ6wkaCIa4BGsq4EbgytdIuBoTZyx6hjTdSsPwnBhKgRghP/eSlHTwVq5o7QGCRJuvJPGOPYwBxEsZDJFTcBzAmey1GeFBf60PGswmTGiBJB2m18Saj9DyMKvAC22JlKQZkYJcIiBTFjb4DEKSahJjAjGca501p365V8tq8JwIbOCU2/tqKG5VAbowAcwgbCgwEdM4XEvgeQutPGCVE0QoeipJNS5UsT54NJWLo8ZyQJCCVBvxZMoxhps1s1QlR0DVDSzJohkXcAATITNW1B1Jy6HEFQkftq1Ih7BCzAT5V9oW1Ax0U1DDiRPUBKFZymMM4JMKEc/k479IcSPmJFghwtyIF+VrQuCQ8KTE9fi5dc4FPvf4o2AO5rWZgFCQsQZxM2G/M/jcMs1Hk8lM4nuXPmQv6t3PLlMK7fkBDOPV6KR3zUcFqjDkHLe6wFOlzSFXnWV2BrF04eM7CkIdsWqJ87CI2I2rKfjHLj5Gl719T4+C0zJOkphEX5+4hVbqsSkvFNXn7Icyw+20kYAxwFZfR+2vttrnzp1bivexIIEAHKTbOOgG5ne+850nnniCp/GrV6/CmAOKkRUlLM7hR+k6lJRjtd2EJBPNvffee48/9iynpmas9BvZVClDpWeWOJuSXIiKXS87O5zg4e6APdTPPvssED4f1WDSuA0FUkkF/5F0QyQxg4vCrow1yXhL7s2SkIoq/NB2Y368//zzc7+8oaekOX0hlNl23I/uDOT9ra3//J//86N/9T3QcL9vf//7m5/8A5vcrQJyQcMnISGV7XgHse/M2yY0a6x5/NiEzgpGFWpVDUYruxURQoU72aNqBOLjDBsIs2+RWDiUyGySh0t9IOYuIARNo5oGL7rdF6FgOvKg+MVudaSwUjlGPWpI+Spu0VzixkJRrE4xcFpmC7xEalrillWuKTVLB44nW8RElRrcyu1RkMGhQvFwxIcHa0KqNJWRVLWKeT6hMHTEVzW0sYcaY+FgAOnHzIr+V6c8xK6RwpwIOIQaUuI0GpZbcGBukoLTiNg+ukZodE0hyC1B2NntVW61oGhwG78tulpn9FaGMUo828IBSOCoypOCPceZfBGI5cLBEQnP8QI8SgRC6RmhO+gq7/FpYqbOIe/xE19KwP4nz59kS+LHn95S7qqmjDiYIgwFiJBZ7EBEzMNnFlrpozUUJUupJPHYxjUWJoaFDis172k4Ab+UOyIA0UGgBwk1q0KH3JHBcU4A7oDu7G4GjLac2ssbSAyHNFOxtiaEWNKw2bijF9vnyKuwwFNPPcXL3GQhzfjmJx3QLCRHhlkGXgzoiDEf6AphoS1xm8twPUzmAAQ1tMSQCxHXCbQJbi/4BHDmNfM3eAZGxSEBK0jNw/aRzAhYcmtrh1HfnXt3KYJBX6O4GPskl6OYCI8/+Tjb1WlYYF6XLHdmWIv4A8QXFxZjS1XY3P0rnoTg2NU4vxgOtHf35rFHHpnrq2L09vVFaU53EXLsm9uPeXf2SpCqdTZyElMYXiWItTUedJTqqRPOAooJA9bD5rYXGQ+1X/83/8t/+A//Yb57G1p8mX3QHirhv8GTC5Uqrrroe+XxqwttAIx5OIC2fYhZObM5hltBoK0QOTA2BieNqoLSjubxI1kika8hcjWVdvsxVo6vAMRRA4whQwlf08qDbNU56H7y/u9e+NaPGOCyd5qsUrSELFm/9VRRcEmJo+PAobaFtuEWNe0wLwV7LFIrMTIIjpbR6PMtPVw1GiDpb5xoEXigAodTVpuhHOaq0lei6m94D3H1JDG25tf2JEJzWzo5hLofigIUfaP7AcGW5FrnHUxCbYFscGHleCDFxWZM16i0w7Qcq7mhJuADn/qdEfjNzWUCWZDYEosSJ+5IRURi0soKkJT9YVjihmBD7XMLO7cOOkzaad/o4cGNm9eee+45dlt97Wtfe/fNS2+++fErLz+x3GETFud28i/8NhoR5Ieesr2UybKklf7U22XdhOmc2jfY169UT+VpylKd35VPP+XwDV4smu+scOb6rcP9zXbrbhTG4cEW9LzYwdW1ez9eTEizzsjJsqSHpZEh16BQT/IJqjQafnHNlhFYTF3z4y4QivIBmnExYtTTGVjDpLpeZ7WHqbW2NRQd8fCo8CVfpPub1KlKjOI37pTfaE8DLqVHLFBjHyEv7VK1MmFsZZrjA0201yl1gffc27t73eu3N1ZXOS6ME//TC+8uf3JEgEF2FXxGn7EOlpSRPE7xaO31G+Nsk9RXkhKizrvVbABXgmhH9AwTkPE0JTpa0Z1rxjJRKHGlinG+nT4bzTcFYMjGOyZ9tJ/iYL7VHexubd/Y3mvNHzt7bmPtOGeVdjTHRGtEy27hViv08EVbEMcC+iDOCjtiHSpEqWpnqbqWKv2LRbOthlz8NW3vgyZ1TJ+EGTZxBRhCjDyDSqi5bkZUNjmk21Q9rNsNIbItgCv7oKfpIKQpoTb/aKs7heArA6tXIlSmSu2wDjeaWhPtRd4SqOzjd9FfZG8XS7cJ3iHFQUaa0jvUWEm+i1V7vHnAsIKmnZesBMm0XWYQD9lBJZB0Y9OIG8aGndETyWqxpX2UlCA80jBOS85s27o6RcMMu0WVaNQmvaBEvxGPghr6oGB4Oz/IHSwcO372qRvv/Y73+ocdttSaGnAt8kdysImaRneWRgGiQl8TS2c2/HQH7XVpp4FkvI+serp4Uo9eSd2gqto9W89M6msyDpjDDk5MmYljaM6gwlN49XIUVr3yydXjJ053znDS4jLHSiAd5GC7Q5NhnliULC0dar8zTIKV2O51tz7/5NKbv/snSAg889G6tThwc2ExXjOx1cUMP8G2kLvqwaFRlKUtXIwqtdff/ejyh8ZR1t1khYektpPS0UnuHKgoc9Owhw+odeUkEVrZz25+zikTW3vbsiS+SZmTSC2OOfrYhqR7/I79rqGiSk0ShCRcu2SRLR9HFu8aIxAB83yXYvf3H3+0ubP97FNPI4t5bzHSMORwdX1Vj98ff/zhhx8uL8w9e+GxR89c4MNcn926xTs3K0vLzNe89ds3sD9lhemg07tHsTxTzz2Il4ZD0gzbSXmG972Df/iH//ORR04+dv4CVuVolCUa/nn1AmF3Twoc3rv54fnz57d6nFC5dLjQ/t0bv/v8+k32bJ05ffo73/5eu7OuEglvtEf6nEdJqgKGSHfyK1UuXW2xSACSMZSKewCGM2UPexBLWatKgRwVKaLz7CRQjlSAGr3he3uLor281J178tjZv/rW5x988P71DU40efb8qbnVxXuXPp3rDub6h4Nb1z79ya+e+tM/3Ti9cnBm9cTrz3z061/P3dbTjaXv9nZpWVbmjy2vrrNhoLvXo7lpwWKO19Z4pNNr0eyeQzeaAumgei/9g4G8yKxS3nECQgzDyJfQpHJJ/EKRIihsNsIqtRQBq0yee3DjhsaoVFOO3NQJDxVvMM88ktfk26m/fOG1pI2yGrYPBaERGcWvEtUoOJS8RiTBVY7Q+prwwnWJh7/RrzWll4JIjI/wM5tkdmphP55HCI8ILGh1pMhNMw8xSkkeq/pR6qu8Onbyhi5RdZQcNnTPXpQMeJrRk1XD/amthDMbXfqUwXsfPLrbPdYNqx52qSyb3R1S3Rvu+601dSN0+KLai+c1Gl/iq3HO+4n5w+PMJCCdtz47NDwc4itlqBfh3einSpr11zZBlvdcDzttzckwAUqOeocHuzT5atnSQ5jOd6A5mlvmmkd34T+0RTQ7Mb1Ga6/+N2aEhcbZRMyriAeMo82CQ0iPzoGnWVkXAByK8aEiVVjBWTxD46QMowsad7p9aJhVF/EBO2HVl4QEGiHNvGgjJ+faB5fKS0NjgAph5TxekrwJobTLCKtppaEMq/J2q1IcQlmNbk/doBBER01h5zP7jZnY31O3S7lonRsPAWVXg0SQYqe7s857OQfzx29tPLJ64v3l2ywN7i6dYa8YB7HxcKP5JMgONaLrdeb4ZuHa2UdYMmzx4Zpud6CHrLn2vpY0Ogd6g4rem/dyDxZ2OEe8zU5pRoYauUkmgYOoFvd4bYsXW9PSBYyBM9LAbXyKAOVEBuiXIaLUSNV/yg4YCmH/HMhWRJM9ZI3xZyL1FEm8zu6G0/w8c828zCfv3j/o73V572plscNHG2DkOTeMdG97x5/NiFcDDs4+9tgzr7/aZWsFwxW+chLjQknMzLNSeEdoRWsmbimoeGz5AMikpPo9kyjtNBrXCHuODzlqw3iUrLdy8OhoRjDkxTcxi0y79M0tSYoMavyrupgU5JdU9aGhEHep88raJ7ibmtCfjtaGL2z1ZkGEhjhgBaIyGw1+5mJEChih2hd45zZLX+zfJezu8BEMmgGVvssaNVEKVZPqc3P//MtfMCLC8V544YXTZ09rdBR7thjV4AyMBiyw6ODbnDPd1XGnfpFr6h1RES6u2GaHBkjiyhCWfDJwPOBNAXxZIz10pY1l1BSzh0HgEx1I4k7OIjtGIUXBW2k/9oSFwdCDK18gp1KdXFr67ne/+87f/x9BJbkwt0piNBZkXkKuPNIztZQyH3YEQcBQpqYOzCGggcOtlB4NmYm4uXTGdWMsznl/F57/pksaBLUxss+wutopzbu282g2h9KlTISsQFJriNHw6JTe/BnLUEFInNCzgEqkKRQuoU8DDv5YHRnWH1IxAVdTkSMHakgtqFYATN+CaRybMcULWY6o6xrNIfssc2Lz16aWDpH1IqLgjVZ4IdWlZjR6yYI/EhlVYyRJN27cx8CTACVHjpRbXqc9v6bDDdm/88477/zmN79hH9DSkpaFbFC7X1hdStYZhEniU+lZEJyEqbG6gSqFBX2+ho1LnGiJRHaH3dPXlwbb2zuJ1ZjykpFKfDythiQbgmxxPO8SwTG4FpuLVaUt9PYck9TsJsc9fJlSXBNIAt9dclGsRnPWaojjwIua9l7gst94/z1O/AeEWEuuVC3bkImAF154lDItGUH/7EWCmQSvUCRXyWkqF/6FMFgEetRmI5g8c1ZCgav0ozkCUkJGEMC0CScWR/EbuQ3jCPoS5tIZhTOS4zPisQ+OnovJkUE7zuEN/esJDnObccUUTNbYVkif7XikFg1n8DwKzkTyI/IvtPdVuGDOiEho9K0eTc3A/O82abbBZzi1CMeSEze7YuXVWEkW8CVSI1Ew3JVBIIS8gMg1ngV4ElQgDYgxC+cUEWkz2AO5DkNGMcR3xDN4wq/45/QhHy03aqBKqjxeGusT7cCkp/MbzEgqTImD76sjJWl2BOQSCkMqL7WY49lm0x4x1Vppli2KoEHluWa26jAE5/Bm9uM8ev4CCnBeBAN6zyTSjAR50tTfP6AX/vzzzz799FOWhPXo2N5jIiDNPufdTMiCFaZDinMHCz8bEMGeYemhGYtupFptcIpZVFi5F8P2VVEUOrXnpjVzrpxJ6uRUgkNcN/6lLTVD5CaMGFNR54cEJUkqZQ9HHJNZ7Pvu7uwyPHj6yaeYlOf5kyutLnZjEzc7ce5u3GOr79La8VdfffUbKysYvN1aVI+zf/Db3/42PappexffW9ImX56WnHH4E1BC3hrKlFvQ/vZv//b/9r/+X8GkpMgs1gaTlRRwOKcbsxOhHI+duEDpvP/eh5cuXUIryvqHP/zhY4891vc4U49mDzYws50lqxgl28nqWRNU4pYHWm4xBUrqNsrI6GQh5o9U6Yh09/bYDnbv3k12J4GPqs+dOMHu8huf32MjM6fBYDF4yhYHB8snTuxtbPz0pz99/HtfxychhGcpZQThYZrWj4OnNDs24PjNXcDL8RFCNlWgD/jSLWV/Qs8lhaNrtiNwmzM6Ie8l6YEiNc9CCNBwrjZy3FbuWFBzpGBmwJfzO17ER+f7RWgt5YtzOLq2MzBdFhMR0PAhUmfkq5RjidRyLcvkhQmVRfUi10cTGrOm/YriCKJ+MfUzH68gWCvXfUu0Ptaw1mFcQ/iwmpgxjzRKDXHNeoExaFvmmHY7QoADzRhNQcyHq4IbgnpuHwBMY4Oq4ExLnQYvGSeiEHiwkh5fIGROTRY13HFkjQNNBtxKNBC4RU9d9TxKVE8cPsLIatONqOAiM0CIE2hyeS2eh3SadwpX73+STR4v1cFpYNBZXWRWbZcdZMwjRwfRi0Z+PzZ3goNWoJOqDQPz88sHbfov+lx04JYrDbva8Jzp0E03CCrxnPiQv8pgKqUhB5hHLkgcjgdQpre3o3wtqvPlVjiyxMEcn0VmcSVeRe3P7xHBMuSdA7scpO2RHQBkZ3+oUMQMr4HSL7OVCCwTGwE1Wish4ryaC6BM7RjZKMFCikpFzJ4nvpWvvHu0WREqX2ZF4YFCLXqc0Nmh0BnPEJic6e0PqO9AIPSKk3FccYm7pAorIOD/PsK5C+c4hptvZeNXrB2AySnmdsWC7wi8E9tGwhe+Za/llOG+lgMOl/bZWjC3u3F7sX9usd1hbn2/pS30fkRh+Kj1s1SaMfjL41NwpDEnvkSB2Q2YMcAnjcJSElnlcFt8dW/hsP3YY6deeIVdEmfjpQbMp5JLBen5kaGeeXAsyGLc4AvYKLbEce8PyccscDXxl/c6jfSaUrJyEbZhwKeeieeEXf5xjg2y9tldDT6MtEdbEJ0ETWC/+/7gn//ub7///e/Pra2R3PfReWrRUh4gKSHrLwBKF3ieSBRgWnnXtGWqruLQjEqF0dAEqGyGDVaphBDJFBHU8NZmqhjmdTMh65FE1XiYVRzGuK6iZIrnWmUtvCL4jxQHRQd+wIuM0sBOnA5GraE4aEZ2soz6NqUIgnSItqQIKJFkmWrFdJKFxaQBV44qTysMq8jQezNQfBy8/uZrhumXdWA5I/6mN1vbGxtbT55eoWVfW29969tfe+eNd3/5L29+7ZWXOEiBFkTDjsOu9NBeJGwydGx8mJBKKnbMuZS9kubSZxc8vR3vJmiL3oCP9qjLvPTRxx9/fpXdTBcfe3zAYfGHxw/25m+xQYuttNNCst5IoYA76nXDvNtZ1JBEoXAtmJHU5KPSM800BQo8F2cBzI7EKqy6KPuqpUTJDulq0eqBcnBMe88QGrsL2dzAB039TfkGE4hKHjMDkKucVtGCoMjMjFsK6/MNcdahHztVF9iMIRVZZG8fLLQ3d3ssBrcXlxj0OAtuLLjm7Kv2GZiku+hYzJcyUtSY5VoiUtgY+mEo0kQWOHKEwkR8FU2GO+4r1qaXlR+Pjq4O5tmXoJpCGMQgr7O4yGcxevtbYA62ugt8malzwBcGaKKFNHZqqoBHCLV609Cn4Th3UDm/NTlKcmuXq+ET44V/zRDgZGSVjzqqiakGjktPO6zrllPTOjSbUt77p1SQOajVirID0GgVM8p9ft0WjdeIGWTO8rgxC8m0pHFbUWUB1r2emfiE67x7Vzl2+2xr4s5GK4LSTueAggNcNNocIZCOe9Y5N+ynUecTICQPu13QvVMpyCBnKY7VD9U+S6KtZjMdbyAlZcWDqshP0kQ8HSjxXOguEW/AcqItAJwCdRw4EZevcXiqJ8LYhofiw/by4KDbjgPTDubYwQ1YEk0bjiHPiNvaL8xp1lUmipBZsalIo2l26zacob61fRp84YD9Ik9KwSgBATDHRmaE0Gny2GNBpu0N+htbmwxHoeOpidnSl198SY9GL3zr5z//+adX34NqdeW4SOYHzPGxKevaZx9duvw+U667exuk6kUlrN+nDqlQmWasazGznCzZwplJWE7SYNraDwm1DtakXOEDBzgD8VWc7SxGCgMnyBRjq8TlFGotGWBDBxOuYZARGgba3AtEFomXnjdGHWUQJMeoXMz6x4HmYgr1vZ2t3779Fjtx+PDgCRreXo/RyOr6GkbTDuhl7bzb3dngun9PHwM8fvIsk6o/+ss/v3ztyq0bN4EvHmrU3d8bdBYZVKsn1D7uUDscTLpF6abFaUrz0qUrv/zNr7/3ve+1pS2DJC1d07QT137wwUGHY1T2dzvHDu7cvPnBe+/dunED0UyCf+311wfz8/8/9v6sSbIkufPFfHePPTJy32uv6q6lu9A7gIulZyDkjJAv5NxLoZDfgV+Hj5T7wEdCRnhHZAhBYwZAA93ovau6lqysqqzKfYs9wveFv7+qHXPz4+6RkVnZDXCxjDxupqaqpqamttux06EzSs6vIMNcFxVhOhSaQUywOGix4rLjih6L1gF5TSnrvSMaTtYskM8Y2G4EuSCwwgGww/bJZul8u3LjUXN1Vd9tqnZUcOSImRFtQOnEcrNa2G8f6l2CcqGNijgnt7XDFfL8DSolnpQ7liw5VBfobQudLndrcjPpSrXGpaLDVvsQlZYLK9XGMoOcsn0zkpkL71uoU5RsLhwTEug5vk1176vph51FKUO6RYi2z88t0f4YifL0lZyXNSwk/yzncLe9WfEiPCJ2JsnRwKO5ZcnNaYFDyzyRwhRDaNV2RzhtvhM4ZLYiwE+4Rn/0EOn+AIlqyTwZ3CTPerGE5VN45xXWU7A4Nio6yXR+bJopRPoRtDrd14NIwxuNcIouD3AODW67H45OHBw+/uLLM/uHdJoLNsAcltpUfFeONd8kOy6fTGkq/dQ1+E4b97VaU8t6A4JS760JTLFSP7wDs1QzHT7WynlcumlYJG3XPHOyUY0d8dSd1Brq2URRK9K0WUnDT9Ie0tgg85vXH56W2oushYQ1xebDfhTAn+EwKSKC6sqEFBFJyEcjKhsbZZlI3hQcy0ozlbocs8tXKjL+JBfw5rQ2xk2lgweToNVGTgqCCVJvQG7K1sOietVWZ0oBKwtgijmdeGGh28YernR7i6fPNLd3Wns7nbUG3Ha4yblYPKxW2NleWTtdXjnFAGuBBlz7lNX+sNfts5ioD3L0KmUdMaYlRke6aWNAyYJTtZ5RS1DcEs39FSYkPCHhz/OGQrmekw8/xJzO87hOXOGTZa2iYykAhNAqTbHAPIiVQNJWqd3u9lscJ63UdKO1VvGsQukcfavfpjMelnWFFGlduHTx6ssv2RuG8FafbuKbhZjIZFwZMf84FkCWoeiZEmoWAA1i01ZequSMsqx6lnmbGyOnL1RQuaT8UnorTckmY8WQbftWucZvuvX+3YUa2ctrTu73KctOxFN6zUaw+Vrv+EKISWd5jFHq15Vh0R7sN7k589NPP+ecBAM8IMzceRqCPcZkYSPfAR7H0/r5kM379x/fv/8PV65ceuutt06fPtnt9CvVMp9QYiA7ZpP5kNCZZIDn8+sjiRm8XCOeKqNnKzgNarEtl0NNdjAdzw+4eMxv2nQ0xIa7+/U6g9pUf2HBdukZwHN+ZKgzC+yKcNZytH04nU+oUImLlJc1MUYIXeDZmHnKuWHn49HOioz7rGweDVOXnc1NzsWceP11kNmzspyO0UOZG2DSn4bG+PN8KXbqn4s/hTQFQLFJ42PRT6tAx/cn5T3Oe1av0IkXK+Vo6pW8M8oLFhkJCGM+lj2v0uk0D2FzOCl5sEajjdwg967C5ckin/332fikcnqOj+aDgUFC63Pu3LlBZ8C8jqsM+co8W1mclLlw+azOH9mOKDmJrDKP9sHiXhx80ECaYWLdKlyZvFPDtPmTTz5msv3Gu+9wh2O/q/1bekccW7vQuvze5WXPwDJmLXrStPBnUo3BEskqL6DUPnMcIs6Y8jn5Imc8roTUzCRVZpYeG4Np+hmKkFMNK2jNlD9Fkqk/8hlHpRyTdCfBzxIiLU+OJ8nRoFErKWgs6pDJrVwQy9EIO6Y/DUEPgjGnDow68Yh8tFVSERmZPyO36JkHn4mA/KrI2IyttbEAjZ81dZAXef+LSz87+iY1uaPzWlxcBp4VaGpfkfdTe0gOmmmZJVJizJHvNGaMwoMCM/FS8Ay/p0tuZ8Q9b1BIa4qt5xGw5VTRnruvotn5tLNzOk+f8+CeCY/N5UvALPlclFN5BidsfrZQQs8JkCtWYp2PM/Dkgt8mMFEAwzOxsrSI8lj3uD9KeHyPEYozstECRMLI0D3sR7JmR7filySABjxIn9FMQ7KY8S8kkfMYaj5vNyIQTNLSguXTO0+Cp7vIgH4Tv2eTqAgnadoHTusAIVGOPjRqda634tNtvDZLLGdLy6U6JLt7m5xjevjoLjcL9/q61EJTvcy5QmhLY0E7hGaHc6yka+NbZjh6r5ZnLssZm/AbY50J0GB14wQzSEZp+c0C9gsTys6b94mIWYEJ/r4IaWkBR1tBYfL4/qx5Yj+RMLxz5w5jg5cuX2W0wAL0L3/5y267gwbUEfKSCnPiYrFWV1mgbXTFiyl8x+Lv/vZHQBBY/EMTbSMBUsuM0yUxOU0m0wknpjlAffr0aVKEkOkD5QIfBid9m68/Yj273//ki3ucBL9/fwvZLl26yhI5bKOS8Xu6cJjpLHZsMzNxKCl3ka2jKch/1nynduvBR04hmEMMrjhj+nNw4+Dhl1+uXjzLkYKgY44t8+WgUomRGLj1lZXOzsFrr7++dOE078D52We4YRNwAw0cgp4pPKiaJ2iUCFEYMM+FkW6IRtkojbxhor6EAJVYJZ01seKWQYjCpRqL+vGkHeGYT08L5KiHownTdI/APCbaM3BI83jMVFKSXIrTHKYhOZL/XwjK3qwpiJ401w50hKhb97gVEZUSup9nZCLaJBjhz+YhaZo4Vi3OZVUv8kkTjUD3zIyiq6U+0jqC47nIUaVBRyD1mXWHdi/cKZDSzPFHVqYrtbp0blFCjz1CHqJwxtufOhvLSrUBpXaPzXAmhFAqTpSAAeIiGD+NUOSTIMor1Dh0y8XNCs5UlyNaQiHj7gfZ+fPk6IsBZV1jY5pKgmy65PQr9Pt0OszNLy+v0FbDDc3QKVPK1dUVRhcrp07zvktHi7blYrUEMjG0xqRACfJtQ4JMbohVGy2ns+2VhlpyT4XmPYqQyjXTL2BUayR7Vo9yanllLxk5+11d/sA2JtnkPT+i0JcjkGuyI/Exy9Ho/JnTjLKgwh8Pex1TCs+Xsz0miSMrLe9tTb10OUDsEgeOz6o8nTM/ObYOFzJjGI5fiJ3POq2rNWxPIvB3emPjq1U5hscPKmmlh1M1x5pYdP7ow2u29KxaRgTc/DnNdh7cMT2WUSIm9Omnn3KYmu9iMDpaWKzzjhpjVkeLmVKlzpzrZB5/j81wn/w7sQAdVqGMisViNFCy87ml1kFvZ7PSWOT1tj63iaJfdkfUythumG5g1tkeo5PuRUmh6jYfttKFEzJgWwmQWQ2yrTGsoFzs8vJdYVStL136o+/e/Mcfke2Vvha5xuN89sqye1uAp85xOOtP7dIwF+H0araO0mJmFB3IfiMMuRGhVcP02DFnmWVh3Hokh5EpXf5li7LKhfahYFNhg4/j1RYz4rZntk+Ud1rcXqfFisiNa787+crr3MhDd9Ib9nReyYwInHGbKl0pJXfhXGoWjL9zCzihTflEwpxHKpl0EUBeKHE9vWXKCgR0p4mYQKSSmc53Pm3y7GTMAwI9fJLE4UCmejrMIh2LmViqxMxenLuKJ9p95BPijIhS9iBP+dIkYJQFyZcrPWhS51KNLqQ9QegM3U4iB/HPuBmCSt+ZpLowIHgu1biiOk+eksSYTjCL0eZBSD9tR8gFhooG3TTECTatFNDQtw+6+9uHGyfXGfRUyt3vfPvNa59+dvvOF4+27jPxYz620OBqMJ/b2MqCiet1k8onUa0W+L5c0J/W8qinfa6Y4ss1jKg+vc21z7ex4R989wcLZ5Y4Akat2Ds8KNTWMdfeYb8yjCeg0/zKHzNrGbG8HeMRymgKMxTZDPgRupzCfjpA5OzZicRhYVoqVDYFt8yG8nKgMuJRJnpoc6x1sv1/jM9bDzHHumK10oGjvL0BeAYH55gFkXvpc0Mm0natBabZUp3Tm9Cjze39tROn3RLUksn507yWRzIVc+oe4qJnXFuNIsIDPayzmbbaVdWgsQYcZ0JcZ5KB3DB42kjMD1+AIQ4e1e22dfy/yW1spWGH7dtBaWmR2NMvXmZIVNjv727vrKyvNUYN2jrrhjLWltBzebgkDHKMm5q3aSU8MSG0lHbzju+9hvdxprogvJ9TpnsDLYVPpxJoj8x0vGVY5F46U4w8FT8vPBUZAD5+mGwz5+Hm4S6nQ9O+Mo+XhZ9NyYG/1V4vNT+VY1WWaqOBqefCNzZCfjkfERVovaTXcbgBRlqoMrnGv5ke3CqsFtgGieOSX6xEK4U0vqTLSNhOnmLiWAKXAWrcaeMr0AAxCVJHXdDBxRmJhWSt5o44cRnb57E87iNfmWACBCWYFZEL5+x5J0BdXVxe39o5qIbRkUhC6diZEYXNQaqWDxpr9zJw/tfQgiqz1Mba417aXredlexEK5RnNCsc8+ItmAuCepnCeVoJQsg4y8R0c4sLi1zXwIImVxJBu7LGdYvLg2Hn+me/pR88bO5z8gIuXj35ACEvXo/T1866jWgykEq2WGTrCwC2RG51E6i1CVLvnFWDjFq/E5qxIkmLPOQLTplNKkWRiTZoLR1ECSwXlW96ELbK25gYKVZmEEe2YZI3uHZWWKiuQJ6OL8uUI/usslZ29vZ//bv3rn3+6dlTp7lkEINmnlnT/ROMLHSBpY/Gy9xHPCrvPL73ztdf/eL6Z59/foMcIcVCscLIuccpQPFEq3Keq5xZYWq8sLW3v/PPP/kx83zcgOuXhsMTy6ukhXnT8u8ftvkiy85ejwtADrrD1eXld77x1vkLZzlHRg3Ghq3eWeaV3Fx3VFuUUSOw/oL9S890NRy60kBYE1eqiqouYH8/jxkEC9BURAThhdK12tIXv/68ePqVy3e7N2483nvv9mvf/vbqyROV7SYGtH721GG1cGt/q/T2C2cuXrz1u+u767WG7xtV+N5B/+GXt8t2pEd9X5go+oK+FMntG7wKtHLiVKO+WBq2u632aLRdHC6VF1btHLS2WIp2HzQH7MiQTCDLlytFucmc8jgZm8U83W/Kc5oSiQDOS8eHE1lOp6mfHeLpHoce+R3ZPF7nJK8Z6gzBU86pfzIt5xP6i8mopw5NSuiySeYIh+NEeqHfj0OsfIoQBhCY3gblUfLhmFP3pElH1GnguK2zFHPadDFgGMdLzhx7cAjPKKqirKQihHTdgD0V7wq9x48izfSEU4028uSlcFyNw52l0uK9hye2dk91lWi1pzOe7VKPrSfXbqazNBOzy5fKuVQeLaoZGy31xY2rheCWXFOgRGFo8nPgUqU38KbZkvE88uZls9cZ1lYU6wXso2VR00wFkIXGD94j1/QQbVlvRXuMh7EJB1nxM4NAkXzcAIIofayAUfPEch4EDJ1s1gIMRPCR1WF2hi/FIScAe89FQ53Ix1eTNOJCr9aZqb3E+ajJxrox9Sj62FoiKPHMzi1ZkyCWhiG76iQYMaZMtyL8yG+OHKjuiMrkz2qP00teKL2LhJYVJe6Uqh/2yu3ht06uvPn2W/camn/dWVsmv3fW1umndsoLze6wOqyDz1nnWrUywH4qhcNRk4yzisI6NDu2KACVsOjR7TDOGPVrauRl1loW12q1bFgKtSyZeIoDbu/PmXwzHiQ6DYXKm134KRVrLuY1xD620eSr2+X9GoLcJy7HJeblMoYcS5axweqJda7nItFzFy+8/Nqr5RqXH/T0ORKGwAyCZSnKhv2RBy83qzWz5iOS0zKdywLwMLt0PZg9s7TBn3p9SNQVo00EIazDzw6nlBWbsRMfU6cglhZx7AOYQpQvjcVpcKyxDFSZOpUXo/U8pIPtjP2Tf83wYUCPrMPsd+7c/+1v3/dTz/D3oolcXBsxiMfktBxJq2MHpokWILwhzF1cWDLaeP/9j27duvft7777wgsv1PiQiTRgNdc4gECiAk4yHLN+Vt/EAvQ8JiTMslTvJDenyRla8jRIljH9Oo4Xqj/JgKhoS9RuJrSmLBQGCYvxHItYeP11znUSdCV6hgPDCe2ZFOI1QylPqybhZxmQmK7lDGKpe92akCpkKgiiXHPEgP0EDrQ/tFcOPcbzkmH9W/nNqZQgzQDCYfFR7KeSFaU5TytidS1HkDvmNMI0VYqZFciYLgeZQM5bi3ARkiToWSOLlGRaYl5kiZhpl+Ppek4TBPinIfmDLeXBYzgI3h1GC0xFinS08rT1nOdikry4VGOtub27ywoixsbpmM3tLZakiWJP9eTJNU7QsI5CrKfOR2nggz7grM7LShm/krZuBgiHYpiq3b6nG59bHe3cci0mR6G79hlGkIldrKlr0bpe4ohKa3pmPmMMxWdunjZSnDF2RpX7hUmKn4t9tuC0YLKTMJ0bs/R00ydxiTATxZ8jn05izHe+b5Jqgv9Mokn8GShRWooSCff2dlmFWV2rUazOPWbcKyM4kDhb0drYRxY05WLSMYmIkoOIYYzLPJFcgKloTNURjVWIFh8zBuoFtaNcGdB3bJdLf/mXf8nb1kj+u3/4OW2yRkgs6k0zzZI+5u+EhEYTIE802TkJ5NQSNT8HfS7Y9TAzemZUTDfm6DhJR6qZCf2BgdPCTEOOFsnxowaORp4XC5Ppwnee3tJCKJwZWOoCaH/18K16X9LN2EWqqaowlgULp+/wejqGHsPnEkLoqUTxgEc/bGDvZ1VyLIFz6QHPZ0gaVp5ETN2FcTh91jOzhQOs3MVU8LBxCxAPLR7P6Ojddrs93Y9R3gWHE9AcRK1W6wxEb976lFaRI7wgQ1u114/wuLoARi2p/JJeH/gR5RWTnumRVm37wWNjEhE5SSe0ZZZ4Co64Ex6pdGIkMxEr26RdV8M+AU8D2ClNbQqJfteJMi5L1sr7zuYWY5LXXnmVUcrK4hJTTbLFlFsISMK3xw8PWWPhrM0Pf/jDkyd/++knn3PqnPZZJmFoYMLQLIG5XkgKTxztM/dmTs7p5n/5l38hlcsXLnLnCQvQFLFWe7Hb1RPMWViA1lb6o20+ucP9eJTsPIslxZijSc8YPl0ijum0Jq2GiwRJRcbGIrS5lKFD/DCy/JzlKZfJ/j/8wz9ULmzwfhlrRJy4//73viO9DYfkjvNjJH3u/DlGZQtvlLn58dNb90nO00LhsCFFF4AnyDiPJYpcD3Z2uHOTLhI0gkRx8Af9U8WxfIKok00EkbhtG7kzEXJWBpLXnKXlXrUSwXe8n8gN9LQ4UvjxOD0HrJij58DrD84Cjc2U3+ExNnpcwBj7lQdEzyfDM8WDNXCensGxzJamm9x03lNzej7CHcmF5Hyj0UX11H3tyiGzqLPmbDKOHFEfi3vUTSFAnnEYtz+TFKp3XIk6rQRV8KUc7lFBcciEwp85kYQ1gdB3SJIsduwXGi2AepCsdfA1GeM5homfl2ZILMugRSQP1Bg6UZtrPPO4Hf6uO08oZxu51D3IU6lnrR8eehyDWN9k/GKOlN9QOGrDaQRBd4h5wtIpwwxm5YtnNpiGdGtlbgwnr4xzWNE0NI0g8MChVuSS24G/68yRV0OjP9QaHTiIQclWqjoZHU9FR7XBIfpzOY3w43hSPkfgK+tZ5unNq6y512rlgS4j9lYFPnKmPQxblyK29E7SlStXCPJNQGmABeE5LVhMOqYSIZFE7DMX0VKgRwJxIE/XjNadk+bFYzNO+vW8pXD5LTUfMfuqkG2PhIoQyQ1TqEG4TEsR4YkeONhISmvgjKM+/ODjTz75hPe7qGVuDJFDKmEE4gEexLUeVtXTXNRSwMkUQpDiwzJ/9KMffetb33r7ra8z7McuxceUDCGqgw9BZxI9zvmZn/MXoK36cJME6dWH3d7eVpkPb8tJ+X42x9uJcH7W9K17zpDVMsyekPxWRb0U9F0u1hF8KcEmXeKv72CSuyGLZBxgW3/ptfL2fvvuF9Au8uIDQNvJKXPyetLFvQXpwqye6QXqsutJuapHp6LZ+BBRaCbG4yTt5zHANYbZHprOQfv5L+S08nMTEgcxID/s2VQq+qCmjdeBsYdgpSsc7pQB595nn79y+UqpqgPkJAAfMgJ9lFacgjx4o2zmTx8pTgJP+aT+gGJ50i6kyshg2TkZ5c4B9ush+zond/BJE1CBoxMQbGKZ5I5vp7+lVQ86j+gHH3+M9MwCKRft2BFMs5Voz7WMOBJnnoiTlZQiwh5UWoGndBIjPXdK3aqfyGNc8Gs3yWuiW4sjpGjSQeLgyT1MBpiARxTPyCSHCRHHUabhNM71DwIes9FgCZ4Rt0P87kgRw2NCUqrqPNeD+5tMRU6dWmOxeDBqnzq9eursGt0AW2QffvQZH5/h/NHJ02fp+RYWtGbdqCyiYViZPJRAyE6/x+WMOijEVO2wuctbtDDnTvqvv/oqW0FCQwPLuo6DaVFlZbU7qrYHqLFeSI+ARXWYZ4ZNJgUxXfROPVZUVjuQNmU8gTBZuCnaM/tjYomwoUTgGewzG6c5ciQBAZtGQsrRyNXuZWfMpWr0DnLG2ZSfmEKmMYOnTGdkJhTcjJj5ID91pXGBtsnVnpcL3ItKf1Io18u9bmtrt7O6xne0vTEQo3F9ND8dH6UBIVGhXJRX2jbJg504viOI3lwsQRA0LsFFKrEzgPHMESqYxBozlQUMcYasaNdpqVKnkqJgeujtz27v3Xo4XFu9dObypVdeYby3WH+/fdjU4QEugK74mQxjHZj+Pn68NiOmEkLgmWlEOD2DEEyTE/2CkWV900weoYXMUUlFnqYJIN5Z/+idEbHI5gJ8RV0oj7nkk76ApDPbni3/PGia6yn2M4iiMmfEPRFkJyMcy40SS7V82fASTaoEZefhPRJXWfI+lksb3vey2JGfXM70DwvezoKD36fn+rdjPXRIMnXemOXJxXehmniRMQi1cmRUAVdoGW6pKMdsn5i3GQiQGwfxgS/dNwt29hE9yWC1Okw7fZjOF8/4OsnikwrSeSqPmYTinxl/9DhCFAs43w0t6V7ZAGNakjY+EXPaYykGsPwMz9Sv+Z/gNGieLl1niuw0qJr1OFYtSY6pEb0n/SCu2dpDhnbngCVCPqZgbVdo+kJilsfoj9lUEtnEmXQ9ac8YTFwAgJzxjrTRE8UDAWREwuOc07rg+LJF5U5SYSEykkx7AcGSI9c2x8vaZ7fMrEQck+cUIINkI2ASMn4TgrgMVjlku7wYSXIUpbd9pXr1oNOqLy9WFxtshPOnzzb1h3VW97mSG8Oo6KUuJDqzuvwff/gXH1+8wpLrlzfvgcV3KMkX3EzzLK8EeZBTkpjE1ExOiK0sL7L7/q2332ScAzLjHK5PabYOFmp8te/MifWTw2azXx69+8bXLr844JXnnu7S5CZK8QglYvUrqiJ6JtqctLVMTDoWWfRAzvCJoBciwYqNHkkLSPZtEtNQcYictCjggI97oV0d3nhcvH7/He4at4PI27/+pM6NGdzZvL52vdAZnVxunlm63xisvnDmYPde4cu9F994o14otbqdzv2tJWyKN53VpctVsAtz0h7/ee+wc7i73V9f3+Ad20KzwGG01nCXL/eWFtd49apYqSEk90EjCa2chNWMTEMGAhQAgsqjM5Vyo3JNjaSf/7Key5IdP+AzDkz6kGsS8P8lIfI1L9cpPPXPybmXodekGSiuwByfVKs5f4oZhYweEiA9/kJ6GqHRKEiGlI+AOI8174xYgx/xSCWJaN6s5MwlFQ9MT8ufzgTrlB1O6hwI8MjZCb0uu815f5oiuN87N38GecyAPcUUP+0GOQGtBcE7D850en77MxdoQKIzlqy7TWRpbBsuQxrrkNXu/plS7/xo2OoV2ro5p1AeaAnyoBbWkr3exNrTrhV0R7CNQxgbKINWOtThZn/Q0pC4OLCvFPgbJfNqnacOWzoT6jiNBWcitemnwYaaJ2XfVmx4dxlnc0eULP7oSn+GE45PmwwBYnC3LlGasyJTgWqhkve8WFdQYwVDmVx0WflaIr78bTbpSKGMgzVOwCIHpPcSUIrJJv20DYCAQwCezI/U3jGYwOrMM9CNwboAWhdzG9xFpU8EhyZQmbWUKFMoGTJUyurusMbqcMANyYvbTZZfW3f3wK+vLd3f6uzxJmqx2NJXYssj3mZh051jrshZKVQb9W5vUOF4vX0Xr6cFrBEzHGjpv3Qy/uCAb0vQQINvxk46+gyv55alN6D6/hBzd5PKsgb1szhy5M1+JBZPZVcbyXDmWeAUbYmzz9KHlzsC22o8MvslvagNeTUEohc+c+4c/RS2RpNTsiP69CNEiavsLSb1dB5pz+zNyShlZRyGPFEO26vUFAT2CzeULUYlFomCLUcQqtStdIm1nAZpxArRMA0bCxkfGNLbMrC0xpLRu2WfOw+O44zhHFR7/bBYwHaGn37+2UcffXT//kOEYtMBo5rF3M0y1AlDkLV4O2YqCNN5LzLKFEU5to8PnScmxBdfOKz1Lz/5Becw2K0/d+YsY2C/jlv6sDGBM0lVPUukp4DNX4DOmJAY4yoG6KvkylwWk/9V5NNYkJdrygUVo4g/+qM/+njrPpkPBZ9izPEHVokJOiISTacyh8cEWFmR9bpNjqOsqltGMxgQKhgh2ihVyGbziy++uPKNN31rNMPK/zr/PPTYYRcsL9zT1N+oGck80Al3PmKKf+3EWk6KiJmDp8GZ2XGg+JvzBhGvw6d1mzJ8Wn8UALbRnzIBaJLQFAYrjWjRY63OmAi4V+Dj6zWyGnOZ4zs6+0p6yvaQHyCVEZac/amMluiK9pu6lZIGlX3FF198kYPMN7+8yzH865/dYGK2uNjgubKyxIwOx5Sb2szrn4yceO7uHLLrxdFmbHhhscoZGQ4KMSeHIYt39Jrsg9092KYmckKadEkG+JwMPTfwdMZhDTDVbS44L+2ZrBw55TaPXFV7lvPSSTl4Z+KQFA61kDUNnNPfzOL/e4Whk6gWypT6QN3v9RaPSBQ9uO0JZ05GlE3vp6cQgMdYT4UgLvqN6wz9xKYDBE7wRSrEJoqFD2eiYKHA9sn7v/0tFott//rXvy6uNTj7du+zzzB71pUwZr6LBT50TuWpP/HpMsyzhCeSk1bUdkSehsSo43uMifUApknnmWYNSBpMOUcBogdML+Uc2jSHI9imtM/LHyVMGeaA06WTIkxnAVZerCnPab9yOgl1CE8f+6eRfn4QiJLLbDtFEJU36W4Ss3By+GFInBMiRaJeWBsVNRCSmMSZGYriQHsEVTw+nGNyhALF7UiZU1YueSwjRoBHCANhxEyZHAEnihYgRzVWmk3U6SJpH1gTVUuiFpEjz0VuMeYgrwsT1Qu3nHgxiCfNNExw3kbFNgdIe3IBGkjKkyB8wPdCEYdkxuzIPpxytGAhsEico8HB043zlgjPRjUJTeYdDzp8cJlmKcOJv70exy+QUIsRLjBSKTmNUwq/+MUvuFn7W++8w5b2qNdl+xyoNtGz4tCZKebgxSKXQoLz/u+u/f0//j18gHjuXA+EYoqyLBdpVHj77bd/+MMflvq6Co8xDCMTPLT/b33tLW4z5EtOjI7+l//y34BwAM2Wp1uUL4s6oGVcIuO5HleaiwSSBx07AmMw5UJsqjxy5Pj+ZEzlHueJSDjwpThzHMwHUljVgQOOvmGiD7e3ycWIG6VhPBrxlpt0ShnYxwzhJhZG6zp0/q5J/HSL3DPDfdAwASsM5EYVOKMS6kjZlq2puZIhEwNML1ljPJF9hzzVU5wTpcD5qchTZGiz7KbgfwU/mUK9RyT8RIQjaGdGpclJpZlZuj5dLTFR9+SCkACJQBGa7URILl3gKSQXdG7Hr1MpK/fPS5dYZEvzG/HdAJwwJw/4Dkk9IpzMRYx1ns/wpBLZ5MiqjNGTLs6bXDzAXHj87jkiFWZnvMXX6sxcZppBF+0/x5nUEczmejMGn9OMTM5gwLDCx8PWhfVWtCQ3a/HkqLHCMYPnGWWIbGMU63nyT+rc0ZSicU57IleRCROZSbEumceOIzKf43v9S2kjVYY4+zeS4MGieLoDm6AgWmSWGDg85EiZApKVrAeBWPtNhPpu1MKTrofWe4/C6HZ7lRFBuiTWhQCC708WnuXh5mRzehOrrHeRaZBp2IHBB4NwPXsQiMUoCj444J5f9xMMQBd1VhGIbNJBJUJjZLnUVqSjhDxn+B7ra33IgKU1irrD2uEwAZgzDIDE8rUhFhmIZbhAMDLPGOs3wlNg6p+HIOGNHE8muOgsGB4EpbQ02hCcUNhZ0P3+DCkaD1+ldr1Q0YjSyS2oDJVnQE7pM/+EWBlw+hcOrD4zfPrdhx9gA1Sy2MVPI8+DhLSylhA0ZZynaz1r7Z3ctUMJMjZgRMHyEZA/+9P/gcLigNoTkpgXfTx4JRndBQq35ZKdOJa4xdJqFy0MSruPGMndH2qpoieds6yiAQ/GxpNBqFVKh+vpZeLj55BrKpIKyqj0rUU82r2Rx7SDYeyMDhYaCy/80Z/yLfLKUHuAywOpoFOmKeRXju9cu2fqiVkXmEGAqe0V8Mwi7EomVC8ql4f0aPu8MJzXhB58h0BVBl+oJ1Z+ULBfYzz89me7oMDF4AgBWeYq3Vsf/6axwfhw3ZuHXpiQjmVm/Atm1rIKHswiyc/MpXzMIjQ5LlWmkEgnrioIz+k4RUegZ8SjvGsvSB9Jrw77t7+8vX33Jh2p77g+Kg8o5fWrrzFUbVcWyDXFiz4lc1bi8IeD89QZLuS3AC2WUjc3bhEziEeQ04wy6R1tiw86Lx3nMP2ccV92SC3gqowCJNNTxkUzOUmi7TZtZVEzY2ORSRgHYRlRsLPsYFMEB0/MUYwIWTP9R6A85Nr23CLQjcgriVO56vRVNZoc7csVO2Xp/7BS5G9xYdlKX7losBtbKu1s7u8U9l986TI8uRqatzjLldFrr7/EH8vKj/gs/dbW/l5rZ2eLWkbjAivl2mzA+zb2TrHS8xde5DXPs2dPs3jdHQwPWu3FpRV6Sk7m7ezvFXr9RqEyKPb5IMKo/Jit3A4TyWK1XfIlS9ezWZpZQsV05vYcjSTmOvV4flOI+6OBpFEpcupPcWb5Q4HMisrDnG3sOGVLUy5aSvSkKFkb4r2vNnnhaRKgn0lLHZNpJxaXtgBpBid1mK/RYzaZL7zzkQX1a/yxe3lVQbl/TdZVGtVoLqoLqzsHB/sHXUq8UNTugh2qMDphW0PP0Sfu6rJOK2bDhHSeYcXExZ4U2NoNjVN1KMAGcxriOA4MGXs5lckmQX3szmQYeBpVKepNYa81HNXutDrtts6bcB6fkqoetH/7jz+p9krLK0vlzvDnf/uPzcebWnSuc9ab83ed0rBbKjZYt1ZLZs6ZR2ljWh4bn2rwaZcmFT/TNiKJ8TSlW1ppQjG5iBw81uOgzaheb8PTM0fCTMwOr7fBymAGn8ffcWSL0GTIqQyOQL6i/aex0e9o/ozAP7CH1NMUc2WRiwVzGgLQx454sjorljHvyqCVHS9SRRznE7lFT1S+MBPdcnRGPCWsJBZHa1GYaoDmfSU9sUis9PXBczzUSVWR1OCsIVHkRMbhq/LiJ+s3qV/s07Qbleq5U/0vD5GwPtIIyg6aSADS8nGHy0AUOKSm7GbORFOAKs8p1driUn1pud4vMymiVyJWL7FNtldOrQ+BwtBHIBm3I36VNImPSrVqo60xm3LHtmgY1icizWMi1UXH2U1rM4yPa8XVVVpeXt3a2ul2mc5B4adrw2utjCiQwCnREuvPy5xbLZY4L6qrCblGkVajPv4MI6mZmDHVYD+wdfl5aoBo73zQ6DUWGkzP6G1Z/gMoHebLcMzKmCuaYoWPjqaG7oPT8ULT0djM2WhmXPEBB87WuiqfphzxmToYmPFQS5CwjODZHsljbYcbgCMhaXfIu7SFetV7CpIDCZQiRcllETubjzmn/M4bbzCq7La53VKdD/dCl4pl3quUTWOlleHFsydPrn773MbyP//LL+/fv9/jNHWhX6yWuRcFO+x0+HhBkAqB0QpV4sK5s/xy/TMlTzdiatNLynysgnNk9WINcRuLC71BvzPsP7pza6FSYQLMAWMYObPY57quBJ9SB+KFhCdt3oCqqG6DjpTpnLKQ/qHNIIrvsygujRebBweMx4AIif5lVNhoWdPADaCCSp+HjNC7g43VjUa/sKxPWnHgcVjo9NpLo8bZU+3tw92F4mKn39req/N1pGzNyrlgLEpa0slxPl39rC06M0psLC7zftv+wwfFfre1t1nstxYWV2vLDC/1oXUKx4j0mM47LUL2UmDEGns8s+PwU/ogT1NMqb8i55RV3p+O1a0dziPMCcfCnRM/Bkfho2ccN9fnLZiXZx4pamkmQ+8QqfVRQvfkgs40AifScz2kmsmLMCOMrFi585kR/SRQlMQRyQW5A0gwF+UIANX9WTYj79jWOa2TOwc9rT54S+L+UG3th9kQOENm8UrR8pG0Bro9FmeaKRe0IvFyv//+p++fbe7i3y+qitFAInV5UOd1D9ZPcqXjwrR16SrnG3lMjj2Gg4tnz/yfz59l7nZ7t8mz1+OFxdHj7iEJb5tuaTKQVD16obBTKK+UKpsdvp9VaSM4SZpsg2Jt56DTP1fVwdgCVwhbPy6K1JbGpUQbqNy7BsgeOUdubXrh13SaGG8ntQCjgQK/QBl4BCa+MjDufqwncs2BDzclbs5XYOw8rM1BPGV1CbA1CSwtKPiOHYMdf/1YcxUxCfKbDM4vCj7RUo3zltOwpRCSCRLpRy2lHZFW9jKXxKfMxdv7ZY2NUIOac2UQw+MpQTXZ4VthOiJDATCYWeh3B5uPOMXM4vIrhfYrxcK9XW0ubi+vQtLnmwdYcl3Lzd7vU6DMxNuUp+xJAtEmUyT9sk5n0rNs7+4vlYvgO4mPDUCDJ/LQXaIx/rT+pdyl5Q5ATpXLfdkTCOITgg9ZyMD5X+9TJA3Iw0G90Xjx3HmExOgXGnyhilusWZMt1WsLdHWwcnqoeDfp5MmTr33tjS69TImFCunJTI20TE5pDpeRmAwWni6xvFRp2BOFDR4o9ZB9DZCGlHjhDhD4FhnyG8kNqPf/3BPhLJiNJTM/DCBWAfHgLnjqvg3OeNsAuNdEUyfWov6UiswTZJ6iFK0Wr61skFCXYaMHIEXuIxiOfv7Ln3/w4Ue2syzD4NOVVkAinHQaJYqZtVdpoUpgWbahW5liF7IQq2h81MT5eE55+mif0RSlwYtq169/xlrmn/3Zn51cP8Wo1c9BQ+JiwAe/hP3KzkY8T+KCfDgGlFqWipZhOnXSnChZ8Cj5PA+Qu2ccHGm9/8KZM1//+tdv/fzvxF+ZdYuRJ+cQLAdRQZt2ZkVN4SYAxJ7m5fGx+KWIpIqm/jGnwYDDd3wUrtM+AAG4oY3jn9lnip0no7hmmp+dQoilZS+V6nalw82bNx/duVM43APEfVK0fYjKIHXr8885TrtwehEI7+cau4lqKV3N0Py4mI6WxOWbxyTSzkxidt4MSr5iSR2BlvJPZfDK6YSTSY8teRIu3BnlMUszqTwkOl15HWQ9GsfpGAGo5yVHjGyYVkEuBBo4UY7TxNJYPl4/sc7+KjdBc9gTEm0hnDjFJOdgX/cucQcgbzC45JQb21z0hfasQwVDHHv50C6trnG5R7XW4LDM3u4mHCoL9pmarMVBEp87xexA6zUOyQyoZ/BGpKfxGMOxIRk35+n8Q4txHJbHMYaUD6qLwXm2FHFcTvCjSeQWFxwhokXO8zwRM3qmMIMGpuApYB6OdUiTBUNCfh6KA/VsOGFVaEwDJw1H3NICZ891FAxPzLX7o7oijlN6pwsQB1schB7EsKMyIzeoAOLASWXgDXkGc92eXovhlX38/a4NxUc6HcabSnvb2xtL1j1ZHdF8gG7VDlTSg+Jxf7V2rC4vZHvOD+KREY/Erzf05rs0a9NYxHpOPQq2YjjHHc1qDlEeHJmk6eaR5odzAoMYGU4Tjdup6bhjQuaZs5HHUiB0hBgzkwr4oUUVSqp8z2bESTko0ckWXkWW1Cynyp4SzP0pkyOUf0RUymGeH8sniuk7Bu+DYIImgHoT4E5ILnyyqbdOZzmQ4QA3pkOdzZ1ZKIK5tJ5BnlF4PNNAZ5JGpSVILEH6FxZtHfOIZ5oQaF7f6cGdeYyFFQNXhjER4ql4LYsSKiETuNPtkOvbd27RaAhoznlmoRm/zidGxLToXskRDPf29vmCAwipiiJ+9OQSooh0MpVhgE1VwlTfmWQ0llYoQWOeRUz+Rgnl0SCDZlYLl3iiLWemYZCs3llLTCKEBZIv4kkSJQMAoCfhsRQHzk2Ie/l504pjzhvrqww8ZIS0crbOCTIFxEwUbVcrde4fPHfx6vvvv//TX/+MEQvvJsuGWavW9TQTbWytVmQVlaR9yIoYNk8rw+3atWtcTXbu7CUWsundcKz969xZlwuWW+X6iiR+fi4qNrKUBpKKANyVg0Icx4dzELquImHq8SgOhgNkNYqx2ejimhbxewPedaOGAIcDby+tZOUXuXnRZQWo/hQ1yuD5CjH7MMMDlFbjsmydlOwBKZZq1PFKWZcQuou0HnTB4O9J8ATufreKVPKv4s94ikcmg/3ObqIMLbHGr5L00bTS3pNcopMJ1AiPnonof72Ay+NPVUnL478FIZEBrfjzieqJkkfMpyKPVE/lwVCpxbymkKMiaeQ5puSRFnxyrP+lMmssr565SH0cDau0V/d6mnBtlQb4t1sd5uY7rRafc6NZox14fHhAu8eSucoutC7iyjFGECL/IzxKNHHkCweAJzy9Dlp7NkZzBMeJ/oTHDO909fF0ecqxMIjSvMYHeZRyrmSF+Jycs5rJEKDDvRytWBBPQLphS59MSznyG8D9gppzGSHXkZtsOsPCJyXC68aUL0srvCXvuYMt5Kz10ztoKV48NSkrjRZp81niBEGdoMDqASMVRexwkWROUpoFZoDwCxAf0vF0fw7Bg4ryLGbRJBff7XNYzCNic8QHBHoTuOKBHGPkqZ5uKQzhCMKVfp97QcmXLoaR9iQMC6ZRdYSzNL/qr9JLsmECmBAZ1BBmpIJI86LAjrHCMUylowip1GATWQBfsY4j37EcjH7+c1afP6Bw4Yk+Tb3Hos0hBQEMKvm86BMZx0DDkR1mbRe0rCxxCPh73/4+ow428UF2hjxBAzlCcuk+VXDGbNxXYaPeJKVtxAwYQ5885Xt3XbZd+Ni0nQLmNToKYegfK+M9RjlVFX0RlTz7Kn9iEIqf45S32irEj4et9VfeOHx478sbN5ZtZ5JDbyLySpjshvlGXFj68X1LqjPJ4rc9NJTlDahOlSKZtxsDysGP04X9Pa3tyblChBm2D6IiLNofVnK6MY0bkyzeH6Itjpr1ar11787e7ZuoCzNSUuLmpLJjoYnW4LP4B9RZP04+K0awiSUwKxEgkLBNg8UU+7r6vVzo0Bw0hqXrn1/fuv0FF1mdWVtlubzYa3LZeXugS+5LB49v/Ore6plLb7zxRnf5BFRdchBqGgYhJYqrzNoUagK5+vECd0nY47IYf5hVWPZpepyWkooyi6GhZ/sAFJzChpmwOZbXpVKKOG/m3H/MpyftyFZ5A10Kd5CrZYJtsPkxzKmyfKE6Cj8ZLxgiMM+pSsr3L1X1JDydTarnVIb97f1eq7e0q+s4zm2cHa4Pt/e3mZYMinqLZ2V1AXKajkLhJAMaIww6IU1sk9rBDI/0WLGGAySMqx7e+YSoUqW2trzCLJCiBlm0pUq72+8xFWT6yIFoa5LcfolN99+CP1jIWA/H8YUKkdHCGYg/nTwgHIPXTMzUGJSpKRftbZpcynRVQJXQOhfWpYyhl6zWdfzccaaZsKjqCYZUsvT9N8qTeBL5YgVLYGNvsLoJy4+xVlYhMa9xtnfK/bPVUmPp0ePNqy9UqhXuGSNfMrlMgHHtdlYZ3EokU5ADXbHW3gbbgITCIzaqNMqjqMzhh9atkREW1o6Dz7Cnq2BYeoAcE8Y4OUcAEeejOUzKBBp/aVi5+cGnX35wHWuunuA7BV0+Jl2t1wf9NhTs48CqtbfH7kptifFPDa6eO7FMSjAV7Kv405ym/nk858ow1YbM5UApjPPitTuUmgswL4mcBjzo+vG0rIOEe94GjmabyumWltl/GvMsfk83UiKq+3NwAecsqkbasU501CY4gMwEPIDkWFkImZ044oiTKaoicuOW1GkEsakC5wkYhdtsgrzDlm6OSI75gjii8dRbCLJ5rJo/ReMGirfTQNg2p4WINQvnuEmWQ2SgR/URRS59RyEtX/grrHKr5L4OkgpP/QiJcj2ho4UnbTgjKuVZ9UvOdCGBrc/DU6zUF5fX9h/dxUt+iTdOECr3TmSU4weYpDUOyzxDXYse4ZgbDHt87p3ZMzrwxXFO0RRrvO6Q7x8jw0jrHm92UCscs29ySEzu0YNkf3en02oGHGPhMkDrjqDabVm5RGp3mvSAnW4LwQj6oiFPmhGjfsIDnuiIloohE/5VbvYol5lJ7mxtk9ATiE1RORwtP+NMtuySfqtVpj/d9KjDaAqk3D2PzipWE+FkBeFRKgQzDRiENISjSEpXEEuFM2942A93qvhkGsnih+Og4ZgQ8hL0J7LSsH/88TX2ODlT8tprry02mIWO6o0qamYrcdTnUhJlcTjoMJI5sdb44V/84PKFcz/+8Y9v3LlfLVd7hS49U0ffiFFNkRsWLl24eGbjBDQ2RCr64XZiSJdFfxagtzb3eLtrYXnpsN36+WcfIsOFb/6RRDIGVjXNlz2C/VuQOhsLy3OXYR33F2MUh4wYv68P+RJDk9fsh1inVfvQuPrPuEVhEsPFmisn1nuc2uJS8r296uUNZb8/4GO7I87YVUoMzmpbzZNNNG8WHPUzKSa5xgjRgInDzc/N/e3uSrmG/vd2dcEad2c36i/x2puKeNjQwa7wJ/PgGJ4ZBQXKZItTfXLwnExkIpTGRk06VWqlqc1M0M8PQJJGpgk5/zT2OfrTbjGyJWsuQPQQlfodMwoZPZHDvzVPsELPl49AbMxJJT6m8G4eE4V07Ey66nhGiqjM6IlReAB6ucRnGjvP76MRH5mQKZikdd+pPLPERCbpGIa+StZ/+1790RYXV+IlaDPriB7q4zic+Zyntz+uJW/TFnuqU3yETkkfHvDHMRCQVws6W80NiZXVRRYp+/3ze+0em3Pvc86yWHz8WPoeaoVEQxXw7dzrqMlkr1waWGvns/VsKONyeCnJz0QOKsJwo4ajSbXJzA016OBbMehHHroCmlqeRCE2b7igQP8j79B6vpw7fZZ7lJdZTsiM3DltbusDNaqRj3VYTxF7qEBRsYQGH/xQ8ceS0+OLdzJOdn16gk44Lj+DpkI6mpKZPP4suF2joXeZ5HwRWOm6Dhm/Kb/W13BuVD2dWvrQWXIYmChCggsGqtaFatt36qPFtUFjqTD45sap1d7ozv4WkTdPnGLMN6ipyyHzOg1gC1blWnVxZbl1uM8urD7dg7IYD1JAupeZQLHNTjnlpoPINnJCG4jN4NBUR4oaSSJBTgVEzHEzlTMT14piuFQqn1tbYzDrg1YpktJkq3hYYEhHp4UGfP5LDIuYvGNtwwqxRE5XUcLfS1YGI7H1Q+7MY0gBaP4jHtikjEVaVPbdTzDYA2GVY0gil+VcMKZihpiZYoQaHylb55sZg1U0krb3G5y/zyvCWN1KwXv/rEDMWrOzz1Z/sZjSz3/2c05T2bkpDrn3GEkmCT7BS7q4tKXKESCsLMacZ1YQrAmHIdnUmCdAzZQHg0+vf85O2Pe+9z0d9aatMAs06vBwJinkmMUUSY6VPU+GVg+yNAH3R4h5MvXGFOZ4MqqxhTlzpydF3DvvvMMyPK/ZgUzQOKXPwNpwPVKxGWaI1ZBFUxvZ4hEuSSJgRQlzDAl6lHlmsLS5SpdzH699/wcYECmD5IlnPMdUDkkqWi71Mab7pjFTDNemC+ycOYWCh8wzAH14797DBw8WG0Wu+m32+gQL+s5J+Zvf/Oa3v/3t5s4jrO1ffvVrWaS9MMJ2K7lYf+l1DpuwmQdbsm4GOhZyplKVoqHMjJ0Q2DBdYH9mpGMslWDgNmEtwFJtOHlEHtObz9nylCdrgFKcEJWCMn/6zmkG028m8ESliAge60Elqjoul/nzefEoh8a8uFSUHc2dUecfrBpTIkxxOeOzdmKVSRfbkjQfXVbv2DbgsztqzFR9+M4BmOiSAQ1BOOOhYyjX6nhw7LHDRLcT2r2KkOOGNT65qx7XEyYWJi6VQbwuKDbTvMlp6I6Zl3iMOR0jCDYDIZ6YaMZ5DJlNOQVF8inYkwGkG/XvkjiNixHzHsUjluy6So/gnuKLJEvFSWLstCfhGUohgSTerIwMNI05htD7g+NPfjk1trPJDHfv5IaOpykqYxU9STJzvSC73lIM5xCfzh+t4pHN2cSYBpMTcKw1Y6sDW+hxfL64iWw0n7Dlyg1wmv0e22NVZuAYsH8Utz/kyBuLHNg8R0JI2pMAl4rgkmC0BGvtJbhB6KnPlDaVPPUfgSxuKWrmN1WMdZ6Bx7+GILtxkGd5HP38fNJVVjRHpDId5RKi/ucnyzNyCrpKqGPFzEV58Ci9G5NIlVm6oBFofrVCjLswp8jTAFbTHTklNrb+cPwgIaM2itimIlbtVLncRKXwOYJCInKrjPjBJOhsYxKUy3SRGYRHcYWPB1KVDjQ9dkddI10f5gMx8ULD7kkAdIHcJGGivHPjU6OxH3jox5IIYRCsb1IwMglxhpmTWSlMOuuewJKNOWc6nUmUiVBM3T1jTVqfDpB8ORAy2H7++ecstDHdjYQT7LJAjKUZAXbx4kW0Nyrw/UVV7sjQ0SOygrMqBw0RTQ3pIgC0BFeYTHY6pnPnMfsJZycRpnH2pIOhJG2FawwuEiaRAXiqgdnJGBQ6yxwcCFuzn1mUmMhCEN6XkpULZ4V87icVIxSYSMTAwAIOAYujcNngZgy8traKNn71q1+xNPytd9/lOxOMNMCpVrRJgMMPOTw58wzm1atX+WDRB9c+5zTQVrOFBdo7YI6rJxw8CQdBi8wZpMg7MeWSbj65d+8xFxpee7zFAQsuNJxnWtDG2mR+cXWG/vRUjvPMZJjCDfYpNfLKGmaJGpXsHOdKJl9obPvxXVTMuTm6SMbk9Gu8rkRCqIuBHwws0bmsPAWpl1eRdehdByRv3rnPwWq9RtTrdUYlNHZ1UV8KieIo46YEh8R84UlTcrhnJOJEJngEnJ/NFHPajxLII3DXRoowndY0JMWf53/a8p3J5+ikn0sSM9N9XkCXEFVP6/l5JXFMPm5Ijpz6gcxUI0BMGv3709GOmdZXQSPdBw8ezORAFG5m1NFAUamuiDZaFB7KhREsQL9UgUVaWoDTC1Vas8qdu+qnQnKhbwVf1fx4Dv44q+taVsbvHQ8sYRtWdwP/CY5getg9MTiBNBkQw4wKP4549/BkfIP5RWDEdB7Ac5BJ3k8RiklM0xCFs6EWsqkvzhWkZLAseE3hCQRF8BCBxQL0hVciScLVxDyd8vKipOFlns5xYWIpqdh5OT6lR7KwYqLD/cqQcIjGRBXQo3jSS3pjHWXEg7jCtzYTkhhl5LMfkjmfy9mYEQoJjo1eBjbVirJAkFieyMw7Nthkt9CK8y+irly5QoduOGrMyYcrUIyMNjKPnnnwiHB8zxF6IMoTOgJnOiGnSmkxGnKE5pWvOTma5pODcACU1WfV6AKXbj3d6nOO1bxgzGb0gInfrTfIb8Sulg8++IC7U1558VXH9ydRbmNeiPPSOg58POAA21fQnWwQ9pcUcnil1S3sN2urGmVy5QnPXkk3bOrNBAmrgrRqSIzMUXcFUcHCmQO8Yyd0r67JORcmexBwjyEsD2o6wF9ZOvHKn//VF3/3t1T6Ndvx44Pt4uI3L9tzOGooGa/lioOFBnn8R6k+VqqzAs2rnda6eV78annDVu4sF2KTOXFgBO5BpHXbCkF1E9iYx9pkyb42yyYoCFx3o02GQW14MNx79Ih36Pra67OTS0bvecebeVwVznv8nFu0aU4N3fWMl1z4Hh0DeYK6k5EDTAO9fv74Fpdt3BwcbFH7h6xAn1xmbbLc2qqN+pVCZX2hcLh1h09jn9ngG4RLLEVxnRXNYmnY7Gwf3P715vbS0ot/9H01LnazXmjlrGR5fZK0fNcly5HECvMneaOz7m0sf1C4aNEwsod2kLCWOMlBpHSPsOa3lTLBfGxajp6wEjNu/oyl4Ck4/jjZ6XPKxMVUJhglcKcPCVjAJ5BAUhWNkzGfl3jgaRWEAQFpsRfAJifbP8ygityFaC0mfNyS6dSglpzcn761u7u9R6/A/OTE0grwwQroulBL6doHf5jLASGfWIU7Ootuu8fqM6McWj2JYamzOIE/fFlAMLb56gdtjWyADys+NxjfomCZmHh4jrypihEOJDhPFRLPprtpGzcPObL9PXmitM5/nhjWBKlolK8giteLEJpHOE/sp8UXn9Bie7uUt2SiY1rhLjYPF+vleq22cOLh5uHGybOC0a5afXyyDNl4KHKGBI25HgDiibNVjA0Fde0KF4ZiRAGxdlrvZ2n5uNrgVFinqFcO+x29Grx9cMg6RWv/AOOs1sGqbqwuaxzT6i8uLLDnTBK//sXP+EIut5SPCmy26F171rQ59Nza3xydWFLFKHLrZ4XmbtDulFd0GRhUmBZyzstglDnm61k988riKH4u21EYs+Kkaqu2FplPF82HWMOLDCapAviYAjgtz8jt9+KxgwRZnVIK3iw4RDJgZh6YwpwnW6Ko0Jh7W5rKrxZbSoM7ypQ+lVFLTrCpXAeIH3twIU0qr4dYFH+ZpsTNZLDaYcMJONJjgqLzM/xaX8aohV7YkqbL1Z87b2I4Da1GPRXa/N5nFZdXSx3qwYFgdpi3wHe0XVGkAKGyocjA01oPr652gkQoxPYL9friCeQAbbx6LbrgyAhRHogegtQvq+ABLY3C70WAR59/54xltkAMhIofaKZ+RGVymkdJZPkH4IvprjHlkw7y5pdf7m5uYzBcKgWAbpRBDRykaDvN4VJBjBM3IXE7WeOgucfJIr1poYIqcvFtt93BEyTKlO7kAKft58ypU8SSNLNNGjFwkI8n2eUZSxZ/zpEKhAjjXy4pDX12ZzYT0jVrNCPQWfpMGOcDLQ4rggPzXYBudzxdfmItT7qR3LwwCqZgAgoNliCZPsTVCf0NQmFLwKAKyw1V0lgavSO7MPZEqyVO/KJErir6+Nr1/b3D73//+8xIFStpVFv45b4NvKDhup3m2vrSn//Jty+eXf/5r95juXapsgCO5majUbVeuXLxEpd048ccYr4qjLThp+PHxYPmIZ9AvPbpHVZsW43FpVUNh3BQ6Gl240+CkYMwzDlaFjrWby7jBCMT+VmMkH5UnRuLqy++/PqNTz9kpaISZhkqqdT1ivrM4MNha7u5XaiXCwsLrA81263+/e2Vy8u8/AxyvdUfbu3XTG3wjsmlfFK/qYphm86mseHMshZdKKcL6mWqdpc3Dzg1OeTdjJxJGQu9kRENwiDp2DiX9zRR/HlVuNHkkP61gv5ii6VuFeFZ5EDzOQ045Jg96fwkvffI20aKH/qXIH8a46CM1uqmLNDb+SmBU0rxtAwI6PPcyUYmRZ7yZymOI1zGcTjzTWNmMVk9JRytOqfhMar51CBRfpmcTsUzNFShuXJUPX3GSrudGifw1Mh9pAEQNqx48LQL5AsLvR6Xb6x8ee+sPo41rubiG53pOYaihx4Pv9eA9Mn7UrDiwvfM6dAcWVqyi+MbJfWJsZcBs1NZRJ6zjfJOu7vT41hGuTrQakyfDmM0OBgcrtdPDJvSsL9WPPGCqqUBE8X6LN7yCDb6484F+GT5YgeR97k01vDWEhx8lGj6Z6kYU3943i1JOhlYpRXBNW9pwZnuuGgr6Mw69OKFekmM1Eoz4UhyQA2eQk2MScCMkPSWGUYaTS5jU0SWcWksJ4hJMyMku8TyDOME6cFEhSTTJL/FbkOXfWkUR1+vlWQb0UHMN2xYUO6Nev3RqUePmKR/v6ST7HuVOiY3WNN6V7vCLJ6j57Jib2/L9YY+68Gb63yyS/JkpTAoMnAqm3F7qflaieWIA9MqomD5E3mSVE90yF/SYWxKDVvVLEmaMWuBlqC7JV6dJRFuB9OgV7YEGnlpNGrM7/Y7HVaoKyVpo77QYMOYKBAyNpLC+ciXc2FtTTxxoLnnaZ9YGpLlV5Gs7sPSa9M0c2XWnEkbkhZQyg8tDD4itL8A3AY/lAJOGCz1cR90ojHgnpYzdsxQOkalNpkNrYePfvazX9gmRaHHW7xVVWePP/KJTeLUqrjztLyWZbDZv/CnYsZU8KANr6quFiA0V7/9zfvsJTBg0xhYeZFUjhBbSCDecjqT2enNgk4sQE8jpMUDaxYCGB3i4UnaHstTzmomBojXGihrMlTxXEFj3sLNMuB+j3Mzs4JTOeJIiKMo9ddff++999YaCgLzmPEzKyTFZv6YhIQxoGsnBo38WI9pEmcIsacym4ttWjJuZgEatGlMIJ4TF8zq7wSnaRKPdi2lqFhRDDqVmNv6DmV069aNB7du6aUeKDngMBpxGpDN20FXx3MoR5AZ1jNX5Jgz5wRhpfMmbbtgxM5MISHnLLiYjzcoV09vgMOZWksiSMTPtFQuSRRs2pNHUAWXYeCmPTm4Bw0zeL1QImFEME/e3uagTRAFHC+kiZixeA5G6iNTnyTOQqHcs2D6m4oHGi6WJq0SO58+aEhJUj8I9+7d4/UckBtLOhPKAjQHsninBz9lTffA1Qag4ccB9HbEc4Efq0gZRj9wm0uLDw7B3HP853FIyL5L4sipNo6f0HPHjGJEzxOTeAZMJ0mfk6mECjIJnAxllWgSOiOUTrfoYBhSdzrrnITybyWlsTOIExCFlQqM/dCYYFdaR+b+8ZbOboQCZTRgpuVPFotZuQATo6JVaR826V/cLOnqgHOIH9qKKlyRBWgGbTisAh7EsnB+586dRw8fsnrd78loSVFtV6sJPn4SBdVtm6HeYmcVCA6eLnCSiefs9VSeM9MnsSPRIwrfYmU/0eP8YjB6gKO041TVJ0n0VeMR6QgWaayET1DTqAQcvB57tA0EDqEtkhh+V/JMgVBX7qa8VHueEBA3RY9KJYQnVUatnpyMk5+jxZvO1DSEQzcHu4+tv9CsKWWY+klagpn6XDYgcHMcntQpKhd1eTqJmRATXiYErfIz6XJZAw0XZwnEUrVT8SJ1lMchLmqK6ekS60CqPIu/jG+AkwS9oRKa5cCP+nHkW7du0RYxn3BCsh8TAmEWjwkYrVmPDy/Yi5RwxjEZI5jqED7AJ8iygKcVEg0zhBn2ILQpYeAJZ8ZyL7zwAm0oB1hanS7iwxv8nPCxufAUCbpHyGTemNtT5Km0GT7w0OYASYDKiZMDlB7Y/bZruBmH8G3kv//7v2dGyot358+eRcOO6VYHnacOnH6B8f+Z85d/i3v/NwRp1WHIbZIcIsYTaeFA+dbK+npBpz1gfPvh9etc+rG9p7tQiY3Hn0FIMyJBJ52EMb14diYj54ZS5NSfEACWA8LoiuPMvfYeh0KSM8cJrnmRk0xppb6obhEY9tPc2rrN6bMlHbHX62hYabg2ME8+M4wAXpdhhZ9yIeiO5CgCn66hJchRRdSwS+5PouR5cj0Yi6C0pmx1HD3lUxnNqa1TuH8IwNE2M0+CZ6Oax+2YcBL14puJTyl4IZqdj1Ec7oQutpfXzIKLTMb0WX1PIc/RP5Y5a94jc5eTBktSWTcWIBHjeXtoUuhc1KzNMumZwGOKkNLG6gXQ4Z6al4hD6JorFdZkgIX1BE+I5iJ9m0HRU22aQ3RaN3N2lomAV1U96QBBY+yAYeDxRDP0id+JqKAWYCoSPbPGQl7zA4EhbDE2IeACfNwHxQRc1Bj8Kh6lM2mryOBAZ2tBtdLRGUQhxHBJst8ARHAgXl4g42e5HJ5qXrUOo7a0Z9cp0NICoe2lgE5cOOnjAUiwJS8vVT2RynmKdAFcwgICDs5c1wETJCao5JO8QEJWItxjp58kNw08JgQBwPQnHRAys5EPxJKGsUoToPrcRT9UpC9IsQjGm9bKEjqZUO0xk/1KaCSa0ueCadQT/cr4JDdInCFPKda6T3xu1a6o47Bl0MsiJ2NOeFCCFPoTqb4KAtLipjkAdJnNo3iCvIP18ccfM4KqV7k8TWPiY+Zrmv80ZO4CtNcnlveVpO3aVQfd/sEuW+ZUK97jt0G7BnxFu/eEHYVQQUUgK6uaqTHKzqdq+xvTefCV+15Z85xBUYtce2W+lVG78O4PPzuoFu78THx0d2LmTETXIlA8vu8Uhk52a5XzdJpqURMtziOLPq0Hvl/nT+cddmCmJPelOv/sqJ0TDHzsZQoUAXUJv2TiYrbSYPfxw8+vrb/8Bun1TD2BPUNQO6ISgujCpfKwPaf1k0WONeC5gzFRFdsprVnaw/YhRvPo5o397W2+fU1lKNmFlDoChITDwebNz3gu8HHNcqnX7lWLwysXzq6srj94cK93sAe7RpXBaKfMB0tx3EZXGHYf3/3kx/dPXH6FW/wOKvZ9A71QMuz5Dd2ZcNO/MSO+JwPzmaafNeBiwD1KPD13KUPeSba8GoxN2YwRPGMqGSyl87n3uNlNd4dSQwg0BqIMlXVTNp7IP+Ub0pL6g0MhmTf8qgc28aRJGcYTXeCgFLW1wG9IgEaKPUe6H52GMycJLUXH0RdUqas6XQoAGyt1DiHSEnO7yHuW+0TgUgnAcwEx7RQe/Yov8olXXXw1Kjd2D7vDodYjkCthNa4piTIoSIN7glandGApc+kSp8sfGU4yGRduRopWEmmNZ1qmjhZObGUpRuZjJrN8GdpYBJfNKpYThKioyOhBLDD8vbUxfZbKhN4Dp4yVFXMGmybNWBzn1/Ob6mcOFXvsIQa7aSz09vY3t5rLK6d5y0TDhBniCt214aeffPChmmrjoYOD/cMDnaMHRxZirs65ZnN0q9wL1jxsYZAMvHx5iJUaxmLq1PX2Vo2xC+8EY9JUbu6brJV1LoC3MbDzaqG40lhYrTQ4L1GvMPips7z04a9+paZI7wV02ETuDw50bV19uLyyUl/SuWdlkQ+UD4vtg71ue0PfLbQXZrJSdtGOfo4tbaL+ztGP63QyMnAg0bRofcAduSs2q+lHC3Ts2MgbCsnlSUTfMflwOZhojUOO5GnUmCN9xmBsYCeS5nxOxm8CngFn/jpmqnLGVmA6L9849x7T20baO8VmvKywZOSOP7AxwLzLmjIi/U5nQUeMaBmNNYMa5cVe3gTCn5fiU9kGyB3Kq1rnene+S857ToxoXU7ayagi0PAHF3IWFRlE1mu/HK+q1buDocY3kFiMTvlIIUjnf0xVFTGezga0gG+h/ANV4GgZSlWvNyYbYzQNOAMkT2NhGgR+p3VidUhyEHX37l021WjO8JMKQKcyBjxcr+rZ3TmEF3t29naYH4JMDSWKnzjWsCyLAfCMz4xf2je/AxG1sxZMY8hSOLSiMiuCecoh9SfsQkYMkvoFqDAGsGYzwZeXVNhNvHr2wkq10SiUXz5/6bMHd9AwIrkAPHFguiwkPZ26IF7MxtDSGQtg+BrX+xQ4lSJl5X6sjuSYmXGGi0EDS6oUBseTP/7k2p17d7/17h/xlREwwWHmitrLJcm50FjZOLmBwhjG9rrNb3zzjSsvnmUWtH+4x0eS+VTywkp9wIX/DPKlBimWJwOdB48eXf+Mo883GNZqIldfZpi4vLB4euOk93cyWabpXnrTcxMrWXBgiDPd+tO8k48MIUBzQaApxEf4boE+gn/zrXceczy/szvJNYSa1cLlC6d31xrdQuHs5atoxlcrCpvNw81m77WLqyfW9za3sdAWXSNL0t2swGayc6DdTM2bkf1Od9DZ53BaZ1Co1evrqyv8sYxFN86QlwxTN9CnzlUxkEgYTx8nSlOL+TXaNGauH4V43cxhAKSGz4zKYf7rBt325smgXMyysXn4zxMeRoBT1ktbhmVm7fRM+V1sb3+Twj9KOuMzxg1ta6AghBhpe+7xcxlO20+U06NylgZQmWJ5zhZMXQ7BpnKfS9L7LGaVzpbY0DLk8JIgqdQOO/1H2wtNFqBDBMDIQUxo8aaSdoRg0k5otuE9ptJNZ0TUPVOYF2PdAtRcnK9yLHX1Rs6ZWpXq+kCnXWmMq7R1DHYHheF+odssDzr2lk9toL6mav0wniCxFYm4JesSdEoIWdHoWx2wJtilcp+LbdUEqBE3LWtMok/C6qUlzixDIp5jvjEBn4mIkxY76IG5Xdqc9X3s5+vNp6EuaZJYWk9h+MR6E7+8h8GvUnWSMXe3oifYTxQh9bj+I4RgLD7zq0gcpLPPctIGOAbMRKfIVLYElQu9pSQukoogbufcGTZNN85dovfZvPbRndu3q12NJRZWltk9vbCn4zgrxWFzZ+etU4vNauk3PY1/9hp06MP2InulKMB2u/1iP5Jj8YZPYvApJlr4Im+ksTrCNw8obike2UhbYnpm7LA6SRD0XX16afmTJdF5AzJyRU7IAuQa8ehnhlMmrWIxKlSXUeLAIl/WUVtdrwvkz8PREhM9LJZjEC+99MLCgi4/JKeUM2aWaYuOmySs+/akQ6uVG0XOEOMIkLKAhBJTTsE5TjhJPi2YhGdRBYMU33G0lE+1s4RYoBFPu76MYsga2jHytI90Gak+erRJiTJSY9Done5x5AntjK2YWb8d2COdpDq2c2V57txavIywHcqUy6AvnL/06msvy9zNTZdQaNnyw+wnSDB3AdrpUAEeVwRPX1yIUUCoWt5s8ozOC0dmMEsFoDlP55N7prH4MVkG83wd+5ovQOewJ7WcKW4KKQO4tEgF55myZYgTvy5SBFGbaRQS84sxYw/MVR6jEUdpTr/xti8CjqMTX455EjPDK5ktYRfetUsLCSpHF0nxYGvz8ePH2w/uNnd3Cz3d71xl6UblYfKYuqAlUaxKhwRLOirI+RK+GM6onBOFLAlxWgfJ0RX8QeaBn5cpGAE/uHuXCzrOf+1NLnmgnpCvefJ7Kp4Hx7FiF8OUJAZz+gTHIUp/jstQQlPgmBGYI3L4vNiIDMLM9KIYKYeMpzLlHFAUnlgV8af4MZWZnsAtbdgMDzj8WZvT/ljNPhHZ09r/EUwUpSGJ+n4nj8jHl4dELRXv8kNGWDcsFFbhFrMcOc/zuE48W66n48uQ8pygMuuYliHFoUWaRkgZPtGfcnPkacgTmTwVQuQfPU9F/szIlBGHi3lbGQ7HSZqKT2vArBjH6xPombMYWAttgnbFrZj9ubuzy/sTcNaZEXuB2uEgu0dLEtYcaXkiOmt52PrChsHkubDUoF1CSNItl/RNsN/97nejZnO0uoDMniipw+rll19m0YddUZCLXS1n4Dw5pK2H9fBnVtVRhCSUa8fmYYM5HSVyM2ykDbVmGulISOQwDysiRM88zGeWYR7D48ORLYeMMCkkIkSPx+aCKck8v5OEBN1uDTV0P6GhkzyxP8pVEDikkNQPFbG4mLqHwDGP4HjochxuqRzTgiLLGR64DYY6RsGxBc6Nag9xTr/mAoCfdjueBcGkBZ2RhBUDgxkpGUjMkzzOQ5sJh5DCrVi/qUmvVXattZX1BqJLMpNwHhAqdqfoK73NKXe2uLSHRiTiT7OlrllC0vzOzg7Ll8woQUMnwBHpqcRw5LNnz9Je0eAwHkN1MEGNqtSJJFGk6IE2Ec8sIVOs2JodSbDMGiMhQIfztD66QLlTapxFgpDW2vPiOJHqiR4aaR/qIjmzTZ5kJDKBc86siAKUYyskk5mn9GBTFDxI+M///M+cVn777bc50QwV5c6SBO7Nr7/053/+56dPn+VKxB/96EfgcBHhn/7pn7Y6Tcafh21N6adTARn34NEmsZWiDscw/ZOMJa6sWM7hKy8GmikwMRl8tm1nsTmuCk7LZty0YQOVqwIPjmPgt2/szmBhIOeDTho2z9ra2RHEVh7o7yBnVwMznUc+DXcBgMPT0ldRwRNuPLEWn6eAhgMtepwVONRQHLQOiR4PHvMJlfPP4Ufgs7HNcXuOQan9eI4s/FsQHoG1MPgk23Bpo8zRk+b1iUxS5N+fP5XtaJGOjp0nYSy1o3XmYtDA0goFkUIvqUphzRtrsmrlsnNuqinPJpKar6wxYc1Xfj3CwSySo5TpYmo1Gmb1m0BwIPCkguMMPTwA5szY63AK9DUcyxCtsGb95EVMLWK6qSFfuNAWpNlMlWirQKAhBz2HJxep8CA5omucbzNvz0Yq+Vf3k4IL4E9naH51Nh4kk5IkYKrykGvH8adrAWTpw1wkdJg3xLzWUzt5srC4Wuh2l8vFqy+/XGg1aXAL9SIf3Nr/ya9obDuHHYYoLA3xZYJaqaZ1GFucJXXpXAsv454CCE2zCaZ5kK7KzhwCuNcl8aBhxjKJMWYh/j0SoyIH47hJXy6KJDxembY5GkG83gdxOxkOLbq9gUx2/J05xmDdTu/cuXOsQdOzgM8QiEn5mJuJkEtuUhaFXIAnok0TRojTSr0RlHiIjXlMwE/wQhJFwu9KIkPud56R8zxdG/KIbyz/+te/ZpQIPr1wTDgwjeEpD/hejDMxPW5m1BSn2QBoI3/epTt/4axeuShpJEMphrRnkx4XyoEP24LWvoRG4ZgIL7LJpvjIE85GHGzCk1yVIwz7+93NbQZze2VN+5HE5i0yKS31yz6tIEzfXTsprNlVdL4nZm1zyEDY8YgYhZp/e9TOXBe0rl/cXqoUlmpX/+J/xzdM1g8+B9XPNnIyDn+oHc7ZImKLKPUZDrMJMH3/sFeiJSA8ruFEkXFTKV5zQc7QNjnMqpnadGVowGqtDh7QG8Ri4JPgxOqUrpB0j2qxtz/Yb92+9uHrr79ut0UVu75xoM+/CA0U76mcSbbzZnDbYRMr5VHFIKVSQDayKBukzoWFdEIlTTYe3fmMVw73Hj/kBvhacci5wIrdrQSmsjayF9K1IgkfJnlDbuVjclLn6FGRVfIvP/vsOjLQLC4usv/WC1MdzcM4ya4z3byFg630+weF3cN7v233T59ef+ml5cXF5uGB5UIywxx58Ejz2U1SAHn/0p566GijodEcIBvPge3esKMKvF/xc1XCpMXCGmnOFDDn3XAIOE8L+NWWpYKO+tqOIJkcl50L5t2q26JSGrtxyGNjDLKZULqPKQJlO5kjj+4l61hdNKH0dezU/DO6o3513ooCsHdml/owLfSHS/0O844DymswbPMyS71X0cdnihQH1dbP3aN4hERkWlk6cRcckUx7bmxWWTEZkvc6Mr1LT5Tx8bxgLZz/Kq0trx50D0muV6x0C+V2WeXi5yKnczLWDnFj1RKwEjGIlxGiO7kVkMWGKaH8UW/EBr/tDAclew31m4+8rD0tM8KQFrsrIg2W4DGxjGYcUXBp5jxjWSfxniOek5kOMpCHcf4nMBIW7jUNyOtoMcce6yVi0akdeqQ9U2u3LziHs+cJCiWahuQ3qvBl53JjYaWyt7d9897uudMriqQRUG3t07ZwA5nPURlGdFttdOhvBsgMitriwg73tnXYngUXnH0usMmd8kCGffHxdQD6FcuphsJ2nJKY4FwDPCsLervCg17fQxs+LD1+uLlb2sLyS60myzqMdBqrS3xZ07Ss3bLlSu+Fc2sX1zqV0YPqoFJu7pT4ylWdi9fWuHRmd7+5starrXTL4bVn6EgnlBGGEm0jE0q/oc3RiQDv7LLIROdeKmIwtoRE28FWM0JKebosxpFf0Sc9BPvP1GgcvRZY3beREz5y7lDkcT24YFEnakuySjpXrEQP83D08sSxsyxhEkbZa6EBlPKRP0mdIkoJEx7mnSqFPELWKga4mXhWitKb64oUHUha+vy31GiDnvEZhJwUCqaSVUWE7GLDecOYI/eoJOhu6HlQmpp/uKvWcI0StkiENYY+SFUZzmzD4Yy8w0G/XK2vnbswPNylVnJsCT7Kh0Ydqjb0vJxvOmQnqVDg6Ck03juoxilh9W7s6VD3K/UlPkOAvGLsuQZB78fR2Xj+A5zehxGKnwQf1x2VqfeVrsXQb0IDDm8uKLOZhXTbLa6jLdcXLAVpysQmOW89xmMnyEMUPnPeSnd6ba4JXuEVrv3txUKzde9wYWlliaO3JW1QDUsaz4x6ZQ6tw5JtqrKOE5C8Jr+7+7eL5YMyt9GjKH09QacNcES5JFFOTxH5JcNkmYPDjj4IeNzh50QuT3TJEy1AJUJz4LiHYZYmoLbO68+y3XQMtmQOaKLyco8cQCCeYJlbNBm6k5keG4ScDyg3ukVOwh8wfLd3eJWQZEICcfWAC6LygosdrqDOErDaL1TTgEgltFQRKAUyx9kUNMn0iUaDhDJwOPXGrBkIk1WeHlepN+g1OsP+h9ev3b5757vf/S5HoU+c3Njd3hkMtaWpqUi1TLmdOHniwaMHfBSA0SlnwfiONjsKvMrIejiCVCu6YWNra4c3WG/dua2FIWoVjvVnqoytLzS4A3pljbkMknt7ZndckivlArmUJfN5PUWVAkjkUFIWOX5k6GMIvhTodphGoxN0GCEg94aj9ZOndncv6KKY0d4kebFTGq6cOXmxtvLex+81FpdXlpYONjusNz0uFS9efeHSuausRGxvHtT75cNLa2jmzq1H6GFR9+qFdyncfja6Ws6o81kjNG+Z5Dzjwc62f9uj0O1XG8z1SidWT3CGmvYe03cHVZQWD1rlL4uTJxM4A46xVY88lPaqAHM8c5QxvXRs6eOTXP0y/hE9SjKGjGV5Gh/iRcmfhk64KW3qf1o+x8ePokZP6A1N9zrVSxkZu9BzpayTfhPwEQJ7Gc3gkHJTLYmpjSMilYkRQyCMS376JBfCqEQnC9MMKhiVJTAZ7aCxxcrmc7k3iOElbDyVtA/VkU5zNCHI4X54qdeg9R40OYlVvXuHP30SATntTKCfocZWOdGLEVnzKFpGynoaFxjyq5O9govUU+rZwWCgoPn6AI2bUahcOAGL3yX0G6g1nub+9wFNa+n0wkp3cfXD/W1qrS4J5l1GNa+lfV4A4UVZGnFu0LIXGlcYyiGxNXV9214lVqje1kkk5BZnjTTse0E02pwlAUmjBZiBQX4kKC0M6wFZexBGSpJTPQgcrCD821Re6kpFGdRys9C0vKQg7AEMdKibzlg54lw0jBhXSLUadNP1iiTwEXuca85gXl7KSt7BHRtQinqLScKj9JotaXWKvBkzPGQAVC7vclK8VNpifFIqtOpaCi/3myCfaaodGzLbbSzXB9yNQJeC5vgnxZkKeTUcN+rXtId3UF/kYqXypSubDx4s/s3P2MleOLOyfuIEb7JwPLCwurx/40Zz/TzzqdX6sLK0cGXYKj56/L9/ceOze3e7g3Mk1aoO4dMprWAa5S6TL86b2dou74JW6OrISW/EZf2DASd6pH8GTsqU8uZrRxQ1f+iFsnMdmebQg3RF+br+kRkkcme0BNhZVBHwRwuooqEgpDdlz2sEuBSJjq0D1zFn3b+BnwOyJNTfO8Rfq9TJ3WFXaxSN5SGXlHOL4qWTa/1WS5dyDLo8exp1wFdrFGLuP3hk47IJ9ZIMIYEbglsUceQxIovy2I63e7l4e8jLr7McPbUMwxLTb2hDZWGIEKzOJHGr9rVE5+QQ9xsT2beC2BvX29qg0QFSqXEn0nPU10QNmOycDS3e9KJy4vq2imgJIpN6TNVAqzLS/JTL9OMUofOOaRk6uRJh4GOg8fhskiHtWCgCq7CKVE0E1nu8+fCLL77gGl5nPu7js9o4yUkhl20ankImTkCji7EjZdV900BmJQQYLWFezgLklNdx/McnyWHyXWwWHe7+7L3pswxHpBtKIhGTTFGQMCfKnzEvAXkOO5Cd1uMDskzWa0pWlwwCUAhs65VKNz/9lOMwxZUNJ/QnMsDN/Z5T4YfK6awSoQ3P0QylyPkOYCwJ0sx9fu8OB/gHB3si0yVu9OkmhNrbwCTNNQnhEIC1XfYaaIIxUHY2QGaaWq1payF1IBOkpYEJHQ9BCFns3i2XuYxveWldrHhDxCZXQrHdPJ9HASTojWGQf6Sb5ph7mABtGiyud+XEYsfue82lywJWzAJRZAZeKc7z9adppZwRNQ3iJ5sR2UVCLCDPUbzI35NGFUw/4M9GWaPWoClnmY9uIIpmQkpOZOPpMh9TpFxaMRd4TqytUV4uA0EKzv3P6+lJ+zNXtDmpSDEK5qmnCJml5+WKODnaPN6zhp8X2yjnswoypsMApi12HD3Hx3gCQur12VPLeHRgmXrKtUuclqL/sZpIW4HhUf37fV3xjCmC1uvwdvVAp7HM2boBjzLmytZ36/AAbr4ATR7dScJ88xbE0lgxc1Dh1ei3oI1JqBjf01L1O2340x4pNil4cg0OwihWLRx7ZqqS3GqNXyvj7TbZIRYiR07Js2Rn/OZKGdocUoqQ+iOaA2dGRZzU40mQnRT4bH4S9dYpRy5woj1ij6mNHJ8nBo+f6yMwc1Fp0P1zDOqJ0j0fBGRw257HjgKNErrAPN0DSfTkyGUGZgLzEHL4aVDc7dWZQo13JNkJ8mFPiqJzKzjQ3N584ODjEMbHoJIpWgbqMn7QRByzoUDegXN8K/J0Q9IZJ+ovTuPzLMVpnp7E0QnRUoHAeIkMUvcZ4hcqddYuYUxrxnut8OcjcFQxF4PhFI0YmARJGVLPLmhHJDQzCg6mKWUJhIAjqBz5ZWPM0wUBSESLVOicPhyckvbomWvKjVlZkIfTRg7m0ZT79u3bnK6i8eCYFQ01hNyt4PiOnDE46pfmNnK2LAQ5jY8ISYgnqfDeS7/bR+E4FIjCDSecdzOciXZMWfIPYxLHVXt7ez/72c/4cAVr0GdPn2FUc/36dVjxrULG/OSF8tpYW+VE1fbeNrEMFzXysbP5MOLzJNeuXedq6Z5dQ2eqG2sGBN4ORH48PBmcKi/+tGUOy5rEUKyVuwJznCP784majJwdEwEoT57wxoOWsHO+NIintb8PTmyM8dPN0Xvuc8z5wYPtD0cLS0vrJzecljNlcEAn/kIqkws64rVTp2zPQ+P5eqXKG4oy+FkOJt6bu1TsGeCgYpYaBYaOWHYSHIdgjJInmVnEFCLC2PQtDnhkEpGP9oAfuR2N+dxjp5OmpJ5XKtPMvzrnIxR1RNS8dKOEbqvz0HJwqHIQgjOBGdoYf+zL4uKvc3D78WeMSmuKA+EzE5PYIyWJLJ/CwyCZtHCRxpPgqRpqWfKq6rbj/iAJtUfqEu1TKTmmlXqQgbpsnJV9HH6erZZe5McRZJ0QCDcbKTo405WJH/NADKTIZh4heELR40FH4IkjJzELoDvM0ERLMNIKWYI5Dz1jLB6dfqaVsCYaxQDx0hxjfzWfGJoA/oSZlwj6Yf2hslRnTemFV15nmFSoV9u8O/XwNu/c8Do5mOzQ4zi0qHZALffYITMZItabCPoda0WHvJp59cEjGnCGE7j+7hYvoi2eOQXC2atXGw8ejDZtVsVshlFZv8/LPazn0BcQ5OkfvQmdry06segJ7WigV0sp7YBmSePPLjuUYCJPHPjIlwDkFXDSwSQCvMhSCFEENYcz00ozCyscR4I6O3vYYaWkd2hafGCq2z3bWICK+7LQLV155O8eT8WfuShLS00unHMI05Ac7cwgTI5uw9NUSGImk3nwHDJoOLcKf3qdms00I8bYbMgUNAwYJlnk+NdzkSuacXTi8xw5E/NbxQstT4I36U31MBmjEKbFYJItlkatLjufcjG5sYSTmZhZCuMFaJLHxthp0TCM/ZFATNNAUmMDLezsLl+gKqiF6hiO31WU7BxM4sedGDPfKPYRuZ0Zdb+6cPH7f3rw8IuHd++eYvApZ09f2I98j+1RPs3E2RYSke1FTBS8Sc4WlGINWVt+7PfYYitP7Jpz3zpGYeUrNEaWtl/Hrhj+UaFdGHQLreqjG5+c+8b3KEK/MTm8CSAqNRBqNTiEkzlxIUGzQr+Dkq1NCq/CF25LpZpuRyoWum0WnTfv3ubR7+yDTxPJueaq7sHjzLIGjr5wUxp1MAj4YDKyDCLYnNF22yEv1nJVIa0Dt3UDZMm4Ui9XtMcZ86O8+5lljU+ZC1dqrOzwBVbOUPVutW7c+vzi5StMBspVTVCZulGRml191ZbTNzTuSktvnmqvrNNpsWq10FQsWqX155AOY+jyiZOnv/GNwuoG+dLGqIpCW7csP6VvzgrOf3Nw4FbtLJTttIS9UCuvGDfLE/nkIj3bYZ0gsdscGsGsjo2nWAAl1aSRK2h2hUcu6tWDk096OgCaDsMKTE6ome25PLU6H2PTngEaW14uMTmp1wYs/4kr9mAbA8HfV2cZds6ta3GNuHj+dFVa1ZbJkho9HJnCcSodHNzi8hqdR6m2xEo359co9vaw1OwXOqUKdyBMyv7kUDiWYyoo2VZ+1aqBhFd+x6XGkS9BQmmrKwqn54bWNpmW9IaBkGxuYH405qyEb1YEgFwI7bk4T3Ga1VdLIco8zXgGJNHSjNgERPlTlAlglte0x9Y3Mvg5IzavqLzvfXCdWrxgNzJ7T0Pr4au3mN/B4R7t1aCnZVwWcvkvmxkVVtbWSVSs7Onp0QzVVta8FGTViRSU3MzSUe/jJq1yl79kH1mi2aFUOe3WqFcfHuxzRhKTpO1lHi+uoZXm5ARjYhiDrbP/IxbPNSCgjxr1Ot3WYZOPrxarFd2jFqxf1Ee50J4LxaztKFziJH6aT6GH+meU+TiJl0iSCxrJUz/Gcpr9e6sDZ0YR8CI5vNNn9DyZKIDhh4ZOZWq5sIpliInMR8jnTNIMPhE5RTAbVm6cj0cFv5WLH0f2DKaEx/fbkR6U4mU05uS2d3w+VsqQq2E0DU8UtFdGpAUD+XF+1yqeXBLkVsVjPQBR9J0+paCi5TCPDg7szZg2rTmX4uiFKJjq2zWc+4HQcq2zynyonassZ37IQachJAffVyzUF5bo+/29h7F1TUrgtuEZnIxRyHMaLYFg8DMLY2DDKEN9mDDRFQ0Mn50wEjtjJDDNuXRznOUgT4tP4VConAOGtNtr8g4YrUBvsL++cYqkGfww8mEiyH3TOBLlBa+9zYdsrllFR+uSEFY4PDiEcD9PSfT0DiakRVdr3byYOCvgaA8N9AaF9fU1VhiZuHJBMAg1XqdSv6fUcTFpl8SB8Qmaz5J3W/3tjz/jjRQZnp+ws2GExnBiEinwOOfAfxwrbZNxYSp5OZU8w1WBDGY/rD6XarVKjYNijQZSkaLLBoFhzn6EloQGW6PoAl+fvXHjy93d/W//0bvcocllTb977zeff/oJa9N0OkuLi71Oe393p7m/u7LQWKgtYJU4Evrxj3/MwRwGRczlK2VtOXhN7nMUkeZfS/fFlbXVar2mW9FDZqNIAeKi8rShUIyVJw6GpxXuVBPYCsBTioo88UNLNh3oHgE5U8jxsXr9xJkz/d4hy+uVgr2vZv3y0uoKlX/x/kHl/sFos1PiQ6B/eaKIfdRKpdVFqjVHTzjkWKyU26eXTp+8gtq3f9l9dHeLdFjuKJw7uXpp/ZNPPqnvynqrHC80h1+fx+QuLBp0vtxob/FXG3Wsw89zISqs1FKFXQ+N+o005Mv5pM85ehijgABbwvKMwcGHQkgxAwfPtLYzhN/v7xPzcvzkPQtkLZb48WmfAdMkn9auOFFyVMWo4pnMpxWeSn40h1i+kbMXdww+gydycE9OPI3pQhUbZ9lNLG3cjHaMMC2Gt0KpUYbZH+rS/GFM0bDNrVVqTrs5vH3vCmcqsy09R5KEAZ2KxV28qjXpBFaHTGnkTT5LBe6qU/oeVOZi6hlAYsx0jR4v6fClrGL5sFujpeOPXkVfT5IYra4m0epo+J6Tycl37LBDm4zq8BpiBHswkfQmkzbIxQYG/LD5SEvCkgLLCLw7TgXlT7LbMATN0AQVeIlIIOXU8+WixpVagGCqH7C9TM1JFUQ9VimUmKSgpeZ/Vx8Wo0XiSCpdFjT0ieiRQ9vGNcz1jrZiT19PlydpWKKE2m/2w9v3hr2Fk+v9737zymuvFepLrLGBxE2CjdFbZ7k76/2PfvOb35xqtviaMOIwAGOBGdKq5Yh3qfAz+SEtPkVAxtSNUeif3z3x+GCp2OMe30q/RdUo3m8XHzxof7ygFQ+Wi7nKrDesUlC6/rqwcffeKxsbm90uPf7Wwhq3p2wt9jSzKvCm3KjNe/aFkc5Bj8o9EiwB3FOmaJx1xEZHB/wdKW/gGc2wQM39SmhL1isNj51rw0dZY2jm05Jf7O1Ca6GSTXXoQbRRr+jsIuVY4Trq3UPmjSXedi30sQ4tkw+62EylWD596VyRz2QaNwZ6qItvoVlrZIUa+mrrF1JBvfhkA1/JIQj0gxJvhdl36gigF4CWrsfOSyDamXKTIEV4ApNXPSb1h+qHszafX5W+jfn93aMcCUFweD58uAUFdRD70UctknJTAWZaCuTcozKlGFIMsclPolEp3RywPCYyuqiOkeutQhSNQbHIaQBOCbxw9SWqj0semeKBD8+Ulcce/QwL0KQKPQ0Wpu8TD4Lu0pTgxTiJp+ODkIudmRhoM+ER+EQEMFkBoQJ/73vf+3/+9V8z3GSwG8mf1uMqpl65/GkuUv80W4+F3Ouknix4yIZkao6f4yCcSvGLGzdOv/XtiJMWEggwhN6lgomrlFkYfpt6YJjiT5Xn2W21ONrw+O7tva2tQo/JFXB7Ocau8lenaS7ycak8CDmOePapeF+S44t4gCzoIG2YNjBBAhKp8NAwIZvW2U02+dVFyQH59NNPwWcBGjjdEeRaPtZo2zQDDs06PQzrRLxYNBq1OppzjMo6Rl21heZ+s8n3gs6dOqd5oIkHZ3jCUNqb5xIhXVwTZx72U8OfkHrGz4svC83+dUXNjnsaqHRi9saBF6rq2uoqtZVGQRuMKg3NHNR1JZohF0rBzns6PMYSRUMGiQ9xsATVfcMHh00FDt1oF/fwkCBVj1gKiFLTYPZpnKforaH7wzNhFHAcKWNuepMl4MkQJjEyzHm/Tjsv9l8F7hnJJZ0Dxvw62nFsLMeQ4DENGEySw+HRkav9fWa2yNMtqp1358vTjkO7AdB6SQhDW4FtdFrZXkhG5fiwwmWw8EsUIOCOk8Y6rhGp3SMX3h+xMQYaktBWHNgFfKxbAWF1RfqxI9ayZdGrIjBuhtT588R1sxPQxMIoTRQ/CDlIDOaiRD7pUoTUH7FmAmNszgNy1JinJR3McsTOi5qFPgHzVNK0iHY5/TmBnQWiYBlg7m/KxP1H06b4KdMIjx6PDTxT1OfqT5NLJQfuQSGYsQbMzE9spDVPMH780SEp/ml5Y4GmtQCGuGnkJ0JiEnQW6pcrOu9MqsZNqYMAhJb/aFbgcwJas9knIObZYJzTlcXThSepe77ACX5TCSIhLWiGk+d5zDA86cI8FUiY6nlroBNPtQbnQwni6Do9+whAr8og09slTwUgHn8eM90xoRWZZ5DeEwUyOqL3JHfIpsWIjLMLiQrwnD/PtcAXwGfrl49Cs8HcefDYtRE16ZKDjBsnl8gHQpXjCmJoi+sFvwffWi1bY0hw894x17GPuXfYWQTbhcFDvB+QZeLJquby4hK9ACNzjRCs9wnPbKGDYHRICK1SsFEl+eXifsqLsuD4GAvKr7z0EkE/TI2HXml7V9fIbJxcZ3VeE2WW5ms1jg5xAsNLEPvUHFBORebqMiUUOWIGE164Jl0IhWHDy4DuNAEeZQwe32uHm6jMORO8sUTwRyBe4xceThJpHYpU7hxORsgU318ZsquaOZRJvm7evCkPw+ZW64RH8S6zDosUOfGNAIzQQCCG51tvvXVvcO3WzZunXn2VS/8qBx30Vti9l7HUL7SwleRm2wjA8XCU41FRTtAcIo+0FfLunhgEJ/rd44TA/z/UIX/MEVlIivUrZSjH9ivxOpI4CjwzxenSSdHcn0KOTGocmWObC47xju2TDMdAPjqho2OPwX4GChWH4TENk1e6iEFaY2fjQ4IW6095fTEwLglGMwMz+kEjCRqpjHxcv4zb+AEJjuKmQ+GF6JQJQEbmVHPaVeC0i5DZMWg/DWUrk0pU3NQUmIPK/bAF4AK43xPSTrGJypNceFTkEA3Puc18Ok9pInORIUmzZC/OsNbDnpOayYiO++vZEbfMQenFgvB0ZLVG/UWOvn3rW7xOcnjvPh+N6B0eUrIsgdFlnHrllX9/8eKjf/4JvcygpRI3xTJm0qwEVjDBwTt6AHLOjljXfMQBgRPElEjLrntmxcZlAxMxGHicPn+Zp3PzHrbCZyNxtp7j6SIAVMy/GMM4OfxT57l0CP4YlSJL2hiRlT4wx1eC8x04rD8QDxoZdOHZIpDAllxk4h0K5771MR7GjWNZ8txjitGTx/jK4cjZxXN++KezmiJEtOOkL26Z6sTEtIHareDmVmHnDCF7D85BtDZ6iYkSO195ESvfT6UlPkaa74vCz0NxBIajfB/lyuUXQOOjcDlkzwLA6MkhuEJywIpm4lrQZibPsJVNHp2XkCJ0TBZkW/0Pa/A0VcWlXqfWPKiunISKTRzwBrYDNsl3LNzReQtnG1Md59b7M769UolL9FbPvf7W/+b/9MFf/19LxUa5KNm42i5Dmfur/aDM6TuiZJBXKrQFhXGQWWSkmK0hti1CZT+6yZ03MHUiYHwOmuU72CMDHHztj6EqGEqFg808K8NDDPGDX/4Ln1UZ9OxmbXoIIqwy0+yTOEcJrZZSRlyOqubAv1rLmBraBdt1bO3usP/w+M6t1t4eZztoAqpsjNEo9PXSKLNLseTkH5Bwj4zlaMDxIiJUlvDlWamWz589TbvAQP9wb6fbGnC4lgkPUUwerM4k2UdXOgqhrU7yzi3QPMvqizimpBPZC40GDVB/yEWxQ87F40qjCudTtCNo0wCmDUw2gLOECX6voq/34hEma9n0gIP+9s1rG6+8xNUue23uomSfVgNibjhCFfJIkxMiCZSUqYJyUzgOPt4TqSLPKB6kMysz9kPuPEUaYGj93mcxmXKGOQWdAjit3R3OjewqMe2JkXc7A+e1l6V/ip3T9zu7W1xfyYoh5aiOk3eKmU5XtZ9kfKiXvvpmls9GpQ7EyR58udl21XTHE7XAMETHhipfDSCh06cu8l4nuAy2BlWWAQuDXottxJ1Os9Ub9OrcM+YWDFXezdvrqw60Y1Sx9qQ81ETLLdbpvTugLyPou3lhiq4zSdS4GrXMMTEOPJgjT9cPU1FFaaKbKT8Ygtm/4r6aCy2SM5/mOW11jpnp9asl/lWoMYmZ1pvy1Nl2xlQmLCPVJS7dXFhE596GgKkVE97et2OJwrQXurXhy947/8z12Dkqq86qRlsVAIz9CpKqxzQJKynID1ilLS1A1SjTMEz401aXbr8Vz57m2M3NRzuPHtK+CNE5F7tuw9oiK7X1p9MK3IXqaUBKXnQKklaq1z5st/aWFnWvreoJzU8oXJAz48nLLKmO5WyvO81ujgqd5CDHDB5RiDGKV/oTbu43FRg0jVPRoKMMjs80nlAnXkcmFW8S09HQjBEcpTIrjzG5hPGEdyaVZ83tR2IkFCZVEp7nDW2gRwflp8LEdKMncsolF+F4HDnlk4vNRZERDvXQZWrAQe9s3aL1qoEu4quYBhzXYAxGCclwqQHspcjan9KpB6bykp6qzsKwXC2UDtTW+3CJ7opNo36Bqw15j00NadqfJn7OrCBebWmlUK0XWzYQsdGFHYVyqVAVHn8ipVoGHFRegngIzhOfzPE6BUMITVTM8b4FK3HezkfLjCqidoIFP2OrZ8bZbNwWNFEd5sq5Tr1YZr0nR3IY5HLzb32hytxch4ToZvo9ZkqMedi9QsLNzfvFfoviYT5P6kB8sdgtP0vlWL9IBRWO6SIjK4IsILIBhgedwJnGC8fhAUFkFKMzWnm+wEto+Lvcb1ysXbx6gXvn2qM+e8y7DzdZTDzc22c0pYv2OTdg7waz5y+BskUEvGoGhoW2Gk6GqQ1S8PpdHurufl2x6QKQupF6vXI9I4xgU2WlwY2OYSsCvz3FBo+vZLbbg2q5w/zEJ8/iYwYAwjwHPerFWCiC8xfO/8mf/Am6koSFEeMZfWzk4AAEpuX1qlLUW8yLi7wHxviH0Q4JdVqd0YDrvju8SMfiBRCUDqaLj1nh5wDd6srqyXPneV3Rx9joxWwVTI1hZLVmQcjsolJq7uGp0rF+IQIdzTNIfxQxE49SUNeCMwRySlKOip5FlXWmym9RH/cuVhcXV0+2d7Q/4R3Ruce99i+ut+98gehbVfXCeteSvG2sDJfr1c0m15FQpRvck7280Bz1NxeL9fWVwak3C+8N3u9vVk8Vzp/bqC9/ffcL3UW+1JaAvPhDs0MxqXRUdpxXqFHvFusNqgBfU6BRUBL2ziOxKIKQNxhUJTJmJuACimEGkN8c+TOs+fU9w8z/IlIKgnMa/P35pYr5aUkJiT1MieH1JpiBsSIXSD42oRzJ0cnlkL3mTgEFgA8OD4YV5J+fC8fHIvG4xNAalUKqc1aUPN1lsbKQmH2A8zhkdHN/I8MMw6Xw0FxdZcgzfqcYzsB5vqB1PqJQKLzUGm2/9+nqYYdNI+bCngTCeHmwu2s9qTb8PMrLJARtzui51WlTahZDZM5JWM/kp4ZRuMH90IV4zKsGtUGBmdlKubzYK3DBEK0xw2W4Ma7FIupIdNBequgaBO7WlAA6WMiiprVLGjurCSCWRhO/j+ORR4dyFecvhmEZGrUwegHAERB+6UddLHBITyNotaSaJM50tBgggIy9pU/PKaRSFV2VPoehvTvdUu1tES9AE8Erjty8rIUCuePbjcaNlhFLHQH0h/P5aZfhUbHS+/73C2++Wdh+/OFPftL/+BqxdY51m+PewLUXXjvz6qunv/fHvc8+e3Tt14fdLge+kGSBs+YUv30Lx+c4PfWJ5E/nWtBypT/gaDhshiXdDlCxrdbSQPdKN+h+NaAjmrZYfdZpOvvOwdXF++cWCl8eaMjRLmq9qFXnbn5eykL3fMqjozGCWhXa6kVGLHw/gKJC/y6t0uKPEQsnqK2RljWoq7c5SUSa70E2iXRkG+LUyMZb/vVaRWnxpmxXq19qGxgyWb9WqJVZySnVq6fO65t1UMWDSuP0/Q2mLDlYjqPMJ2vJgZ4+KBuQFVAmEz21w0mSTKcWFVOQMp7Gud540r9Cp0sK8DBuUWEr0r8/5yzDCowFRGHjTIXsNmp05Wjjp7XP2UTV+Hu+vEqM8VKfZwu8CcVKFkuR8SVxip5aIogElggjY6lJ5i0L17b3rdtfchEHiUUtOWaafIhKIqAFwceHKaYG/bIo1kB5xcCuAbX0NITHfIlKaWTQoxFDw+I5a9RSTn8QP4MnXtDrvPLK559/vqol06/kXPteJP50dqnfIVHXBD0WWvw2/g6RWW0SRcrB1djkwoy7d5cuvcT0gRVanPAMk6cr1qwiAIknCYoH/XPiWWc97t3mO6o6R1yp1Gy30IuGkhA3K+xUTjFKHDi8jMvZn+VlnWFkbZGGjCEySaNVpkmM72kvQIMJzkUynyDc0exZxg8OLTCNIjjcEqvUtejDENXTU9ZA80CmqHFNJ4qWC2PjGXly098777xTsv4NhsD7dheEM9F4OXGReQL7g3pzAhBEX0iA2AiPi9LkghE+15PQpjhw1rzLNl34mA+aZ55Gsnw+njJldso0lXJkOhdqsbUXlJWLCj5W5tqObAG6Awcq/JBzbdOpkxdg/ujRA0q5WFskXW5pAaG5rU40kh/Tk5K4PzzNZF1XKY6zDTo0bUR9ymPpRwjI0HowMpE5/v+daYASp1jxoqKon5xuFJWBwNf4QmvEQYdM+HnJIovXUg324KePvb5Ti8FnegwOLZIHPS0VTdIURCZHeLwoeSIHFgumQwhi3rSDHpzmILntJR4tu2VOApgDgJ+2lxUNrt30AXeGddSvZwQMV6PziQTwdgQlNCenkUOkSj1wSIPud5KZUdPIDonioYfMn6/vOVrJ7PLb8+jk4AnnNC/RZnJsp4NHZyflGWmzLAgQEaInos3zOKaNwEOz7JghvwmZYzJQTGDypsUyTzNRnmmEGOVsgzz84GwQ5f2FJSQTcsF4uodMK8qIieWXmjVnwcuQnvSg7OihxUq3K4cGE57UZed/BANEol+gHThOiUfmRzDMRVHWkQo5mQN76ZOut/aOT1C5OJ4D2QczoIuPOVLBoUYyjodMMQQyxeqjvrQMAB0/tn5IQqI4mBAbn85w5jMmBy08cY6GhQHxJIDAE9HovleW13RJsd2TqGhkMHnpcMnCqZPrLEyXWR2kGbfpcafV4qDxoN1lMrD7eIvjMztbW7S68DT+KlMKVnm0xUMEJ6ps74vEhj4K6WllGnJJTWNJswSyuRBrkke/agrvRx0caIEmw0yIA+KMHyS8eP6CJjOWL4KGNEIbTJC4rRtuaGPY13a1f6qaehEtlljyS9BZU6yuOCnQGg0Q8MQ9ACHgzIq05pMN1dTVZaZFI+5UzpOnB1NgCvG0IrJ7QIgOiKerzsx2XhVlsyoVkFmjSm1Y0Kn87q5Ot1k1o3B1uFtfOsD2xFgVcDTisys82c8g45yF9h6NgwgeC5Pe1762c/s+9nDQPkCT+9YdkKaLSqLQ4ocnnBng4agILnl8guAuQqY98JwJhHAcZWUxjXYEZEw7C2k6dhoyi+4o2ITAieVAg4qOorS4WeRzib6KtJ5QLrm5KU1m5Ai0NCoyj540NvrJBQgx+G/Q83zFI7+0xmSTxpae4my2VwokTQg/jlrB03VC6xWV46AQZXUnQ1M7RvMQMY/jCa0KSwRatAndCuYK3I2W0XLl5AqtBBD4Y8jkwh7qEEja2zrWDEkuymwlO06foLnQQqoFc8nHKM/oU/rWgEPPCpvkMb3RFkp+a1oEzDT5VMlAZSoVkfGY4OM8eSfp1VdfBYHVZw72nUlaMxBoPzmfR3Gff+VrF15++Uyl+9FHH/Wa+zSViKe21zs7F9T8MUXyBY53Z5QOW8UuhqRJqeiuDBNC2mTa6pXysk/qoXVyi2dEFAhhCwkFSthzoQjrWViwUyaPoS7HgY/TOpGAAeDgGU9InEqZsg+GqRtqy344XEg/i3iMdoqLdXaLF06c4BR5Js9c1pBA7mxzSU4DpyE5kplBV1oa5VK5vYWKmkY/q990ONYqJSROtu2kn1mOHLnZ4HF5phEVNb9Yo9Wl2aRPx4BxmMm00iJDj4IwMpmWkaQjBwyP1o9XtXwB+mjkXGzKJ43KTp2wdVMuVOwILxs5tGeoLDQDGTqL+gzP6nxfdftx1W6fQXCL1M1rz1CQMWNZCkf9+kBgv1rplktf/6v/4/X//J8Ptt7TvCjboT+KeCLOGlwX126LQPtYh32Bg5IOqMpRDEyQK0CszkFrgKKgY7KvJTsbWxrtqi+FdIccuhm1v/j4t2+ePLsg7Wpm5Tf8ZkshVo2tSWJrVZ3KYNBuHW4+vM+ydXNrkxaRs4BYQIXNNb4A7q+fO/+hzidy6E9lbBB/ZV6S4cy8lhbqDFJpc2k6793VFfvgcJ+CJqUcfG4279iHSkEnCXKHUeKsb+NHXVq5xD0P3CepOxn73OQzGizXGQDzBl+DRqfdIo+hF/Tbr1iRlg5lF10WqGgduWBcJ7rGm0F99g91VNFSPLjx4fDs6sLpq0io3WBOglstct2yx0a602ftVXJk0UrtiPIS0tFOJ54o1oCUVEgK2E1vgh7MmJxGHLIfE0IFMV6tm6A5XsC5Oa7v1nodY6qsIqn7Gl/o0mgOUBcLajjKlxkL+wo+42VCgiRQMLmRXchoGYiQG52EQm/0cMgMue6IYbq+uMrSMxMY4HfufgZntjnqjYUD7j4uD6s1LK/YbHco3GLZTo9GZUnWoyqL56U+2MdTHR3wrBV35C/IX7Cbnbn7C+8AG9FUUwof2OmwQUE7qIOizjgPCj5NUqzpWvevy09dVMWTC3ubtmfoNXRon+t0fEN5+ofvQJY0wQ7OIRaYWCMJkGBGtAVZujNMKOP1e//FBkhDrZwVWbTbNGFqq1sFQD9E6E27L2H4BrK3Kj0d0aJnsHIxFuwuU2yA4ECPwD6GJ+dtI+ni2NcllncFhMPNYbYQwzMqyIUxUYOc4mKtmWuYF9g279w93NkZ1y+/lTs8OYLNZwo579fXB6T1zotyrbvvibBTkET1e63mwX6/y5WaKhmEUSLmZvZgEQFRHS1CZgYdOPM5TTjdankBRXJIcpAYlXpythVFVZmogwKgI3gpiXH2HMlWWZvkqWZiolIHinlieNml7VWaROqfyRaEnE4iScwCCF5E8zAjydhjZ1e9ReI4Ensn4pHli/KOwvhZA3Kh5JL23BFcBq87Y+aTvqgBEy/UelBSaSfzonkRNzELw9aZzAZkhCSKJomgrjGJDHLyQZpyxdtD6hfJReEnBZkbouCt/Ve6pXKd80X05hJJ37fnxuFRm37bTt3Om//qMDA5qy1UF5Y73PrHViWCJBVnbtpJhHI3rmpJhPWbDGhw4slU2SIZOhN0nQCg51IJop+sSXU98HQPOFC7n6f7ecGWvxHXYeo0hdRn80rOdY6q3K5bb/A34O5gfVC1uf3oLnbA2TGQkZTzPJQFQy44U4N43ZC3nNlwt0NaYmRSWYlP5ctjyQ84UnXmQn2z/oiJJIOrjY3z+gBRSVeFqC3E6ZyaRkUQcXqqslA/bKlnBAHHeVeoFtYrS3TrxcKpjLMIURefbxgMtrYfo72HD+8zqeYDsPTjHBUGUmnqvjUVOc5vODHJexUdjuULRxkz/RKTWX5ojS19RXkuaE1Iy7JYaHDJoz6RhC473OHH5Ac0STvZ5og4cWhmvzViRPrSSy89vHOPCyX5Q04SloB6D4qLMlVTnCj7tgpvXGKwmraAhrIY5yCJTgMim72XYyfFaM10OI+D7o0VZvarnBSlm0IqO91L9myqY6xTUfF7cvOejqBnMuoIyDYqkH4wNf5PZh84f1En5A6xJbmssdLB1srFhbVTB91RsasyWrYTl9z4zFmx/VpxcWmZS0X1HveplcN6cb3VHz3cPtnEZroPfvr+le98p31mGea3l7nx+szilY1bpVK7KSE6CxXtslgnWoVxf9ButmRkVkYUFgqs1GuYE5VLg1heZpLtcvowDGk15MPeSNyGW8p7KJOxkqLekHwMtSTGENPYNG2G75oL+icvad3JcH5fvwgZszA/DUk47VTa41zBhiyAyXO6z1UdP0ZC04kIEgnHKp2FiN6imU3Hu0wxJyaPQqpMWXl5QvNEdQ40hpbHfApHy5bHnhWex8HhqRKiP2UzTT4NSfHln7BZi+R4aQKGw/J9Taxqv7t24fZmnWVlfW9JfRY9uPFHG/yq1cJDbeL0Yz6VyTDILj/IOOoXT818eU7KQ2k6INQNoTNeVzXk7vblcq3Olw5oCIxRVX2HZlPNg9ZoQ7utRS7kpbXTCwxKg6duFzYmZIDKrS8gaHGHyTofj2LMrSxYt89bEZBQ+6FlXVg0EgYq4LC0mUDW0Hl+DcmYpw+rImquURLMkcEP7nuOUBqqIEINol7BpzeUtfOlKfWHyKLPk5kzAqFaHeOZpudwxRmCymOyOSKG48iMNbYatUsXzjOP3bl1u3D9yyvV6spA15CSEs/DqnblN/rt4aPmncPu4quvVl7+2rna6uYHvzlotTijRz5oisEs2yl4BlWopUc5VGv8dcuVwxI3bvGGeG1QYsFal1iWeSuJI4CFlsTzpZJRjQFPtdtlhHZir1DvDH54vnbzkGNfF+kH92v0y5WFvqTa17XTTIZUikxlvH22qZly73mksdbCkD5QRqdCPjQfIxZypWhO6qDssmD8BRj9CBn9qcfhmmuXinprplR6cLBPz3JyX9dAFZdrq6c2yo36An0N1tZoLJ06was5zgFzM2tRFzPPTac7lmkezfHg3H3O8TqG1fS4MynS7IMwLclMqhww6CdA0TkVzoZx3uup/DIXvBpLO0iFMkvtWbXXhMHe8JZsIHMaAUJv5M+cOs26zZmTp3wT2odD1PreUDvNfC71448/3tnb9YSoXhQdfhThrBzurNwfn16zNECJ4tmZ/UcPHu7v7rHiFDG/ikdrT9DzRAi1VsqhrhQgxzxxkbvri6dOQPNjzmlBy5QZ0X9fHvb8WYX9d//u3/3T/+MD/ItjAZ8xRTJOLbUsKMtkyxml/mnWxAqYIUPlTDTQNWfk7rVnr7e7vc05X05wJ9DQfICMo8h50miw87bFIjEfxzzcBwMQ8xU1/Jq3yvjw6GkNLXVbVjU5hTA+QsMutTdVGFJqzElYoOQtemi1rNxo8CFtDLmxtCTO2eIpZkrLQhAHJlUXbixT6qCETUFRE0pj9Z4nX4kBbttymj8YjWRDZnjioHWjByh4ZlFEOQSrU8fT67HleOH0VccBwOkW/AHNlO34v7+nSzuTf5TWY4PoU6iguUMzRJLZmN8p3DkA0880FQqBgHJBXW6xaJW0MBUGRmx30yJwkyAnqvADxIEMiSYYZb5cgzxWOLaQylhFDEtFTr6vrp9i3Xl5aZ3io8GCsMANwFlFIAmj1VDGGc6Rewx2syHsShhHzPGRhGw5K2u3agxI8KTcXRJgeFz/EdMZR11rgCB2Dp79jBnMRUc4uSDLudinCpr8SQbmE4NJuvPj/xAxEsA0RsYlj6UZpcpUro9VEuPxsaAFEZHoncTtjSAe86s66D/OBm2BYUblMTx9WdA8DFidQq1Qb795sL1trCKuPEEGhqrWeZE6DmAYd2klwEbWpl5snl4ZO2eNJOZogt0zBY5g5VE5hFwwpgkcaWMQzzQkjX2i37Qg7eHJqS7lHP14nOfzFSMnZ0wlB/dgVnLPUkDinBkhWeAdGno9GjSaRFbi6KcwEtpDPxS5tLQgm+ElRXQeholmLc5hpnCZ2UASlZYiup4ns2A2DLaxtd/wgNBgYgCINhm2LExhn/TOACky/AzohWBfTaiEIb1Iju88afCpvPp2UjbagbkWJW1daSY3CJmgEsWohmkGvYKyNmGhY7pUJyKctOQx3pQPnlFCMTFzdQ6hJc9IiI2JJ15Fp0H3U9Csr0W4MdQQFyAGQJfnSaCTrceP0QM2YMhKwVOJtBQEfjlN656QNbDA8eqWVjrqoUVpX5+yBof+FISIQyyKUCJmD86EAsAB1FM9v4aBKgWm5lmunYQRIp7FpUvAX3jhCmisKuB4VZxaUN7fYzx5aF+rZw2XZpBZBPViq/WIVnHU5SN+41KAQ+qcfwoRX+EL5g3v4uJCs6mjtY7mHghdYymt+4mhI2EBGg/jUpjg59VdvwuOvFB8XtgkBAkL1DxBlrRZKkCIhbZjb9RFuJM4Pjw1cDVatBeumvHVFKCo2p7+iGInsKfwIp5bjhKadMQAFALOxHYhSREANokxICejuP2tpqwxcWSQKMZmnJLj1XBiKFBrO5QaI/Br166tvf4DmMiyYG+2wbSGFKGNnEiLRFEgFkiuwUTb+F3aqD1H44kzYcUgepxbLhiTOKYHcpinyClkWnsp5u/Jn2b2qyThfOz5VdjMoM1pbAbGMUBpNo/DEIN5LsUxL60whs8kH9fcCevIou03yuM83RQdfVYq8xlNcH1C4B//8R+ZZ73+8D6nOwuHOoySpoXfJUc2v1JpXi+ZSyZlMrNOxcymhGDSrpIiv2on7QOJkwj6aBbNiLWiaiVEYFQwZGHLpaVx4BtyPDmNFmN1e4DcuKdTlJwGJPykMhumOE8DPWr6KUbznfOJ3PDgkNbv4p9PN44BfxyYLCaHu0rrGxucSGF57oS1kEgluC1RRgnRDLc2g3OqWj536dJG9/DDDz/k3SO0ShMOvk7UZdmh+eW7BOgCMAueLLw0R3pPhUQlkmUaZJWaNacgitZHdzYYIOr8+fMlrh/THrFKBFq44cF5ruSZzKAXJZjR49l84lN8NF6G8bhECJJEjlYZNZm9t/QgHRZvYnEZGD1IdcQJNY2sJKgNs+nIhKaThOHtWDjk0sqlkk83kWJKIpmckxvPHKd8EGSEQaX5iFlhZxj5z0I5FkwZt5euZrLKiS2cCbOdSMI4qNx9xMhHlbETHG2R65ntHC8UyHyAhJEx8mFp+u233/7y1k0+rcFJ1gmmWQDmOWGymBm/pMISIiNJxs+RMFomBLAyaWfQzgTpijgcNCgLg5EeMBs1bbDV/UQMis387MSuzqJ0++396sHu4sJCs74qpbBEiaInx4WemER03+TziSJ6E5gSxQEdtI+Gxfqps2/+8f/2l//0T63SJkqp2ak6x/dTNH4hcTTSyIpFO/wZf/1qb19r0NRCTUMl82QNj7TTHl19Y7ugxkecswLQaJ5VPuWU77tyk1F3rdjv3Xz/V6+cOzmsrWAlwhiNOEVM6hxLArPU7z24/+DezS92Hj3SNirjTn+5pue3PKv+hJMgdjOjtgclro4qcEBHabkV25rv+qpu1eCKDL4rtvX4ETNwjj+IgzWdEo+2YiEMzYFzhJozEIxGFUEXhSn71Tk2BdW+pPbYlENuC1fLQnb5G3T2mtz8oIkEDj7M48gRN08qGMwLsO7tNScQeuur1yTXZSbZKLE+6m/dvLZ25WVqVBt9FUddLpxgvmEbIV76HAmCVpM3GEjZBQ5R4zw2DGUMrjY+c+6VsJN1w6V1rNSf8cvo7RcEyCdAswKSzW71Aj+QJJLMosjD2DcDVB5qpaE20ukkl4cDbLXFpVJnW2FlhuNY6pkWGktAmIRQvkwnaBeYpfB88cWrfKwSILFBEussy+UVSqdeW+DsFbdksgbRHTB969+++ymzGqmIxsxO5+nuKZLRpVLcSV0vl6p3trY6pXo/tBlKPeoEPwmZk4dXR3lW2OMlF+X3QFsofESX/K2TdcS7sLaPf1jcons+2Fce+22d0ymW+Nz9IqeeGTxt7utOya19tkg4pScr5UZ19v76Rd0U2R81BCnK6tpd7SWWGquS0+B+vo/0xVnX2GCtkqQ7Ep+C4ajdwmnKP+oZTteeNQlOA2g3n+pUKIpWDd1o93k96rPPPms3m5g/Q5D19WVW/FtrG+Do6jdZpg0p7Dyp24pt5EfNiDMNJU+PxfgFmnSJJkMEqU+iHBWaJlcWsBnbZsMDgkPc77xoEwT01ppfNQAhUeAG5qSxMsKwi6fXPg5MiIllZqiTyFpBAUmsLFFLyPkoUczKBkkqETtwM/EpAx/4qmVBS7Z5jBHRTvL9cZZLPn7/PaVlZgZYsthbINgncJUwRzcAciU0MgJwPXOHGu17uUtLxDgdGQa9TrfdXOjSxtSRlabKebKfZnzyjxRq2ZlEsJoivXiuJyM9lKMi6NqbhTsDBr5LOCPuCSAz5QwHzU8bUsYcTDIxzmsGz4gtdzM5jDHm+FLhYTsHyw1jHHkEZkCaWV5WE0kRa6bp42UxvpH78NFm5IatEstxGprKkydP8D77mbOnaHOKuqzP7FZ9lvRmiF6wgqe5iFKmcFm+xk62RGsYQKwWWJ9rpq+rBuQRXHY56TBkCkiVjVMsGorpIxuerj9ZAnZbd7ooUvSk/Px9Ba/FbS7t5yi0VYtyv8qx6sKI2w57u4dbPWThyw62xhe1lPLREIa6wwcH6rUdZBLyIgjFwmGKhgx6x0mq6pg8mkSZUSkL05wjRAeL9YliWuOavkeg4w9cCs13I5g8I6daSDjgpq3XBVCLY84n25YXpU7vpuGZtRjMnWW9ZT7ut1qvLS0trq0ur3HLAW1Fr9dptw4WGtV+Vz1R1IbzJFHOTLU6PW6EYAWQQQtjXY+a9/SstQfqcbgIkCcKgIrbeun7Ou1Dult7F6m43zzkMEu/qv6LbziA6ePJkQ46V0aLZ9rltYWaTrYifGip4KWjvZll2qDKJWE4YGnpTElQrxp1FDCqraxUN668+OI7btu0P4qgsnDaa3+P3r/f3Of1qe3HjxgD9A4P6Hm5eRNp108o9Q8+/uDxzs7ikvrchYIGCdxV3OlxtAg+Kh2kJarW0DDchVGBZUJGj0dFBJaFT6yuUU1eeOEFFndgwl7R3qY+uohWoNo4dZJvUSw2FoAweJHEGpxqfg9Ekw6sbVDqF9hiJ0so2vojjQot50RXytX6gmolOuMYMPUPdJyG264uPc1mVPtcNujcw9P4xlDiiWZnMOcGIezk9wqVMXQyJenOwkKl8+EUnZYkRrVKtVsYNpbXuNaOOV5FZwCK6zZnHr16hqnmjx/f6dUvnrZc15oD+rRzOgFdWB9U++/fOTj38avvvnt7mbSL26rvhW5NKtpYanS7LSVAXgbD3e0dRtxKmqaB5eladfXEOgl5lpEk5tchPDUijAcQE6V5pqznxSs1znSR+czYFAhmGvy34c+30k8jFUYoY0CrmOzTEIL7jOm6Jce0VHqZVt3vQe/tHG0aJ0KiJzKMnsg2Qp7Vk8vpOEjqKc+YoukzVFUQHGsCNSU7hj+tytx5AwXtBE9/z5jbn+kpXvrsfSDnusVqv9dfqiFDo6taRosU5aTv0p2UdgI6zFuPFIt0yZSjBI+9H8M7CHAOo8RkIMId7sBxOobGe0ul0elif79aeLGpD99tlZrciLBm05r1UeFMs3ultMDb0ZyhhTlNQaDVgFgvu3tw2GUhgouXS5yhRpW06MTa4gRVWis/NAm8Qki3yMgEUUlI+aUXUm+NssBS42qnq+k9lZuQJU9A6QQN+eiFlymQxzsrR6Ef4qyAVh54O5Kb6GmqEQ+2Uo6WAuCuUYLmxcbfyDK7VoBXu/QcabWXBLNaALKSt/6OX8lBAeLZaVQK504V7jyo33t0uoVySn3rZbb7XWrMjl1Syqv2YC4fskY/uvv+r4pbD85+7RuXF9Ye/uY3LMdUBy3Eq9V5U6VTrDYo+O7SWuHi1fYX93rt4fb68sbLL1dOLbJgvXRL+V7WFWjlpn0QuGq9Q3mg/mvEC+/DwqpsabD+6DFj0T+rLtzbfXxQX6SlP2ARm7URjU5LHd5g1qCRVmUgvUgTUtFARxTKzIj4Q1SbGoEPHUJpVcud0gpO+g9Q+3GwyjAGUVwWVH8B3JAcNqxXDpkgc1SeYVqpRlfd6/K+Pke5pbFOX9fk+tYyo0UVtGZ+eruLzXClIi7jHse7Xdh76uNnJsAYkvmibADwT9N6f8dbAkQxLmIYQC/GtFEViPRtr111jCSSFjJjP/GbYx6FSmWIBGgKuDgrh2pF+A4JJNx7BySDK9Zriqe+sbF+9+5thpm2HhBKwTgIE+3pgZx8CWNt/Wtf+9rLr77ChrRnfMCkgk1ltKfWaOz0jkFoQ0YvXLnKQekPPvjgvffe6/Z1J49bhWdBnBMdOjAykvRjyTXgY9h47+6Dr7/xhuBTpSYNROJjeEK/SKrYgZuCVKiaH6wOJqSZPsmwr3aBBpwC1dMcnpyTucVCy8VlwZCuB+eI7/ydFbc94PhwNlOCe7+7zZJl77AdW9iM6xN+4eCKdbOgsHIEnusccDro2QPZZUupHELuzINBDvncKsc9zr99jhVD5kvIoK002t9+j7w8vH1rn6Vn7uuAhLPGwO2qEy+kwDnsQ2rYikMe+CoJKyZ+2YZCIZJz2GdScf/BXeYXfG8HbuA4JpGRmxCti3KgkK1+GkIoDCsg+QGSl9hUAGcP0KcfTFrsCAbLk2pYY5m7EqA124UUgDhYNRBPT50nTfn169e/973vAWRpkuVxsTcRqHwmgzRBNkDmQ0H4eTAV0XrDZC0iKjqP8mD0S4h0TsscJZMYcKTFA0kaJGkF1XDJOZV8hkTQvDqnA8BscoLcsI56OLazc64Z7xlUHkWiLrMfhaYUWIBmCc/Xo4kDgvaYAeOHi7bRihW0zZ4YS3vD4gB/v9ClHN0msTtPjFxYFpUAJQIycGeSPgHOFBJdAWdUAcIPfvCDN998808ucgX5cq13AzMr1/bF16bo5ZGW0Ye+rGzXbhz29OGm7QMtPh50imRtv8ntl63bD/bMr83Aw04RyT11okiqO9DHGKkE4mwn2kasneRas8zaDQ71hIMJYntjwtoEHr5ZjB4YSbAADQnmPkRhwyHnyajFa69/nbWJZkcn02EEJvt4MEFN4mt2AtVEGlkAtHlRGYqbfQw9i8eTIC2rQePKnuMF2qSlT8RbjiYgMwPGRMVtiXquJ8YHLgMI2Vg6z8aVr+TkNHnDLHlxJOK5JMCde8DMFOwCiInRMoTG0nhbLqaLGWMzGA+VIiOKvJ/Rk0mbJ5+GT0NyNBHBFBgiAabBHMlzCSrd56WORKAodsxXEhm8FFYKTDHNfyyxnIrkKGgW0W7evMlXInSJqu4rlwV6KvqQF0P24YAlnoODPVaoz547zUDi0rlLEOKcD0+3PxowZHN4zIt7YjAVPvrFQFyCYzAliw1cA1Yu46QjHJv24KE9mU5iGhJTnOcxknEdpIGCObc12OkVXd41NfCZ4OQp8mRGgR5EeqQ1PoOEmZJkgU7uQpKcFg4TRyxhF8Ax9Qwo+iHK0YGjQKZzzBXx4+DGZbecCmE+y5NGAAcQSwCTFGM6APGTU+DYD35iQQZC7gHGJGJykRaPi3flyhUw9e1HdbgcFOqxAA19R6vccqK1Uc2E9XucvbfE+i8cpAQT3jKhh/csnrTiYudic/rA2fmbNgxF3wsSbxsq0SFHySlWGtjaiVUmjb5pwavlOD42DHq1phb49LnTP/3pTx9vPqTNFBPWmms16aTVRUYkxAFEWn3qz5xhHfVAAK/30DIkoERYY2VaVaVnoOce9CgXJkv0sAv1BsOSaqV2//79rV1tWtcaum4SscGZmQbMvUDZdWTbJmZWGbNJO4lHOUNPYwuFM3oly1pMBVYQ+jMCcx5iHSF9RrMBGQ5OIgvx902NLTkCTrlDuL3F5J7bd6R/zJg1+h8/3GPbHhxG2t3tbWcCjo/Zrr+nCwkL5zUt9CiYQMsT5zITRHsxdQghwXnxuUg8nYRnhDjPSJjC3Z9DnkaYhsDN63IalZMkjfoD+F1RJBQ9v49En435M2j4+MKbSAF9WjzM5vjlQqk+s6gk5EKIw4T1Cexs3Qgd8/hShbwd7wfmMQt4cAwkGiy1yam5A+LyuDDO1YH4AcplaUXvGGRIijdQRIAzuVbQ4cbBW3iAOJbQhGOuN9KB02ZVH+/JugleKKkyT1terr9YXeeApGtJhNZn4bE0o2hi5NmxjteiTSgwY+3HjwONdYaYRxPhKR7OxJ+++uWnmFwUXsYmilUD8sJtV/Kbs6VnZlAqeq2SW97FxFIG5hJkgCCP5xoSHLmZlhIEej36sMHmJrSgAdE1RTZTpid6+c1XqqurgOk0u7f2eDObDXF6n8Pa8ksvv7z+9a9f++ADkHE0v0hF7w4TiWfTZNpV9k251L9ycvWdd98tLN98ePv28EBvWXE/oUk1IRR8yC4y4GFisrS+hAxAYM7ZPDxwhopYeQa6nxpk5xO54ZkGTiQzKwCVuVlxkzBSjwDGQkhVYWiN3vhedKnE24QAW3zejK30avH1118HAXyCEEIpfLs5GjtyPilDR8vBpxqAkH5KGEngEKKzH0Mb19NpqkibUeh3mk8a+1z8nkRIyMRmAWGaMwJnKpfGUPI3vvENLl9msOSb8ZiHqKw9iKjTfIBQTPTyLL8wKP2nn/yzXjiraVzqDlqEOZpDhqu6icgsHHFWICwzxrhn8mjEA1M2xKjtDE8VtCU+QrZwNOaq4Sm3DhU7jFCbW48XT5+kQUQaezeOVyS0nDrGznyZEkMYPWYxs3+nWTAoREJmgrRSvh7frZS7g/7C4umXvvfDncd3uIOgVmpxW2LZdsCQKu/CnoTATD94ZrVgPOtwIjQRTXBy7pNnmYbBdEsHqHPECuazWS5wJRtnmwY3P/7VxuUrfNK72eMT9MPu3jYG8eDObd5n0OIqebRbmNh9la6G7MjptBRwDg0D0dqtZNU9gexKmhg82TIsMTxdbGjpjRk4XebhwR42OuprIqFZLeU64mYY+KlF5/AD7Rm/xNKqwQwt44/Zh4/Lw5uf6gOkQ3zyM4g/c/oMx7ZZCtzZORQH7YXSWg+rZaap1uj4bo7xRGxySkrGXGlxYwgJwh+IZsCjQpWZd3nYfXCje+9U/cV32F/lCiUwGIBLKv4zcbbTjtBpVB6klaJVmrbSp0y4SsAHbvui5i207Y5sy6N0LH1m/ZkhAJBAuOjBL8UbUH5rLEwUA5ka/QEzrzuOQw5x8GFriiPshn3cB+UIqvetLJTg94ZGd1dRUJZTYcj5L08yrxNDNA6WKGujDxYWq9s7QWZ6RItVQ8PUTJMQy4uv7nMTlloBbISl6J6mqW1LnrQ4JacREZvtbPwPBs0Ou6wN9jQ9myKadsMaDV65dEBMdaQXe8+2HrL6/H/5H9sLCw/6t//Lw08ebqzVeXWksqqZYb+jLfuynUBkW138bI96tVYqLGFnWjpnJUBPO0VSHG0MBmujgb4f1WmV2Qnb3tXZ5K29Dn6uN6Qj3N/vM3E97Oom/uZQ56cOe/oedKewToq94QaJjMrn6BR7JU322MGQDLYzX0XH9J1UVM7eUhjscDy4cefLL4v7fU4XoWEkWd/QGcC7dx6z27v18Yfn33xzodIocLtif8Cg0BqYQsfuHWYrCcyh5YhWQvRWe8N5AeImHQiTgD9QSC2Y2gINdNR+oR/bUbVzwdiWWUFiyKFt9DpuA9NRSR+xBEVNBU8ZmBY6VC/IlX3cWsHMSRHj/sL0710DfNTOCJMz8GylbF2/dnDnDgO0IJWqAG/dEGS/n7TUhmi0M+qx+09DpJtapegiu25YDQtu5fIyWyu0fZByALrTavd7nIBe9Jrl7QBWkIk28RtltBxNRFnqoYpNRCSBHFWuS00Q814xT4whF3Rsqy15QgtLV5lzLEFQuGfWo5xn5CzVgRNaFeUrFYAoIDkOzmfm02khmRnrwMQAnqDGI5jEKJX5aMS3WD/55JPPbnzOjtSAKwyxQrOrUMpun0x2sITBsNvvfPHlLY5Iv/TiA5ahT62fgolKnJza2qd3ZZ5rV0jIl4wwphw8QjDFY9v4cfxQo5RN0vOncAR3mtQejFSnFDkNVK3rbhBMG1oNzhJLyKc6J0zbbhKMo10DHBelhnCvf7s/avY5FRqWFMd4s3waLixo1Q/nfcc0VhDSckbSqczyW+lMU0lL6IblUfRjpood8poCeqD1pYl2I1G6mRKiRw3NVCl4ErClS6kvL7ZYg7S+ni5kbe0MndLaiVOlSn3r8QP49Xifttdcqle5mGdoN86rCTFHisgV01JBiA8Phl1qK7gFwzHjkyjQ+mW+X196893v8vFArh+mP/rFL3/KkB0FcBYVnIiP/MpC5hRlWnKd6OKsYmXI6zY47zvAyMi9LUVERxaKNcqO4EDGRQpigkKTzNaxIySAkDATaO6Fbpoc7H4oAUh0X7JdUDNQ7/zWN/9icfXKP/zt33ASkOsfgHAhW7nKLF6fOrRxCHrR8gGlZnoD5UluONLN2rbkSopQKW3JzX8ZD+uwJV6P2t8/sBsMGbSwDvvilRfpYRn807+D5JikFPqjiTTVl1VqjdX1Dd616/H2U50pM99iQWfoRagg6MdU6n4LCxZdenGkNJO1J4E2wyPKY00bGWf4OxRsMx1Lm5LSpjythAZgdqhIN+zoFQouE632q9ygWVrqtfVGWq/L7ZUHXz7aGtS/+92vsUa/3S5W2qW925sbneJaFw1UaOXYY904LD/+x9/yfTS+rPVgXePtLis9fFtDb4vLIQhArNElYki3WNUl6fypFVKLBxZfccDHKoc0wXXQKk2OwsQzGWGEJobHdKmiUn9CTsunxu+4lpNQfnUvImUFl2Pm7TElNnZzMMcIqc85Z/zFDSNwDhkwRY9+TzcG5QF/IpwEPAq2R/IMBI4zE1OjYJl3yO9MnCzZGRJmUUeJGnG+iicVDHt+LjZDTYwOnua3UaW63+HK3k7hzq1Kb8gbOVRY/ioaeGo9wulM+UZE8YJiHGKLJKX6sMD5WtOjqjbpyAh8hEsroAdPScLUjNkPXSEQX9RzOlZjRv3Wcqn4+rlTvDDEbIjL+FkSevv7L4KwxJd9+P5qUxlYUcta4t73mY57m6jtmA8lz1lobE3NRrHU08cjeH9QnYdehA9j+5k8BPTNS2tdZ+DYi9y6/If2p9ft0g7TF6JDS6tYo7NZWGiQPd755kb8SqVd0f7i6Y0TSMIlI6YNKZt9RRWMr5N4W6S2KgwddCZ77JQX4WbVx/2MfnSq6szJwkJt//Hd6rCtY7k0ctY9Hnzn3Rffeafw4jkuOSo+fEDC1fOLb7795qNPP/3yyy+3bn3BkfJTF196+Qf/wxc//zEbgedrXOlWb9usubK2gWmMDtq13miRxZyHDwujPV3K8daVM6+fb315i2M0g8fWAtiudlUDCrKHlMN+BW0U1loIMXip8uClauGBTlu1PtKtSpVR5SSq4wVPMsPItaurrsDUWo5YKJc+BzMfD+/3laPQqLpVExPVIyzrzgwoe/RgRMh5HF/9Ase8Ov0yowZbbu6OOAuoTeM2V0ix4sKseWl1Y/0Ea/MwZdDFs8o4jgUdS246IWRLa7ELOU+YefCctBNBndWnx7VXx7KmCz6e0ARmpoSZUTnM6WCULXrAsWMkPt2XIRqVtM1xQJ7sqpAWL2Iyz93Z38H+O/Zqgr/rwD48OPThLB+/8847rD7TWbOm4fNWUkFvdP7Gc/qhiqI5PRyYUfPuWq2+eKrxp3/8J//0T/909/49Bga0UVFUUKdZRIjXK1ce/tbhIa/XsxSOwBHn2TxaekIIXDSCKAoeN1xnTdAxCVL33O9AYwKDtP470cyhoaIgDxiTP9NwDdO967YkRWsktGXsFH3nO9/57//9v09TTXJ9uhDcPF88j0npJCAHksnMjfkUi9xGyYUsb7zxRmfY5VR87/ED2SUzAxRio29PERJMzTsh/DhaHJ50TlbqnoDEo4XCFDAvZOCuSyYJjM5VcDbl8ELRWWqHQGGksAKS6W2iNlpakgJaELBSPLwaI6/pgx2bv/zLv1xcXmKh8yc/+YkmAyMdxODr684zYytt4FfSmWbwm0jGSPEMboO2yQjTHZYPLp58gaH24YF9yd04SOrMRPGAKfkkoT2dmSXhgwLPQlyANqmE6XxSVoIewzlDEF2fQRtZppSNKVMJkKzJO0YijqLM+Lw0XYCe5g8aGUnZggOEzGIhjEg8mzypKYiNA+gayyaabgoqBuZ1VOqdw32medzQQRtX1pcHaV+snzQmcfbiicJ5plSpSPjZdvurv/qravW/0oXvffIRkNdf+QbCcLsW/okMGKXzdJvh6D4wJskAuRCGFBkNKV8F3YrYqC2x6XL2vI7m9XSZDWWgBeV2W3swD7a6bMM82h9yzvHGnX0ghYGa737Pzk1XtGvd9xdymQqbMsVfXKR+Tk6VBx1kPvzyE4iWwycQC9/+9rdfe+MSG4C//MX77IqzC0wtOH9RH8+89sl1ps3nL15GNsloPI2fGejx1GU6eP6PWFjoynWbpoGQBNOnBwELOF1IGfE4dxnEf6VJt09vvTUOmeBPGEkmiRRyQpeEUalfe6K20cn1y2B43J44pqAGJ4jpatBmDj+/FF9sMYAwsAMCExYZZ8rg3J7qSSrT+DkgQRNfiNNFMJPcteFRIjc9TGMeDTHCo1Cc8zH5I/kRSosSwm1ekmnej0CbRz4TTrqwev/992/cvMWrRMxygLCE5/BI4sn5U7uD1rCwcfWb3/yGVuLdt99l0dCNB3ODyvOA1j3XEHoJxGymnCMQNHcYm8RSLbJ2RRM82TGPVAmRCR6aXJcKKsgd8wiFp7RH+E0PimeOyrnRwz0+57lDLYgkEnKWiwqkg6CLhwTMmNNIEdEi5Kk8Xk8hgQ/jDYIuGyHPu+Cmep5R1AixtGLdUggcnJ9+tV5Mh3ZpTyBhaEHeOQBOkMGSgGHxxdhkDy8g8N3RdcJQ7xGJXXjDCRwObwEHBzqCeC5fvswJID7GQC7YoadfIGlwXKiM/YxfJWQGKU/iUlRPK6Y4kecMz9LKApbqOJD5YO9eL3UPZrCAhPycSiMKhpr8/Pt/z3j1+m9vAS9VtG+BelEjHktRmKgnCwYmR/ygSCyK8qXGidaUGyZdfO2ca91qVTj7y+wg4BiTkDpUqFfpmZuZhITkqrF6neJm3ELxLS6exMOcWNJarwR1lDb1pwxVdTMdpjjuj5hupRacqEemGVk1tgFOMIK+eiWQgcua7JABEAe6WnRHGevFhzqFxwD7t7/97eXvXwYfR9BHYmBComNyfESa+3n6/fannz5YXS1tsNEi50owtiIkiDUydAZCEOVgmS6A4/MkinRj0D0izowjenI4zz3oKpXKslFBLonnJYnpR2UBf9dMLiEPguCYBCNy9Kck00y89FOc4/tjok8kmU7XSVL4s3Gblj/lmdPM8ZN4Yo5mIqRJTws2k+SpgPB3BxUeag3dxFVj4eXu8Hk8Iw4I7p+A2GA6QMzk3M6ByBmJrQvhF8B3YX3Wb1/7DskyoKYuM0FnvsbZRmpxq6pKrQpuzakNY/R6qJya1tDgOyA+bZ3QUlLi1qZODvdNKEeIRHM9qMvjnIog0y5EYoWcJgtNUl5aLmacY+0hPSn+TkGv1PsRE86JGQf1pHyYyHrtGo0bN4CxSLfG5NS2KlVAdjUi/Qb43PwkQNZwKTaTBI8ylgVBBo3WFRDDAIIehbpw3G9Q2NjYv3GDW/U5YUTU1Stvs9xx+tKl0xcvdm7d1hz28895W+iVd945ePiwee8WXTw3caB/Lr7kDB4k8OfmNyB7S0Mt0q0tXb16dWFt7dWTJwt3Hm7fukXClqg21/0QoVNxMgAqgJTpxuIq41iXllVdtMEaLmg4+lxaIpc86hm94gDOc9OxghxJkmMFvtbLOfjT13vJC3xOxGZwyIAH7fnlKrysw7ocry6DT16AQwJOFM4liZK7B5wcHJqcAM8WlNgmYUruaQGJqeOPwBTzmP7IBw9F4dxgiPGhHDuEpexENGdLFCgrK4vvvvvu3/63v6VkS8tcvWInPtGezV4vXbjMPc680ACmr+qgUvzoFjdZWWcLC7InjYeB05/8yZ/87d/9iBWM6XPQs+lnQTnog1VL1K/mwkIeekEX2lVzYa0w8GI1/LEMKrD2/2gVOVY0KjcPGr0O6zaMpNCWlD5rNUGyTVjR3BWHJBfj0Y/P12GgUaBOBMlJTvPs0XIVixe/9r1X27Vrf/9/Ly7Ul2xCVbedHxFNOufmO3WkQW5oDg1lQonkRRnPmmPXhwMn+eVDjsM5Bq7PwJYJZpIKs1poAmFBDeDup7/43f2PD9t896YYXkWxyuZ3TtGzgK9TwShP+3uUgi0C2nEs2k+/Zw8+bNBR1RfqdXD2drYwqVbzACrxhJ4PFTPJ5hArZcc+iesjKw7OByIPedV/qxhmpdCpxEZ+KNYwdCGz5LRaYZiMXOGp266Gozdfe40dFY4ZUSHEzAzGOEq38ijdcbnzrXMk52C2Ys2RIX51rVGhsILIu9u7H/xq/erVxvJpIEw2YMFNN6Soz3+qoVU3xlW86s96+oxSt63L/vEDoYrypIkEh96Lm46pujxv9nVCv699p6ACEoU/zjzuH8uZ7eVKqmlHFuDmcMjxU0QIiB/HDm0k4UbL6D+Wx269pJRB9nuvdOOt7i/mSBfiefmIE6dTqY3ygU0/plmVnmG/XXtfxNKIqfKylIFgtFz+BVW+h07uF626D5tNlHZvS68IceRm6cKFxuI50DsY4nCwwCdta+XP7jyEttMmyQbJwUpDh6T/cuMhivPUfEnZTzRXh7to5n96d/RW/YO99/7rLjvAvYds5fUL99lyRl/wsR6twNvJYmsn1v02Xj8O53XWa74wlDt+NOLgdjPyhZdpptpBdpH1/hbddInrmaunq4OrS+SIO6MZb/RH5zGJ7f0KS8YfX9/iDf1PP99mcXOv8BYyDEdnoW2pLRxVh7pmZKPO670PHn3yXmtzE7vhJdXlUovW8fU3Xv0230OuDVc3Fvovnd+9fb00rLUfHpbWGtv37/e37u1uFQCeO39+5cQpq5urcGvZcmhvZLdzsh+rfMjSSJqnl7UXpd/cZECrN1l2Z1uh42U4WWj2LxO5WEbU6Gi9rkMEcY8/YUHhwJglWpI2P1ZoZRQKQKnolAc1104CipAz9AAZWZq4/hQGaEqdTHuRWa5pKBNWYmdUxrI4ajep+AvD/rWPPiy0WoxuMGHh0ILSztjZZ52DVn2QVBrVjjR0s/QZBVKoSE8FUqs1LHHwjc+DIwJlUG5y7Sk3sR7WqovrNCm2SY/YYj/tMFSAEv6rORRp2f5KXBAjs5nj8nHJI6FKZ15P7dlMpIxUucSOYOLJ5fBj0Aoohp6g1cgqesaUUz5w3v/wo9/+7gNWZ1BRtTJ+r1xnH7Lii0eRgHC81EyOnlJ3et25y7L1v7zTf+e1V15Fznq5rv4lHN2X2j3XEKoIdFyR7i4vR0hITWOwZ7q6AITQGjI6TiKz2mEc7JwCdyAaEW04CfDpOL6irX6fQ0hKGjvlI9SG/vQPcXaBuvrIfWVz76BNH2zDR5KQSRzJGkVUqg2u0z1sdWhqjZlabmVN/Y57kibG0jveQ9zo0qVStCbF0pbzDfkeVZsRJ7BMzxqHSLFSu1SZUz/CgJAmqgVovkRQ4KWKUm1hjZbq7Jmz3MGzu7tdqxb3D3bRi0h0MFiqZsBgQwwaE0IcP6ZR0RIhZ9BPnjjFXPqwpfGG8swAUtNFmZa0V9bo4tzr3+Rgwca5y/DhQBo9TmXQKdfqfEGhV6z1+i3drWYDpFTI6NegwuTxGkofy90YQDxfkjNx0rxGCHJpFC2swQTkDBJoduoZXQmOhixWIRBwIUVvRZ3SGPihV1aagfXt7ca1F17/wdXXeoN9PspU6x9wuLvXPFhZXuKCEY4kl20giybENF8yznfiibpQLA4olk//iyPjLrIJK3zjJg/5kH4Y0DKU4Zg6PUJZbzuxy6SDPEKZcKDzV1vkao9TPV6dY2TYayOk45rNintKI5WmAKuVUlym0uhJqdyvGurHNTKeSB7x8WhkhvAUGasJHDG2vSWn9XwZvtLSUkORC9EXFrn7szvqDDtr+tbTqPvTz1747ndP1Ae7H91f3tfgtkbzRM/H64NYaNfumTzscelMp6furNxniYotBB2a89Zmv92yhRoVMFfYNarchr5EurwrIPnNNmQz/PnIUKM83osb6wnk6bw/GSIrC2aZQ/ZcuwZyUX+oILLJqfBD7jyPenqxWrwXzUT2M3yP19OKbwJnHCefaptDkuQmUSZD0YQmwflQLt2c2HlsCxvJRMw0ZCJ6MqCW0/IzCX4OoVyWY3Ba255YmtmvnjwdPt0trRI1q0qAFbQHDxqbm7XusEG1pELQL6hP17EYktP7ynKUrAoXYTRfs3mYoDbgSCUP7Y9ZAQ8sHzQQGKRgfXYwiLqpVlfjE/jzQib7bvZ+iC/XAiT5Mm0JM9yOGpmVdsv58OSYNmuYzHnguVDVh9da+gIBObLjOxCPnSyfHoxZk5oFxsrWu1JR1c2Oq4PG+fwxAtccdMSmoFjQpHjbQhaRnISQhLYLh4duku6SZUrNP80pluRYPrYZEB2FM7GnJOH0ME+aHnKh8QBfNjpsjw5avmLDEAE4ErL4cOnsWXZ5V5fIJS9L6TQJsfBnU9RVLdZqG5FazpMWjBUA5aW0sbrSbx4u7u7xkizdCTLvFcvnz56rLK7sXv98/5/+rr69vSgRCntfPujU6xvnLy+dO1e/+uJrJ08/+vKLezc/ffnrry1//fVmdfjo7l2+VcF0dGF5oVstdbcfbNSLiz12+3rrzc7o8LB8v7R37Yuh3TFVevHciXNvdXb2eOu9eW8f/kt9lUvXFpcb7Zao9ofVdvNP6sNbzQdbpbPo8u76oQq1pi8fVMhr1+xTHTsa0x+dACM0jaFYhrHlOGWW0QTGZUpVTuY4lIOpu/3MQRmDpUkKe8SNXqhe39s8HA54MWDYqHG0jZl/rVY/df4sHQrv12PqzDTV9ch4KCHxIS0vI/zRQ7kggAfB9KjoEdmzOjFR/0UdqOhCK7peN9+MYS4IeBqS4R7rN6ZIkUFAonTQNAqojY+QUBzebngqnldGpC+99MI7m9/kZCrftBiwsm+6alSqHGV466132MYgB6zSaIpNedl6oOotOrQ5xbRk8CfXWARRzo0xiDQ8GJ49dfo73/o2q3atTry4WM3IpGKmWSoxQW1gw+4I8jCEiGRe12aRHQVTG4dYCBYd1uBMJdKkUA6hGvgCn5M48QSLyEse06VB5uMoeroOhNGy0SKTfjNmsAIfMWh+uBtl5+ZPHty7RzzwNC9GOvcBBzI7HQ0Lcpo+p3FmQjxtp52JAFAq5Qs+HCGvai3Ps+Ri0HrEeig8a7LhqRVtZc2wTTCO7TB25HpbCNmLYCtP1qlOS5KjFkrI5mjAbJICLX9oL8susz04Q+71BCpxx1kyStqc5BGlxzgX3VJHoo7DtCHGuj6BKMqo8OBAgJmjEVRmrVHwvENFlCHylUTl4vaNG7yyWj15iYpHLFG8aKDFRHO00XR3XKwohi61z7Hc7yN7Eq1UuO6Oc210hMrmKZ0f0VJ3JonIn+RAjvK7J6WYjJVmM5xMmSn28fwpB/enz6N5pLSuGjIeyfFTNMiMSlmnh9X+YUv3Gnc67OKun2B3ebHXUqeImnmCzCJga9ChrWm1dMqeIngq7cGEtHgnlK2RvcePPYjxYALArWsKtuXW4syD32PMatx0KLcsg2Ibla+34kxakautHzLuwTGGAo17QHjWK3UWI+pL63w28NU3lthp/OjaCb4o+Pe/1p3ssFXX3tAlEo1ag2wSxVL1QnNX0zzMhuNUXM3e6zH6YdJbZS2myi1hq+B7Y6gM7u1h5dQmDPD+rVv3v7zdOHly49zVU6dOoUpkk3iZS/0ZTL+ewTSWJJSL5+3gCecc12gqDrcgD4x5LHmOJAaR2cnhTIc3zVwISTuQKAMe4h9K2TiCjLt16xZrRQDgTKzxTGqZNSyeKE+SpWXwoJP4EwiFFOGwpfRxlGO5oWH6EzUcaU208HBg+kxjPekIAc1zB2RaMxFt2uP8ETJGTUNi1NEeyWB80vxGYPTkmOTguaAjO1uicrRpcDrXR+AfEZXyjH7wOSHIAjR1kFaLOugbPCkC9RQ0LDMqk2CU3FPkVYZf/epXB3v7HClarOkkAotWorI207k5SWQSk8CTZ0j4SJ2ktFgvQdi6Eo00PFCd99Ep/tP6U0lIRSMEdZRju3oiQ5o+Ogg/NDQTOdNJaAeUYmK3M0mUQ+vCEYZsQul6wO99kFM55/RpvAHMlR8OjHCQGXuA+cLSIn6Y43Z3tiEkZfo+LrnAAxePiskx4gIegfh5mYYWo8+JZyspf0Z8tnZp51994w36F249QHjuMvBYUqfjgAOSQNXTieEZTplRdpSoWiXrSuQ3q4BAclp+8aT0BB3isR7lfinBujaeuk47U5cL71SUER4vhZQvEPCJcSpItIldKPyH//AfuODyg5//o86smVT0kOSRdCzpuSWSyowfzpQInTKcWV8hFU/I0fBLqMQFSCaPx1jnPDdFOHNmjiP/B0O93cWQQOutvqHI0ioyOGnGQEkkYwwU7zJMC5PIFbxgom0CXFYdYyHE70yIdT7Yhs2ChQ+EIgLHK7gfYkCZ0rYu1lximQk0HRsYjVj3p327/K2vkZfVZEgAEyMX1cLVq3z4fttsBj6MUlwGF0ODokyrRKF8HAikEWUGIeJEoHvEaqJMBE755/AJOqt5DFN8hE+D/4p+pCVT/nzuYqRsU/+8hFySebHT8MgTs3miSiNy5OOQCI8eEJxhLO5IckzP02bE2SJA5J8Kk/ojwnP0xHQRm+PP1FmUSS3z7B+RF9C8Mjmm2hhrBLIgNcKqjLU5IMdYsPDzENC8WeNkEMWNa5/8LNhmU+9Y1sBdSGELR879MejA+CQ5HB0gTBzo+EgecVKPo6EfPPSFMcqFoeowl6SZ8qZGxZQIEGSwFs817MXr/iCA7X35MCjQWjuMkEp0NGTozqXMuKVGnbPJVy6epYX3BeiRfachiBQWn0MTBDBmED/c6P3jfJYo1qsbHItutTjjXN3fV9/EHiylyXduOx1uz+Ds0sqlvTMXLnAamhSZIfK23JlLl3jD5ovPP4Un80Ga3JC6JeH59fksCpFrbtI+r50688IrrxQW9w62tphdK/VsXcY7AgSjfeZmhnqrhD4lnc1TeGZ64zc4tGQ+e84ptShVzgMhELIZiiYXPSsoZEvPpcKLB7F54qdEWJtSfbExc8rW0/ISj/DoISkQYlDImQHPkuLJMDiISeacs0MsZhyVoTzjb5TZ6QkiOP9RCBAGRjztlScOXZrmbBxIVoXvQxDb6n7ppZcYYH/58JEIBzo7f/n8GcZaWIINIGVabDQ7W/zs5KT2Jm6JQwCyKVFwmR4cgkW98sor2P/Pf/kLWbjJ6ZpJGBzlha2veKwuLR+Fd4w4LhuxMyfWTE0UjDa2xuMSWLn0ZBr/0qBXazfrLOdzJY21Fn5WYkaKpoEZ8CmQK8urq5dLSsp9caKgWLVrpxg/MbTL4dv+6N2/+j/817/+64PtT3hbY3nQjYWUJuKNGmsjBrT5Hpt4NDJ2HnAiq1rIBUvLlZ5rDeDRQDhxknKV33cY2G8S/mSkZwpYha/D+1KZBvu2E2KvzlUKmpFwfARMu5oSI7VFQ21twExyBZk5llypLGoauFitaBllc2+PCSHv4CEkc25yrSUYNBRqr7WGrkTnhN+DlhljTgZJ2obj0V7VZ6jJ40o6PQNN0BiAxXrj6qXLr77xOs30jesf/sPf/6jMISPbkLNjFJLZ7/MlWWTLqiEtl2VTnMUtvFJiOzllzuQySTKEBge/24fD+ze72TuergeekHFLr/EXK/Sop3oKdpeUNz46R2vI7hAp9AqdEdcSth5+8tt7L337r1aXl3fKtmtqSrCM45MkXsp4cs6EByYclcOkMyplgiizFLxyYCGVv1wzSfF0oawPNRvQHrSLgW71l64e+DnNTEtKBRqJop03qFSaKIRbullJZS9y0XR/97P3sJ/Xzry81lgcNLljsdocltjqtxNhHOrSTUSDw+bh5lZl8eVCscE5J7b/sTScbnjKO4lXKnZHrPMPtQnRKNzW8fP1h2wnDA9abFBzlejK0lq/tsnbCzX2cjE5z9TIavdI9yIVCwviM9Lr8xy8E8QaIk+QTz4YpG3VSP5RUYvmftbAdcKRIOW9sE2JcZU0fNjCwM/hADAXl5ZLK6UXVt8qfKfwv/r+C//5P//6//WbrUa1cb/5Ao3+4aNb927e7Dx6wAG2hUVNz0ojvhtLau1RkcvLOidPnjh77iR6u3PnATa3UGYrpHf35md0BnzVjouIqeVsqlNDu1s3727ffVCpNFYZtFw6sXEBy3zUWEWGzFlNMWsJ+/zeOtleZVb6Yeifkcz8DYahuKTRjlfQOJyaLK1EZ5h+VoMmB/V6pLUa+P0XzpHEU1EwyobfoyFHV85bBhS8AMwqLEKHOZWG2XMiTCa94NRoHGtFB7u7D65fp73nFnCujeMz3RQe996BwwloPS1hHla+GgZhTTR+lLavgAuuW+OBWnsGDYWD+ff73Cu6srTIyJLREoRqLGY5iWJtPmnJmzjPn4xQbiIqw0qBqV/ZfCpH0lG3TohB5yAZw2dn7qn4dQQZlxlqcbRUaTnNZJLoF7Q0GP3zSKbh05DIxD0gfPTJtd999OHe3j4LUtxQpe7TtiTdCDVrscGxwGk5UGwW5qmjTVq0buxsNd//3YcweevNr/EGw6grAm5A1mQklDKKx66sRTVuWHtOJFkLPRl2lUVgkEpKDVGGbGdLs3hVCe/+Y7GC6ciyc5uzReRn83hZcPtzub44KtfbfFjHWld9Nl2dvNd34511NJ6QiaSF0cXlFT8Ye6QAZDrkMTQCVD5VTUGzFmLMQO0OcC7B4NQTZ7LV3iIpZ0TtTDQLcZA7v3GTYj2gyMZ83Ocay/xEs4DLNY2lldVTDGaqtcbW9i7sS9XawcFjjoAzYjLkQYXDYhy5zpzmWL4WrM/xUdcG7R6YyhrW5Pf6eVocUEMzHFXWMVO+CM9AzLZv6Q5kJ3baCw845IJnLF9PipyGTGiESQvn76XpZknOGNjAzEaDUp0VUyZh/E1UOm4TACIe+vQpio7+2Bk6mIhP4giAObDRdagv1rZkd2ED0wACHC3K0781zrz9x//r9dOX/uZv/qb/8IbyzkdguOFtsAdnys2n0DEVyGNqrjEP4gdHd5sGY9Eomj9ZCX9jIgHBZGXFDRgvicIEdduxPrBVe2IqwcNVG6Xi+qnTlQZ3rPC6MGfRDrUAbdlXbSYdI0KESJury2RH6cQ6a3jOIZJ4bASydUJUDOJ3BJ4Sm3dyuOJd+90sadFCUJ9k7ohPZtQCyWkjHADGUl9c4oXT+m5roVS/3C32rj94eH9nrVhc5+Q3tdf2VXtVWieVzfra2qnXXudo82JHrW6xzZmpZrU/qqksqUSj1uGBJgu2wVGp16oNbExjrQnHqABcq3LkwjKC9figbwLxmAFlf6zgHNHs3iGH9HsNqqLH8jKbVNCS9KcXicuQy8cR0pNrVJc+KVO3W2d1nKdUdwwXUwE3Jorf64snGllZgQabBCfCnVY9jaqbxsnunHkWUuy0S5lMxzrkODiO6VmY9s/j/LRancfH4amc+qYUHcTDh8tNvvY35I9FHxrLCjcG03NU1DKkFYNKyx/NDlpyPXnbomYtmk7W4CG2TgGjbb3ZzIxFdyyoe2CKhfFoJCwaXVFEi2AMnAcNBuni1Nha66mjw3wyQfjces8Z3yLnEr251r4h33Utjcp2pnhm3knBTIXLfOVRY8oKhQE9R9BjKDAn09E0SIx2QsJZE08ca1KHe/smGBwQQChKUctEqitEuQDRMqUZJadYXUmL/Rm30OYbxJp/1UKt1Fje/QLMVovDQ48e3n/AEvCZs6dYqqvxzVw7fgd/t2BP0ZODg5goxSJNZp+rlActVvc4uIvF7/P52xNrhZsPy5/fvdxtCLXIu9SMCawfH/YK/d7BJx/v3r65cuUFXrG9evUyh/AedfrcOHH2yl+wtlxZX3n0+eeN7gGk1Z4dB+QlFK1yaGtQX5BEA/u97Z3dzS/ucC9l5cqV5Svnio/Ow+dgf4sThBe6XNo92BjoXGrj8T6z0e9Xug9aO7tL9UKv3y3pklU6Dt7stFdW9GkeGxlqIZI23r94gpaBK+OmexI9woVRmZWCNG9/8/BJ3QdF6JYEfYzdKo+WF6pNFIVrVE++cBmVwgEr1IDGGMKZImPFABRnLis3l0JmphtsxnrtmQgzgerHqVuqXsFxEplD0HwWiDCCKCM+184QonEiVwbL/1pUYAm+TByXpWFenfPu25fkOpU69Ixo2XLYuHgB+1w7c4oZin2bAb1oBgGCnkOtdXB1LsFbn9/krENzd5/hyrBrX4lTrdHpbUkT5lOyJao8D70thfMVkkwSAHZIWlEup6GYBY5K7DpD+86bb3Hghp0V1nnSCgKmu5gvgj5SYkSF31daGEdsb+1eOn8hYD/rj9Z3JGImOqKMAwlTA1pWMyCzd7woDpfBnvH3OBwch3Jz8SIJQfZb1hcX3v3jP/71f/kEuI+ANX2JuUrkAgF4Ru6FpxEtS3i5YoiYjj+TW8J47HVMyHER6sEIIUEMkNgAt5oSkuBHzs1FZgoiI0VGz2ouRwUWvw72d2jyQBJ/5FYLRKUj6E+zVE/cYEIjiP1kQUwKY7YVOUVGF3gmkscoyWGtGgKQJgbATeQ//vGPgbMyTgMKEAkJ4uGpTJiDg9EGTsDECG7Wt8RBDfs8/gE68HSspqCLjFltp0DZNCIJZ6m3CFiWqqiZo0MSc1ODdS284KBU/Ile4Il5gM9BVz4zBSv8sdUTqrlUvAymeuHwWXYUsYIHZHwpH4fkgHmyZwrDWbmwM+xPZGC5EJZ5QqG4Btipxq5YZ8HD7YoAkV/Vx83CbiNluwwgtkeD+MS0cgikyAJ0vb6LwSxXq9goaWF2wE2eHPqxggjj5FK1W6k9FYyOgM71eHGATnqeop5eWziojK3SK/yn//SfHpVu//SnP62eqN64cePBjY9AX+Qtftt4BN9fsivaZx5ZSef6Dj43SCfxs5/9DEwSoix411WJa+iptEy9ejK/JPZwd/fa7m5l6T5Xwax8/ZvC/LfnJLgZiVXxvHy5WkOucxiEnUMOngZzCJEJBQmahpfWMjja559/riG2uYiZcnMgKDHWkSMO8KwVtVeeGaFnYnN8j7OKGCTInlykmoYIQbrJOyecJgcvBaZ+1Jjn8qzhyDZmKscpQxiXFBBHdjG8EchRERRhpigP5pJwPs6EadM0ByBH5DQTLE83DTfIWP48gYlKu8TxZ1ow3v5DTreYHOY4p3OmYZ405GDSsX744YfcusCFgLWyNtIAEhXlkHccklFFyQMf6wjg5QaGME5NLB7g85TjaFF4Rws8kxQjgnucLX7HjLG5YISTHXpVpouMyD3FefJEEjxgMiNyzzzOjp/LRcok9UexAeJHV67hKJKnoudU3qeTGBdxYntecHQ6DCccgZzSqjAkYP+eEYVPpQAyTnBkWiGCjoyx4Gd/UWLbZrzjYGVRAEqTSGcIEGklMOryvsD87NNrgNTrgiZWmXPkmGvxtBERqUCCtsUwG0pBBH5MN+Mx49dxQEalTuLZAZUoj50ik+QexTP6nYJgdIz6YMvhr//4H//je//tf+ENMzdyEODJA4+351NJ5AE2rvMjDqGC5DEs7FJ5KugKTToa4sZ8zSREEk6lgcPKDr353t4B+mfNWxnMijhH6GkB9OxAS4MdgY4cg46TBvG7nKlgEShyvnSpe0I1go3cQCCIw+MOcvYugJBZjfz7Gn2hLlLEbrXNFow8tEkUyqhubUtWdmBC/vjLLxsP+NimHCRu5JASxMBwqAW4Z8QKMJSgy+bPNC8p3P1Om4OPgc7UosfABDsCXcIjeCZEz9+LGC5A9Bw/jRxJLjjNJ0XAD0Ka94jvUTE4z+PcIs8YdHw4YwZpCUbMeQwdfky0o5mksUgCzxTytP5US6n/afkcgS9daRcw1Ag8QHa3tniSoieqXIRqZwU3P0+Gb7WS3iNbemNB2/3GTg9bgGZd11ez1KYxDranJNVQwxyQOM9FHqIEsUU0KrdQTJIMORNRPI5yMHFuEIJH0PsgNQSJI9fKeOaUWhIEzMFhnj78MjbqFiMObHG0Zjo8Z45VMCC+yK5Vfqb8tv3GCgfy7GxusSxLGuKZrfnALVlRJEalwz4/c7HtnU3mZS9cOMtinyXF6TPbnROWnGhN5jiUpzGEHIegPNVuc9h5bw84mEx7K3yvUHVT3ajPjnk7Fkydprp9++xbr164dGlvuMdZ7HNfe3f1yhWusmZaUbOiAQ1alupg5ZMYen78ZWu6GQdwx/To4SbTz9PnLi1dvsy1GgU+8Hv9g4e8j2uDBMSgzT99+YLm5nqtVl0GHPCQF5xyZQ44Qb4XJaAVYhajX0iEneDH2BQIGi5GzfSQEB/JVakFTFOamS3qomTpbZlV5fgQdFkhdHmmEWYm9xyBJE2iOM/yzHwSOzPFeXDPiz+dLfqxreXB4okVtiVOXnkRnVRObWg5hfeiyL6txzd8byQ5KMZxNfhwOzPPMyfP8sR6McoC14z2+ztbO7zEz7CBd7KxPV/3I0XlZ47MMCAqLV/xzJzTUgv5nJW+YmVfSEL2LP5Yv/Dn1ZBjoR6JpPkVCDx1h4NdWEgjYi1HWh4IxwuSagt0SnU06lX6B4NDq6Gl7kCnSkEQHyPyZtGv9AkZmzxTA2bfXpx3fL/NxG9Atk2vwMf9IJPbhp1P0akGDf+DPRHVqRzWG5XeYf/1F79efON7fCal2OAURqmm/TpaABuCZ7UVyW0Vn9GdKoNdCsP5aWHaF8QJ/yUAAQAASURBVFRLurHF7gkiVnlVTpRtv8mRLVDAvrQtes+zljsVavDJwEKhOWqydlUu82VXDSJlB6YTRgGSnPv0mF1w6pmzmQXdNosd4feGg7SB0BAKbh/G4Xah5eUlZlBQ9bvdnc1Hu9vaKwMBR1Egrc7oUYjhTxyCi16XFSgQhIHnytJSYwFt8GIiLR1NLPyVW1L3+bZOmWsHy2QL/MAXOVdk3L75P//P/zc6TvycfS6TIZ0TwToGTGk50ExXw7uD4PtH5CyW21glkPQpx8SDi4K088N8AsyFpRXaWYlBA2dHjADS2rZaB+DUK+FdQvJJlnGDw/83d//9ZFmS3XeCT4t4oSMyUquqytK6dTfQAg2ATZDgDGcIzABjBtulcX5ZsxU/7U/cf2DN1gZju8sh1pZmS+OAxuFQDIBpAmgSLYBG667uqurqElmVqlJHZuiIp9/bz/ccv/7uExEZWdVoYNcz8j6/7sePHz9+/Lg67jd8olBUuTPJ9DrxG4SFnIsdDee9m1d2ZyuVY7q+mg/WkYJYns2caPDdME+V2tOmiKIZEyieMSMltH6MPsHDiXIqbA0tebGBPgDuPDt/JmETfvtu4dsLsywgvDUhtxJ++wQfl/vp4qostsphDgZacg2iZs0XM1wCRZ+cJIBX9WRIKYxtN9VZNjMnjxyp9PZW12+tnHlsplr66fXmmdPH1ns6ntnY2WtvNwvNvfliYTff2dlda2U1aPCFfpeE0LeAXZVg3Ohxcxps2ISe+fq7nzj98ULneiVzu9Hbgy7uucAOudirQEO7KDHmw5k8C9nHuWMlk/+UqrZzTGXJ6M7lfr6JH4Nq55uGAtkGz2J2g1T9/hrg+bJ2PrgiiZB8vwGBuhaSFWQrcssWyLI9WfpwtTDPXEaQ/dI6P/XOa8fPzP/Orz1/763/9NorP8zdvz9f0qF77hoWfBjTmZ9yM6PbvPlv/uBfyqIAmK5G9r3NXTqUqjfyUB3iAyTx1N3ratHaq8ts7zXqtzZvvkNzXjp5lsXobHmWYG6mtDJquQr7N0JM2tRClMocDZ28vB6TsPSv5WUByclvvTB2SgON+BkZEaKC8PTqNAivX9SAwxNPWZS3tTOujCPcJS3qZ0Jca+FxhDTT0NADFoU7O4P8eLg9C0GMRM+U3ZJ/6fXXeqvrujEHnYIRgo6q7BGLfT1PIxkinH5MC9H2zWK2wJ6V/nLVUq+UR35047Mm/N0Cl/C0uARM8t8+QoPioyiN3b1Krc/hKKqZEiZnCERQolDMHzKTP7pQxvh+oGe8vBH88HjEvZQ8RAx4EiQDGUjHequMIQmwtIGq1XquiDx60CYCsBatmh/LPYYw8gVJEP8o9jG/Q3tAmIYdvEYdRvRAJpFsTdKw8PzBj17Z2NjCBBUTV1qQbzkEyQwoTZLD0nOQassrcGyEsTmu6231X3v9jUKx/MwTj5NLAuAepeLr8DzxiU6JunICLGD3IRR9O1YXHc8FAOvqSQDHgE43VF5Jbon91ILRzzKZVsuwqmVggOwCQ2pCpBElDMJs4xl8Q9wTukmO5DoJwpeLFmbz6/dQmdaPkRZUA20TWpYF6ApYounx0AjcZ1ypdXY3SOAcULv3WH6s/6XEQFrmpAn8MOoGFEK/ASiNe3r5Xr1TXygsAMk6NO20vrONwi3ldc0ax3JU8IBVpQ7ylrDdkDif5aUHQLaRXj4fnp+dyc1Md3PZI8eOdhpbOztc29jc3d4sW68CK5QAOu3KDLQN5WQN2gimilrZbqdmA4JmRZ8/Mssjs862ZDx8bKCYfKHV5eJuakR6ppepU0VcBAZMZaqmA5M6v+W9ghK7E68k4TqyRb2iK5iT85iZmtHRHRWZ8anDitTEL2Hz0BCnV43g4DjDKLhFS6jYxw/FOkWoLvCIVKosRQjrAoy0dG1b03s9TjkxEnMN4FlqLKd6RlNiYVAsNvvFuTMzH/nPf7v17W+/+/2vUUflTAXu5Rt7qOtqeTAsMeWRFCAhWbl3ezNT1WnOGCEMMgSGLKpYlrcQKSsbVQUsCWmdbLKAJ172dqs5hc2bjSFd9pAKMGObo8JZnc4tHOHS83ahjylxud64eef2seNnqF9ZMgokISjFypijh/nIMMhpkOcgadZ21C4GqdWP2vzSxo0JzaHWlGUuMzVVYQAv8x0ueS0UUUswwHNBfUGU1brsIeE28oPx8WxlrpQpT9e3WWemx0K4dDl4u9ews4NUNoPu+W5/9a3LS1efPHfhQmtzl6yv/+XbmRs3zq1LaxUy3bV7qzZfMwmx685ZgEZQIR4ALwVPXy7hRlGCMfVHbLy0zGUIGZQ05UsnTwWbN629R+MO9W7qBwonAE8M3K+3DdpjApoQRNldrnhP+5Eor8f9kw7BRzBHkn4eBpUnJ1XEs68nzHcGrEnnFfFQKDU3aUU1fJyDyZfevFdzCxxI48EPoHNmhH6i0lQOvwl92h0c6xQCH8GiJyIZDyEqli6OQAiMJRVAaLMRzZDq89BUKTSS4dW/vjC312aRpX/73hzfhGCQKVnUbQNQQr4VKUv1VrgUhsArQvjzurHGhCIUJLSRVkoMbSkTInpSZtqch9KTOQtqH0tkYtEJAHJ2Q6MJnf8zxaISgZl/du7ZcuC2X6fKmrNG4qwykNYJg061b7t+UugHTlKByqTjYZwBdZoZmjLxDwXhhx0oBB/fMFBmYkenTLgS2gcP2Q+r7+obQpz6tBwllD5rBgZIJp2EF0plbJhK1Qp9jZ+rhnlkyWWagDlNXJsMJLbHpJg9dqRbLty5do0Q+MaKACswQCoNtSMPMzSl42YrnsVupr2b+cnbN+Zu7Lzw5GNoNj4ArOEYQw3NGQ0SaWc2YVWSp9BiC1WpKTtEF8HW6nR3G52dOsi415gzUVYiZga5UhslkKm2dwmZ6uxRiTuvfbe8tFR76YXZxaV3S41zs5nClfWt29fOdjWfDZf4GgewgSWk1VuCG+/OcP9kf8k28jOZ3TvbGxvr91mmXDl5vDhd7c4s7q7vzPBNhU5mYXcv32yd3N6dr81c3N2igWBRS+7vr13NZ3emG6xE99plFwmNz1usdJGPjSW8WcI28qWCjXHyJpzGQ1VKhvyPLpvSha9UGq8Em6hXY5gYT6kZEvPtBz5eR41n5/t8fiy/VcoVpvba3U699OzKskZpCJtqXjhC3yEvn520H5n9D/60A5GizyDCwzFYFYjQGOohyXvIyYmMgeMeDi/ICPqBcOMpR0PEVWighGq4ur9br7kOV5k2O0eX+ArI8WeeXlhcVBNnJTmXHLpK+KmRyYizY78exmKAPCU0AdKjAcrC0sICOfYz5+GC6fyrV99jv+fm7SsYkVTrWi4rpQoWWE/LssIqW1uv8NGOd8iiPZs9sri0srR8/c4tKXaHVq6Js+ThxfLFTyrfNIKe9Xu6lY6FbAKTNAf9JoQNwaj1EuEVbB4THgsJrwYf8wigtPmiLSBZLKLpR8+cDyg1gjWWfEhHLqEskXeGIUWJKl5CmziUL2ngIE3ik5/8pFb0N65LyxsNjg5k7iHcNJ4qR3i87JaF6iDpoa04VgS076F4K/yeBZfN4sHmwgIsUKNm86AAkiwiYyCDuEiMwg0eey42lDRAz2bZAGGXb3d7W2C2W8jekyAZvcIvG+ym2qhi5KKcU1CxQCEsZ3MvHmSwBANamU6wymdMdxqIMmI1I/AK9HBARKdxDNz48fIsmiWyyLI9OoAdA10GyOEqclIqVWgk/iEF+jZSsaOoaU+SL2BsWGopPHFeHTzJi2ezF8y9nRjFJpQkxOgMo/lFm+aQ5oxOYWh322wfnT1yVsGhK3eQ0SdZeCrltX/tO23pxKI9IdjCBxKVBjvYr0xxLpymm0NIKhlZW1bCjwcHwXrqVxQryOXZXj1WgY7c5JDaoZ9mMZRdDTzAYCTOujO1UJqrse968fLb2C9zaDUObiLyFC2jXodxGqh67HfYHSWvKp/MMYseBEUnciDQPzxoGpHT0IQw72Uqy5SSYU11qioht08bbdW3KSk4/UkWkAQ2zT7L5VZ3gyjEEwx5n/iFD4MQPNmBgc0DJJDyshBPwbHkunFjGj5EFpEy8Xs7CK+k1aCIvFzgmD9q2ct47mu1w21cSCyNBm/muH1pbe8i51/mjvA5jaOs61AWJv2wK1QoY06rYqee1HgI4ekc8PCHfToeUomkn6/zHJNnyHuEHiulDNy4c5yjFTAtwKdGKGnKg9/AGCQjt7kpzahh/whmIDW39202q51mu41YTiNtmBtoISC92j9gTTq7GBoDoydGuWe/cK/Bg2FGUKVfHW0sWjrqA/jBlkYVX9Me+HgYzJLVQwEKmZdiBG06MO0/AMyjMD/xD47TLtJNZiThAa/eoDx5bFxIBZ9tZzN7frp2/vx5kotXSaOBQvf6kygSKjBpVuOeNAHEpjmfjor+CAAw5cJJrD+EA0OaKuY8uI2WrhfUVESUP6AKIQkFhbN594AUZ93g/QP5IMCdUhsh0EUtsPIcWXF4xKR1YDx+6wJP/D5EwYQEv5Y7E/Hmldw9iQf6K/D0X429XfoFDKTTBDhAxMCr448whMTYKFfpQId0PPgjMH6UGHcpopRGwj3J+NPT8iSjSGUaIX5VsAF48nStSTcat2G4OGOW+9CAo79RWmNOJD6+Mnj4/Oc/v1JUS1m7dQsukZxUTgUJlTZVtFhYcpTeja0mYb6XN4KlSwoqGMIUmECGE1iH0XHTk1KbDgYAkkPu7GSrRJkc42fYKCLMYVEOMG5gyJ/OYJKf4owHe+mIAmtkRaQ5GRUMpYMqAJwM93g0frApF4sjMMJQIxbFjeKYs9SI6rW08ME6gFOFn2KqjmxUwEcWOYr+2ve+hyHVwvmVH//4x/euXIHFRIrOnm4ylcfIIC0jH2SbEKfEn8RSFfofCHFy7Lm/fjD4NJoP6x+maojCD4t6LL0T7zniT2c98krSiSGOMh2V9o9lqIARgPiKZyL8eKBBCnhiEg8cKVQUm5jKOQtwutTjeTm8w4AE4ZkI84EDnTBPTi4TS5RGfgC1H542cncCyIXpBQs9M9bSHfMB+EWVjfTTpKb9Cdn82sKVnmr+LOgoxIxX4KxePMTmsADgWL1WsDl+BKPFU4y6rEWHKbtCab6C30cw0vTs56c6xAHLzVkBZAhM0kjNcp9jq4M67dgJIcoBcISPHohhwaE2P4uHuT8qi+uaSM58B2RJmQLemIp3tHdzfp4lNjSq8JqU8nRQvaY0kosQ3KBf+NGPfkTaR86fDHuTY6yISEDlCUMIi4mtFn6siQNBqaaqHI3cmBwj7Suvvvriiy8+9swzdDw7q++Q3BM6pJfOKab4R0+dOvHSM5lyWd+vQuf3d2i9ma3tjbW1ixcvMuc9smenskyl0yHruo1Oh6HL4lRF59XM0euRBV6vZfd7VKTZPRQtwoxEjbw6tSOB46+AgZC83IOU4aEfAZIKxaMPYMiJPIAgwLsnR+X0KDpxh8w3AQ+/hju0hZGo8deYBaRH/zjYQ4VEPO7hSdGmKjW+Rbf0/BPFxUXsnbXKYecLicWN4CckzQdiUzAD/sRUAk6CgTx79ixXuHR6HyHfjUt3mC/fff8GD6JwETP+iMH9zjp/EkVtchn0jbu3FRJB9/GksQFCEkYUCDytbCRFxD8SPvGV8ZzUjXatKKRUHr+aYqApcLajQkMXebKGZZxtO+KlbquK4XhnA7KK3LTb79dsaNi2JzsDqcxsDBd2wgf+nDXVgu0GVGwMWbP9WO6OwTk/tHNoDvx8UQ6vzUdQ21Je3D2NK2Z2GJiWO+3W/XtHpsq/+bHPf/8//EvUYj7TIIEUNJtkjsWe6HJpz0TDEKuraDP9JnubmRyfKZeBs9+NaLlDA9XMdTuiwcgiKfBGTma7iPEvVrSyre8UVepq0c7H2VyACibtpLr1ca0y0G4h6HyeiYFrLjczVUOB0p4J5HPtLD039rT8ytiTJ/tePEWyO5b53Z4oCQjh/JAJOUAUpsblPN0AMwSCu83OHtqugfCIhbTLgMmaAasx1Km7BGWoTfL1BVZaHEkoMuu8rMVRxvpuvcFMIJdlTlIuydqUBedKZapJvdBNdVqIiVS7yDZ5s2/x2KtnrieZxhd8FMvfVdlJeSmtIDM9ysLNHAml+o1pBW5LhHigDYdnChPZnXXuDdE0yQomtPAmC20hrVuTkUXo28LMPFnJH8qBwqsuwGCVAX2BXLJzIomKPVmwiyE7DQ6Mn2EfLPAWYA0ehG1QlrCHSaiKTMvoIANQ0caYPJspaw+XZqv7jr0IslgEB3cyCgm3/7Ega0STFxVN5hqxcPKIeYusBSu1uVyxurnXrdVm7+zUm61u9ujsZrG3fec6PSKfUV+/n1lamcYMHbnj+uOcMINfNBuxVhkKkvP9inaHxT7u8OWmw245U1+eKWS7TUzJcgV2CpHDqoyLrZB8xp5UvYxNrjLnON1ze+0MC99LC89gdzA7U1XfVtIlKtMzC0DCYUpazndYNt7buMFHh3e23n/1R29t3lMtsBFNuy6Uu9RvaUpHmLkQTP1fTpaz6C78ubx2sCkXz37/JCbX718t/af/9Of//t++f+3a2vTMPPsUOatTazwU0hZbTNoZO0G/tu/ti4Kix5Y1uZsLXsBf1kBpvgpHEKyrVu7gkGUbtUIzkcqbNmtuZKS3tnNr7fbdd/Pl2fmTJ09OL59gqtnuSXu0qdZsn3oioS0PIaXa0BJVpriQorRskCQ6rxd/9RqJUbG2TCZEEVFuuSyLZjJThLUNpdGLcrdnEkDRrO0nTYZwt3egkIIhvXlkNjHkFMvGgz0V4Zte8snvaa2dbreu//CVSl0dW8s4lrP+KIMBiibqnkKccS+548/rfi/u3Joq9avFfpvtaI3ZoUWtEop1wSwCD9+Ytmv7v5dHZfXaHf76Rfu6tA2Iwe70Rw9zd88y9RS1ESwVHr2BFcl7fE2jioEDVFbFSSL7lTyNOOdziv9BVkfADvcKS+GIw4qlQ2iD3nM7aGQ8xnox0vBpqTsgZyWZGG3McJaOAMQEFqtIB0MS2EB9991LLDfldQMvAii6kFhzEzjnEannAIam6t0EsV5MDD02N7dfee316szs8SPLBNKQpdulu5Ah5RV0oPXdjpazGhE/DOPov0zsg5N1SIdL6OxCVbNVTWL8165XZBRCAc0eF60l/dMjN2mwIQeINU3X+CKa6MCZIcChFwGY/FQK2fnp6s2NPUJcEL33SaDFGTAqMjWiRaticCSbLRqd83kgxUlSSQ2h4sOg7uiwhcqQWnSAJtzwkJHx1iSQ2wJps50WfY6QiECwUWBTpzGfAz1q6Jne3s4OLZ+J0sxUttlYb+2uU83d5nYWE3dkRjzU6ROckElPwFQru5W6MDXPCubKsRNvvvlmZnsTqqBPfa66eHPUokkdNssg4du+BQ59iQNiJ6Bub6UXFcH/PKWeljMo+dUYRQzXVZGQXqhMTdPRAKPx9T4OejzGPd6QOQ5CvsKoqpYzGBcWDgT6CMTCQhnk1/Xb2H1pF1hGNrA/38vTf6JFxRN36q8piU4RuWv1i1Nzcx/7td+aOvvGG9/8E6ZAfLSzOjNHX0ltFnoNmgxdV0JDkgzhptcxiyqimGOTo/eVQLhY98xyHAMrQlxCGNKvrq/durPKntP1mzeY/LSl0bszS0dMzFRr3neBSivpXHU+Pc0FFhyYYtgCiwr0KJXK5vo63wKJRR+hzcOjKhBOqyb3eN/n4ze4nQaLdSELfqtvCwkrQ+TCqzuhkqRJpEWW7abDeFW1zNZFAr6etIRWm61Q+WK5wq3gWGlYofmGdIsRDpCOGc+JbolSn7nWvPP7/+n8c0+VfvjDJxqsdTAnwQAwm2v3uNGUFkE3COJSUesFuHBSEIM92oWRJcExYsDJVIkhFMTI/7DONWFk9Ghyiv+hnFM1gTZj4MOijmwkofvTIWlsXr8BTDWVjlTF8U4bTCNJ+5EZb6cj+HkdQrT/iyAlPxHCNQev3tAU4WQI0Ohx3JFXkQYg98nXcaZLYdl66ZIiRAo+gCe2HbjhDAFJDMQPtfvQNsgtloigiGQQ/fA+cnRjwTwWr5lMeWt798rVEx2NG/1QrnEe/QqTxVzPwVgTMoMkgGNB/JU4kerNHQ1ns0Gebunsts/0Q+So9Q1dQ8l8IvT4DPyV2B6eh2sh9V30L9bP2uq0AQUqhn6UeOAmtzuUtLpiX7cxSeQklrQ4w19SW18ScPRkJ9Rrddp8x7vRYpcYk00fPlFqoL3WOCXEfrVWA6pT6F30PQdsjGbOp0orWr8SKEsIlJK0tV8Ets322JGVY3v1Zq9lp2BtmFqwtZwuc2GVSouw3myc57620+1wbJqPl2+yioKSNMBB41Lvxp/GMXwvpIiy83Oimvujczs9jokQQqqibTykN569aPCBfqeni/ezmVbx1tV7J5/vNnc2d1955WRj0DkOWG6+9qOPZy5coMo2btwtXrrCPLdY7i6vrGQWZ+bnFytLK1w50r16mXWZPQZnWL7vaZ1nfmsT7n1hfum97Zudlj72e4lTa8iorRQhGvCcGmXH1bPTQIM/CaCpCNX8yMhxiC7vbT3IJcNlxXrTIUhNQYv6vjfDEt0aLDHuU5dwUhlm2vNLtUqtTNUwFyOliERJsd5gLmQBS7VCiDSrRiggTmz8oI7k40ljoI8cXEQYD1DnXdvM4NZvy3486YNDnFphNh98RjvwMeVjv/SpmVOnMtNzLHPBGC2V5BEwxnNaN3VKwK5EVlw8kc4H5gpw4JH9sjVGWk6iwd7jT9SOPHrmyZefpQlcffPi97///c52i2oS95PRNQIrBDaytfMU1E2YzXGX2sLcPI3lYBqsujQ2UxGM5fgZg+F8O//g5AfEaow7wgho1QAuGWMry1D8UNm8UlpcxEuIOw8cXoA2qMA/rwDBFhndMtA2RhTtBlVfF/RLtb2osdpAwZiJp7SDBmfKGuHX04ZkxSxH26YyrTrfJ/3MZz7zrW99S7cUqGEIhtO58ptj6KYmVNDRTi94myVdeEAxVWWAasIHLKcKeJJw3JkQqeU4AE8VyZbFkUjza/FLHnUXmlyNIyEEAhBgnoABX6Myp6cZdqNoGM2z68Wqn4i0uuCMpBEMPqsIiVSYT0rpROd+yq35QmZmZppugG/skJYis0PY4fNDMnxWA4jLQCI7IdiJEelhTCPUljW/SUUai3yOxBIhmPGjj6gFtuwAZgKvBe66jueolWr1x9MOaFUOA+lSLtGBAccrbLFA+QkhCU/VjjmHB3+6ptQ9GOUOSRRIppemKTWJGHY7Ek/raHk6MTHQPTHQiUkHKsqmxGRHuFbiE4LTwAFh0uANoQmVqQPSBQD9mJAYT1KB+3pd6qh6cMYcCQwdiV0XhX26uGEyCQyQbqHTaakLhy3IA7Es5XRrWaxx16pVQq5ev4b4zcxXbSOkRNfYKraYAfb70+m8JlIGAbhiTvkCjDy4ABA4IGNSSgyASYJjHRYD2I31DLaH8wszJGx3Mtzwdez4CpL83tuvEbIwV6AL//53v/5P/+k/be02aablGsazmfIUt6XnqjOyvK5UEUm+06NPk01XK4xFqlPaT6ZcPLPZ2z/5yU9+8MP+K6+8sbl+hkzhD+KR2Xe+L/FDCiXVqC8sATGT5EJM4yoFgipUvIEMigckLrQat49OIglHZsC2d+8eZSnNrsL/2eXjUO6NjNqBG0g9KZS1SZdysZG6Qic57x5CzH5AFu0INcuVG7Rre/0reVBkMjWGBPyJf7Cf/9Zbb2HeNlsuURcIjOB8ice32QI3XMYDZzB5M+HSfjv4XRexO4AfDDzdUTeeKzykC8ECGpFGQopT2urDBZp+pj+gHedsOq+0//A5e6qEe4dNF/PyhLzigUUuVBGLh/MaPTFqxPNAgAgPZPSPeDzqAADgY2z0QDMf0ODsAjWIBvSKHsF8+Few4YK8WTIaJj0F+F9//fWlX/gMfrIGhicukBTKNCharBEH48k8DWDC8dM2PS0hTMMsn8GDxg4YzoPkGSAWBnIfQA/7Itrh4MlvAINYG9KrO2h7/DHTkQSEe72ZR6uEcBv1NwI28VW5DDf2iWAe6MBpeOqChuxtlvAIdrjMBQ4GRlCoaC20sUcuowRdg0vNDrClVCuBIsDjfHhTKmPUMzs/Ty+2vrMlLiUaLJLk4M6ftPwkaPSrhPs48KQFz/EAC6qRLNII0gjdn5aNkYQOMAI2TioYqF8Px48jR1J5Qg/XE9VpxeEpYWhxx1X/pZde+sTjJzhD9PaPvsv96UpjLk1z9BPjFHqfKw4kck6mVABoGfkzFX///fc5tba+tcm5RrpIYjmcTD0y2MLlGNxwCtNq0xGmCwUwcwE6fY/yJ+OZ2zfu8kWK+eUlMFDhkarocSQO74Hud7J5AgDydF6GKiBI+wlK4/FXQtxF5CSB83q1pXMPJxAwMnK1gAchJIRFHwAIpHV4fRGFQ2R4Ul7484Mf/AAYMCDt4CYVHsZ4DG3wE874h0YBsOcVnxQKABWNsUlKaOWfwKqYbrJHeSUx+BPv4DcGel7+jIEDuGFfJCx6huMf/JbOyGuTNKI2ad0RhQfGV/dMDByBGXlNJxnJcQTy4FfwHAzgsTE7h4/lxaPKTVykJAmY8Ouo4hOINJIBLktqcjgByUhQmoaRqAe+elkeCPZAAOfMRLDINHQOjhxpaAzuPev005MTsl+tOEMAkDPWmepiVVm/rE4AwBqdntbYbVWTWaV0LwtXBiMe+9yZV1XE0FhdLcwxyyNK9qOF+IMcaZXcnMOJD9aA8VBlLjiwQhOfeh39jKURSViVdABPKj8XNeWLXAnMGMMPJrNZSCrgDY9KtLsnwyC0Fv8tk8TPEnCxyN1CWkmwKVyzqREUaJ08nry6E02pcRRTJ2KPruiDhMWCGQ/5vGy4MxWddjIbttsMS8gIO/ApGCdAXDFG0U/hOXn2LNXzyiuvHK/XvboFOuZQuVwa8N7bb9Cjndyxjf96HUOrnXyPTe7a6TOcXClPVTa4YPrqtUgMTAMTaBmHFLYZS4YFbvKNOThwfN3PY2BDqQh5KGmBDIqMAHjBmWERgoOYciHH7dtkDUpYSqANdQeiSLjTTJURC5zTCTAI8adLtF8RYrgIf0jnZLv0PmTSyeBOMB1rrlDm2o3TWMFLmq0sMq8yY4VU0vECCubhCxKTOEK2W/DQZJjPYhnNcPeVb3+fEK/pCCxCkrwUafwjlhHRqVOnMKsPzTtF8AO9lJ3sHgh2MICZNGq8y5gGAuGIlkuhDAdNvEGuSVNYKkM1UrZTufwJ7Eqa3HOHlYfb51bIKduVBZ9bELMPrLyDFaEN+n1X3JTOYmmKyLJdFT/FPUes6odbGpVo3PlACqM4CGNjBfhWVe1/q3mXsVQJQ9ZWq1jfxl9dPPVf/t3fZBsJ7mT5Vp19ZwM/R9lAyw4AqVzTIv0qow2TGJXCUOZjVAZjNZo6jV95sR3W7ZKYNMWu1j337EMia5USVf5mVsYIt1nfZdHNbjjiZgHS+iUDZKesdG9UImrGjdBtWxwL7rRo7mRG4bKES9pbN1bZkTDCtCtnbZo3W3dWFVlqYU0aIb9Uib+5n0F8Kc9aNlqP/o20jXoLzLYAy7VLFBnS9F+4rPy0TLoSSmEeGo+2rqLIwodQFpuA4xdkUXtfGD7DAdgi4Hb73t1VOAk8Joc4sFGuaklL0jJ8VUoVm4EQ/3kTZvtueNi0MNsZGC4MiQMPfODNk5C1QlJzAKKE2ErDPp1yMZYoA/Zeu1x8nC3W73OJY2P9+srMGURc2Kzjy9sXb5MtEy0lH86Jc9DJE2LApjv5rIvGjxtB4pQTOPCMgoilAPSztjQc7oBW9zPX2hU2fauXW9qxt0UC6PnZbLZBjLUyLPRJDnJ4BE+pEThPPVOpoGC/khEU6USYmJLp02NMlbcL5a3dxvTRE9xHjBX0+u7unY215eXlYyeXZxemysXWsZXa1sYdjNGw/d/d2Cpnp0lqV6HRAlVHIgkXPoAgnhRtj7rWXav0moul3ZVKM7NXL8qIlVh2z9kwTAQhbG9IY3T7i8XsVC9zMldYyJbPtDKry8dXMlOL33v1XQpSqNRu3+53K1NHCos/fEsrCB9/7kUmuv+X//P/kskc004ROyu9LeHJ2/Qsj1psWrPOzLOca9dMw13+YI//YaVNqnqDJfX5brasdfk+NrR5TMHAA2l6OrPMD2vVNHTVjGQPq/DNjfX69vbs0hH2P+maCQQhsbCFirC9eVUlWJwSBp3aM+tJZTOI41nNFapoCkA6jeba9et3r2TKVfg/d/wsk2dKzTYdx8VBG6foapnJRhEYJrioFtQihlwUSxdU4mKIW5d7CBpyKEodgaj18wHoBfzqEiKTvN0lMu84lYXJm49EHGEcp4s/xmDnj63c59rNOsP9W5e/D9mtHcvRtCWqXU2M61Ulb6LNZImnDCRhMOpApiDZnV53andnvcDFoLoujf6EvlkNwmnmxEKOoyxcGa2Nt3K3zWW2W1McYWG1IzHcEO6kXNZQPCA8vSyEg2E81oFGokRy0tKjJ+jqIdyTX7y0I/UYQVGy7nc2xvAHekSkS6Z5XB4Q4BgeMRCC4rBX+MavaOGd5E4bABH4YT2HTBvBzJ5d1YXDNo9PhnZkf+BG8Gaqo5j9uKW4Axx4YrugdDLdxIbBDuG+d+oEQ0ykCErcmQhQ8sFinC6exXVt1KE2yixSNpelE7Ng5pflHhx+QvXkBirqz9ZDvQOVPX6vt3bvPp0pW0/AFG2LHa3BqEb2C8kuppeC5JAZSrtPJUDi6J8l5mvgNXboKoXdHQ59WV+f0RhmhD/QQwgZqYnQaool7tLdhf/wxk57qH99kCP5g0AYJHJTrzpQo0GS1ulzI3ETc2urbpEBrzUcVaFdICdg9SbGk79effvm5Yu1zFZuvtKud3qtXb5O0GnsMDIhl0AVPZcss0Sh9MSwk30oBjuMdMoV5xKtwdqz9fuigoRiGkZBjF4bHC9Wp8wqak521xBsvXM3V+TD6PTXbjmRzoQhBCNbCgeWgE9LD3lGEzLUohhjlWLJAwfECoEgGwFrVEx4rFRiiVhmeBKo0CV5GpKzHQI8Vzx6jRMuCTYnGC0R21B5KJ3QkpZnvdHulJdmz86fzxR/cn29vXUL7c9WMoYe+cyOccmzUv785+gYEoRNSTj7InWq/oI/uMEg/Ic/fpWrde7eu6eBpRkuIPukpUPNlKgR5QuoZsIp6aKjBEZmwtrryS0tr+TL+iAM7UosymWL5dL07PTm9ubMwjyFQ5ycrPiMAh9DRqSXV8obuQQYrw6choyBRBEuwdbJBgHTK6WTe1rC5WAXktO2MZVHJE+oZUxfrdGJNfs2hACJT22EnNG4csidrGtQytkB0qniqUfNG3rtnT224rlHlYEcwIzJ0UWePMlBdDqphGhsyaWZ8JzfpIADyFDiGPBz8sAhzyl6PljGPvaARyheRmFeHbCF4jvm6HH8jAklPykHQOpt4PVw9rcVZOoaXRapHUE7SHZ4X+h1JyQYIclfY9YTEgwHubR4iyCVkeo9PHBD9R0zGkceOem4/dX9zsB0yHD+h30zwoboOWTKmHWkfyShh5t9LOcQ+e3m79xfbHRqdmTJ+oFBY/e0cID5hEiirmWzzOgf3eN3ELkMKAnhUGztCOtQNGauaHM3LTEzn8KPkRzxrJvQPJkmmHTZkTwWcyVpauGm5FElyBMhwc7IOw0CLBBQ5/NI0XiFPCUbd6r4UNHqg9R7DfQiY371UKiDPjRLt/S7LW6vazdYRuhyP73fj8EETNrNEpLLVG1adx8VtBICb9DnaGYcfvLnIbaZCx4zMSEA4qkm+MmKAhtpzYquYfROU2np0/U9Bmag1t85HltkQDuizQp22obVmna7Xraz+OKpzpVQdsvaGEsWe9v1Up7BGGcf6dusN2cuDyr7szPljBXa6iNI77y1tO4HQz1bbfZyrUeOZ54+n/nxxdqlO9NtfSfWyzX+ZLaR6ewWONPZ2uOaSyCrXPeb7c/zbaTVu937q/nl5czRk/OLR3Y7fTZcfUWi0qgDeezu2oWZmdut+q376z/iquxujy/ewERqis4EduE0anBntRB6NnXc6n3G6UmHWBFDjaTD037dU5gvFUoVxq/86UpxSSKC3y8UCxy3nl6s6VZt8Zli6ToBkaX+XLfHebU699Jo8RM1LrTCPAL34V6ljPk2lG6CFsc+GLIR+hnzg3buzPGTT19oZ7XVTWkFw0cZ9LQuw1r6fswV1Agx46T5DQ72DSQHDkmSgrCNwYIhOZ45cby8MNMt8Vk7Gopyt8OTSVmTXil5VzypTh0/8e7b73Bbbip84PUTfuHdaGNATirkiufWxubJ4yfwCMBirYkPkj/QpwVoJIApPP/VEMGTLP/Zm41LgjgrgMxoEpgist0U2OFwrrxsPMoioFRJqADtjwXlF7pPFZWRpXRS0562AD1lkykvDEmk7BIHPPwhxBegqVocC9BQkp/ik6TXl+Z02gIAljBYWSMWLUSJWk3tSoGToWqrpQ+UccMEWMOSilOrCYCuHIIkRmaoTugCFYFGjKhlARpseY7P6tLxApAsQBNeWLv92muvkTUMOffYE8wzm40uAnHt/UvAk52QmFrE445wr2o0JgDkSyqG4qjpjY01XfGTLCGJCb60x+TGE3lNm9gKmwtrWrpZei7nWXfGQSpZcZellp6ZIXgHQF0kkubIQAMkDmJgp1iEeEm5ScKIpUeIMF4hgPkmGHyAUSTBEUgWdBulvu7HACmBKgIDYr5RgF9t08SMmlVlyikXt3D02relYbIbcyIaYJ6kcjxpGEI8lh4Lf4wiX/dDKjTzhLbtPU1UlDX02O3DTH1iknFPGqHHptVjzJq8YHh8HcdzcIjLBWIOBn0SIHEwFm+c6kO2RjnS7RIwL7WGLSyv80WIel0G77Y3S0JiCQ+dgXVFfqCevBC8hYzsTLmbm1TTCwu0I/Sn1yP7mcV8A73WzvABN2XnFqMUodHTNqwfNudsJ36vQV8c7HHSE5tlrgnhanxbdunvigbAeLrMM8EGofPQigb3pdUghnCepPU5Ek8Sbvst0pgGm97A0mrj9Lkf/vCHSAKQBIK2Vqjh6WjwxnRBeDSU5NqcZCecWBwSZELEkrf4LEEFZ7ikRxzHdpsQW5OBpaKZKSF4aAeE2zqM1vdpp3AbmvFDQ5kFfVtUgvMu1QCPuFDSRFBFoZNiokfbAQM5cTf07fvbHG1eOXGajYFCrQZODrDBFpcQ8AM5gjy+pvttn2jFqIkem4NRsNArQ5IvQA8DS6rJ1wKNXFNHSV7iDASF4gxSqrXGQDHX09svWsZLIemyExIIGDt/MppgON5VC/XNy+TDg5Z7qgfJ2gI0HR4Yylm1ay7BhEsN3TCTr01JMDgJKDToPF/Hl5qCIjV8nog9hcKJw14XAKe0h2j4GTmvu4OROQ0fgIDxhPuFRAIA8Iyix6P8dSQwpooeAOBUfH0oj9KOOa+RseBBwEgq7giipXj0SNQgzYN8JoGhO0RmkISICnp4dRax0k1LPH3iJNKFqqEqu11pGAfwJD4sQ+NhyXL06HG2kUoVtNdU1UYj0ntmqU17iJKg3E21EgISmiD0bvJB9/v376CLr1/HwMBLIEhI+aAMdySxaNFDF8BYhfIb7gkqhXwZgEQuwiI6l/uTqi/CpD0xo3TgRD+Q7jwWemz/OiglDwRgYtrxQMgGA/pEA5K2zCPoyNDYM+WClHanQ3VQDYCFXCdhJorxJXgotfFH0o6H5wgdThjPWLMjJHmqkUB/JVWMVS6JwkS0JsI/bKAjd4bEtGRKSHx1GHjiI24PB4CCBzAbyXsxHdhhvMiOnLITxbDh6aeffu+H9+B2rWCqdpRbSgq/aQ3g9+4yPfiiyr7zne/84Ec/3t1todvZrHEW+TOdL8kJxHkgTwjg1Wn27gD8gCHCArO6oyFjg8YRK75vQTOOaYNneEIYkY+UejSVvadhCBAZA9LCqJVwcIb2njRnzwV4PEoVTj5Zq7QH4SD38rIXJdmwdQf6bmoNnCBkjOcenkDy1JaRMYdYOlb8eAiGJ9imoKnELsHJGeRAn3tZIAaP+x3sr/EZyYieD0wMhQUJkoY+ZwHa8cAcMd9Y4Vk42AfOxVG51I0gSedF1ANLZLVjOAZVNIJy8HoA2QdEDdInvgjsHp7EpEklJMqPJ/JYF7METfidGEhcOtzxBazeZkew7P+KcE+MHIj4cPQY7SHawykIhHEM1IflNCiUjYtHGo2ArUaAJ5YWhsfW4IAKVaUQd8He2XoT99tSjm+pupfJhmATWsyvR+SzaPAcfdZsETGFUTiZD2myR/yWXKXTvp05ByDc1zcpHOq019IaApoEYz4vKEnQRTDKU7FYkcswX6lNz8+hZPaamj9SLQAkMEb68HoIefn8UdmpErkms6mLs3I5lkQUG8Jtgm8tl+yMZpEJGH7+kyOO8cnijI44QyqvIAzKULAmvSZXEMZkAeVpGkBR4IRQB5afEJ8vh1m20oZw9SRZBhJg+MhHPkKqq++9x2ADSjwLBxPSlOMw1sqxY6dPn6br6W41yMi7ePQw8K1uh/Dd9W1wLs3NPTo3V796hZPHKGlwQCQOs25uJAeY3HGEJ/RE6Ujl97P2kiM0wzG4ikcU2AI0F27AZOylGEwi/6LKuz2ral6NLUMyCTxbLukiKMTq8WdN9QAf+FW5VkeD0A/hU0XksvSkTzzxBKt42l1JXGz7oPdiHpAPAF6PB8CMRyE8nW6L58b9NYrGrJkZR7/deuSRR3qfbnKMeG9rN0ztEzlJU5L2M2GhzTY3N8Zz2S9EBNtIiaa6H8whw7UATQHAiIjrzjecNT+dlkCGUrLtElXo5Mr50mPVuYW9znFM9mRfqdt9yx0tyObNArrrdygbMhQK4b44Ffx6Z5mb4R9MbCB8sAMn+0DdbmLNWAub3TJ3uqL4AOK4hw032XUSPBsZrNhOaXzMFzlPogfvX2eyN1PUGlbp3hr4iyWtf7FXhJ/PXypL25PZ2dxCSeVL+tgaV8GTIyXFX8eGg+Unbxh2S5ElgXANUttFjSCLOnyfbXKDNGsWGZkYv5EvVZud8kz+9LHjmFfQC7Wa7akqZu1nWCYDMz0KGkocDrZC+KWRWb6n1rW+1uttbm5sbqxJ7VoPQHYAhOGjqcVQDVSA90QEpupFdNorS89oAeE0nQXT0MLsI4JNS1H6o5GodVAKqiw+IQ8HS3nqSIsiVNvAiBLbJ3FgSAMKG0O9YjrUxbhEBkS9LouGCiMRHyWolivYFKor1gpdo53lJCDGFAAaoaC2hSSvFQ8EHcFyCYySJC6JlSwZZUlE8suSLJmTRIxLAvWqm0WgLFdt38S2eLq7lN26MpPTYUbsmwAotmO/EtMJT4JDv1auEEiUMWvwqlJpn8iWzKxIxrQ0Ai1ompO8hT2lsJQWwiN0oWwbGz2dJ/D2smB7X7Wp6Uyfe7v3fOYgPNio5Pjkjk5iCrTXu/PeVSZ+i6eOIwbNPa1EAwZ1XEgBAJsFpRLdnpaM2/kiX7d599JFtlsQ8Pfee++TS8ur99fOnD5S7DZnGuuL3UJ77RZE7FS1KNNvr55byn+8uk3ae9xzAU/6GhyklziDbLYaZFrJcytCrtZo1Hp3d1psg3P3pKaLsJy/Llt0Ej8hJgg8fe6L7lcKmal+lu8PV/s9FgiKuWzl7KNPUq6r1++uru21+uVurlqpLTXat966cv0Pv/LVZm4Ba9emTSPZ9ANPB5tZGVNpQ0HqixmsWUb7UiZW28BANf+5Q1tT7ra+m1QsTfFkeAUnyZhYVAKk+a2YLuE0T1oTYMRykoCVfuSA+xua29v80fZpzpWaDIt6hXCbhwODx4XJd35cD9BrE543y+JMS4O/mqz8w1CMZdPunY1rd6/eKZePPfo4Hwp49MwKfcx0XvLjN8MGpFAz7LwWPMxrhyQRZKJfgSlpTPfREEYsNRWUA4hCy3Wc4oaHqCYlD0krpi9ISDEEAnQXbKittYGfQJUceCkbdLvkLW926BzeUKzVCKbnSh7oHORLGYHHnAvJv3HtNp/D2tvcokfiPiNaP4Z96GrMJjArw4a6x0eJdYUAbZDm0Gk197psgdqqohOroqpvHXBMmeIsX5XRVyjG4h3KAdyfPBOtknB4EkwCa78CMCc6h0N4I9Bj7enxxg0pziSvBGI8JImZ8AvzvTpIZXzAWkd5uQ1Okq/n5bUmJCHvCfgmBD0UPRPSJ0GQ+ubb73TQ9NK8GOh4yZ0bCdDD/HrtI+S0cScSqSAXVATf0F29t3b12nUuaqdz5AiW5lzZPOLY1kUa+YUlHfk8d+4curRYUQJyRjnAPGRP7RUuSSmNupEQKg8mL87OLZ49+9hLLzClufTue2+//fbW/XWmfvNJ6qQi9G6kqrKSyIf4pYALM1N3irntjlr3vsfPkFv9USKZdtVmpvPlPMowHJ1zqdYT8mHJqASOEDQuAD6NsUbsgw1SUI9cHNBjPM1N+Cigto0nk36TwvI3WteJslE/6GK8u7nWbe4Vs00Oq7S3tho7O/oURrJqCSUOKZK8FGaxmKYQxqo2Od1S0GodlHms7Giwd7P6Uy9jFacomMifjaLRMMT4WIRMVSq6usHuf2jIpAKzI9fTX9GdoNfwibuQlY3nopzMDeCN4bKgS1qiedletSrTizoXtFq6akbkRdlaBg4W2gIdtWx4DB9B7MUW1YNjPg7fjIrwGLCRuUBt4ZOf/9WFI8e++c1v1u9fY7U0W991psUkwJMjLIlTWWcTzEHhbO3Vr1y5xuyJc3MZz9G2QNyi3DPmVJss7zQa1uY6mD2c3psWx7yQu4tLM4vVuWW6aIxJNArXBXmS2UKpXJuZ5ZzB7PRMqWLXKDv7jD7nU+xbnc/EuCcWJHoigKUePFwCiRWkmo8rUQHEtAPoJNCxeVoKAoCMeCAde3jhYXyeKVWnWhjaNzqY0jBkAQx4WMrkAfiq2QNwMpFcZC0Oi/C0OplGu9BR2wQYg4MSK/+lIsV0Ozv2vWG+DgdBqI1JzJxaH1J30Ymkhr5A/DDZgqrEDcpllAxek9IlgEPCkwSO/A7BeF5ARM8I9Ad4hWNtblBrsTs1k8YM2bFo8tiuoH4SrXKYvAIS4wMTKm/UqhKTh5FSjLyO4w+cNGzjsYcJiYVKA6sZpt9T/gg/0eOADyQ7hW+CNxQqrZUmQD1EUCR1vzSDHFNCmwZ2AHXUbD12dbo8t3pvodWebncxp6XhPLDIaA/+Cqpp+Rxeui6Dba7ZtNCL2ryYForaKmo4r1bNk7M+ZMFZVcGHtIplWs4TOnn63IFIXhmcSSYtil6FtAqcONAwGJU0evSSuEELDiFko9k0maB9pL10py+v2U6PI4OcPyUngolnLikpkjrnxFIXncwBqem5OYhh8ZQBjDo+Du6K4aaS1KOJwSqLChH6NZGOs7kDXQAdsy4oi32lOh1MyEkkhQUtZm4OvNhMzRDOgg0MwKS4pg/J62wrgeIPk6XEjMAzcUI219aP2EeDQiBI6aq7vUq5AgFKKEcDIQv13e54EQ2W842TK8c/+tHCzNTdixf7V68dYZYhyimpFSxJEn8rty/1iu3cyx89/8S5Vn0b+ycm2iCsNMxali/c9HszLIjdvr3NzP348cLJE3xAq7m3g0lYsbHFIfYTx8rnpnIVW5NvaJEAQr2HsC4w5nQIjxg3mcx9E8MO+msYzcF3gHj1BRdVAbddLdWKFU3RVL1UD9htpm9djFhi9YZkSRDkbNzOeCjy03nurwbxQR4qV9p5z27jB4lCgYthSv0Wh8skMz7mYe45miqNYX8/GDD7X1laXD55jNvcsxlbO+SbdCLBJDoxaxOOlPZW1gM61dMNhwh83PlXYQgnba/fajTrbKKwxHxkccVXhLQqWtEw9cJHXzjz3JO0O76Wce3iexiy9Oot2kfZRtEY+YOBhUkeTiUrkceOnVgLC9AKCyPtQERonbyRhYeRC+IDBo5DIPZep96dWB0rLqR+0E+YR4gkumSb5OtzZ0M8Uk6Wo+kA1ohLJa1wNZvs6oD/MAvQmr3I6Ym8mtfQouHMkQVaA2+npUtgGRypeHbTGYNgkkzNzBBSs3U5WA2GfNXw2EmVnC03ZzsNks6UdPG8fyAOhKQqlnRVHDwDD5QTSyDNCTmh1IzVScVhPYB1YQNOieQUYgNBoIH0Lhvx9UAslRACuIGeZWPwfkMfhZyfPsrCX6O1RVocadCMPNMNHisMcqS8qGk+igI2HTAHRjNqYwhP9+OBbmo7rS+cZzw9MJdhKZv1LzQvOVIvWPZBiU2hQQgLhMyKH0SfQlMJXkC4AVvEAQ4Aap/XwpF3zRMcxpqrEQYk5YWP0My5GzCQiyhHk5ojdz46CB63ETMeyCJDqFxOrUZEU8o5JakAeYcCnS3DqUbg/ZWM0uEggVLKwt34EA/P2SNaPvO4tjT6WbYBfAEaMNWRKdZ0cscG7aQlnFc8uESegzBbktA4PfkIGYkCEkuTiXQafuBv2AHMQnoBurkHztn2+xBPNcHwkhk4O8Hw1nPkSXukXNW6rjK4ce19Fi4JxM8eF/Y+QKKMtpubXDzP5xkYH7DuTHOg0VNrQFIuMEAhnTiHgDJ79+FPrqYjitzewkbfkeWnALtXPqqqcT1mx8Y9bdE+UViwu3cL/T1w3nv7IgQTi4u1OcqZGGVVTF5iL/dizM/zpDIpEXdBQryFZ5AyMH/5y1/GjmmuPE3lEg5+HmShBGoW9rQxS5Kv5M39VDX+ChMwc+SI5S1c7XT1mTvmdgSj4iXhPRa3WQ+VPqDuoQFHFkhK0c7RUyOEANNC7NlA2t6iGdam9QVRGqNwmk4TSZJ/J2BAiQIZ4NiaCDA48ufpFlvIFq348sWL6JndR88+88wzR8+dZBm6ZCu1PoCzREOP0AFYmCtX50/gzHDrGKRMuhYLGXTJkOchDinyHFIjLZzJrYU4ZHoBOt21p6myOR1JSeFIhAjyqC5IZSjJq3880xegZYYhthh8oN/auPnZxoTJ1BFSAcORFpLDfJIYfmXhfn/GTPHAXgIteaCEQGcUqQ52h4c8GM8BsWQxYFAClw47JKlJ0tHfWITogRsuLQ4aw3lN+8djR1GPvZN8LOxDBaCpDKf1UCYOHwCdlTekg5nelp1Ub5XewLmDiJ6aHJErxttq4nOzKFVMnenK9Zkd2+EToqF25KaINoA7PIU2UwMTmvO5559Hh9+8+j6K2oyBJtRCoP7hfygmXTllYcSF2w8BbMFR/wDgoUcglfcXHuLsIsprmFcLGVS3JZ+M3iBDFH6qw3EKG0NBG2AckHwy0iSUDk74rQehj0SwwQ/xHb6Tw05DohbSNHjuPAkkX4oJZ0ilsaIV36MSv8qYTh4IJnTYgQpnOm5UDmKRPUePlhpM1h2GMelNqJInnnSDVbQ5LyxePCPwaYId2EKEE4/86us0Ceep15QjBFY4T2JaBw5zPT7OlM9zXw3jile/8R+4RmPJ+n9gHB4PXLICijachYvbOPx0dlRcuSS7KtY3LDAMvRyDP4klI/wAOJF44Boh7BYQxYhNYxuTKPoFWhMntHEQj+HI/dV79BQyGbHklksa/YfyOzaePg5xDsYsxOJhrsbMIA+C6QUJccpDKuESc1QALlflMuiMejqWBCga4UKYcNixeQrPCFQO5lEwR8ywMWS6diNV4lJKwDxVkkt4i8D+vt9rDI+eSCYhLhNeNH/GvD6wx/HE7CbiIRYxQA4jW0SMr1gZ8/11YtpDBh4ew8GQxB4yx4lgEXn0TAQ7IDAmjB4H5hVPmjgY6IEHV6XD7JdjjI2e/SCV+/BobXKSfRg4GTjRgRQB5U/rorHgN1UjXaTmuX8xaTgRrbdiiAReq6M4ebUAjVe3CIHW1J4vQJNS0d5LGCRpgTRDLjUUxfm8Jim4UJp+I/bDO8vdW+QAmUjl7Hgng3nH1u6edI41ECAoqZZOzLGcysokeqmGKWinw5Rzr65Jq5yNLLypWxG8IB434QmH4TazPW+baComVppv27jIMZA1Hn+CApLo5aDTqingpIHzSqpIsEd4BWFbTThJIgWEK4mF4Jf69YGbVTqZEEgShVvE448/Pn3mDPx/9dVXH3N6wpwoohzyQDDT2Bpn406fbl+/xek9smM0gjWWsrNVcmBwcI8FnJXHzmPTcP8nrzn9AEPzBT5juHrboJLO2jIhZKgpDuX8gBfSplvxOLQVOQRTHdDj8Hjkejn6U2brlGI87X4hXsseS0LRby7t3y/tQ4WnqXJ6Y14Rz3hIjNrP43SCkPE/YmTtVUUYp98JSMonfBHGozwLpyEd4uH+VJLkHcgb12/whvBoVUeL3xL+iJZX5ilsCz351FNPnHuENrt26y4rP3fel9RVpm0J1bB5Ep5cHv3Tt39KQq9DPElu+gUg/Rr9gNFIqUr4EAMf1qPPfeAoH4uIrlBMwHRfAtl6zj6QYqkbDwNDvgV2nBP5u3tP7+g2selcnpn82s596Y2Cbn4otDV8ZJAkzGUpKdYzpYVLGvrY4KlemZqlYBCPwgKYJxqAEG5SYT2MAZjIwpldaY35WKcDKxm7anGBFZpsdqY1Dfy9e3fBOT8zyxoz+0WEbIlj/UK5wDIQNm8Q0tiTVaZsnckjU0GZtnNNKOfCNAa7dbsyoqWBcYfPEVIIdQ3KwYyCbQesLXJKfMuGn3YHdud3MZ/uF/kqNx+nxtCOv3ylury0/H/63/0fUBz/3e/+39559yIiAjxTGPGEa0f5DFpVdpe722trtzfJmlhI4r8YzTM693q1UkH+GuOjJ5d8Y9D0JghRUsgE2kpdg4GRWkW3ZWXlpVft17gf0SFS1opcAM3WFpWuDsNS2GDdZYuKAl2wfUbnYPzfabMtScUtLM5RIkqqmmVOa6dcW60624GeC7h6fXhMH6JeF3mTxMUiWJFVxHTxLTD9GN6TScWkJvw+BdK91CPOJlTN/jQXV51urX/msePL8yj8fLeIles22yeUgopNE2A8CViMRep73KLTo3w0kc7Hk4cbhJOIiDNYGKVoAyGxWt60glNlNALosKurYI94qzhiq6Jtvt/c3N3sUXfacQER9nVqU2Ql6uku1fyw2MqzC7HOkuXW2sLs1PXLV3c2NxeXprmsuZjJrt66tvr+zbMzS5kqtsLk2V5YmNu4t866YXZz4+kTx6ewF+7Uu1u32tvb2f7Ocu3o3e0uiw7dcq86la11bpPXyeZdUnITl3FGoqLiEtDSHhKaBGp7mT22odf6d0r5+z3Mubh+HWNU7ryfqdJWuH5XNR62QGzlmCOkGATY4AMk4Lv49pvPPvssuyhSOPMzWv3pdVACteL06o17X/3Ka/n80V5Odnvdwg7wYhSiZRzWNJSNMd7RJZk56PS7qnVGjE2IrhYg7qzpg4Q72xpRMbCiy+ewPKlQZuACA98pRQ/RcuAu5aWYwmMZuWBbIDnYZq/XbLu7s765s7HJha/T09oTqtTM9lyTnQ6jV4js9q2W7Y5v7iRWahiBpbaqESkUl/g0KxkVzWQp29jrbd1/48rbl7/9zVdW5hj3PPvM+UcffXS2ZlcBtLkjGps07R8gCKoMbh81OkO9ICi6nw1n1WRw/iAo9TbqTccmSaX9QjhtWsmN5qiriU61R8fo8GwZpjIwvzdXC02PgPotlUUCjTMCaRfyGrU8o0cAVFOmRcG4BwVd22pu9Xt1lBHNB6NtzBn6OX12lVaZZ/O+n2vu7hUqLSoSAFoKNUi9l1vNXrucLaqmcGDzflgKKzof/vrTZcAgYzweCBtlsUKpX4NKkI1y3fehHdEY9zx4v6ezgjxiXe8HuV+4aLb2Gz0OGV/xuBWkW4MkGVnt2KA8KZb6jPFcSD4eOB5yIBh5gcTlR/kyXnjnnXe4VCqhXfgsdxFwAKoYhbxFatn51TwE1UEXqH1aXc9Ia6UtQXu+VO6j+AvTucrMVjPz5PnzjNXo32uzM7QygbAFy0kgpVbuUcz1ajXrMjEiGsQ6B0V6Ujb3o0chBrTs9DLMKVZnzz75DH9/9mf/kU1ubByVk5QEPDEcafkxFBYlggRk/Pen4/enyWSWUerSzNTO6ibt2MZZaZDgF6/IyuqWT8DpCnouKUrsMVzTjieLOToZAESPAwNASEyoLBh9MrwLM1bZBmCF1uXzA1xvpGiatMB1E73YrRB30p5yxg34KvMEwrBGqLNVl8d4tN7atk8IYqREk0f581SNM5ZrqicK6ZU21BojFWUAIi4+rJS1UilLRomXCSPmCOE2+Vxfp7vICI2Y77awSuXchRYthAx04iuJ+Va2JrUsqorUgSOEqQPvLciGPKuGxZXFAt8wUYYseAyA8QmlKJCX/6bqoyildCwDFhsbqJQmnSGRJYu1Y3gGD2zIVDzLkfEOsA6ZPJ12hTOWVu6W1BgZiCJUFBdmzj3+wvzccv/rX7/+5l8yIrX7kDJlS1EpdFp7W/lim8EwPZUkgYzFMsqF6V2xySicL7xTUtGeUS8Jf4ZETWMPjYV0T7Eag21qZjtIpt2aRec0f+pCdeUsq9H0vSBmyFHPNDXhB20+s7i8dPvunenZGZXAWoox1sqjYgcPP5N0+iB23Od4qFZVjb7bkVSXVRbhI0lEvOo0KC6VSxRpGKgxg0Xp8kp0jOqRY/JcEsYkCEkudro7srZnVAMcTZOCWN217amMpCpUiYzXJUhWLs5pgbBam6Ete79MrVEHDOdJgVmGKhb517gmIgrkOT3CfDgnkky8HdySp5g7AQk0CODwGaUh03mN4PYo74upX4wqK00ORA2gAHBUGjxQcPuD9og/1OkgxZDPa9ba41C4Y0OdgydmAcTB2BxFKM5QXzGE/ICXJC9JU4LNf9XS9kvokj8i8wmqIAOknYghAaPxjaCXnvAkoUQW737GR2noQdpRJAOodKUMSjeIH/LFosaso2cILmmA2C+jqWfaHSx4pu6u0s3zQTz+UNwcNkUpWhWrpQRS1dfgl8wko1ypfp+xAkyw/rNyonMbXBGopuVrKeRFFPoLIjG5A8bXXlBr4CdPRKar6kJBMjSBIyqNXysXbnc15tFcR8qSfvXygmS/gisvsCPq9udpWdKB+EqpzKijsbm9t7XFWlOZ2ZH1a0aMzgDBK2oQAljaYMLDsYJtDhs1677CTjSQZO3KG+Uu5MY49w7RbR2WjE663OMY5LbVZF1IUNYV8GN9tFiVZUyvdTc70cuyAgODXK+wt9UoN9qs/PSLMnwlLaWGAC8Uz25rj+WP2k49v7FdOnl8Y2drdk992Uyn1bp1q/r0U71quc5yei636MZeNk/hAZ5Wvs3hkTfndCniM5/8ZGZ6euv3/9WZu9uzLc28Gvlp8GCkwNO/Z+Yjlor16XOtOitOV95+c7lWLX7+Uzf/8i8fv1JqtSH7fUmUTCq52lVqv4LlLNy/cZdZ5JHHnr927VpVd2L3OJfMGst/cfI4Xz+60ZlDgdyoaICwV0T4dLaTfBMX/cFjCzgxUGUx5lDfSYrh38D94bGlzPXMnozJvMBNtvv5wk6jubndXF4pdzIM2Diq1KekWtkgH5bq2i0O3EiczaXGM5xzVSOPtXMYlehIPtgTPrO8wpdM+lmWpz4YjpAqx2WfdI/FwvT8VDvHhQDFXldjElqnN1LB2ReMvOC6CiEKoc2daY4W4K1S+kQpELMwXVQIfYcF0wQIlonV2vo99ifYWcdx0QI4WVsxGCQQ+KSKWc7NWS+GvUi5fHx+/uQzTzZ39rjm5c3XXmcjpL+nE94VzppTA73M0ZXjxT733kvnWC2IO15TgNHYvLWGeicBVWvtghv8rNQiIRBu1PgjVdep0GEvkiFnAune4A8CGsL0EznoI3hkkW/qsDCEIsCmspPVd5Z9fM9KKxN7KAMty1yk7XCUMpdr1ltY8LEALaWGJht1xnQTSjF/2GnOIJkXTo+BU5DkY0rWXqHKJyrEEs7ojVyod8BsGKhAhSdDCi3UCpkcS9tNO8aCeiML6lKQ5vwKjo59DojjHCCXKTU34ea7bgENHziqS8iZM2fwc4Edxw/ffPNNQnCoKkQNIsslCQcW07h+V2Z65CsInvxR9NgkvHy8WkbQAz8Njy4eatW1QOMXPfsdC7xSUl961rJ/5JxkHgWnjGACYLxahoJQVPJxJEeOuABJMFFOm8N4WmBwNDoqAsYE4pnC2Xo3T8dJLM5jIwxR7hRiNCQBP6df8nUXaaNQXi7C3esAkSDnFa/EmrJg51pM83D47B4ASDh4BpUhNBHAPLF2FYWzTFkHV7apV/WaOFRPpMcBLHjCAzwAkAVPzoDTEBwzssECKB0ZnbGuB9Jity5+QRRff/31Zz/1cWSVrUtgmCVhXQsGHDWrDyDs3CQckfesCQcP6i/c/myTaKZxAEeCIiRbHzA5q0i7atNONsiaoCgJJIqnA1u5IwK1bUiCfjUQS4VUc28CNEMnX7YEtFotgIqbsAikvSP4qfSH8iZsZxnCbMnNmg+ekDWDG5BzY8wIIlMIqj7jkB7OcwfjFYB0uQiHWevtJk1yamYW4lnCwHlL2bFtJ8ZttCZ1GjivckdnuaQ5A35KikKCNsYi7GS+9eYcdpEfe/lZFM7cjJZNWVqlgjhrLGRWwkgzIb6ACJ4kh/DrIV4XI1GjryFpCkPAprIPAadAPBxy8AznLiCfppC7CEBrJVIROiRPPOkJKiVJnPvTNeIxHp6GdEoCPewm2ekN1ZFY1+EmthRDRCEAw2QnWf7N+00X82Fp9rTOVZ7wx+vUwseqc/+yp2nYH2pCzMEJPTbCQB4Oy0oQHaakwNA2KRGNiDaocdvUFC2C8QndN6iIpSWC37LQcQQCQU4SNCeWztg4nL/w2Mc//vGXXnoJBavGCJcSxuBHe4RS8SKnFmEjfolQIqlJAgON4UCHtEl4+jXt57ZBxhXMswj0VBG1MknwDDJMEo+HeIxo6PfRTplV6dsE/KBf8oVXsOUAoBHaDoD0KOCVJDVKCeKXdBYBQNDWHg9FqaBdJwt5olvwU1IPiYECTZz4kAAAhqggM0Sqd0ickg/TENGSo/AnkPEXpimV9RQx0PE45+U35x5kzJN47FCSFJgzKsZO9IieB5U65s7xE+CRfzB7IK8eYsjVLka2GGPa2GqAh9/0Vgz1f+VXfuW1qQY3Enb2tgCgULBIytbGw5GrHuX0W3L3Dj0Jj3mlYUirQU4xaYM2skJEGdJQfWz2ksozYovSWzelgzbmTmgALq2ObXkov9RLzDd6UpGjXqcNSGeIR3sgIXj86ah4xY2isHcAoNZjER38Ls9UAH7EEjx7Ldn4g5GHJi2THIWlj3M8xMMK0jJ8isJjUSFtBANyGIAAwTjACB/SqSJMCLTCRQA8+5BJuod2I2QcPj1kwD3k0OuChBEVIbb6J2RGbSh1BJiYC6k83D3Ouph8JMl4+HiI5z6S8AO/juDnNRJ8SJzjGDxhlF3nj4FNRklUjEj7YyCe/cLTMA/r3w/nfuGOn1gWevioFFVJc6N0zJ6sWtXWoHREHvwV2bY2Kxz000kgvxonaxCvID28RbMkjdenlWwUKZUt6ygef6qv9xACyd39BkLA0GCY94d1EXNMCKnOHLQ013dirseMjMlLoYgBnhfflYGIYQmajSpUCoqF1Ym9XTapuaNjaA0hYj7YE4s2EWy41ALxeqFPwZW5WwH1bjqK7gBVWZ3VNaG4cbSE0PY5u83gR0Ww/o4nerLa7WLM29tZJZw1ITBTYJCQHU9ewUxGz738cmZu7upPf8rNzYQ4wAjZwmwyj27Azwqi5rO56r3Ll5dXlrnPrfm2TCdJZY1xYFvteCCGMerMsdq5CxfuXvwpMHRloOF0EfNfryOXgYm5jxDz4V/JkVrmCSqeoiYRQV75Sgp9yqkjC8RSKKSF2y7EAVOz1AuV5WmpIgJH6AGSkIhwJPYDvzramBz8NGbIwONOmZrMRJgHeiKpeCQeXhajn5CEJRPQWKwEKe0cWzok+olK1vLk3d3bpRmiEs6fP0++YqYEc3QsGpMrylgK5/GAgV7/1Jkzp86cbW5t3blygznUvZs6bUbtINIMlvCTyvijHB2V44FJMSSdxXhgjD2kp+B3q6kaWH6ykbVEzDuDpHKcCKfIlu9Z8ODURXer3ttptjBsWGvUu9zUh3Eze1/58m5rq1TlO1IdDnr1SjUqqdqR6f4MKqyuVZKl0txeMqQHudkvEAygNJyyN8UR19QF0sQsUfUN2h62O2jepqwXm2XJUwETZQaarBPTVdjlfM2cWsJOexcVifUEnOK2EpTFFtt4hdJWszk7VZ46toQOOnbsGBPL+ZVlhq1875yBiWxoJFv68BdW1Sx+7bZ30GsZvvbNKe9mHf3FVw53C9np7duIBVSxbHf53j1RbpYuLFvXZrgihM/xFWTmkcns7mqNeK+5q8LZgosLIyaRSsULvzRMD/XXLHtsU8yZKZRh2C2VOGaixoy4UDoKhZLa2dmDNolCwjohRE0QAGXipRwhwBgIU+58tapVMexSiaLhiO22C8f6swMBT0buR+K5+odXbiXqtDEZ1v6JTVa46YTPlHNJnXL0XMiJP6bKlqfC0y7WaToQv9M+Ehhfkz3elOYaswsIditmL8MGTUzrnnJ3i++L55u1AsKpy2Bbmaw2RfhEurSpbllQRcSyh7KEq2N4E5sAsPZpr/Yw5EaVGDzirGYJU81orTZQaFDa+GZXC0sMU2EYUOkvmKGIjOhA7H5hMoWjbWBD4g/qxXfLT5w5oVMCO7u5pYU9NrR2No/YfZ3lZr2ayzS2dpbLxRNPPIbq6d+8OTU//8Kpo5XdjUqnvTRVyKzeRKq7mzdLq6u1JT4juLOXb9Ce9bmgInert/TX1eYHrQ16OuE+5UAHIZI3vTVReKxCs5rLGm+Xq6ILU/nyTLevtP0+t29zgNR0p92R1M/MIqXNXqncK3UK2Tuba6371yheqTL9xpsXH3vsnAy9M93Z6So3FW7d3/mLr3/vrTcuM9JTdWiWpzs3jT+qhYTjoiO4PHWLZOut0/U7XnXqglvGGnU2bO1LiaUKypeJBy2iWV8HkrrlKesH662odJyHk9fQIjV7vD40iBJsW1xc4dlvdXa4FGV7mwVouudStVIplSolfSiv2ca2W1twoIVzhpmBaCLeRq5f1mRGEiJGJKFhs9mbt7Zv33n1W3/xXS6TeeaZ80899dTJleVabaZQ1i3VfIFRTDGLRjCK7iArQu6tLy2HFIdwd+m1k3R4mq22VyZGe7pBaid+gCzBKaNGOUPofifI9UzQNhbrxbfddRNwtD4JnfM0FCExPFL+vFhXRLxkQKpT8mAGR0TK2IKBHwcDdJTO0lJ2+gGs79DzKGmI4KbvRn27vrc1XeNqBWxLA6mgBn3ilK85L1v6mQYDxKMCdPITA0fwJPH+G6s+CY6kJAE/4990/SJavMYn/HSee5Ye5ZKTlpAPT1CahgdhC9yDtpu37qytb7oIhHOSIbFgUpWo3V8WwlCPtOsXn3uejp5mKBirEz4xRK/NTT5sojMCo/i9gg5+Nlr6vMzc8hKXDr30kZdZ+T39yDkYQuNCFrGzITXSo/aoB9YBg7G4WpiaBiCKJT4p12h9MlJJoga/Cgvbe6FdEOc8Xz6y/PhTF3765iWQmnRL3Acpx3wTeUtgOhwrrWqpWKuUt7fpDb3djSKi4Na+FC7LrVwWbWYkhRIZHyx2oAsCEs8uAoTQfX7IhYvauZmPu/Tkp4lyqRGKWgy1mrVegyNwIIiNSn6fqBsM2lLqTtF8xZ6ZsG6BZxiGKmbuwTqwjLb8ogZpjCHnEq7LJmX+rGWB6bn5XLHU6mLgJHNbsc62XCm5qs/0PNYFqiF0OFbhWDToKA+baKh/SQWTFOWR5esgfMCgzOIW3x4w1enbnBSOz7KX6Pcx5WLrA3tsxJVbevmDFUCScaRSVQ8y0IagIaEa2L/I4COBsG5LqaK0jAkeCCm7go11jIkc3quPMTOv+D3bIHXGcw1jldGg3bmowHYaQjObK83MffGXfuvoyk++9Rf/nlaGUTpNssg9m8UeW8nqXEGHQUozrCBDia/hc8cV6y9dM/MDlzivhzJzaiGYKrAqwwCpwn+y7mGI1OtVlk994jOfOfnYU/3iFK0R+vMFO62Vkcm0d0dsFBw9cfzm+9dZiUYnAOMWPeA/jKOOvKROUiQspo0heNxPFB5SOT8jpGrTdK97CPcWx8ELK6sZIthhfcTW0koGWG+pUPoeB9W23WqJ1Q4wRLTRk+t26lub6gFtaZWVonK1MjVdoyEjtmoONLQcYw1mAHK9HEdA2NHWHtuQeIUeSmGARfxDHoNB0gAIxOwHOZRs6GWkFCOvQ6Af4gVO2hhpSAm4tTI1AOJBEQ7MxcVgBIRA70AdiT3VVjD0p4ZilhHMs3MkXl6S6HVsVOAwh3uSj+QhoBqk8fwna/sB1JgvjWe8UizWNZ5SDgN4jkGa0njGMgkBwMjeZ6L7oDyJ+UbPRPRMF2BNba+e5Zp4aecutzMzbuQMC3ualiTItpfRtWLOzkmro1HFWQvVXdA0MlnBcq5E+krpBjc+02cUCFG/a091OErI00cUPsmjGyAw0U7C72cabHofBuP0NcA4vzwo9hBGsD+EfNyBDdoomFNO7lyqjFKoVaQV6/e3Gvc3OT5SKhTp+tBfunPf8gDeuj3NMUiFzQ1jJ5IouZ26IC8vUczUrcLjKx6NwqHchF0+eUbpDEhcpRgPYYGtJDLAy/HNsDJGnroKGsLcgEC2VkWub5KhrlaH3T5d+KkLmx7NtOrZzbXM3OleobKXaRW4P4Sp084mH5efm5+/eatC++UCqDxn9jWTwsRYZgrXK2WttDzxSObTH8u89sb2t7999H6LvdR1zqrxoeOKvjDBKXhWk6aLurgSZvHIbm6z8rPZvE/anV5n8/6N9e4zR55/Iv+Tq6wdTfM1seh8DGODjCk+Y5PP87ncU+fOrTz13DtvvDHXqHMc7wlstGanb2026bIoGk4jj4hh2JPWMMMxgzcwOPu9Lsb1gofTLbHwJPFLRhSOwkjINvuVH795eXGmxnoax9+1NoVkG2YGMdizM56hHRFCHXkH58lN6oKc4HeCBfazc2kZY82YZUHORanILnaunA6dnWNjzIcrwvdmnYVIpe41kEkVEQEOFaJqiTMUFSqlu7zgoTdIh/upnGyLUa/bUO/sbCEnbBzPL8+XilPGHKarsAj86ApXSgMRCMVyFtpTIQlR6IrC0uzp+bkjTz6SabZ//OMfX3vnMiucBW435mKDnmwQSzpXpQG0yuWjO2ua1Kk5/bjMMBNhYp6IhFjpo3GHG7Rrf5/0lPQbL4Qw1ru4azmkRQE/oTxx1n9LGzJ6q1V1KyvKiXD2oZAhoADABhOVxHd8aHi0JaE0CYNk8mLhO7qEUBWAwgEWCxhh0m3JAHRCjVz2eg2ml8khD5ML5YOzz6CbHfN2R7PQLmfXikVGnBztn1lenpmbyyzPcZOKit1ub+3tcEMKKpQcS5qxlmcX5stcrDw7zXWPzBpADbslvjRYBuW18vbduztf/rf/7t/9O6avkEcWPCmA9hPK2lLgqgpZWtnNvFobAkNS1Fgu+ihNF7w+vYJ55rJwj8aMSINTBtr2DTRSaS04rztPCAE5HJbWInlIaz9WeTyoEZIYNwIMtp4UjSUYj7LhLXVK26HxqI7ITnRKGByjcoRv9JzkhVMZHSc+W/rhiQgmUkjk30TnhYJpUOvFFPXQ7yxIukxCvCDARA48bHliQs90PLkBBCaTHZkSYoEu/GkxFpgINbaPoyILRKtc1NYLgoezNWO+jiCbIAx86PxiWlV9rsBNUogWsVwhxMJroattjMzmBpWb2dmCHjUWTnhWtEcEWmrf83U8qDAxMFGaXkaeLm80EpJwBRis9oS+vpMPZ8aECQCgkS9LJD4DyZFzjA2VtrGCwJcKGbZeKBr0EAiFEA9VaExCNOdPnDLe31lGakQC6cuUG2x4wYyH1SWWcenLv/3tb9+5e0OcNJM3x2cL0NY0ADWaCadcMUcCaXewi4RKklACQfyRKQqDqEZLTbVYKVM7fOWYJ58AgpJ6CpVgY3Iv0OBVLCLW1YSXHFZwEcGNGxdfeeWVx8/rhujzjx6HgRzzA1XHpJoxrRfkAzxVNGvmI2kjH8zjWsJyMfg0sBdHlWWsMIROzxBVjtBiB8KfxpP2O5iHwCReqYCYBZoqRJkewz8Eby0aIUf7eRSMQv6pUOoIfgpbUmr3ODaesWpiyAhADP9r96RJ/ZkTCfKfCc40kQdwTGApYaG+AObrvodJDp3ULCLBl8c5MXD6xEnPiHA6cB5cpoGj07906RI6CounVocriRa4YeNjH/vYk889y3U380eWad4ahJm9QySV5NGP5wB6EsgheJK4fVMaCX7ti6QkELR6NT25W9+B2vtruxAMJOFEHJDvCOaJr56cERFlT/N5InDIlA/MVjQ9Sx312Q/8oHAvZhoCYmiD6RLxiiMvLZVR5OHmnE470U9asGHVSyweXBps5DUd5X4o5GAfzNlt6pv1kAHCGIVHVUAlsGSXTMksk6FcIjwwOOBR/vRuSjjsPAQYJBYOk+Nw/OCNXByY5gD8ICLl83AAIsyDyjtKTxre/THfVD4Dr8OIH+bzDpEnXxthvL041/7zP//ztVvXaJJYUECeS5Gz1NOCi3KRJL4OsFsNRqbhSfsjr0BLP/jcp76AldBmj8v8xCgNQgw+8gqekBUzF4LZhTp16lQ6o3F/pMczdTz+BJXnkk7lYDGVR/krT/eMwHhgGon7ycVSqCC0d0pKhp6phkxc4N7pNHdlSOGbNCMYSAuwxngWAQ6NAM05pOfL0xvLSPK/ulfPF/zOB3/+1WWXxkzWMEXP1GIK/jQNI6/p5Gm/CVI64LD+iB8MLkgxJVHR/1Ae6P/AaffLKNI5AhAz8jHYSCyvESBGucIbCec1mfoKMMZa8GQ+TMSjtDGnEc++ESNw4ZWsaWWoKbZ28FA72sG0XOVNyYkHOj3qpBFmVzSurGyMQddlGLSrZCvMJDIbQ6YiSq8HTyKpPXsxPIlWJ8TnWRQCGKTF4I1UK5dSK+OfZdU7TvLigMjaqmaFXLFJJqgIy917CjihXlmWYaa0seByYVaEybDjCRQaycOPh6gYEMKgmJx6wekD0Pl8ic1aVidkf8iNCGH8D68gm1iqxJlGWgDAQ6Do4/LNjY3pc2eZMbXv6fJYYtGTrO5iAQ0wqeiIKXKxoC6pWJbtI1LBGGzqU59iEvjuj37EqOmMffCQ/WMw0PUUT5/Weg79C9NB9TKya8vMztcYOfSOQkGGaS0HZM+e5StqmRMnGHbyup+TTs7111dXF05whnm5ee8qIUwbOfmae+M9/JG3FGc/JPuFP1QS+Ak3GLq0bXYPTkI8S2iAM4SAkC/Ac0yQezo8hECnkCij1i67M4JIbr+DBzAA8ySt15HYleiEhy7eAPGQjyxAjoMej/BMh4AO8aLiW/eBlphn0+VBSZwP+0GBKkYl+pAwTOa3qW4kiVFopTIF2b4p7HpD+uaDOuhh/MPi+Kc+9amPP/8RruYo9wusgVy/dsPrTrVgdTRUlSFD/VA5zgQ4OV6bh6cr3AFtutNrXIvaarfGE9gMdjcqVVuSdUd3aW56ul1hjYndgHypyGV4DDPZpG/n+o12E/DVMqa71budzepMtdPkuyTZWbtEeaqLOYdsrq0Aklp3yWqSjbnJWAWUmnAZ4U0cNwVH/mrdMlHOZcpV1nfvn3/mt3/7tzOXrvzJH/7hkZyUTtNMB9cyGGV3m+XZ2mJt5bELrMSdffF5eJaxa6Ezt+9trN+/98ZP+LxPfUs3h6jYuI5aNQS6xPDEfpk6aKHnkinB1NwMS2MXXnqONvmLR878NFN9e0v2LKtVCWVtfhaF2OGqoc1VDqSg1PjUhaGzvRPNZOHjQODEAfaGKLWFFcrFWq2qixFMrXMOAxGEACUy5y2HVwLV8D0Yrecu4rYA5ZVkNTePQc8MNHfaPZbDqFhSyNTG5Aw4xwyBsJtexdKq9rkAkSe2z+1WEwNCkgHLqqehhmx0LTN66Xp2+MHpWfpl0rwaJyN91Ot4vYv0UZ4obODCJt0gYH/fPtuB+e4eJkWdxma2u1fC3ohrc32Cat8DBZ2TLRMFGzqwLwzliRMTfcnARBFp1F9g/jgtxnPSDmLMG95t6ZbsVH9Wi/rSBPVkr9zLrFTWu/Mgnih4zi/B4oMhj5jd1tUCs4WWWkxve6e1uzsP0vYmHwGeqxar7UZmo1HpdWqZdhnT5Wyuss3HL9e7fEx1b4+dVPDncg0EPZPfqxZz3QazROycq3ev3SpeuLC7uVVYXhQlVg3UlEjqG51GB68qh7VXFvjp+9u79JS9xWPLue7U/MzzTLl9v7bTm222WdMRnbIH4Rp7dTeIzdFev5oplY+ePlNvzF28ePn6u7LI5rorFogfe/zJlSOn7t3Z/L3f+73vfe97bO3wcXuVFNkznkKAERIq0f3+1C2cCdPYGNcEuKBVANYljhxZ+fgnn2cVfnuzy2Dr3upGnrlqUa0Yw/6Ik5KBiobDE9tnLJdjFB76XxqO2wSIGCgx6eBXRJkyAwF3mWkVOpeD8dA/XdNVAFNz87Cq0dS3Ctu+j2pEW+V7yahyBQV7EPXQQglJnIgiptnIvH9t8+rl73/7e2+eP3sMm80nnjjDIKla1uGyTI5L89HNYo5au9yg9fEiGMPv4e4NpQsbDIGxllYP3fkmN9BIYV83AIYVNCBgA6i81YRjrUnbdJZ61kaDeE4SVwL+FAbXFYZZ53PMRRgviWkWnbbQaE2FxIIU2wX6ohxPBqkF5LltY3fwsB/QbmYztSw3t1r28J8v+bbqMIpdUhjg6z7ixFD79bz/6p7OGeN5UlN/dZlZvafQw3wKm36agKlSnAnRk0qEN9TAcOCD38B2MFACkJZV89Nos5m1tQ3oQmUKyQRMgoRsmhRt9plnnnrmyafoT+nCKBSYpatM0IDBnTpzcnq2dvHS5dK9e8dOnPj0pz/9iU98gqNquvfRBZLdHJsu0gHgFJjQb8ZDvEuPET7ZmW3UhCgp3RGnUY3j9raDLIpcs7culrivrb90dO7W6vsmpcgvtw9jnDO4fGAEXfo10kyu1g70xP4RiZurlu5k7GxLOsHAT4tOxEOLuf3y9EwvX8C+GBBMJQ0wjOYHiRAOW1sh34OYowRgYOig6uQGSaY1+PjTOKHT5nwSVzcxywUbeVDefFb5uh2KW5rQ0gnxcYXPiuw8EAMQbcRBMkdnGNcllaYsRZV+lREuGX/KXwQ1tznnMsuLiyuzC9kWp2Z6lWy+3OO71hpBkJa+r91tI4PUV7fPNxkgT8VgiRpTBiiRsZumxDBZ0kjWmJVStbSxdo57H9vivhw6raPpGisLWGazi1kozVWmFqanZfdttvCZ5HZusdFSRY9GbtY3Oa607YmRI77KpUZcI3XhUoEk+WFwwcKMQJs3FsQ3GZuadnK1mcYJ98DDJJzk1vb09IzAtNfosOB57PgTf+8/O/uD7/zHH/3oR53uFr3SHN+QEGp9jwFgymImv5w/5AB1m2o2Ftp4RicrkQn6enJSXcMsvLrUHxskMTu32ymwlPDsJ7/w3HPPTS0cadFZ23eVWet2qsAgS19MR9SBQbLU3YlTZ7gehI+Jpnedgcc5Z+SxVx4QSeAIA12rJCCD33R4TCguJfWVDhwkS/kAwKmc5vAbb9EajENAo94wX+KrX5ooaXubSViCnNwjpr3tbcJlQYluwdC+jAVKlYOeGlHI8lG1zbaXGGs80nkyFJ0bRkYsPxuPSpAI5ThGjx0Pnxwi5nxQl0c1tBrdpj6Zk8meCGj2QZiudOdwmr37kQDkRDCwxTr1tJMhrbb3Q/5XFw5XReHDuAgP/9y/X9VESEevFh401Gh++2EYgTsk2Eiq/V6dPJammBJn763V9hpVrKFtnYUknhdP87isShvYagHFENPUhPypm1nVBrk1VQ3PRg7MkBEJuhBYHKyeJQpSUdbDih0kAiN6aiA8Nk4gndEgnChab9Fq1qagiCOVupkP59R70u3w7PW37/NVr/Vuu4vtc5cJG8tMOgxbaHHi0zoyhtk6ESbyNMOgNjlfogaiEYv4gjuAHO+pRwCUZFIhCPdgPJxjoseAEljCk7l8KVOslbVVAJWizRxjiS5dbaGAbqxxfFZHvdXPc18+g425Zit/fy3bzpw+eurNdy9xkXGls1dgCWt9O1ObKy8ssrhMT0+9ZnsNDn9vFfPMFFbPn3rqs5/BBvHy975XePvGo9XqOqdSMrldep2PfEQfOtrcbPzgL0jbXVuDEr+4dQobeup0RlaN/ZMnGItu99tLFQysSlqJCJuDJlGh1ctPctxUZ7e3sbs9XV08euLO6nZzr1uobx0tlz+6uMhFHPeqVYYMmxqFUDLJhrlJHEziHuqXbgGuOl6OgmULxay+n8TJew9T/VI0J5Ve/vr6duHSzY8/dZZgqwfAVBkSKh39ESzSkqAc0AIGx+PP9OsA6EP4jEKlZ6jeL3BcocABO7eqI+rhEFvZOQ5XLBUL/Xa5iyn6ltRYtqFhlH+7Qn00mYUaEQeG9LlrD6vlYMehsZaTwXkw/Hv1LTWlbB+ucmhJKyS9KVOYuljGdUwgOwxeHafCgi/JXSFDuYuqYP5bq2qAVSouzJR/63//j37lt/7zb33tG1/5ylfef+9afW+vxLhK7Vtl8bMLqm9zooFxtaZUWG6xQeWzZo/0/EPZQ4L9f4xTFm0sEBfIRn51lMoxssb9LJuwwNrekyUsprEOQxSv+HHs1aycPsnKUae5hdpqN/ShQlagYKVLnq8TpMZIpA7OkfBCpqDy0FhsK7DCiGUIy2ifkM9+9rOZpaXM9ZsOTxSaApi8zkPkP/ervyr75RXbfer3L/7kJ/evvo+C2Lxxh4VdPopCjhzfAFKHI2E039ch60QalJmNJEBOuF4z+iAs7tLdm/ivV7gLaC7T22FJa3FxVstJbd1wvVff2eWTQS4ESUEihkHRIlp9Pq7IpoQNhbVMBnk4yLM8B3UBEgJ5pqYVBuLkQWPCLw8gc5bLucqDTEkI5WZlZSN+jHpoGWFaTo2b0FhyJxW2aHfRbH8gSZ2QMV8rbgk3vE6hADzWGzm9f0OfsBTiIU4MTNwDaQXQuQdkLC/+WI8j/hEwXt0lvWjyHn+t5yYXkDt+J43BDVngBxC5juBpj9MA8wmkaLzS41LLjR6XPLSrtnFCWzO0Epv2Vkedd0M3TM2V1DX64UsqEAyODQ/wXBnjEzPwkJAWx5kxoqQaEke4y489xVgEF/nPdTQvkvDY/dFcjL62sYUGOHFq9ujRo9VpmkupXJQh6vS8pn+d/ipaop5dU/k5xrS8vFidY1G4219gR5qs8f+b/+l/4bSBk0RCFVmDLo0SDnAAxEI5tSU+JIFC76vlgvnevXutRoHcIUk49WkErSzh0mhhAsAsQMfwNNoA6UlCfaVTi7P+TipqZ2tzB27MLixiiq5LxzBTqu+gJJ3VQCsXyz/mEtGJMij0RTFbGccMgNrk3nkuV3nllXmW7J+4cJbnsRPz1LVW03VOQvVLRiBMFpGFElSGOeiZGGLH8hQjngw7S6LAkNa6t4TOUeChpEMcVYyJpX4jGAWJfggd+BOpSwgOudNElN5OnyQ0KMrJljTZharUnT5dazQTxegZzvAKZ0zRqrEgrhKMMTeS41j8X3+AUxiLHwkifGJgBIgeh5wIH2E+vMfpPBjPOEwMoSzoFi5vBQP+GD6OkOqmfp82V6tOqcaTyYmnjU/6NfTAZz7zGQyl+bgny1jFqSlZPbOdZtwjF+dh5OR41hFmnJKHDaG39aaq7CzrdO4utF5wQSYUei4e/sAcY0GApIGAk+NW27thg+eA5CTEobKSZnsArKJgOw1qnConezwx8GlgXt2Fwa7ZGoePJIwnTkJIQs6RQggmhtr3EF7JQ4FjuihBoF/IwBAeMDw4QigImOG5nkw1KJdhBkZO8jgATqNybEouEBFjP5Ollyiqg54IT5pAT+hpecbS4T+MG0nuSShXOtxxqrBhi5FIEelPksA3TziSu8+A6K6U1px7HDnFUSV2OvSwjNWZfr/x47+kOqolTa4Ad2w8Hd57wIAp9JmONTwNeciLxsuFLeht9l1ffPHFs898lOEB1+vRk0K3cKbowSslkMIJAZx4uHLlCsZrbV/uH8rqUC8j3NgvTWROBIjsJWQkFpzwzCEj/6PHcgwAiIofFMs0dVYsIo8eFGb046E6YJGzWkw0RrvH/cAoo3Sa/3/0s/TMIVBGlV5q562zBc5P5CSQDvA3gR9OSayyvwkkHUzDCKli5tjA3WvhYDw/n1j/uBb0IAlaMjYPWTvbba8sIcQXiK3B+KIqjRAwX/NB2chvjtkxv+FpZYcD4KfVWryki9Vowdi2WZKdUGu4PxA/zRORUsE7FR+6uUZMysuWUGgaqGIWAAjxvFglIYqOLAyloDpVg2AADADgRZg5x+Yh0Y8HR7wD++thnpSa3HE+n4VV+Odm5nhO2cWkfHkAmqHDsXmnj8ZjNEh2zmdHwrUBBHLzwPyRI0F/WhrsBemQuKAZjw6bUBxDBjDDnueffx6rpftsWr71FuaHpCD80aefznz84/Do3ttvYwJ8YksH1Bhxkpb5MPkCg7+x02Xdabej85ezzz8DDO/EWrajD+DdOZeoC0ZoFFOobODC5I4PApHMAGC85sKHdyB3+P0IcFQSTXOA0XHjJcRytHw9zmggnL6Y9SuOJ16u9DCBSmN2P5lajYRkFjjQqMQaZj0Bo7DkITrNpQXcsSWZH/TrCNMQhCAPIJecJIOxmEsa8mB/gkfrYFjycU/ufvCey8RY5Wvt2tpcKOL9tfsS0YKsxTmsBKneG0t8YfVDVfPEXMcCyY4RFKaoiP1v/MZv/Pqv//oPvvX9r371q++8/gaW0dyFB4CyNnY5r3iqZZijssZQPkQA1xAJkQ/HufuLpBJxLXCruLS+qGZ0K1gm+9Tp00hisdMvYaxrHyJr2WINNYvFWT1Xevrxp6v/xW+c6PX+53/5P6x3Or/5S5/lip67/+Gb2BoXm5JgDDDBPbwqHyhWDpazIBJngVimsAyP6Yg5GeS2L+VLFx6/cOoXP8MqwhuvfatdaGxg9dJtrc0uwspnPvWLp554IrO7sXvz5q2vfRWVAXWogOOdCl/0O2GbdAXZXmTzZtJINUMbNgI4/3qp3xZXIV+y66FuuF7V6JKK6/GFdtJOl6ePHj1/7drrO5tr76/fBrI3VUV0hBdgX9KVLqJ5aYju5PNFQl9HC6u6plht1oEK1QXTOFWK1TqYLL0ehBBur44pefqcwcUTqL4W/rhLgFymp3TrJVZ+4ORDvlCYY71Kc1cbjjtO1UiQbcgkLfiAYZFQkN1mhwG92T7LrllFUcFAINkwuUQtARmbBxR6uNMni6RByxloyqFxbuBNUqIH/jpvx8EG6IfjzAhE5qCs9rJlyBqf7kNhL1oTb6h1ze5pjHitp4fGb2UkSuXSJFQl1Wq78SokGRRQrJOAJ6kMQJKj/3KBRG6ekuksb6ghbIHVhff5XDkQcNlyc0kUa7z2lVoOTEIiYbBuOBfs0Tg3ka9w5wN7MOvrmFZhzqKiYe9Jf4n1Vq83W5pubLK336z0+9yFDakduzMd2y4RjH1ZP1PqF1p7DTTP7PTcluVnSCBBMp/c5GXNvt+CCK32oYckF/0mt4T3OmW+Cct3ge8vvf6dtf/ud7/GXattDa22Wvm7mcy7uvcS+bQ9IoySNSMq/0cmqNnKOcKr0yewSp4t6hDAVE3d/NXrN1599dVb19b5vm6xjAUcVQaXxCg5JAEx9vvXxmo/19Vk3gEZL+R68TXHPhGjCsYfuQwXV7HZI0mAIZQmi1yoCszZWJKlZxVRNkMKNJwUV1Lj+AO0QqSb7UwA/DRoxbkn18akDhvsYr/daa7eubF+v4A1NK5QKs/PztEvMnnW/NnMCITfK9pSI6/khQCDrmO2ugyOCSz0CtRvLlOhK7jxfvPalZ9877vvcH78oy/xyYonufmW8zv9vN8cooPkCLFIF3LI1zNIppXUh9EWaY3EhBkYd2KOiFFds52MC0078EGDVHe+kIHOARpFa8mH+ipQoeYAxrTQkQtvrKxYAT6Wd4IMzoZ2ooA31RXXIyDnqCMxiPapJ4GgojbpXGUNQTep27LQjP1Oc68wRZfX4k6mXrtb39vstGtwniSRjA/pQeM5BpMP83op5N03l1ALnvJwT6N5X4QRh9epv46XMsX+mEIeaW1GAg9GP5Tqw7wkVZA07RQupIWe6969tQQmFTfmpQtmQRnb59naNB0f8aaTBReTu2dmfoGlq0cvXOC7CrI5FXf0HXbkCSc6xISBfCpUUmobFalgbHM9yp7pmjQc4zxMhfiKM0IrzD7GcFzkm9QWMywowXZDH8nqc0McJVHPjpNOME86VwuQqI38WcMd9M6k5a5Kvqh9c3fbkwyeod2ht9WkothyfVBlisNnvrA1nqcQ0AhdqEgquoNzMnnBEwOTOFp1p85taJ6Xys3Fte02CgtbaKqPhsv4jAkxkuA14ynZXcWTWECrI3LNwNVS+vCtnT8TRdITaACCnGZxO3GDkLwucc4sHj22snxMRs4Mdbp9esgy3SFS0W4Sm0VraNitHpOvvPNtdVPKuWpnq9bbqXSKeXo4W7boZWSfnu9sz/T3Gv3dNsDdDqLVARVaq9epdlvV3g4l6mX4TAOW5VkMT7hhEEMIMJCW3pZcYJievJry9wLyOuRUOhtO8JNw2oXc06afntBj8dN50IXwqktg7FYygC2Wjkw9mkWFdmSWZCnuWV58FE84ma9otdkm/Hw3hK8K8xknWku5q14qV/mFz37+7MklNqQzW6twwno6jSmzNhYHAT0CszooQnI4cUQI7EpKoxxSrjs1VemVTj73wgvPfuTTGDJ3i6X1vTp3zNGbMz4jYbAEtwJ4QlAhS/TQlIqWvXxk8cq7W7dv3V06upTCbG3Kmp43LUBxzsA0WNpvYhnGD4TDMaXVzwBKfHS0YyplAGQ+EQmkdWGWCHo1ztHY3CwVgCJHSOJsRHVqhnFXxMDqCcnRgTx3ud5ReKgdHSvghudipcA0h0tNXe2ATee9QK1RC71kVWN/yWDo2CPan7/nYIZ/YHrafE67w8mn7ZtXLvb7nztMLqqLYefVPRz2EG8DjWOJwEZlyTsYJwxhm0ikAkfpGkoF2RMTDgE96OUAJCOleBCmEI/GRt4OCfwzAUtX35Df23WKlmK7KZONW3cX272pNrMFdLGOdyRsHFqOgTbve8N6iSkLwUpVmPaWRz03epvWS26UnCfzMtIyKibUMNCkda8xtaV3Y6vrDZBBsFJJY2h03bMT3mkxccgwCHhIfjk3UCqqEntm291eo9VrtrEEZ56GZHJCG2cqQrYr9qYQrRHaqFqx1kB0eIJCCQJw/RjZ/MpFf8gUSEvlQwXnjMNEyHRCW4wTTjw0FlmLZHML07MA57u6WRFVSEivoQVflBgTHbZ9WRVttTV300gHarMdTIxmGx1mcnc3djOzS3MnjnGtWa7RYqTS2N7JbG3nlmfLi0udtVXQkAsTg7cXZtnjXHn68cbdu2vf/P6pRnM6O9NpdDZffirziY9nNjb4tGDjlTcezWSqjGGY2+q4TpavjqFJuQoKmtu5Ap9nZHnq9OLR6m6z94MfN95695i+N6YZrt/2a4MT2EY/y1hH9T7T1mdme5lC6+bN5QsvXXrzzUx/s9XvPpZt15dn39/RrKU4rfNIfOcs5ZA9/iRGVJ+YkXK2ghPeAYKhkMq7iSRb94oihDTeuj22WpuyI4D6PIANi1X7BqqmAQKCqXCsxd+8eic3vfzkCh8pQLhD1tQXlAAXaEF8STNEl5DhoBYwgGk0vAr1IVws4wHwIoAycQE0m+JIho2XPsBdcmTBZdwllYZVzPbW+++cnM5nji9iBmWzdsi2OnX+pRuqM86evpJA3yvKxY3QgrjrGV75kc1QFhsHhhlHUA2O3/hipRi0yYm88lQhd6/VAZz0FGM+ZhXomWqeT1X9wt/55c986Qs/+fGrf/zHf/zj73yX1RtOXajNJcSSAlaqAjVYFIKUc/weOJpXCix4ByUhe9W6iEn6wgQ81K6JC+tE1lNqoK/9GJMYBnxcmIMfz8WLF89tbc2dOPFf/9ZvcXt2aX66c/MmVgYssJakwyjlCMVJNkg8zpjljSFEmAR6UYh3IhnjHj1x9GO/8itgfOe118BPOEusLOU884u/ePKllzJHT669/falH30Peqq7mygrHPYO1R21SR2MkWSLEmWq0Z7oJxS/GhPl0njPzbUVQ6pAt/GB5DhwYq15snvypxd/Ckmk3dvZYXuKew+uX7++s7ULCnFUT0smTBk2wUDmSx4gIYSMWIx2ccRPiIfjidkakVol1+RnCJ9ykLMuAnxwmmqarpn9aYPbNlSzJHRzel5t+rSvcADAGhlIKB2Fag0MM0UoqBLaPNdAqgIHRVSJjKZBQfz1r/EJhZEqcWuYwqRQgcD4Gj1E4BcbzflrgDZsaUjCwT8SEoE9llqAmTheeXbtcBMtiFe1dHMOybOkKdkQhwnEkRBAdmtjXtFD9dGRIfOlomw9mOwJvr2LyW25U8AOl512knN0SohMDklLjXPAeG1tb3plkc57xyZCoCIKByDZCZ5eT3nr4QVhnYAokmvmI4OzHHuDf/AHf3Dp0jZryzjC6W8NGLB+syNbV3qZrS22I29Q8Eb/Hpq32bkCnlqOFXJy0p3d2i2Amb2w4GK5GwVGiaASqtwvtKm6Vlqj3BO6nyctjjaInLN6z14flDNhBgbKSYJzeH+OvMYo8cBcDBEn+XNl4Uh4xRO6VY6ucXZNJJERyakIKClWYLYOQMABPFSWduMTKUpyNz4E3RWEGQyMZwe52/iM5Nxse/P9d7jX6YknjnFE7LHHzxg2FzkboqX4FtJbZxkXjKAwGT8Ezef0eF6QxIqJcjfHKwzE8UY6ngzjeZUQJkMZA9TDAYTfHONed0R5SQmOwIQpib17rD8dQBSgz+0TYYSnowAAbXzi8eygirZE1XMjKyHwmRDEbySt45/4HCZvIkgIBPLwaA9CdIg4Mjo8YeP4YvLoGYchZKQ49DkTwT5A4AjmcQzU0a1btx4I5kRy7zOdoAukhzhz/AkSVqzYnrnw5FP04ogQY5cMtymkHJBDYpiK+ll5UZ8PhQpZhXIcgv0h+R4LB6rDlNNhAGaBjwEMvZRCHkSEaDVI+w1l3S87YFQRKRnznmVobf9B/AI5hxpQOzFH9xCOZ7+sR7Bi/iwOm5p0ktDJ9F98U1AYurICs33QTKtjVt6a60p4YqZphGAgFQ6chMM9PDhDEgBjQvAQBbBHuMefhBOYDgmJUz9JOgV5eXm6h5ARtJ6OQHgDcujkyejQwRTqCx9B+QphQoMtjyY1C6T3IFQgAL70ySgajy47MkdhWV8hR+5cZoR86+2d2NsS6GjxpHni5E18WqXk2ah+7NnPcY9htjJHl0daqokvK4BNvKZEYzVuzEjEoM9VgdMYl3ET9MgCdDpTyKf70TNFZxog7Xdp8Vz8pBHyz2saBn8McQ/IY0iEtHx5yA0CQ08YZEmp7FBCa+t+hMHqmXC+hYX281GfI2EAQo3Adl4BwDGHM2JEj3PeIR1VOt+I/Gfu+fnk4mRTTCoI82cGWlv3bjAqJpzAQxbKIb2K90v4wOI4Es+SJ/CO0J+6iOZvhkuzBX8sV9r/wShNY94Pw34wg5awX8pUuJActm5DMpQGI0OOWXl50cbUT0RJLP7IiuiXgiDcn0aiw/h+IU+9mpjhkZ/VBXRUaMuGcKxgBgbWoQIQGKVVAPEloSoVQNqHdnAMOZSuTjpQclFGpvqMn6JHQShzC495EGYdVzJpSEoXAdJ16jiTqFBGAJiOiTPmooc3FDvAKDRqhz4KJUbFQCQOgoFU8zEDbVYuoL/D9mYynCChlwgPgWADHg/z02PHjzMOZPZHILFMxG5fv7689CRDx8bNBj21EaKMACOaY6aZnR2IaW+1mUfzsQF2uG++/fb7779/0pCjYMGDJac3Z2MmAYwurW+1T4nU79177bXXHrdRiuLMQVO67iAvhJuuRqUvrvQxqlh9/33KQixfKMm/+paNbwfc8xrxrAPeh/9xVsCiKHqEUC9gUqDXvvHFceN1rjoA151x/8Fy7ilxzEoBfZDkPPclg4laLiLxsiOInp3n8rN6gpPa/DCYveiRJwgkU/jy8UCgiE9py4lkC0ZtVw4AfzokoxHDr4eHRM9EVD/DQMvSMoX+XO65l17i6x2XfvrmV7/61R99/weXL1+moyK7hB61IytB8HwwSgr3mlrwYitKTdmGoYyyVUk2aNZXz8mGlW5ZOHfgzkpdazSsBtHcN3T/b0ZmyUS3pSD4GvWNO2uXvvqVv/8P/kHmuU9O1+utf/MHX/va16ZLU0Ct27QF+2lBcgHPJGdqDWw6LIY6gCjdB8etKLoUO7fJslSuf7VSO3r+6Kd+57/JnD1b/9M/eO3LXy43polufubZj/3arxXm5zevXLnx3/8/mazWm6t882sRYrudUlsqCSsa7lAKmztGgNMRj71EtVmwfqcTBgSyflYTTJyv2hWafWbFj1fKW5nuJR2y7Pempq6t3YUfNDk25mhFWW6qE3YpJgplLdm2Erwuu2Z56rscQFD3noWJJl4qgKdqQfq0T+cg8k3nEEM4kkz5uGSINy4qZ8GxmFdevW2dKz/e2nr00UcLszPcgn2v03799dd5Etvi+7EMOm3ekLdtLwxmCedGbxzjM0jttuutRp1bVMjbV9TIllizloWKSKnEQ0QaWUYnVkiJtlJTc9sNgw+JAA+lkA9qDYP73QpgfIbtO6XCNnBpv4UaZzzebXV1CzAyl9VEMdPirt16u1EiqsRmLjQQG5gZKBNLvThEE5VUhAdilgWHKKvUTNTQglRFWVK6ZvVYhPFq3KCtmOwZhwkhCoeE4MeppLaIjJ8Lbuhg2H/2WPx28gCbKeqKDLU3YtvQGKNzKyiaXF9b5m4mEGKNzP4NtwiRvpTZXZhf6LVLC/Oz8wtLlerMxffe4GTAselj1OC1d98ulqbbfHGVTUr7SE+r16Z1b93foDPu55miTO1u99fvN9/byB07dqyfqzVb/UZHS6J57sNEqjNcBUX7ZyoOS1hTJaynRWTo7fX3clywt/eHf/Rqq1WtT2U4bZufnYfETkZLfrl+gVOg/uVo8ECPs4vSgZPulVIzhBBGCoTohRpXGblM3sLZYrYFb5ipaaZYqqcNFq2mApOtElSzBBYoKbt4bExncjST+k79f/z//I/IOQihvKDW2rDKlSQnHUSoZJIrX5tgg80dgdZcZGaLl1L4CIl2x6KJmqWLbFLjlsrEDLtdrOdkHIaJIOcTGtwa39jbQjkw6ClVuA+E77nqSh8cOL2AiSi6SlKL4SoqcLZlr223zbF/kPOFVIXX24X1m9vv3Nz8+g9uHp8r0Z28/PxphgV8EFIlsVunXcvRusnC2xIed7rkqBOk1CjXw0eXcCKGUH344RdlKZhlKMkFKatshDNAEuu1ExMmjcvsDBkiDiISn1l70VB4Ry481PHoac4ZvL2lRQcCWL7nQAuxNAxWpUlLQqcnkSIuYO1h9Uy99yX6lDLPjHS6w7ewQaC82H6zAiqLUBaTpihShE9wJnvjWsu56uRJgoZTspoVA4bEJIaOeWLZQ7nGANIB4/TE5BHM5UmvjJutr8cLqdBMFolmtjpNdH5M66WLrwd4BhIjoKG3wxQEGBzbujxJb+XSbYkjOSIyhDx+4cmVpWWH9PJKbi0hLX1mTvYsL37s40S1Kr61xscwqG5JThqhSzuybU5lDQDWVhLQg3jg9Z7GmfYnmD1M3MbOF42DCS3dp3onO+JANDf6EzvT6tUanWaOz+0U9qyL4e5+Yp0CL28av4oc/hg2qIAGqw4DU6E8e4bY7GT785XC8VJHq9s2M8DISkhoHXIun/R6UoBY8aD7Fyo17jiQtCQsiFkTogrQ/+AMRgNu3Fh1BRhOnGikZOPQ+cxuqVp67pGzLA5O1YrT051qWfQ0W/QyGix5XjxRQtFfLpRRlQX7HNCO7CEK785lb2W26BKpxGStE3gugZSuSlMYr/qhsqb6G1zI8MUXTtMbYkCNSm82mGDMt188BQ2yW8HZWZlQ5L7qBfNoby9kxMWMTN5tiAnz1Y+3jpR/6fwLpAM/ryj5YC+P/HLVFXbR3P+jLhHKTBpz67oSTl1EtquBGIuGsouhuCSnqxUliaa1rlNr2RhGGX+CTQnwrCW7uIpC/7aEtQ56Nxx3EoGtzEZhl/NKdOfFEhsw2UyrF7biSEt9AanraCi0MufSZhtb2niGWMGY8Ck7+vGyhETkKRlfCuk/Yh9xYjuVQIA551XPVuf6O3c5PT03tzI3o97NRKRQqqCHGfMD5fUlZZQ4kpuz+Qjf8OhnHn3pE5/73Odmjj23SUSLkQxCwh5+16fKDAsIltxISoOYgpkPAcNtP1rebWvLeWnlyPaVKxyOZEEBcLgBGNny5NUdIuQvrhCS4KHfdJRGaJBofJCkmZ+SEjIEpsGNHNlRMJ72pgdsVJR9sJFfqHKSJLpylIw1LBsLUQOF3OKx0yyndhq7kgEaMHOle/BYDFBxsrIIAOfNmzfnFo4cWTymsR8Z6swoqJV112YNMj8CXtMOGOh5WYY+OggBrm+GdLgBTXikC0X0yOtIgjTPPSodMqQ9BqxK44ByRaRT8eqs48lRBuSey+Ux/7m/eq/T0lEGOBOTOCSM8kAPd5pHcBJ1gIvJ0zC0CJA476Ay+ocwozi8RaVTDtXESISq2Bx1I6z+RHitLJ4b8aq5mJEX05NZjxDq1QRQfjwJpXgCoPPBUCEwQbcQF/KwJuNZJBnF3IfqPV11+zUH2ER2w/2j6HeZC/gTwqBhoiySkdmSBfr5URFCKqXwVA5TUuvMzHEwcXV1ur5HFnQ7fPKKq41xnFYh1udinnugwUj0muWyZJwdI+f7ClIg9EeEFLMdmm2B79nxY39M19QD6dQrfQBmu8kMSIYp6lUJ5/Qr1OJPswtselWLlm6mTatA3IMPpJWGdmswBpWkjPIMWjHVYHykBIh44g6a+5k7t26jDMs53Y+E+Q+R6E+xwMf8+Rz6uqVRIYhUumCw7RVp0h2+BYW5qUkyuTt6Dut6CK8e6C2aQBFm14+UuA3AXv1JuDu7OCGPUZzWn1td7lFc4ksJzDBa2+UiNuosYQR9SPFM72kZmlkkHUqVgzIqiIo5ZabS7ZLOg1Zb9zev/mTuqRfnjp++v7dNjkvZvf72buPmIlubN7Y3bt+/f7x0DMji2VPZp57PrN3Yu3X3fK7Md8xatalGtcIgQXcc/fTi6UxmpbMNZDfnSp4TPWIYITtVzZfFgX6m1uje+enF8utvPwIlJYlLtlADqBmnYdibs1Er2TDeMgFjUtPdYjZzr359+cypxvVZIpd26MLzf2tp9q133ruWm+J61PqU6ivfVY3ROVAWjB9hXc7O85tWpL6s4xdy1nkAFyAk2J9ecerTqR2LlbjjMjmMLGu1WWKxfISnEj1Ko0pjVCjDOJd5BVJkjIMzmZ9cvvZsbXalxgo+Mq3ukCqHKnKgv0HeyRT7YZNE0lm+8UUBJvwkkd06JVGIaLFfew4/ogwnCkXAyLP620S8yQVUlA4j6Hyxk2kaCo15WO00xPYcRjzyls80S4VSv9Ttl3PTLO11W+c2mpW33uvOHynNzPSrx5VjnwEe+Qob1WhkkzMvqk1pTnFSp444Bs18hEAGLXx8WjKf8XGgwN0F6sPs3PF4jNPsPIm9QJJswq/B2AjQIxk+RCivcUb5hOi/FSBTyp1/8bl/+MKz19+7+I1vfONPv/y/ohtbu/rSXkmbapKHeqs5rx02Ey0pbKfXqZKfJiB8oRh4h1yhwZoLwxxkAcnNa4cchgEC18Q4mGTpecIdNtWJ8pAhNBYIPMlZu7x2587O5uZ0r/f222/f/fa30/DmN1qcwmEslqciyAt6wAa8O8pMLOE0+NMXnvzil77EdTz33nvve1/7Gpv82/f2uGDu0d/4DaT84ve+B7NO3VzjOtFqTYvpjNk9H5VIekocDkphmIDDvxkqdr/UhaCGqjnW3zVwR55QefBIfzZdQTnWZmZOHj9BLKRCVbOp4TiOEMeDx0NGnsQ6ANTS8l2LUgiKQSlIhaPtUCAuNGBpiWVWQhr1BkP5qWLpqaee+t9+6XdYQHzz6hUuMXjn0nvkni3pszdkJM6oP1LWngtPAlkCg/MUDVpVQIEAM0ohwJ6W5AaSdGyGxAXO0Y4U6q/lFQol270WUw4K5QTzzDEQTzkIxsEEnqnggZck9iIRcueQ6SPYCYzYEmAkbqqatPNaIMQqLcR0GCCjdjXdCGn9TABaE1RJWMhaMEamA1tjDQnRaHQb7UZYRaLeaZi0Xw7C4keWYAI3DUkGmOrYlI+WdfnyLS5mXFo6Quxeo87RpO7R03AMHQleSuDwZE8qnNsm8woGdwg/UyBWSNgVxNFsSUJa17O0Q8Dopnj6ArT7FYhLK1nbrUFIDT5UB37PESU+cDq2izxLpLUYbs4ZQgi5A4knZJFhA1v3L+8UdwgBjCZpRVPtRBjg044oCqLytrWgAE5iPbk/1UxM+3l5Yb5Wc9RQEymipvzPQ5JxMZjtOINyAwPIYVRurwGFOPCwCkNIoxG0CjAjDVEEWO1QcsOihzsBy2XZ6t9d3WaX/r23F/jM2kdefoqoYkFbCN2G+j9fYuBV/sSpLNavJAGq9NiRjAD7EEEdUMIZPAYTcMofpg/Cl9BmMFbvESG5eFotYYgkbzg2uldSEgvAMfgwCMlxkaaNs65CLK/wjU4jQuJxJ3nwxmisdJ5Tg8gtK+agdQIAjiQlSQ/6JeFE+JHwkdeDMP5c4kZpTkrhfPDGi//nQosyGaeHQAleNruxvuGLOAcTAySmIlipxHqM8ESxvPjCSy9yUopBnzCHGxhDAUdKmoQ6goOY4DohZpTyWKtMvY9700UOftMwklLagi1Ekoqyo7tQsD1bkB3HA/FpVOMA6RABJ+8Qj6pBE3obcyYQG7mBtnY/T4BRTXgOn1eSz+Rf4UGroTtAbQXHSPaFF17ACok9JO2FWw/SaGoyiYtZk4JUrt6be03afj4r0y0SUhyu8/b8AHC0IaHnNUxLxEln99GPfpQ9e5LUCrLG4tIkKQeuPTENxpOBu6g1DuS4hIOvwNmxX0KABwMeX9SjoyOtTeOBEr+RSY1DvKczPdbTxzX4WKKW+5MFaLcCU2+rg9C2PEG5UK4m/+pPbYFbZSAve/CDVwQo1JxFDfykCrEp/Um0BYZODZiRVHol1MKBREKUi09UTICiHHlCYGK7I8T9eMBAXpQCccYPGHWEIMEfDnw7lYT4OAEPXGL9GpkEAHi/TMypRcEzkvniF7/IMsGmzaQNt+OIQjtorc6aEG00uF8s7fdRBXSydJEsQIs86m7SZmhMvp/H2Kj6dYBYZLKIgSNpIwyekaiIBI9jxgMquqwUsKrb8UM5e+prd3VwyuEB88bCK8eNoaHMB43KZUqKHR+f4lDuNtlEVkHOXJ8nmx08U1nITxb+JOqBTkR+IJfO9AMhOFQi+ACFVDFzaUSLNIREDo/T4GUHLO05ICcHizhjKk+Sfo1+PMSOZ31ALh8sKuYYk0c6Y8h+HifSY9P+EXjPIgGYIAkWNUHakyQm5CNI93n1vPaJHAqOyIdCx168CrQXlag7BAOFSniUEBI5mD+9hO53fA5Mm5Ey8UXhZDoZwfDgRrnjSkDPEGNQQwxRqtFkY8UwCg9ZZC+O1IpJOF/EYYxBF5az7WcfbrFEDjY4gLTEMnpCPX0Kat1PWFA0CiPH0gmjvMEcwln8c5UrGLtMklArtR4gdyT4i1q+T0Lse0hOW7PLjjiXO0lvO0lUlecCzZj40KFUqupNaPJg45wmkO6A57LvWqfDCkn92kXgnRgOxDCEOHn2rPSD9vdl1wIp66urzJ1BjiN3AlGLqFM3ngHnCM95HQkRLnMRGERQFV8hAJ6AH6MuAD053S0hkMqY9syZM9z+zCtJsKLgGuve3i5+MdBqxzmGP1/S5deMBoyKyUJDwkDQsFTHQDwghHs8wQO0CDSpZsmSwFD76QTm39zc5AzukWefElW2kE2zwe/kQRg4yVw4UzRENKLZXuADAPpSpxVkInBMlfY4tekQ94OB3R5y5zXJZBxqKMTBPGuKb9xwUQlgiBaXX2MO3y+rgAHtgLVD2NIvANMjEwJyx5+O/Wv3G1HiPF+U+p3z57/wi7/w9a9//c//7GtcONFu6RvvrF1AP+XFGfBoE/CiEbtfWQr37Gg/NnGIR4mxYa/DAASBWZiaJk2ZZXqeNmCd7eQWp+cZe6ve9EVwuGY7b779UZimY6/383yGb+WFx6afPJ15+/Kb/9O/n6vfByEfcQRPmo6wxzJKFyN2jQ+Kfc1wdEMvK0Sm/lYLPRr/3vHa5//L/+zcJz6L7Gde/elf/Nt/2+CwTK9X/Nsvnv/tv5tZu/PnX/va9vd/gjn8Qi6zUCt3iozu2e+WRUxTgtEv6IZAFcpbwmj+Y+/j3YQ3P79zp2SWp3OZ6lKhUOupJrYKtlDFB9uKhUpZXxhjrMwYFyNHOrZWc6e+12SpQ6SEWvEJQ9DsMf8ojjRaGb1YPCIdVq40SlYVoAGW52oMoPu9FQAb62ugne31F0vlL3z2c//4H//jTLHOMfw//sa333nnnb1yKZOvNmzfi5sI0R0s8Csj223GagIuVQqmrNv1plaftTbni1OBMJMEWEdehCitbW5Z1dqeLyEW7lt63pOGnVLrqLQ9N8n5XqgvLSX97yQ4YR/wyjEnC5eCT/fqbvscdrcyva2NvWefWKlWuAZCU5peR1PKxGLUymIZOuddPCimODCuxMNSqeUYihzKFZNbA+FNDNEuG7QZIp6Ok+U9eWh4FmD5ECnrS4gTaoMMEmil9oEC+ByDw8QngThxoIecZ/qNdmavVcoXiq1mb3trqlDle8Ht5hpTwLmVKbYovHTcicVEZfvOLvLZyebnj5xqNJi68P16nYfCIh5RpgmBuVeEUBqod43QQFbKMRCA/XIva5DZdjN/59Ymxv0MKpgr42k1tETr+65hWMYdwjDHbKlYalRhg1KwFmHW0Mh7LB0SR6UwRfeqccby5NVC7Ci0LAjEVYYO9Ao4TMmIZdPV8KgULK0zLOEiUy1CwSqTZCQ/ZGT6xxf9VTY5aKOQvvHLIIaKJoRclIJoZyMeqKN/MnLYlJoilo6WAZCau288koZOnKdEWFWvP4+yzhiyoV8HvNvN+u42yr02NcOwY2ZGN3OAiQk5OL2MPL0t6JAIXqsM8OIXcnOCyeTQD9nuAgGXb2ze/MNv3rt7m+26SrWNDDDuIxwiDJK0WG1YiGHBsI9yAUAZaaH6x9pD0vwJ91g8iYv5KgocYwAJoP2SXXQxAkaSypxbfolh4CXEYSSEIk+vrgywo1ehuz2kvWVjRmoVZrKGLxifP+RtAMQdcKTv1rEW6GfKMjHtdZv1vU6LWzQbSAXZIOKGX7m5rFrT5S0QoIgxJ/4k8em9ZQNMIqziD8YzhnhyQORGOprASHk6HP9E+JFw0kYM7lG9+9DT5GEciQWPZHXw60B7HwwXYmXFgHyreW7cX+M673Sq0GuktUS/z8abzPxtx4gSSR+aDPDE6PLjH/+4rntm2MRoByC7+8yZ5tYWafwYy6Vf8QcYW4Abifrwr8ZziSvtTRUmMZSGhPxcp97a3Oiu3y43tzJ8gbuLRbROkyQntJS5ShoFzamxEGYzxgQ47xJKKO2aFq3WDQ8xr+WCiQYrVmavoTNW4pgSc0xAzs4EyDBavVy/WpOxBlrKMxl+AiEpVOOnRZo5jgOAZhhSb2hXCVinhQpjINlp7C3Vyi+eP7YyReF3Uca55i5zT+Swy5VEWArb1MvaGu0tKApid3tNNdKs9pAoF/us56r1FV2YOGsKdjAxMBmPbBr0L4wToOEjj5/6yIVTc9kmh3iLPd0gVOjsFjrtFiw0B822w+XEI5zaJMhpFqzxD/YX3HMgjpmel4pnrmWVSWqCAePqeYosPEZFRd99gGxbHRbbMCmTnw1zoIpoXCy4EQWO7nS7DA6S3I2aMBxUXeTt0HEGPpgDT/BYLZOvvYoPmOXxymf3eOb46gNjDiq60y9jvyALLGGm2xek8wlTF+wfuCkYaZF4CjP+ZCRmYoqOpX9OxkXgodCAuSEjefv+qD4A1OlmW7rpmw/fGElhKA5CemGedHDdel1zflLRc2OympQF+WOFn0nJ7NzC3NFjdOS6eBFKwvkYuEp7sTpNZA1qZRCm8ihIjT5pvAwlwE88Q/SL775Nb8uHE1h8AaHIH3PwYyxscgCZgsSMEiBOOoRXnHucgZNTjoUasAYVeMRVk8Okf1Fbcw3GInK+WJ5ZWN5ptHNt2VWxicGIzgpIn9djrX92ZhYFiHn0/Tv3GaexyEIgT6eH5o4HmhmzTSi8FAIgLhBOpXNjCNbLOFaIfQM8632jk4ggh8nrB/gFg9jf79QxdexyYcldFnSsTkKrHKlcGI2oAqBS25NMvXQPpNnBwOA4/ZXknjCNBH/EFv0x4Qco5n5JIvIhTzIXi4H7JR8Pl7TAkKSVjQNMDCGj8fCHCow8jB5HOAGvRUxETow6N/3Yw/yS5n5/DgutO3dmuygoLkRlcYROj1LSLAQUUnkrMKVEsyTc+yGUm/lpEfpSAyoH0SFEgaZ05VEmROk3aUzoB7tkmgbOhMmmQWCNrKWk7k+XRQiDipW9Ktn4MoxvTLp+8NqBZpdkcqc/BRV/aVSEM0nj2AS6ol3fqxTzaMI8HYKWHVQyO9QKYhFH7wI5eJW7+aUVKDClVTHxgl2xgWYLCA3BZnCk55UzXDzxQZsQWkqVNOW3pIZPDNXGLWwCntIxtmdFhQ8ekTtUs9IPJnBYG7eGpnJmuy0+NsgKbYO5Hp0TyG2zjbrVTO1oXwZ2q7euH3/00enT5zldVytpR6q0c2/zcn32mRfOPf7U1RuYQd/PVKqZcqV9dW1uN8dJK8Yhu5g2Hq9lyqX1zY1FO43U6ZkpTyTaLvSQifSLz7JReidXZ0G2f/EGJT1mZ02KNkNt8XUE+uyc5nQMC3hyrhnOMsCD85QXTnJeG6wz2xule4XWsVPbpUKlsVPIF8qt8mNHz517/YfEbmU3wbxjts8wiKps9jtYJ+cQZFd9Gm+o9oWLmjJtj4ccLSB5WO+p6iAVQshqLR8ZqE37JgTyAwJWihyDcFnq8dW8Zr546fbdhbl5bjMrqaY4m0aBNPMFuVY4kC1EStIjQWIMAeYRYoB34gQcZCuhc79fW4XaL9LDc4VirtDtaVmjXbS5A2wmKoxefEppoGpZGvMMXDGf46BYNd9h6ljmox0cHGJI0evO3byhz3s8VeayEg5hQS11RzK+0CSZtPWNhPeGra/zuZhM6CXkQPOCG+Rm7dmgUo80FR48HpICD16Vi5KFN/0MBu2uK0KUi4W9hNoMfLC0XJ/c7598/In/+tHHfvnX/s4f/dEf/eXXv8FGCIcSN1vNOdN7fo6Q8aFqedipWscCHaQgLYgI2LC+Ldt45Auxy/T4+h6cSaGiLTFY6W4PCgCACZDkBgy8sv3OYO7lz38e/6XXXsOCcg6fuXGykpihX8Ac0p9gxgHBK9/WPPfFzx598slMo/Gdb3zj5nffREW60cTf+rVf4+LuV77yDfb2z/q4ISGd5D6mdISgIiT6Y96eS3yNHnFnfwceT8hIrmzLmtWqrlBgIQN7hPm5ZVQ5RLJjsLm1jkGoluLQlHZ/BzwDsco2yYEZByr4ilQaD8JT/Q5r9EW+ADvLML1kg++NzU0CMcNmi2yun2Ek/Y/+0T9aOnHiO9/6k3/+z//5m+9c9CU2YDw3ry9EnpZCEdS8e7IZLJerKGVbfdZ8AzeRQuD99vE0MwEmnOekAv08wpR1KnOnJDzV2jNcDApLWREU2b7oGZaExFKvSicUMPcQjktTb2CjXBmBITlNkVTOXtI78vSTWE9FIB6vEQrgjTEidE+ahCREVLlgpMnzWJBg4tHcroMcv3Zui3m2dltbazQZPkRDo6Bnpa672TzNtrHbooHzhRpCNjfrLE0yoWH7ZNuaD2vVRq3lY6SQC2h9Zs1lv/JzqsQsSgDizW9rxeP0eMGJksc6Px9i8YrTFTU4U8SO1Fa54Y9QKUaZarro09qAR2k02rFYgWEBbWGDB52NSb6l5c4FbnnWimOTfX6GICg0nqBNEwk9nt49UEcsfhqyz5AjAGCQJwpFaKa+u0ed84FRcAIJGB40A8oVzmDDHMhK8JNY9WfDixBldGi7ydKS4+5OXbPEGdaQp1mJRp+wBi3zaqsF8iWXQPwkXUIUjqtX5qeXgbx26RW00Pe+9z2GRC++dB7tUTcJicNdgKPkB5KSH2oSLwAqrzn8SaR+nS0ExvDoiWAOE5/RA4BUkPGTwPGEjt/DYypecfE12M0lIUR5vq76ACMLRtmEUx3qFBJlwQYMrOZZm1aFepv1tPEZscWQvy7PwZSMxMYaGgmfSLxgEuZ7LfB0hhDlfJ6Y8MMEHoYwp4H1FMlesn60X6ZIO4I9LsZkxMI0Fxhzzp+0hVpVla1BdmjvjIr3w/nzDBdDTHRDFXje3S4tlw5aFZFQY5SHGrZUSYT9EuJuKNRe1BASzSwe2B1/NJNmS4tlzhDw4jH26FfN045dE07bQRHRR0xSOcpA6UxXuEdB+zinEGjws1JKGzx16lFqkF6atExOIY8NPBosUQCHtmn8CWmT6hOFTI0h1Z5oSwbGrCMr3BQLbRyFrAnsmIuYsbwGPvYIlrt6N9aWPZFDEpLGwSvhFNsgjS5blCCUEGAJ4scpcb+SmyTz6uERv6VXteKhODj8ThIhJLNwMdnryJjhqcPTk8QgXmMW+KP8ACDicFoKoJsbFIpXh+SZJsP77oA5ieJ1GKvVpi18OySiBfMdjBypSvyUiGrlVRkZ6wiUyTM2I3t7wDR1ptC4pBoUbYA5B5gLeIm00G8RFi7vuItlJ8qyUjXh54mj6Ixz2LVCvWAXDHCEV6wJzzjOiSHAx3AjNb4NeQ4gdQgueQGVeUMlkou3SnmifrZNU4ZwQN6+pgsEcnbpCh74XKqUWII5eeIkszOmIG78i5EaRaZq8mXtJ+WL1XhogFS4JP9D/VqhJLTREfJwKGLKxPOwNCTpRn8ND+SoDSKHCDqrzwyJFWSNBwCiUA28xkwP4x/NafgdnGn5iXkBFZFHTzpwGM2Hekvjd0QxBA8hI0QePjMlnyQkjtbwDMnDCGYnIwUsnozAHPDqwLGyDoA8fBQ44QY2radNUVB3OESDXLygaXlOZw2YcjHyDVjLsXjQLQSbntar+QeD2xhC8nTJDy4aqSKj0n4RMOwCfhO2dExMTiDl9Vc8ONuUmlYbsY/j6UOluMQ8Bfh0WscfMQcm2LvxbbS8eW0/YhGk7TT+4x+Xn4jEM4pZQAVRLP/hqZXLdA6d3TohNTtDA+fxw0QRa3zmDT/JedLL0K3whUI0obbsVMsqWIaPI2WzGH2zLXfs1Cn0YWZzF/IZPRLLog3fpj776KPwZI3dUDssC7zINsdyCT54BTDZiWlGgmctvykTWf8sLBw79/ix8+fbC29xP3JjXVoXGkDFLNFLKjz2vYSSX4BmdJKd4bRyWec4s7jIku7Om68DT0L8x+9eQ597D8u6vtNmRNqn15PFR+Viefkol44qFsSTTHySO8xhYgUlACDQRi2PQ+n2t956i4HiI0eXSas0uGTORV0QCH5iQE6MP50Mz879SuTNz98f9HRUE6FiFPlSkWE4PQm5MoUywxJTQZWlg3O+JRyGZw68evt2b2nlqMaustqOqSZSEgoYsna94s+J4H8jAqGZgrN08A//4T/8pV/87J/+6Z++/cZPaFxe/Ei9l/2QFBe2tC+EZCEKGT4SSMXY5x2za+0u04xWRhP1PIYxmczS1Ay31mAogd+26tjAUC5sSulpd9duPnL8b/3mb84dWXnr1VevfuV/PdPtFHv61Jh/d9B3GLAkJCSSiz/tctkKMrpVkRlCMyvr9Ht2tn36Y098gk8OdnpXX3/t3a/8JcriSE5q4pVK/u/95n+1+Myjr33nO1e/8Y0V7tbhS+odTi5qMIFWkBzYapbfd4wIKeRBk1gjSaVGkOT33YCwUyHay2bMydoX7J7P1vir9SXQ5fnq8vFlBoJwcq++cf3SldV7N4Hna6i6OpAdOFMm1vhdkQ1dAaG8zCHESdegnajQEvgeKlkX87PTOmS60Mn32Ny7v0UPMZs58ktf+KXf/j/+t+jTP/6jP+bDLE++8NIbb7z5f/0ffu/H77zbrcyw5lpeOgLHtrd3eKLN4EMLzlCn5liWhKo+i3P1PTQ2MACIFit7MIpwOpzE1C4okDjubyKVLMB5NrsMdpud9vr6RmVKxixCh+q3fUhHkH66tXKinRTjfXI7NV0c0Xlgo3sBMmt7j6yIy2+dpe8AIwERD/OXc2cXPvXy8+UMPdc24dx2oGdO3YZtxVGT7ly5qJZ550lGPN0RotdUSBITYCJwAOF6SLoi3yMa03TsGjk2XxczVCyM84lebYDqNaKzxuYtyKkyyRSMg/CE1ugvY7O0vZPH3J7vCXIVxt4ehlnbe7vFXGtzs9lidblYvHXzPslLs0u9zNTKyUeRgqvv3qLLnpo/VejssBjd6E4XqvpibIKWlossSr3KrEzb6do378uaioMGbExzoTbWVPCn1Gz0d7bhcDhey8f+VBwuBRZXQzcJHhz5wiHVgltkJHZtlqnrCj8WqrYm1ZOwkRILxk3CrGsEmhCCcJacHfteq611ZxSl99mlygx+9s83NtUFMrwAkpYr6uheTOw8LdJhWVl7L1VZ6t7d3kYH07pt8UXWygESOJfXfqbVsIVmjHJpovYlTyqfSyfxQoAcy/TJnieZIojqX6OIKUsJGAUgHAzNVr2+urt+f7VcqjKaqdVmphYWG1rQ1kcUaVJYQ4Kmqy9kCZ8545t1GE8+8RQt8eTiMXiyvb5Rr9+8df/Gn3/n4tnzR6SmuP5TbU26SCfWdDeonGPBOAFPwvAQ6FF6Jtk5E6gbwqxtaHgHMxnrOFescpXCyhRFWyEjjnLHEN01rxdC4I7CjZwQLwtWyZ+POLHAyFGVDHd67U31WTa4tCR2wSxKDGsAdp6AVxnrWElyAzD4WbXC3rzXbGAcxRWnGp8hjIioVa4XKmQ59hPqnFIZiRY/xqWxVD+7gAfnlUjyYfME3qvbPf702oxRh8U1DCc5Q9iHAw//hvSyZBDhvVtOeoQB1kqp/OTjT6iz8UNakm0larZbtJ0zFy6g7vNT5UK1itqyG3edLhO00CpjJng8NhVyqJFDCv4hvcEqwZZQWUIyvVBHxfVXr3ZuvT/d2m73G/TQDCa6Ocx/eu2KWq64YcU8IDf4YHJq40Vp745fWMSgD6VXKWb5q7d0IRDXQaiuOfPBGSlZRNLSSIqO0PEIFCWtLF8udfd2vZ9NWqg0s7VJqjlQAx6f8KAB8A9URkIo38YgDV9igNHlTv3YTOW5R88ulLmib0/lUotnCa3HCZp+TwvQeYyxOeVgsz4oN8nkZi0mzJ1ivsjRlgLDmWyG+9yPsjufy+wQpa+IKD9aty62tjv4vGZZ804IyVQzzWeefOaxk8dJlcs2yDvHUV8SckkdYK0mIsKflSKYDrofGhg2mb/ACYy8XeqFpRXhqhkOWal+1FlRFsDKuoXau1RAekX7korZgZFA9JAni/3QJnhQyCKFi31lV6Z6EFf4F+rdcEJgBxpwrmkVnzi3TAlk2+gLYhgdY3dFdn7UF8Mp9ldtyAyZoqFk63F081BDWkFmOQal8zI27qeCVbVAUud6quKRI7UX0WtWdTyxABK1VJyh5Yk1eavJBeYagXAJBzTLaqhSpWOlO+PiL9BhrSwkNvGUMpaiF0ucMd1cYWHleKlso8qWWNKGW2KbeJN2dID+CgY3CAAIP45CuUOIGIGsHDmGBZzWKewGFUKcmUCCAcg02v386vJU6cqCJLQXdagPg2EiZsMBSp2NY5SEj4tj3W9RjFexmieAK7D5Rmh1dvEItgXky4G2Lp8H6XQWZ48fO3ZibuHozNw8RLbQJ502e/AwGQxc8MWz21fZZ5aO2m3agRAVxIrj3J1IHoEkH4lSstGwAYik5HDOtffhYA+Cgh7+Oiw6s2zU7924egV2eQInnpJS44ilMdbE2JpAjMUDTDrJSH4xNh0uPuDSaRNDv4gwegxQuUQaLLEjGOQeAx/KM5wLmQyaykh2+9UbZTEkig/YTAN4LwzLwIi+SGMeo1CMTXgiLGMADxcAGZ5gP0SDQg4j9tmid/hhdcIGnoW1e1M7W9UOBwgYGdPdoHJMA3lGY71/6EFclkxTkQrWShny6ieomCmDxhiOB1KD+jHiTJ0ONQhnIC0SD1MHgw8M9/IKQHqAbjPIMCHOAX7w+9kLL7E0OHgMMMp8YJoKpbE6Agdm2hr9FzcucEMuY/eGdUz0tOhiLaOot6WrFnrVtYb3A556v8O7MjctbGQbsGZ34kYhqxVoHoSiGx041qAlFEL3xCce+TmqKfbyCYdMoZwrYCenT1i0Ot0eVzxzBQeN1+Y/3NUknb+7u40wkh1+Kmd3tz6bn9aoAYN2epy26OnkG1TW0Y3V3pu7mU985tELj733Ux1lqjXXULOzjeb2xXeLL5w/8vyZ7oXjmaVysVrCGJxP9gBTpQvHoJDP2E7VGLPRvtssn2W6JTtgqTrFcvn5p0qPPVZ88lHNO5ZnCueOF19+8unNL2z+ybexhi7/+CZ7f0dOLhUXF3Mz+hb6nWvvYmRwzKZOcxVZGtXaKnV1j+63N9dvdfdW28Xs3NOPUwDWds9lmtlW6xePn/rundV7zW12eC+XdO9co2itrFjiMFFGnxRBAMRY6pdVJdWPzbRNsBQTziVbuD2oAsHg6BGmpjA8nWF84/VgWl3dhjdeF7tYiZ6KZ5feKtvb67V/8t7lmarMMfPdBvWnyZUGtdIZmouZs3ajftxn34Q5QnJ0fQIw5YIV6ShPG5+hAcB5NbGAGb9GBrwny1lgZvSTK5Y6+RKLP3wkWfDWrn30hXQCYnVKhQoPuRPiPWG+30EzlLJcFIEM2/EICtTPTTM+zGZ37lxs1+8UzrzI4KZb1de2sl3dzU3vzNMGMPo1JzpDiKshC9XHi36WznJ5WIROT5CBQLmqgHE0rKQWC/nzTz753164cPHNn/7gBz/gwl/K7qsPWrGd5ABQ2jEnM70Y6PVk+0a6PowEeYY0OhOnTSFWK6hFZMaSKFUQCEPAfhaLMlw6PPfEE9hbfuMb3zipb3343Rsxhwd7yAD8PEHOky360tGjn/vc52Y//SxS8OYPfwjmlZ0+5i0oSNAxq3z05Zcz9fp3v/vdBb/lx/Z2fHAM8TgvYbqk43RMilU6pHccOB0Cfl7hT7kuS8PplSprRozt+NzH3dXrkn1aAFzzNbd8HoI5XZ/GsJ/faybWGq2PYfH89AxcNUnoMyFH7S4Vi1x5Ob1w4Zd/+ZdfeOmlH/3why+99NKXvvQlYv/ZP/tnP/rRj6CHLpTVmU9+7nNU61uXLmMJu721ysCUVgmTGfeDslcsAEMl+qqWl2s/2jzc6wjWictsGDSbKCyiwDxdrvz9v//3uXDk93//9wEwGGvMKXk7GPl+sWQEzThy1/jAeqwYwtdmCfFm54eSYD/uzKnqr//6r1NBOlBjHySEmYTbEMO/9aJERqlUAB6eaech4ZkcXosAdK/RDzH4XSFyCMZwhkeEcQ/doHGO+gkelkUUwo85wNLYQAxkRGLEjMon8M4iwFgpoBujXiyVEBNLwbPMlMNlRrrQk1jCbQLWxkg8V5nRTMz2lmdmFmJ2JJffHggeSZxMJrH4saWm3tHuyE8mVwSD80pNgET+NM5Q1jROUtHiDVic12kwc5bKasGXvD2NdtA1gjGEzlt1jQG/dZO2ua57RXC+aOUFhEhw4NcypXEJADOUk8GyY/DigdA4ljCfAZPZMgPDp/xI8s1vfhNUMMGJmvhkog0raH00Cuc2fgpLqpZ9UCvc7THSzh0XgXCMlfFGA4qNOYyiduHqzs4eBFSna7RWSkEId0RPJABqveDIfGuTq+ebGP5AEjWOdmLbHK3lxbSjVRpTQpuX3bnh+YLcPEMtImGXciaW13RImh6yACC6dBR+TxXT2uBWIIRQ0wZslZsa+oNKAC4arg1EtRbrcV4pBa4bShxRYEM6PcqrXlquKMkRKi6P4nu5uixFZyMcwKMcRyQvQfmhfp2eD4UiSZwmLE1wEv9X/psm4DCZaejpAp9UfTrVAdioHUQ9DTzRz0jXWzexMSP8hHO379lz59gqq1T59FqilIaEeki7TsT/cwsUK/iDV6yq7uzwcVhE1NUIHxqTDvfOzwga4Zu/8sS5MOMZLqhknqSCMA+Yca4uCId1mkQPO4cM7SveWTQM8wHeQEu+PMnzueeew6gHP8TE6vONMR9aeHE8FwBcfRFICE9mFgQyr+MJfKqrVEkjwnEiiZqbncP8GTkBMhZdfnOpDkLvHq4cWc7j1n7LiW8k4WEyTCxdIzh9GcKoUyqFJMKPx5Hw9EA8PtpUxdvLAMYXeiyU3h1UzOgHqWICZTIk9hYwCPTMI1rnG9HUKQX0tF5gCgIYuRBItjy1+ICTSCIYkg0ngNHUwI8vccA6f3g6JHjw04qRZC4Sd3kDnJU/Z69zUtitZg2x0DkBCjR66PgwyyLc1TWxEb+gI2Hmhwz7HTwcPuIkLX5mN3SqHNmkT1TjMhfTjOCP4Qd4PIk/xTZzB8ATDzDPNEx89VjD5pyXpkpv7Ti3AYN4DLrhzPUr7+mklI3JKRSb0ChGYmE7ls71nW0yYruJGmH5nidDSwYGxKYJGPGPUwhAJHIE+G/aK0VEwKCWpxboE7GJRfDSpcuYhnEwq4LJJZvIh3QdhWRJLceM0p7JqC10hJiJkJEG98RXB05jkN9acTrQwQ4oo5Gq9j7uPC+a6HhUDHGYB02pI3gYfvv7eEMmPJK6b67DDWqAeh8fM3fXKmAOfZDrotAu1VJGkkKYa0VXXP5EexLMdqmIDDpTnQUc8KcjifQLLHE0KfPahNS0aCwd4YGHxsSIikCihMTxGrYEMgmy33Sg+UPB8INNSCxACsGn2IwKbCXXLq/AkkYwKHLgmdvy9D6FAXjMJlDCj/EQtOxiw0wW00WwSYhvB/JKLiQUjD0tkR4Rm0exL0onRQdLlM+q0GbYqpeWlgkBidAnt1ORlokAtLHCwdOzQBl6oIVogOEFJDlRa1eunHniCSa/7777LuFA7jT5hEz+6rvvPsb1FzNPgxPlyRMyIIlUWCNhic3JEk5SkIROmEAAhJnvwufzLF7N8ckKrtVfWWEqTncFhQxLlp97bvnEic7db2IsyCzsCNQuHa+urCzNVu6vrnbfv8UydJOVUZ0nltESOQupEYzl5fL09OKZM3zGp3P1BndAc/Uws7m36huy2DSpgwY8kKSOrOVb44QFRW1VNmCvIvZxXhwIxlE2Su1dKaUlRUCxT/tySpjWUxY+BceXVxZqYUYGA5EgkLludFID7gSbZw2pnk2kOe0ZoVrAiUuJTxKU/AqztcekNEnE8G8aW4yBn1ANBuqC5JSCKBVE62iqImboxBZ2d2fn5ydiiKiiR2Cx8TrGGPc3zzPgnl2hQ7U++eSTl95881vf+lafq9WkGQa1cBjyuSvPvtFp0qS7/7DgMzFrY+zQaXULRSZsxVZ3YWl5uTDNh1hoFxpVS4voG97kIdsVmiUL01i6Pv9UZml29dvfbVy9U8o3qZJiR6aFWFjwzPZNBA8U/l52hmJsV7UedD3be/FTv/TMpz9dOXas/5O3/uzP/qx+7cqZfH4F65t2i90/WvjZf/DbmdPHG6/+YPeta4+0tdBW1Gm/TregvLjPjqd3D76wrhI+lLMvV4YUcUTMe3eGxx5X7XKTPfdNX3h0iWtEW6219Wu3b9/e2VlDqlAsllBWO3gee/zxM2dP0ZhZFIZy9ItVpzjjlkTpno3im2QKAWwmFeNj+HlqlzW+9tJeE/Olk7mp06fPPnf0PCcHL+XmTxdnMtWp8088efbpZzB0/H/89//k333lK93qHFOxdnGqUSxvsKfZ4VKgwvLJ06vrd6hGrnpqdrAh4RJCvnir6xcwH5cmNTvEsHtgDHOrFlFj/IxPiKP5ueRxux/365Xb3aNzi7/zX332E5949PK1e1+dz+7saTOjbx/4kjTs73xlMtnLksbMdWVPisApCzMeBQb7Jr88Bz/MoYdk3ZsKR0XnZjJ8VIbjNnQh3JzLs1LU8cOPffRlzg4gVSdPrDTNvlvWjmhSHQPChekuvjR1IjvVogxSAG5/FCFNl6kepYnojaT8TScZBKlMWw0wu85SJAMUq2bLRtoWinSdlrUwn1dGYMEwFwexLddSdBFitcPWpryy9Fe7JLWfVNANR9KMOm/iddrrz4MQa9lOu1+dm0auGg22crfre42pqWOnHl3pFUvr2zuV2aPcgkl+M3OzLfibU29tu+hGhU1HRR7Yk9pCQvgzTiLIxaa+aGWtHsJSPDQu8SVjRREMc2CYyOa/c0itzGRQyVRHfme3wcsqzCCtKrAOiM73GG3HUhCJAw6vQQktrxSZ3LkDEWlnJkyUzznx0DUm6YwGo5+yEkhCWt90bX5meoHTraxm5rXVsr9zNaMV5Ca2yoy/NBrIZpnjQQCoMLmWmpLRsekkyCQJkwIKrbRijf54lWETVoDa+iNmd2+Tv9y6bnaers1yMUdudtoWpnWyIWyEeO6ZHDXLTY8cWM63Fi9fvry4mN3cvNzs3O7Um7e31i/MPdtcU9tU5mSExaVy15+xGTL0K2sFAoeceKIkkxzpbZhKShcAMY//Xul4RhIN41GFBwDTQiGToVQOYCJEXvqCgVLA17m5GQ5TM0pDM2hUaO2RHD0LYaZtqoG2u60MF9uKxWY7v7uz1VmcbzXqqFkV1poSWNWypPGU10SXIl5U7QtnyiFicCak0saYQ3qkZ0ZACUnz1nXpCMwDX0klXjoqeyKdabQPxPBAgHHKD0ji/ISzzAx9eGfV5VQGqyK3I3C0c7ML8ZUJkiSgoJbyxS/9bZZddO1cqaz2hkRboybrn23pDijLcFQs2VBw24K5fZji5HNm8b1zp3VvNbd+d5YzHNyWnO3RZXMAq1nSYkquG4b1jsWZ4A0Mv15RLxIWOckzbYpU2hZHr/qBAPjR5w71sq63Y/uQMZNEy5QN9snysNEIjGdhvSa20HmUkttASWmBGb0tfTVUHF6U71DjHQUgFkMHQl9+7OSnnnmknOOjfdz9zBwXAxO1qWZrj2botiGMO7lX31uaIDhTz8ccdIW0OjgmxOo7wUiz7nMjvG1RYyxurT7PV72NPGKT0ogYcimXyo8fm3761Hy+z7laFIPo4f4PegRxjFJpHAhjNOWmlUuuyCGTbXczTW7YtSEQOZKCWSMwWtSDaK53hs1aYoWr6D8pG5kYqwBwjDMZFiFqRZl6celcEIBT/i5fazcRBY+kVvsRllj1AoDEBYyc9aOSdZxNiRKTb3lVL4Jk1CD4sBZsWWEP3tEdfEgaCLR6L5y6iluaG71qJSTEhEY3FqEXhR+AwECLVPGJU/Gs+p3JQgusXc+FoVlJB7sYc/XbdIj1RrdY2N3e6fJ5FjZje5kiVm2E1MVL2M2iKDNoPCY42KUyfqakNg7p9yvVqeVjx1kw5RwXJobQYCQZ8XoJHQ2ZO38MQMXk3R4Bkl7Y1xQMsD87N829YfSYvgnhqRSlVEpLiHssbPLD+7gQl1TUxFTjgekQ8LhQxUA8RhJMxSP5RJokFcb5TKGowYEZDOVKU7OLK5lrV7GG5lgD442Z2eVKdY6jA+BA6gqlcqnETSfUhiScageJL0B7qdPPyeVUqBVvLDoZD6YjJkMmEAfHJlAf7hdVxV+X2Wi/e/PmddSGNZTRmoWfEmMTOzKMrwnzAzxREWY/ukhClP4bHp5BNlxpWjc2jgTuSZo1ERAllvpQD8/OQdP+kcSxIIRHPx6yi5Boo+hPPBbrhUmC4u9QjQc945GO03SUtyBXTzHlPh7ocQ6k450hkWaP4tU9XpuDMiQpPTx5G/11TcUT5DbYzBTX1mf26rPcEEtny1kVLZTS7PkvZYYipE5SrNLaAmsfYhjNECgEQ5rTl6TDbEXlt8mRICBRyli9lPpjqSsUdOSLojUkFSZbqjaxUe2Y8onlBQKsZIy5DnN23lys3OgqljPCk4UhkUDGQJXNkqmAnHxCM5jKBwYa9Fpg5pdlSYjkYIaKhr5xhZo3Tei9YZh5ydLT2w7E43yhmRUg9ytI+RndprvEFe0W+yjc4i1Iuack3zmi8YB4pzcR0u+YmW+PYSHqmi6VDGWSqkLKsQLAFRf2RrAxk3WDpGewjrJfxFAq1y8XdunZdm5cbmHE/MSzR9qtrauyRFnJ1zF2PrmdXf/2N7NzhdozzxRXZvdu9BmhQd3Kdiu3e6t36fKxpaUrZ5bv3L491SggLsW+faSHEUcuX7p0tXF/o/3mO8wpukfPlPn0yHxFSxWsLG9vF6pFzrEwc9u9erVdXzty9Gjm0bNLZx7NfDSzwrR0bQMzwc6lqyyOT7W1w60DSsVsgYWvVvPuxTfZha3VKtnuVGdn++89/1z74tuXr76vHcc+dVSlkmf62musY7xurRmuSgZU+7BQdW0VIinyVqDDTcZc/ZhDTjBa0rUeWm4VPEnlmN2Bp6Olba/OEO6x9lRbQLCL+i7a9fWt4o3bL184Jyypr6owaGBwBJGSSNuwh7WkVkZqcaJNVWchPKlNEAAbQxSXyLMBekBKwhWQjlGUiKY0DB4w9jd5YXwS0dLSkAdyU4iaKOk1auKkHn8VDgvm+PoKX/KAr/bHSqkaF+05c3yrm9naXC++Nls6jyGCvnOePSoMNmazFRsRJOe1ovE1DkxWXrKVkH8Ad/hUVs375pDC4xQG5g3CdXoOR2lRc5CeLzz69NMYRH/na19/7733uI6dyKIM7YfYToZWraOSooNmRswgIlYz8x2GNcTSmNhuSqcnD8mNISUjXg1JBgtT5gc0A5AAb+1fMe7hXS+pDl6vw46WT8D6+jY2Dl/85S+e++hHabFvfvvb737jL9E1pxcWeDJmBTkFBe3KkSOUjPt6xBMrMyWCNi+X60ZX7xIn0elJh3N9+DfLX6WGBlayfvVXf/Xi+v3f/b//7i6G0FBiVpbl0rQosY+3LC7oy0jsd7Frp0+XBj1pYgeWAfsDKQmrtJWHAw9oKZSXHe3Dl+JfOnkOLs3UOYfR2uHOBFjSas0vLa3ev/8v/sW/+PKXv8zEu57XkVu4yjCUC7J5drkOPq/1WWhgR4EnWoCGDQxPLlmCglHZCURN+CFTUjm3wWkcznz6059+/vnnwQt5iMQ7796BRX2OyHKpy0CSJ2AbCQJ5u8U13347fzZfYh2Pk/PsJpaWl+aQyZmpKuYeC/P6uArXkoCfCoHPdD9QwifICEG90slVykWUqWwgGXCbgYtvytKrkQvaiFLwAwH+dEq8OGmqQmxQH4MY6pwXGDsIMh+5pxES5jgHM58kgYdzSa3q2o7o6rwQ9Ng0mxkHAOktAdDi0hR6CGxXKnO+A0/RCPQnTCMJd2boe8cdlRojXXLkQ8CkQgZoX/lKiU799r1VbhLPcNTAzGqQMTxgBQxsQsJTRA1kV4Vq0b5z9UZzormiA4PE6QHViCMtIcQyBnH8AjALHX5JaH2DciWWyROBniQhY0BMgDeaDVbAyANEqhRWg2gqykVIYItKNORATtr4JBWqZmt7bQjogS+MmDrdFjt53KfBlriNusjR2yBW2tK0anPIirpC/eGiR9SGEC1Dm4E3VLHMurO9x4p2eYoRjhoCgdhHW1qxkaKTxYsvvsgJidamYG5fv8JueautigaA9XcJkBSLzWPNz0MYEhGSJ6UPPDbCGOAAmHBKh/Nwf8UvW5BUuAOMI7FUYZBIWRwJwBMhY6DlovJSWC0ydrV5SbWCQTtqtG1TnsZOjT0cs9RdIs/AeLdCKrgUMTsBBzwPDxmLcwC2w0cdPt/DQ5J7rBfnuT8JHSEMnA45Ev5X90oDpIIkriJmpI2GbJ1aDP0AJohXHFV/9tFHsGydZ5xAQ7bBroszsSMEe8KRwJ//K+0faTFisnx1VNZ69+9zVYGzHb2R78kuBhiKkK6LWNfu4QmM/EmLjgDjhQIVahB20T+ifB0AGpjWGR7x3JO7JANPLOvkaTYCEHlKLKhiRkmJYoA8SpvIEpvEmKizSdxrbwizNX+6PnDSKtVr6xvJUt2EoE/0tN4W5WWBbsYbNpKJBT+8yu7iNXMbYyn+SGHMnfEtquPChQuoUD6D6CR6Rk6kUplD/+PXiFjO/KalIFjEOQPpsa1qgHBUPAFg+mEhIoxXnqxJAzlATpAi5ZSLFcFjHQyGRXg8IPEcPJCnp/Wn0RlaASFODE+cR4VcDCQgMjD8YGYcyNNHL3S6pGLVXFnYGkLE4EiI9Szcgx8HDA5kacLwo5Opzb32HiMQGijjBK4fZg+4UpvCj2UGaemmhcoso/mKA2LJkSFRZY7BCVoaPDEXPJEGajNFxqAKgHFnVEnaAcPvgZBE30HujM8RA6LIzil3eJ44Byac2IBu/x/HYAwL/AFDOtOJSSPykDyRIstfBHjp7CkivZUlBIcWx8ifr60y3ejtrAHA8NikUz0jfEN7wEhGDlzkJY66JLMuzbK04U/EcCKB/z8ZCH9gLAXHMQSKbIx1CoDXDmDGWxUzerzMDuD++ExjiGDucZwREszuj8LjCCNaeVisQMySSo9p3RMhR8LTr+MwIyH2GlKMRKXxHOyPUgrYOJeG0z5YmiAjJsGvKjDbhRjoHmcg3BvJcQTsga/p7EaAGWY4T8jCq4kn7oAkxDJlEbxpHuQInOMUEiLMiWIEZlBmI0JZpOrd0CrCS02sABLnr3DKAzzKAhUwgLNo8XMkyJOlnpCHQxmwroiHGAtQV8UcGh2C6qftoEbAxsYqeel6ECrLcmMHlyQAk8qfvgBNGOFMXoUtoUEANsawLHgEdsmXdI54xh1kSF/Z6SiAsTW5f+V9lBuL5FBCYjBjo8WTXOkj2MNk8EC3AbCFGYCxiVJAhZQn+3LWF6AWuHDi5Pnzc90GxjpCJSfaLl68SO/AhwKAhCpCcEzl3n399Y98+tPnXniBwP7lDa028N1D8Ory1w6rNChbTp0Su3Z/h+de0Sahdm3uSS5AsvJSnA1sKy5dqm+vs3Yxtbw0dfRo9vjxo4xG6KauXs3trkEtKxVAwnfWrxkQgvZcWSaJrbZObb788suU9LWNVSqKV+iGDJXOqFUxEueZJm8P+CWhw/N0DwmoY+E3i2xGXwejgGNckcbp6tls5+mnn47wFEiokvk1fnDGwJhXGjnA8TVdohg40ZOGxB8xu8djGWxNTOuBQJoTtbgofgQC4E88oOJmT4pL1fCcfuQ4q6u5qcmIk1TCAI7JQH9TQyGexWcx0yqRLW5efuELX3jkkUd+/MorrEtk+ARmitWxHCR0hhPiAHyge+AQZF7QonrSaAtZTncDVytMHZ1frDYYEPYrbWs26s41IkSw8y6B2b1Ot7m3ejtzZLG4tETj29nQ4IYz5+TK7gp4vE9hjRA/iXkWurYYlJ3Cj2Uu+V6qSZHNPff0L33pS6W5xcza2iv/4k+4MWcm11+C+s31mm3ACEMpn60Vj599jGtnG2s71X6RK6opAdteCGpTyGgquj2dS0D1YDRtqpC02a5NYLRvFuqeeCWwXX0D5Duk4s12WYjyGU1L2LHi2e1Lptp8AzObuVEu0uyf/Qd/5+yv/MozP/5OvtqfqkwTCxU4LPLwu03iFiZ2717kFccaErNJlCYMhDWixxSoX5PHAjEh1aKOc85OLRm1rW6z+9j2BhtEz2amOXPxseNnEPHybjfHnLwrVTg1P3WkVMuWivfu3Pnd3/t//et//a+5noMBZQcbdiY52cLS0WO5uQXOmLz40rMsor356neYnrFJQz1wYSp8UO/CnfhJl+akxmfCHwWkm4vpZ3FZX7Xb5fLozN/+u0//vS89ncm2tnd2CP/f/Dd/5/r1mwx2a7NzdAmra3v/5J/8v3d26VaxXUSi4JSOp2Djgk0oW3pMRDlBC22L87MMo/PZLvMBltkJn9ZXMKew/Ye3/izKpiOoLbY3oY07BvW0iWuvs8PiPzN1zK7bZS2OU0wpZa52FP9hMNtYQQZUFy7/A5GwhpC8Gtqgp8LWNkHmLKFyjxgiNuo/zTDyBYwnv65t5bPNdwuEWgjsMud2zMKJNDP7k4RQkdajaCEFAVZkBIse0ELG2q3m3burC+V5qqVY0E0Lq7t3qOJ+W1es7HTUPWcWj8Dn6bklur1Oq810pdHehdvzJ2ax4m9N95cuHLuV2YUAljsgig1b8DCKB0PTPhLF4jiOu4HIvdtq0402MzRQ/D0MnVgBwM+4AEpVTJzt79F5iVEmafSTCg+O7knrLLxpS9R2XHjS1q34VuBYbNqYtfGeWQORQKmG+UEQvPPsyQ+AMp+wFfPy7Sy3i+f4QhXYuQ96r84wi51ylqVVpHCTtVGV7VUYZGWzDUY2XGnNxdbtVqNWq9brO+xvVPu63IYBFqtjEA9nGL+BIejV9H4mIwc+8mXZU0dwEjLgAxVRqfQ18IU9XBklE2nxje0S2rVzaah/RAgQctPSbG7R5vbqOzvFIuvQuOWFRTDvNfWhwrbt9vyrf/WvsYA+MnuCAdbarVvdTpMvACMUza2tFldaN9VefGPDFTkmhFZuPWAEFIqzok1+Btk4zpZIq4z1McP7MuJBAoMftMKsqrfyeMMyAOHH4TcJsQxhnbVit0E2wziHMrDgFTZSacin7r85PzvL5dtcAHf16vsKMYswQJB82Q8YtGqYfRKVRbUh6070Q6cPxzrcXN6ZkgWkKkjh3hd4v5DkGX5Fbcql5TgVPETtYcLTMJP8YntC1Wh8rDlgQq0lkjgKOvFd9e0sVQv1aQMFABaG6KnuTAWNGaXR7MeBw8MMMTQkkzZjlMCHcdtNWxwMGkP0SHJx3sr6BU4ESP4RIgIZmZTLXOnw7EdeEs0lIA3YEihRkD9ZH3ihrFiTSFCSD+wmcwXqjaXSP6JAeRMG+7HR45pgyWdm607z3r3Ozcvlep27/aRe2JKU7QrebiaHXTBQC05ZUuFB3uLrCN3S2F6X1jMRy7k3gugIOSpUxd6WCWiYWGqWKC5rbKCd7zqmxih/Wb7w1Yt5zATs+BvftXD6lRX5xqyttQ3xkygCcQI1YCYnNN2pTOOZ8088deb4VL+V69cpZN6vnbTV5wIb+hir6GINTDyRSzaNJRXQxVYqYboxWAvoLJvKLJsCUcRFfaO5WlrbJk0j2W7UrfcmHiIZYmxVeCrXfOTE6Y8+82Sx0yxmOdHHZ95NF8ryWwf8QO4blqI/0QUy0YY8dVMMB8g6LzMxOhVGl/qEA4al9Nb66Ac5IWBMhqnCfJFxCmbHciRVAZgwcwGU6TFfkkZaGd2RE3XOEUURih6yeoMYwyfqJVjGecrLnzKA1K5MQ0QaGsyOTvtUH2qJhSSrU5LBQqkR7IwpHTUtYJWzhy08Fk6yp/UvvDPZUIvLtuh3kBP5Zb5HGf26QusHXCMJuUrqPbuI0wgbWynynmaobCPqzfUNDdu62fv31/fq2qieXVrOXHvfe7SNrU3OrHE1I1nMTE2DDT950b9BvxYUms1ybdq2ktT9a/mDXI17vOLw49zPm3vgXhJitZm8REjvi48fO8l5YWZQDIKspIKjOAi/YzDcwikmG06e7klQ6tfR8hQSMT5QSAjA6afiLAuekXUeEgkQRIITT8QgERA2a6M6F5jv5OyEnA2csoXq4rHTmd5xknBzJaMx+lBGasU829IFnRniJsycLdAgG9lMbYrPJnT57g7kRfYp4w/kxnkyjmYkHy9XBBuJjeEP6/HbbxnhMMrLdpqt3e2NtXU1Gc5Ryol7VpMUWqIL2514CMDjIe43SAlSfMUf3UiRNWwCr/HSi+bPAG+WwtIm5mJU9BDsWQeIh/kBCXk7kTxj0jTyGDjusYGugsUKSx2GgCnQtHBadipo4hnkmEoRvA4Tw9PkeSA56s/USgRzjwPDFl7TBESw8Yyl6FIcSCBpNqr9dk6j33ahDs4jskVpFe/fm6vvoYC47KFMc0H70ePK5tdHx8pBr5IBSYLOdkOOmVRaT0SDxwiQf0Qyn5AI4fE1O1HiomaipX5fY4+Czo/LFBKa+IIOOAln2Kuxtuz2/aMFmjVpAEMMOF02kRFQmiI3DSzkYo4owgVVA+WSaucb8EaF4MEjQdFDWl10yqlLk9ErHS64yV5qnLLoIBGLDTIfthtFMZPSBxk4dMzaSHV0pc2YRIwcOeqZso4Hn4I8dyuM0atyEWg2yxbNwzp3GhKBfNnLmCJUvOI6ze7q7XvHTxyBzoCBQYBGNapfTHM0INC4Ud2EDj1ndVUzX4hRuYysCiLQ6a6Ud+hl1q6+22juVC48PZ8tXb/4U2bHVQ5nFbPz9/fyV+5kfuH5wtlja3c3q1zitruXK+XPrG9lvv9K5m9/5twvvLy+qxsOZ7OM3zKLLGtk8hXZFaF7d3guNNd5ugsaSQWF+VNQsWjHYefev5t5/+5evo8NUbPYU+4NLBAahRI9Xa5VKkLh5tuv3Xs7U7F1+M76fRbTpttmspYvnHr6uc2Lr16+trrZ0Pe9NrkMjPvBbMyLtjeeIC6BBnp8RDR5UyArUTCQ/lRslJmTzN55sjuPrmQkEzhPtJKppugE2xI38KgwZKqX4BRBB0hgJycz1st377eKl5595AR+9dyJQ9CRRB1FFx5Vqus9z8afREFJkmLyb9LiRmNjkaFENGm8Lcdwp1Ats1CBn9ugefotxi6B/1/u/jxG8iPL88T8do8IjyvvO5NJJslM3iwWj2pWdR0z0/e2Boud2Zk9sMBAUEsDCIKAhf7TCNg/tP+sMFho9MdqpF11D9SY7elju2emerqrq6u6jq4qslgki2cyM5nJvCIiM+7w8Ntdn+979jP/uXtEZPKoPmQZaW6/Z8/ee/bs2W0/+zlfWZKcdMLHQ1hsqjB867HKzMKljk+bCiW56iwux7gpe3qjm9tabDb/snTgQHd/J89KS/mwYq2n9tt4vddWvgHb7FUIf/0urWQrWteWSe5DTpeR22QI2AuGofRZATx2/qHDDxznOo4bF6/QluY7anYSHQbKGI/bCcNkyj1tCaO5jxbAqJ1lEdZgSemJiSLgLVpIpmXE8o9//OMzZ8+eOHPm0KFD69euMYyzFGRC+G6fbl6eKrSJvrBlX8eu7t//8ssvH/zCBU5BLL393g9+8IPm5etmfGIHX40UrdgoceUOMciqn9yxW29YW2GaZMY8miN/lhwufBhPCwzQ87tzmjGos2a9icWdl778Ze5nfeWVV8AKSrOB+FiiAOC4B8XT4bs60ohqM6nkzIaZkEGcWRbLrwz5eSWwmNVtPnPVOZaPPz91kBWrzobOWZdsqtNjZmDZ2XfixMqdO7/3e7/3O7/zO9Av2SGRpm0AsNyPTk4cOsRBV97seOONN+AGF1pJKLP4iAzoUJmyacxuku8Ih7tI2ZLoY489zIXdoLF2Bkfe9WPncP/+g0DsPaXMRPXACy888+HV2zSsh+yr3JgWMkxVtZs3VSljMzPTUxQiHR4L6LS9xKIamYqaJmqFNyPqJtE/Pk4lbqc47RXYKKYwkQ2/1bELTyyGvPOrJpX8mu0BiY4owp6KgKUYeAlkCI4AETFBkEUpmaQLNcWC8pxcxHRa/ghfTe3MBwIyj0bHKSmtA3W9hrlIMP1Iqq2tTG9rTQrr39XSdRlNZioF7myYKBYqnG7OHTisJeNsgYM/3BRBElVzvqK+sIDPZcdoHoVD399paNvWiMbF3OTYUmXU6AgJGYP1uPi9TaHXrSnnVLVLpWgXy3yePJsCR5UlCI6L7w6aHiCeQIL18X49oZOCtQyGPY0un9rUF/z8dWYqC0TL5SItGAHQ0twoC5KnuaJAyIJc39QaELHcOIz1uquLsCpUyOCQpTCO5TA0qxravUO9lgSGfMN0CqlcTgLAoa8NJFvul3mnZYK4PyqpGIhla5M6tbKyRts7PTdL67G1qU+JcuDp7bffzrUvgTZh9ZQAB0VpuCDLdh2PPrx2XSUdhsA46DOzTfh4ZRRHJLR4IRDwtJo5mDOINOM4juA4hD1JjEoHHMf14LSsgRxKMsTRKggbJEakhAb8FROn6dTwScKijKqVHf32WGXfD1GaMqkLWAKKLPmmnTVrQc9pES0c8zIWMwDshpOGp8ODlHuF9qoII9RQo+0N7UVuPA4iFKlIWfsTtU0ABTq+4Qxb9jihjwkZET6mBo6jaPBTwoR4h/CAvPQaGLaXKT4tGI5qRkYUbYYKZiBiBGKOArm/qh+vGuQoMvQwPgN9ZOSi3A5bgnfusNOFwINYbZTyJDee1iEe5ZWIMOWURgZn5BH6OJojGhz0zBka0THywEEecUSCieqodUQlBEPpKO3uDoIxUlytF56emmargJe61PSlEMiCt4ogeknxSPJ0qbkAIFDicbQLkLkqm9YcIYjsCDiyByCCKCRk346Pl9CqE3bnSSJypECswoniTcMCQAru8FekKQRMAh4GyCPcvNCVwByxEWK4wYuxBDy/DFgj0MMIQlrXgycDgYDzNcE05XaIp037zpdYTyX5Umq3JweJBWUEpgKG47E8+tYgj4pV9ZJy8BWVUOZCN/SCHHB0BOfIkIPayvl+hhbT+/oyPH2wetXRoizQiXl3+SHOCWjSYnvKRWJNTpxYR/NHk0KAyJrQ0KNjJz4Gw2iVo9mMk9F8TCV25hJE2T9hiWcdkAfwHcETuh+1wSMI7kc6BJQLcwQcwSH4MTsj+E7ZgTQXSmiTZKnaCLokJOcYDBB/E1Qvz5uLchLlpPBB9lgn63AP/233yS8q0iC206Fwsb2oWFcFmSXgVufIIHgUeY8FEXViyInl7amd+8dMk/lkqZyC5yVNjXDMywg8PkaEmEeiZFeWyzDjjtjW4kW1pMADc0oBg6KcRZo+OJGv4+vxXnr1shuhM0ieYrwHcAQLvpBlQIhtEEYKHmHhPgGCMYl10DypNulmCOwmYKr660EQoYew1zKBBRf1UO9CawAcoOdLqUMjJo4oWamShkWiJWFiPUmAWNsSe21n4VFCsxwBxDnNETWLi0lFG2I8dJt8x5bdWWQH4nmhjzNpBdGKJC2MzWJCE2b0RzzwlW+ylWKZ3tIIjeZIsuRRic1ZZm0unETxq0PQy8szs5PMdxxPosYk9p4Hu7yULEDZ80D5ohIfae298WTyu//gsQNHjx6o5G9dvdpp6kQ8rPk8wMF2+5FHHqm9e5MxkgkjCrdu3tx+++2HLlyYf/BBMPsrNUFt4d57LmdhwFFP+OaY+YmgLfPRVgFjGsJchjVyl0oUbSzEqgVhLsUgRyCQqazdc92Z0P3+XMhr50+18A1BdWFcG5EymChBVBEQwo4jPykj2GmH0hxwGlBLkkQnqWAqLpFuDAzrmbTMuDkCdXAqzzCPT+BCEiBcWITCd+tySVRJUlZqfIUTdR6Z7BaQ8EntiDiiY1Ipm+jQnDRlZuBczB+1R4A4uFMWVA7+O8TrVMQGyCkEzxc0KQgKsZ8/yEfaekVpz/toY/jX5iHGffMWpuvqfpNwrLNYZP126cQZVm63V9etIEY5ugy0HhAPC9Ds2omTCWdHNjiHwy0nYOjqhqOVmZlunquVGemokpgqsUD7C7JxXQ6h9b/4UevUw6UXn/lP/uNf/6OVhdsrK7l+g4IuZ9hxylSsrFjFUn/G3J/9mZwugritNY9u7jEuSX74uV/4iqYNK5sf/vjHF/+/f8AFAYf0Un+/lNOaTpET29S3km4r5mLhXr949+7K0ROnimdPLv0wx+lNclFu1fgUdJ5VMd3RrFNvdrTDhOZSG+Wau8Dkl6y1tmucMzPchygjk+F2bV/UCHCeWirK255NvqNwN6uh520+gtholF/43Iu//utLk1P//H/4f/7mv/nXxYmqrwqqMZG5W6fluwekSRwKwYgbOd2wk+VEDFXFKiTvvyHVJHba7/HKbaa+3MhOfuGZZ3755ZeotEe+9wYEJm6vtGr1qtYoNagCf0srWfmjxw5vLt3+N2/9+X//3//fa5lOaaK0xQdQ2U/ljCcbf7n+h9c/unjrxre/9Y2tjRXewJ+b04Iv19qQuWxPV5ogB92UV0hvCr0zS6Te+ZdBLTKUWfetN77w1MFf+ZUvzE+WNleWChWtXdYa64zUYWSJpQ0Wxv6rf/j34q4UtRo0SpPlP6vY0PO2A60gW69Rb1l7JKORSu2+IDjSm+lRDp9g+KwcdzolcItMPJLQULKXi5ewEAlLS3o1EGpAErKBdlBB6F4hkujESjYhzi/k5RsEQlrGt9L3iZkLiukZpmMZalLDEUnEkYY6LBExBWDW3WrjDfKSwZeTWO9FJ/r2u8/6bEwD00DRssQ9+NqU6VQ63U65NDtRLj/46BOsaW5lapRIp88dxK1mI9+od1bW1kR5IsPqcyfLIlynNt168cUXm1UOkHMdZ44Tv3QQEi+nU8MczDVpVRdkqcSxX0qJIAwogqr/1Z8HB3LpGTqoxwL2ONRVKL9RFeKY6EeowY1qPoHv9ms61GBRCNxhwx+6anc4eaYZpnHpt5rb3OySy4bTx8oJhRmaR6QNBhB4cOp5onr65Hk2hLLd91neXbyxhdhldlTzOT5HStOy1WA5vsUniCkpWXssHdcG2D2JQotjt/mrVXLiFuAlZfZLtDlE7eEFl57dz+5rcGQCJULRfM07db4CP8spxWahMHnjxkflOxO0otPV2alJjk726tRt2wovaSTGNW79lz7/zPH9hzA0ju7Bkatl8cFEBgwR3+WRGsTPGIgJIwZBcAEhyVd8hEjHLh2iDfQRAxCWKox4MHKQJbA5D8TH0PJ4lG80uvKTVtQZSQBrRHRqXSedRQ0pa9tNVjGYRUg/iYMbkUYZipzb4CIy9iw5hde0C+S1D8F2Pt9larfqWEJpQtswDJIYf6kAx1yUwWN2tMgRnDEaHxugTnLYeekMw8KTF6VEjwWWwhsZyTmyx0tTlBy9HAWkrl408Nk+VNBKBGULJ1WIKdofL3hvLdnQHI5qvM3Rtqo7MW1IJgLW0nJKl+PPvodErWLp+ZFnn8rxGqbXfZv9+HQ6iBh65/BkP2R8TMvp+M8inFQg0VLTKWcvtGa1wj7ZqwuwudLi9fkbH1bq9em2Lu3hoD6u2yvRDXJAn7fo+xkONeJCnbJwFD6MDVy9aV+apAqImMqSP2hbUATEiDcrjYx1u6iWTTvaSr0DwyfZW+zz2vfNs+VJtt1oPowvBUIam1TYc/RkP0nLhpklU/cQTyynv3gr5dGT+5595CTvmTEWyBW0Oce0Cpq6Z1DfNmEyzFhFbY4WPfXWmFokTmsztNG4gH7HRi/cwkgDp8aAJP3+PJNSyyrHVdRomrTQIK3qTq9fndCpotNzky+eP1umLNhKLNFTBP2oD9OLOORRtWDESRgUx83G7JQzDc9glhLSSxSfRhxxlWuNBnW9IXMtKpXqldkeydUCM71V86QmMml6pVXqOwiszxN29mExyM7BqUKSzkYFogam/aEZ/iBMEg/7uTPlV05jEEaEHMJCd+RPqWQGygtFZQUkuWgjKQSO5iCX2liVr06sMPYIhaiClRNV2cqgSZdR+aEehNDASqUjtRgbskwqoLTSjU6XUfSthUVUMzFTZYhPMYEtolZy/KICrNIAGAWc8lPTs7rAUblOVEP0sAuCBc2JBA4U2cZOzmMZGvE22OUrH/BCkm9IgEuNQNWUo3JrvaS3RUZS5DwWoOM4+RgrIcccsZAaA98vwCxcwyiI2IF1Tqv5lF7CMtw2Ca1l0Kl8rRqJNO8DycwUBEI5IAaR1Bgm1zKnT+q8eNM5jbR2zKT3Zcg0WhpmGC7FaNQnlg0JaDVaTTr0Xqu5srSI2rDP9N6s1KgSoYtxofDDkAw4nK0WD0E8yScVSums7Kx59NJJ0aICwHWot0rFpoNWVdIAJyuI5HY641pNQUYyknqUoTj98bEHZkQRu1o8VlrF2SlXwpIguHTYbDKZJRCfziOsxwwioZH6jWXhRRNjlPw+nOfI2wG/MTnLi0X9zHwnu7G4Mrm5xVFeOoEJ9QI06F4tOG8bLToVkn6l5lB3LO/WVALWcEVNqlQtyULTClGAAidnkPO8zsgRD20U6R3TMFkmrVXUVJ78/VfvYaRqWAf6koEnacALY6AK5cJiheOOKi9Mu6vBjhCpeSQLCMSnHWjpiEVmRo55dSOg0OECVY1xTF6Q4cPz+u4CeFQlgyfkw68pxtpbk5OUA6HQTEoqNBsbHxiJWDA4s0BljTEKww7wInGhoFLejObc8fLddT7P0+ONKOJhZFikgg/nuPN8rZ03U5tarrWsuboUDuRYVyGn3d7B0iYt58Z7P50+cyZz8uz+4uTSzdtsWRXXu5v19c7lj05euND5wtOX3n67VFsk7b5WW23+997L3Gpmnjm/7/PPLrzdQ552njPXmaIdi9EJ68Rp15S21w5pWT55q17rWhKknylrjNLnTBA+cz76kwrdnA5JKdsrpfKRU6f3P/YUusourt+8ebPz4TXgE70NBkPztVWK9ekDc82JiasL68Av8vEmFvEYX1i3je/rxNgkFOj5SYUOpROwNZoadWSNTnBoq1VdORVD+AwHTMMJBeUgOOTX2AEcmy/wgAitHLcFdC9eW5jad3SKN3DUqWtwgJnJ9uj5NZUD4N2aNAWRpMAT0mO/4KfLMZFgNB3aRjyHEsZJQC40tzBjGiA2itXYT7EyntAakYoRXJFviugIF8tSjMK16wsF4dhFqVypIArMTSSymB3Yame21tuZNzPNg7mjbV45zxT3AceQwcz4OpVp3UaYQEy6UA4Wdoiwh523DMOwv5YnP1RKwyHuSK4GiX/5g2ePf+XQL7zzyhtcZdOpb6PMnF3WpzfME6eGgzU+LzypJVU5wQHuDvtjLYMwq4TgeOufEBn8OgWev/Wtb33h4dPV2dm///f//h//8R9nLr1HWqRyBwUcBmejZI2/iZ2f38+FZbNfeC7DRY1H9jN62r7xLmlPJ3NOqlamr4oKPsm5AoBUrCCosWCtoVjkklOOaTc3lpAWCVm46TS1oEwdIZVOOiKDzavqevERqMZbmImkUS0wS4q+BYLALnqsW6YZo9nmxpMHv/Y1qP2Lf/Ev/vAP/xAgi/Vu4zaUEQEop5IK4g4JK9kKotKO41ArdAgQ634xU+RE5ukHz/2zf/bPXjj/8A9/+MP1je/wmv/Rpo3oTRXKFHXDKilr2Zx9/p8u/YhVqsKkDk5y4wkIHkvjAqTFyzN2rd70PKvEWkBnXgkOQiIP+nCCQcT7+CGhWjG22rZbx44d+bVf+2U+h1gp6eUR3h2h8WLYR9lJ1VKpjI9VTrioOTDhnQn4aEzwlAY8iuTSj7kokVHTE0lw/ggXwrxjF9FiwLBkbNbCS2kkgawpR6envbxkF0n5xoCJZPYTySkQKlJkHedIMaE3KKEntbTetFlQnqd1HwkdTgAx9a6QNjytc7L9E71ezDDFpp3ohFTpVsiJRMqRONSIIoOkhSAlvt3bxlRqDV2jUbdbvXTPCWvVeb1evba5xgTswoWHKKZuQZ9TaNabnAxtWH75gKWomWxt3lHD8OzUubOoNzWZbJgOOVctvUmdOziXyiM84yoZOSnBIfYYwmmIwz+x74ycIPITMNUFLdkls1r23ZG+pwKfrBHmQA1DjRefO8fNVlc/uPPqq68u3rpDLE0QPieaOVhXb2mb2hYpbKiaaljEgkaTrQZmhChKwwJVJcDQJ4AjkIR1IJrmhVLjVfhgK7Axh8pdNkyKIuOBF5vvtlq1rTrliCS4iakqOLlWg41ZipgNLfHH2PRCPR+hku8Q/HGXFJCOWo/HjkB6Gkwgi/aKIas82AJ3UsuC+DEVmfAwAQYinkTyhLcxbNRmzfRQEuvMpio6t4iukJAdBVLh4MuAE2TC7oPDbkzkQgwQCWZW6miy3kaD2jFpDQ6FY/BRI3bkKMnHDXzK5B+X3cfCR40RHzlVHIkCNYiUU6tFWHqzricpupjuHgEneA+kPaOjVCNYCM/bM8QCZ/X5CS7m46svNkxUkhFse0znd6f4v1IYmvGs0d52Fhc5+1y1s88A0Tb9CD5hHA0UvoDWgrmUrlj3vXQiPFowsR7laOnsjUCgb42x6gBJcDQs8OeRekFC2iIauj3aAiggHglFypy0PVwMQKjWtJMvv/wyR9e7jTVP5fjG1npemzIBFHfTktMEwR4hw1hO7CS1nBPQYMMxNSw25zQNGMQjI88884ztW2isgqUo1igMCFln7Y/mixqk3Acf1pZWcEdzvjHsHIklgAv1iOjQAYVUBhjUO/Dvx5HKhUkji5QJSZ4iHJhxV6QnMSzivXYr26ZGkgQlSlzTngLYnpQ8yCb4Tof0Dic5hoLvUXYSQ6NEGAF0viShtWaRlw0MNM+jdg3rGL4fHwtjWpBxLKBCKjqsDoPh0flGuHPkUUkGTmEXzAMme8zjAC+GmOwwImLixKH4dMKYI+hT1gyzgcRCj5iRzicOQH+cGpAIhy9592xGOJLAEWCEoyHC3DNBFNeqSF02/IUQyvfrEEkCBRwBp+BiO8TDP1MfCb1EIheDBAP7rMQg77XNTXxMjpOM8IqUpRZzaBWgc8cHjkII4BzfMUED4sljwCl8tr74jqjm/hgkst0D+7MSHrUwbr0Hs/uIVn7NQTCGY4AYLx0PeFk4/r380BbthgYpao4TZ6lxn60eYAuxdrs+vTkECJ30nE7ZFzBYUeQS1UtyL0aH8OCZEjTJskPMl+XLKU1QiOAJpsWkKDiujX49ikTO0Gla/MCLQMSGipIMIhVSWu+1LUzWhEkHIGkDqstJFK9yeh3xhCE6+VEOLB9KO8LGiBMLEcUmj4IYF08LWFEWCwmpOqUHT4sA3AHNsWXe6C2WBmwiWaS1xQ3tQNNoe3kR6wjE4jwML2IhiH/r2rXGZuPsuXMnH310/507K4sLzJu48Zn3Yw6dPs2NxpnMIusbfKIK/EKucPvaNb62dfaxx45cuDB369bqO29DLe3gwqOLH8IWDS9+fRkUQTyJSaetwSgkAQZIsydPcrcuk5RqqXTq1KnOZo0ZqJeR06Rh54z2vsZP7qxpZdxUJJLo0xGcvkEGunIgLEZwgDOCgogjuHKEk2oniYoCRAoEnHry6wTko8bXX3/95c8/PgANh6CfTgVTUTN6rqsoT0znsePCuwYimgdcfg9jGF07wb0DzjAIpjT8MOLHy8WYJlXCet6BGCnVcoEBVtfqz/JOVW9mDqos9sW0iZ0PM/tb/WSmhpYYR3GKkaWGd994nUJ35ZCzdDEB5O3U0HQSoZBVESJ4ZMpR7PXny5OHspVcvVdua/FXaKHNHdJTtq/Fqf2tfv2DS9//H/4/X/3qV0tPXfi1//Q/v/L6TziMXVlfZAGNK+TAaevETbfcs4OeR+f4YF3h6QtcpHq1tT29tbJ/Of/+K6/89P/9rw5ls9Mct2y3imW+3LXFWhlD1bo1WC0bgG72J1ksuPmdV58/+1j90P5TX/zC6p/+BTJxITGY2QOMVEvtakXhyUn8QlHvvE9YVSyXdAlGy8a7ty5dZVByqsM79aXDTeViSvf+QUhL2N4mMAMi4xu6V6B/p6KRdPXnvvjwSy/9aHXlX/0//sV33/4JNT83wf3Y9J2uFq+03sh7OND0aPBZBJ+anN6orWPXhTaVOne4wwVtxSO0gbn8wxcuUIQPf+nnvvDoE5nrty/97h+fW96az2YrdlKbg0LQ2babjO7Maxfht3/8HRqj6xPcOFNowVZzPZ1Y4YpEHEf+KiXup2EBGvk42NnW5V5dzlFx8QIfrrR6pS1ABhOjXal3CUnb6OIHH76TTV5a753Yn/lHv/6lSr7Z2FzkHmqarQq7wfUG58+1ayqb0fW4spxw7FRLRbx0iY+LRFE4YQ4iYHsOZ35IgONX+CjN2yDCSOkIwk8okNxu5Ir0BgHfRXT6+DQiaNxWPVUu3kPCWwixVfB2xG86HFDykJepws5dZyhNDCRM5BloMvBNmnJS2R6vN7XKvlVb04MpGgpkFkfAL1vnwmz2bIoZmaBOtGscLO44Jz4SbhcrjVyxWpzH9LP9KXCuLq7SdbHVKx2Wc8yyKpOlcnmGbptWcm3rDqPz8unpsy9cKB6brHE6lCJjtZnVWIogWWiGDmMzfMwXwWwMpa1VZV6iai2VAIfm8G384HIFLblGJbCiXfKBJn0Y5+q3eNJ6Jj2dk/Klag9r0uX26VBfZ0oGiA5TKTCOkm+JWNWEOJqFqC93Uiy0DDo3pq8IbOXVSHCOntiwZGlvCUlaMHCSP5PZ2Fy68uFGe73zK7/yK7/8S4998eVTtz5c/N73vnflXT65SS3VDVxglkuZVo+93zZDB6UHxOKRyxKyZedFbJGFKFAgLjYo3W53s2VYbatAkDvaue6D2sF78rRv1A6S8IipeNWwFggu1PMud0M3W3X81bUCr37QBs4Ua6CdP//koUNT2d62Xn7iCKGpBTr8Ui9oOWIX60B8WBuWdrAImHh4QVpnDdxjddIvwSGvUCv4rq+RcJoEI3EDBy+cpQpPNri3FklHMGys5qyJJ2BY6lO4zoutGetestPVyl1dKG9qpD2TSm0z0m1Jy9Zkh1xwtIBvsHIfa5nVNThBcGtjfaY6xflSNTuGP9yeSAnONQgYfgY1PQWPiEF1FgUm8DQklWKXYGLPik6vpI7Dk1Yai6ImmlQ2tuU6o0hbag8r+wFG+x9C/iYgXDj6A9AmMyHKtm+NGHUH+c2QNRIYtD+8lRO57BZIy7wbjrcDIdZoUrb8YUjR0sbTcgd6LquukPcSHn3uacbOjU6zwiaoPl9GBlOy2dZFNOlxUj9TCHJYLmx5yM475Ls1rDnTb8pfvNRfX+/fXppuNmd0S2GWcReOAyvKgJk5r6bpyjmmfxycilbmATsCRcnTyEFNB10Sl1gAdk7fTDmgKixEVK2sKW6qPpridKz1tv4hXG6voqfu9blLgTvYuAaSP1L2eS+DTt7qNfWPusRroYAhaMYkFMsU5HesHUGsuWzzpQtnHzt1uNzazGdqjBL9SxiccmZvTBlnAgmvsOjGeR4bvtvBLQ5HWzZE3yZrOsIFxPNWyPVmJydsyKZLopRrLAgBMf6svjwEVV5jO3H44FPnTk5lmxz1VhdregMLvrQ3NHEYuPKSVBF48eiOckQQdoZ5w0SvZtmSH+WipoRkFIKdE8RydQRbJ/c5SYeQ1E3Urus9+aOe2hvupj1yJ8WQI34pForXRt1WTFD1OJGneiaKVk3nzyq/nWWwTkDprW3UyR6PJIUMAlSZh6TFroQDG11dChl2lzjsh696HXo6k0d5kAAWQRYIujxoXvKIoMQjlfoQCxOFBRKFpKgTa9UmIRWScSdfl+51a80G+97VUqXGm26bW4znkQUi7qR7JXSyUCY/VPFidWbWohSbNAgKR0eSBK7bzLAO5dEo713r6S5BO3L4GG818WY306e44RoTguD0XTCNHIZdWgzH8fgIJ+DwNGTvMPguf8yFbM+GUQ5Rp00dCRu9PspQuVM4SMgyEhUDRVoq6cSMj3j1j5Q9CXizQud1BurfQ6JBjbb+WgVkbgB3AzAgsWl4QLUfYzZowpKoxAZIqYr1SZ0TZnOdkSzfJ+INMb7azNcs6fN1pB7DhH4iO1g6Fo6N2fTWBuAMXTT7cP6u5yQ2FEcsl48loxMhiSdP+yN0ImaEj3MEEmPHA3vERlJpHLJJ3iMd2lKcM3A01wNAAh5L449FKYwkVjuGBRp+iqSTQJo7+U3A+jVh7pE8je/hNEFMaYSm43i5045hBBO8KM9n2DdapasL051umQkAVzcoR5oT5O09SN5LjYySUZbVL9m2DggTG0S3ZpRHiUEGZMzKggVEw0GSyiq1OjQbfCqOvssoAaEu8wyK6FgRuyKo9DvWUaef1Jcgi9Ec8zj7bARjBBQhLsGQlsPGMICA94U0FQihnlGL0aYV5IlJJSFZoXcYgCwkIqIZwEY6hPnxTQuxSzQTwtYAUQuBa5CDIDYjB89GcbBDEqqv6JnRNQ8d3nfnzuqNGwvHT+ynrXM6TlbdmrLGNy3zhQqnnFHroBwjJsjuym06vv6+zJoeVzLrb23Nnnpk8ujx0mMH55g837559/bNo3/3xZncU9s3FzgsfbypXnuSb2PwVR7u66jVCj//hcrZh8uTlYsXL+5f1oFIG1Pnij29r2bfIETByl3TDgiWfISmPKILiPULHCSlElm/zJIZ4c18VlvpZ89mDh368LVX2awtN7QefeTgsbV6p76+xOHL6d46nxg6taHZ1j986vnvfOd77a0mSrtc5bpITcgoI4ZocMOqpJQxZ6OmcErahgcgFaamZtgBllhWDJIGBUl8KOiJGKfkYXx/dGhyYYgIOJxp6NJm45Wfvv/kk0+WM/Wirf9q9Oh9vY12KHnH95pLjniM9N0IJYE3NQnlwHeHnHmM4aNX1i7sTQW0IJOi10T59loQJ+hBpeLh0AaU1EPIkWFOPfeKmW4hz3eDZJnq+PqceCbWUiE/7Sa00JcW2bx7yczWu9ymV+y91V+7mjv+aJZvS0wdRonUQOTv9f2EhCjYmJD+yHlKxe6GanuITeI+yW+Sp73SOnfH9PqSTjWQzTQDIcWG0Ro1l9m1mUi+Wjz71KPT+2d+9KMfMcTllPCZoyfB/PEPfsQWju6+1T3aVrQjwrgZUdKFbB7bJtZNQSVj5R2NgChHdrjTuXz5MrX12OrScy+99ODnP8+dpG9/899z/wvjS5qMQk6XJDgFqntpauqDy5dZAt46tO/LX/7y9sLCn/zJnxxjpZW9l2aHs3tc34sJqmRyWkPB50VNNT12EoGDhzQ6rZOlNW4S4K1VXoTvt9mbevTxJ86cOXPg3AOcxdbpd65sw6aohVUIsppTFJAAF+Uur7/79tu3vvtdzhy5/Ele+GWEbuYVKobuuWarjXO+m8ePc+r5t199hQ/6dUpSAgNrV4UTSfsimNTMNJwwq8ycu8w0xIWdNM5wPX7k9PHjx+29hNyv/dqvsTZ27c03ORsyThyyLJTAlz2lO80SAYpcQvvcwBo1dWFWZIykmSqhyampKqnqW2sUB+N0NOnD84g5IuHej6wq85bG1772BRY0V1cXoFwpFrTcbz0BxEfE5hV3I0hBhS8dg4BjOwBk50VRu8w8AicWg1Y2zCkjNBB2c4FbET6YCa909XB68rnf1/jIQ0hZlDnZo4RUc4CLMnjs7n66QmrdjQY6yGeTHw+TPI0XgSJrXSwcnQVRIcA39Zil0bTp0BmXNLCwqBi/h5dKqyQ2kdD+Ak11OKbhqQetL6vJgEAGodNSt4QJUSuLeS09V/fPsG88tW8GvotLN9mhQuVqIJ5/mI5tO681D9c8CKSle0CesHdnTCgA0bdi8ZM1fBoKl8nqGuXtbVYtXKQdfGh6xgkMjWVswBETKNKMEj4R+BkGnLtxQRzVFkqfLJBxvuns3CM7j4piO5wadOnSdTbY5uefR5+PPjqPDj/6YPmnuPcur62tswrKJyBpjmjN0AhF4O9kKDm5S0rfqcEaVXtYsgXnuHqgUHhABoQpse9ulQuf6uZiM7HnERzvAKjZmDqxQOpM+HUn+zYNDg0yjCAo0uaUfzMVkttsNDwaOHjEEPJDagRIhyDmC4GAE9SDTeSMMN7O9TGJHfu1nRflVk7LT44hqoly0mlMK2p4JXhWd5Yx5gMBiFFQr0HjEZM4XMjmaEy64aUnyc9ODHUEA/DmS9yHyyjKEwn+DQyoGiaO/BL04X4C069nBN8DrkA07PiMJhzuPvjAPQo/ao+AA9OUP/OwWDAt0gKLfdd7uEQiO3KNqR89evTpp58u0rnrw2VFlruitBFzp8DPpIXZidEoTBo2M+s2W4xh+ktLGOGEjZSsQVULq4wnRYo2cK4KrNTJjRSWHu+VIU9C8higfXDLiRBiAbqj8fEhJkCXAfUScAF285FT67g7OU/LgOepp55imAdr2j7w1b1JKq1U4jyzaQJAwAAf53BSYR7+qFgb/pILOjpaA6qzIzBuiHSAEKZlfuyxxzAbWRdXr8mFtcu0EsCMjwQgg2CwIwQ7SsErDtQVZ/pJJwHHHcC0cwqUFLEedoKOE8MEYi85EiZhmuB4mPzshiOquzhSxXZbyUN/LV48imaqRXUaqqLmQPBwpC+dJmISqSaXdxy3t50aPk0uyFg4cCdiAjCsGLhIjcJiiBIjgIvhTs6TmAlJ7D0wSR3pE8Ya6c2ZkmCcwD0K34lEVs7XY90e3AeOwySUMhkNRhZAIoVPH4CREzSeKGJo6g597NqjJEDCD4geE9XxqDJN1BgDCfpev59tdsY5RSHHo+4TAgVaMOoXpaP31pMPP3jytPw+GyL7ERgDjuyagRSPUWPgeJL71JvjQ8SNPIoxnnxHTMf/xL7T3DG5C2AIQ/FAeI7ixcc0KdNJmJVHzCEqyYMlj5Y4sEnXKlhOP0Ef8I2QjxUYoUbaEfEcASDvUtPzUig4zoEBwalhVwq2vlToBPDTNEEW3FXkCIbjmCSIqQi4U3InqpY1jDDJPv2ITad8yMpMKkmwyy8soiRqbTphdiD0gYJH8+vE0mlHyAfJbWpJ3qwdk2wM5KlBUoiOvKiXJExaHl0J43RcPBG04zU08QPMlIQjCXd8hBQcUTbUoouYjPo4vZ7gDDVlIAct2cupXMIDHSNlBZfEggY198EnQGaZ7Cy+//780lL2iYMHjh//3JlTrMbwXh13xTZOv/fWW2+hA88RY6FOns/qLi/85V8+9fTT+44ff35ubu1Hf4lUkznNv3CQNTMhEB+Z7ciuKHIDycM5XpRcZ75yXO04RRRWauONDJ/yKlZzLEYV+pusIHmOkJlULE+9+OLzV999686qOll3njVJa6OjNDBB8exLOJAhSN/BbNFjXSSAopPoCmAkG6IirXTA0Tzbljtycf369VOHRRwKOJc/SRRkcKbuO2aCcI9fsglBkIbJilek5iRUcOoR1DsAITZxQo5sIIiMpnlVT18uSteyEUYxoQdYKGAU2szemqekjmoBxkd08AJhwGYk2d/aR1RkWVPuDh0//qssY/rGD/t93e4Xv/jFb33rW4s3F8HRl4CVf1tZt0qgBQMuH2ecCPhAv3SsWJ2yz9yFQacaYlKEhRIlDUrkJF22VFHzdK56cH1x6dLvfLP+/fdP/t3Hzpw//+L/9v/U5RWGN19788037y5cI9VhLiXlRbb15d/97d/aqExQJF/6r/93lZnJH/7r352pbc9VS7Xa5nZRTUl9lpesS3fqa6ykbXKhyPb2UkP3gSxtFynXWxOl17/9R/Ui5wu5PVmLlTl2glaah9YXDl86fPKDB6iiVftCK0mwkmKzS02uztBEHH/8+aczB/dnHi6c/9zjj37l+d///d//8DuvsuA+09U9jGuzdtGHJi8YnL6qV7eT1Ivl3Ob1K1//9p+ypN7Il7KVbMumgXxAnVTc44PzcyzRgAdWJpQhi9MmSKk8XeihgV878ijXepzcIiv9H7WXfuEXfmF+bn790uVLv/eNo5vNSkPL93a8J7NdVPXYmC2Rqf/px9/DuK/PnGAqtGkHfpitYt/WY1JUPvinpdQx7olKkavQ6o16bXtTdYbzKeo/JDIzPj8gYyLer/fAXOarX/25hx460OmsTnFwlDtwJ7X6zOW1SGi9lec5EERUcsdXaNXL57UQjHz4mJxh2BJD0hAAYWJCA9G2A9RcV0AYcaGghpSNen0hve2LvzQg6AdKpKI1IXcg4+PA5+UPj2VGimMag3g4lvHEnFM/NgRP7Y8Gww5yj/yM7USRVyUwXsG3LtO/xksMzrIjHwdr9z1gMHmaiKmrDQ2iNGNVzbbuyK66Iq+ngxpoiaGjFjdRdmGyff7hM7PFEyihvpWnI+Fc/JUrV5YbyzMTM91Mc2V96drC+/QExX2F+aPzBx89y2x8vb++vb49PXukBf9CQVWGVhYT0YuwzG1sfdNOQPP6lThaHpFKEiO7j6is3bfsmmTeRMTKYDC0kc64KwfEkCD1A1qaVCqG4NAUdTgqeUqWF5NnVxELbDqjTe9v2lS3hFXRgnFSiFzncnVMOBHH9M2RPNO4MppEIO5kafKN1945OFP+yle+MlVsTFTzR5489Mz5L62sv8QXWX/y5qVbt+5u1za4yrlUKNN+NDFiLj+xtRBfVHLFMCqAuw4gqghVkjAytQzshHUPwU0djElQO3N5elDqEQ5FIbMyxTCxr9pEha9wSLoguWu9FmY/MdWfnuWzCLMZrn1neVdbjDJbRtkyf607ohGNickaVYjgiBvchZcUKDsvrhCll1NKgcw2mKkZSGRkS+ZMh8qjO0cQmcR5TpMn04bpxDHJYkii1l7Juhm1/BSgWpNsh0P9eb7Bq+8NWqlStt4iIwNCohkT3q6V4nvnej+Apo8/Rpy083YtCjZQRpO0Do6cCDPyO8gFvCwuDXFkh7gPDvxDeE/KI4zio7iQn/ic6G0A8c0tR+CLAmibITDaaG7p+p3mdp0wuUKHpHVNWomoDSxVJrAlmkcMJ1/Rdi8axTfrV6XFTlALktM+oPe2Nduu3UQq14P4J1pX2GNThSzgmEvlYjgOqUxvIFAjIDPg4mFEevDBBx997tny/vlOlw9Vs3uuujOEOdZuDzP5mTy5biVHaLnFpWDv9GS7TYqHyshHjLdvX11bWjrQXpzpdCoamOUm1FWGfpztPVJZobEjKKUX2j1ONrKT7iUIxBeIdeGPOaVNAvrFvtV2DfTmdcFPuHQ4H9jvqtlXGtVZTAP8VluXMHOZIcvP0EfvLkmmUC5OTuuEkQjLp+Eg5NYYDTvKls47yO5okb7w9Nlzx+eL/Qa9HtMAfXxZHYltQ/nxZxUktiOJkMfWBPgcCZuzbXjBCK/Ibpx6JYSw1sioc+hneoIRZXmcrSnSAAEAAElEQVS1uV3kCxe5Ptu5nMFlc1dE+vqgx1wh+9yjD0xwcVqvXQYJg9HpPzWx4mctbZDV9B+aEXFBHVojRwB6xrCJHc5sqZXWmETWL3tPtIGWwh8NFXlBpfzpzDf7uxmOjWjUhkc68kJXBL63me67PPhG0CuC2vYgpyQM9ULlMeBrlhPaHEPm/ZzUQEcEw4QBwgiA/ChTduDOyxTSAHX2R31VOtbV5DxJYduS+rX+MttSTujYlF7cdZk4p1CzapHEjw+gNLUA3eOUmK3KyTdt6/B0QqfPbYG6PDNfnZufrM7pU/KhLmOraXGUAgoQxOlBzi1HMhPC39EaSUWUj7d41frqtSscSeEjVwBxkVo6QJjGcI9YS+r1zoOfgY/ObE+Krk9jGHvkR5RlONZGWC6s3JOScrGJNSNEMxJbSQRickDBSpNWlT3mk/uSKklt8iQPqV+TJPX8MwjSWinXvW5ju9blkxz12vLKYo/LSM0y1XKMOFlNOAetLGhZRCNeBlYgipSpyxO5rXouPGqEWCiRYSY7YsaEYkoZJk5NG3yt3U5brNCslIU4TD9JajED+0+Ddw6bNjyDIa2P/SL5EclHHtOlSVRMZcyGnxL+0US83iXg8DtKYyR698eRhIn5DxIEBJeRs5DM2TkK1c0UFlf21Tszvf4EHYpuPKQhUzPI2Ee+EfDeBZoDLqnMCYcIK0C14/ZovjzaVPuRx8SJB7pakDRvU4zvV6lDSUhYLwzB8BwswxAC13TYGy6dcRUDUeQ/CAFiolGmrm1oCi1xqgrW72g5gGZX3QGNJF+70rXKfA2MuTRLDnSijApp8Wh8ab2996eVcVriZY7zs4TVfRkomC7GnBr/gOj4+B6gAVJqxpto2/RNr2pogiMhdNT7wM86Fh2CzuicInnhMNWdO7e5CZp2m6EV+GhDZMkO2bCz2xobWGctyb0GWacEGRPVSjmXY0Y8PamlXs5Bg1bJNLrrS7XFt9mWrDzwQPX48Uyzk5mfr/zcU6wlLX7v3UqxMpftsKwxXVcv1rzevnN3bf8Xns/t37/v57984/XXV2/dgO9Bfa7eRVOnLmenuDO2aoSNAbDqjgVJjeWWBOc0GqpYp8dl4JLny2bFyZde4kuDvMT+jW98I7fVmn30bKXSvv7OO6etQZhvbvNy+9G7y8cnpzpHz/x09Z07Xa2oYGF0iYw1ocw+v+jZATK3VYBISDkTIMh/jdXpkLMFXit0K7UoMEjBAED4Xr7qaZPSCRU5GWOr3K0fp+jUWtoL57yCRtrtfv69a7cYbXFgq5ix3l+FGVYVMVnY0ceJi3XKBFyAGDAEdesOxw9mFp93CiCzpJVvRkWuufPcl5QZBGG4GoJZnPUdhowtsm5KCbQ5IaqZslJjmYjkrYI4UbP48z6VNQQgGiapkOGZnaLPyWaKS1czqzcz68ucZM+dPgecg5f856SE8mVvWvQyOj6VUB7kLkBMn4Yw4rkkafwRhEhzHP5pIbIEacBkMAllIShAa3SEulnmkRpdMFrCXHKF0sTLv/yV1179yXvvvZf0rFYhXRB0gdOwn9c/dZsFR4pVWjiRsNIhKJNKNSiKMQR8TjRoBlua+uijj979k5sn3nnnwcdf5G6aRz7/+UeefnrryrscENx+/30aAjC9BWEqQpPBpaqczdYEOK8v0pYKWfZ2GAVyMmiFN/S2tmgF4buR0Z5SIzePhMjAgg51DAH0ERmWVFh/7fU41Emqd9//AExOC+IgC4vZfJmFtompWTj+ryf/N9zfunRrhUvlqvPzvEr/+m3mgEu0Jgi21NyEZq6pCwpq9nrgsmXtenai2W7emtLNuQgAfXcgpx8Buk74TVB2/iULCEPTyRt/0KzV1lkHeeCZB1780pc4AMxL/U19Uys4WDhZAsiJJvEjawLuhGaVk3ldLCZWIQmjFjSpT3CxvGKVwCQNCa39CbxQY0yb8B/9fe65z507d67bW2ZZv9AtsXw/OVWR8bCqK6dKFtPAA5pqEANfw6AbAC7TRIHCBYcyJV8EeALiXRrYFKLtSuibqjx6Hil+rEVkdxIYAdxJGluIgRHExYkGwvokX4DmERxjGsvOkMyDyOAhaWQ9Cb6mZqZHkruDi8MT/qnkRJhzHHx3wFhPD8ZskyjSIhK+n4D25T94GURdCwmNnch5Bo2wZne3b9/e1KeEcq16ietZWHHU8dV8BnixoirBF0DZOj704EE+K9+eyXORVqPc5XgsaaHvBJ0FjzBy+oTdgRADHnbWAAlY7h0QfCgMI4zWlyHs1IMTdEAkkorfNYhmvAscxvDGegBzmsG32op5kNnUiHGATAh5QI7+1tb2X/zF94E8/+SDup6yqfFNqTT3+c9//uSZCzSyr7zxHt9K7Xb0EYxcQRfUcAG8lqHtHIHz9XongVWOYgcyTp2dOQMNWpKYiiR005QmtZtmBDj78M2mLpGgm8SBAJBHWpgTh/fR+HG8iyL2ST4NjnhZZ0kATOqneJkAjFU8DByncMryJZ4AAYcwkAGmsHECeL4YvsRYW+bQsyPhawRk2ReOdeUeC0li/SqMwDH1iqhL7kN4sDBXHKWwvq420+UhACnCRn8gLXAg5B8tafjR7Wp9ttmkaIDHtKB9hs7Iih6Bz5Csq47VrBGaXGZKdugjpQH7XnbR3nhxPFPtUIpmOyxfAlX3QO9Z0eXjxYlJtRh8T5iXkMq8OadvzKI3T+wlpTJUgaq8xnOX4HiKj+eneSXEB9rzksL+GZofO3WKtk/cqWu6AGDvMdnHE+MTYHu5jCcM2kBd3DO2sETb21xfojJ6ThndE4iVg66auxtcpfhcHwEmozJ3ECfgsZFRoG9RDnRIhHsqHmFEWg8Y00GVBEil8L7VE0YuBGht0o+7DXO8CXIZIr4/UmQMwDTCtCvFyJQhDJjymLYxC6tBS8sJDs4J4hP2DCAwoxEaW8iSEMfUOqIRAPLMM08zbmG7kVTWzfrkI7RLTnZQClBMt8ZGgb5Y9UJd9KCt9oTOS3xNPBILKSVqOhcibc0R+OIDstUm5+5sQbCI0PtAyhCVMB1Is3AZRnxHjkkc332ATtYUiVZF3CEJPNhhpOmqJ2GE7B3AoqwA7VRdIjnJAfrAQxoIk+FAiSgaLR6QgePP/lJm4Dgo+b3YioI1VogJkTTqiOR67PexTMySQ9CMyUF2nOh7gIy4xUbNwAJGTlwcfeHD7ATkNNPxsNOMpMYRgETJI1qEeHJP5UAsmkeMj6gYSwCI0BJLBhmxsWFPez9+pAbykCrvJ/HHx4FdzObHTU1aCoI+nYCNi+wQT4rKUF5cV9YewtH5kpwgKRwC0A3JaThOit5nENyRpttVZA3OJ+Zk9EPqyGuEoD1KA3twiSoCx+iE8VsUcse0nhElGTOdyM6o7cz9npbgUu3Ieg8gHDESJHdHdYBO7DgsocSNLYeLQSrwJW0Y+Yxy8Iw4NB0GwiNV0OhIyU4HkMvvPnBnBLYTcV9AA4iGOWnVgunxn8citwfwXeXxkaRjheD0JI9ZvhoHcohCAjVTAQIIwegRxoVkqR8Q4tM4AgkcqMRO07Chy6/3p74+TK8oDNPMDmyMDr0881aWbpjJan3VnNEZkgEwZQqcrFl2Amsg9qhlayZHm2vrjAqAEF0q6pAfx3swj5uXL2cuX77x+hT3+T775c8/++yztStrLFhN2ngsx3DZ3q7miOTd99/nEwKZ4/uffOGFxo9znPaFxY5OOTNnscqfQwggJN9bYugdcXKzs3OlXIEuqZf/2te+tvzjn1aPHMlc0RqXSyvfulHKi/U2Wrze2oYlV68UKRPgcTcXScEahcR+Czg2hlSUrFPATwISQCaRdH/jxMF0ykTRGyIh5+EAPnAibPGKso4R7CWbk3UuaQMDnmadntek4Z48sLC6AxEcEHwtM5kDLYpKmNiUcxoDP42ZDg8wLOSEWVjYWFlh/W1i/jCWBnAE7f+fHlEa2ZHVyClnUrJcnwr70ksvaSWEYxoJXujmwUVNRc4Q5fOPH547PVWpbKwrkZogXUgBPnNZJbPFCD9t12O3zJhRmN1eTTTz29Pzmf5if3nh3avvX/nR9PSZz3+OM0oHz51/6XMvZGrbr77yyuI776w0uH6ic6dU+uN/9TsMQK+vLLDo/N26Tjqv2jURnGWCWmeykp2br9swlAuM4cWpN/n9BmtqXXj2emtmulSEfr68RvMY5EVgKlHTeons/vVCZp3hrRTR+N3fqf6H6htvvKHz1xeeguPF69e5P6RrS8/Zbp1GhxsLwOSH9zUb+JWpulZApxs6Ow5f+b4DUGKuy8KJACNOILooQdlRk7OwaTLT517rfHsit1iv/dGPv//www+/WDlUPDj/tX/w65mZ8uJ/+Oa1b/zp43Yane1H6mcr2yT1xkSVOvyb7727UW/enjhiNFXMea19oRxJopMqVGlrb2lH7Nh/d31jvd7c0M0rXPCLptiVVMuu5knnIfu9TfuIX4V9HvY/GazSoHABA/XT7s3hjArhiaaWvA/3MhcunDterm9++JP8VIkWZbvXhsvEFDPTEtesYW2uGe/34koAFBzuYu/uS2/cFagscVbIFluBuMVCxOlYrIaZGCX5SM4siDuOjINGKl+0RQYefXhhQBq8sNjKIy62Vp7KgWkfmv4ohNRinHQu4vRhGrho68scV/DtSCq9AGdMkd0bI6RF6ySyuzhCT0ndVaWlQzBk7ZY3eaWDHKtg+m1bnijZ5ZObucIGlzyfmGMvd6F2yboQHbPCtavtQlmnoY+eOcYbAJ2KPuNJ2rud+uWPlqB56MSJ4uFDjYwW4Nq5UotjZtrvxEzYmZcNB20Pt5voRHkMBUTSfr5YQh3US8yQ25ClGQ0jXCX4nlMR9GVQ9WwCDypPtJaYxgM0OiMQRAIyeMuXcAqDtdn4xE3oCmexKJYqZdqK62aKfJgWUawuBxGoFdvNDJ09nzPN6l0Q2hQlJQ8uKT6MtRk205mYXK/f/eZ332SnircWiqUNLe92VyYr5QsP73/g5LNf++oL77777juXrr399rtrW7oXayJfnCrmqja4qXV66LnX5boA2JizBVbGfSpfTnAP9GYCIIc5+FMeGB4Lp7xSMMnF9lNTJ06cgvvlqx+xb9fmvkN9k5DPReanZwrtTvPGRzf5MkCWW6m7nCK2C1586JwciIOjNa5kTmWdsNrh12JDKZlNSh/g4XuAsNRlvg8f3U5ox0TOL/6xGkfLgx0iKgllPZYp+dYuJdQGFuE75CKi/SRdl0HdgTgGSHJUwe4aj5LK7rEFDSLs53Otnh3wpm3SIUoZZpaRZQ29zc5WgeikZ7u5tbbK2clSVZugntYzIn7mXMIRoLcAHBmJWImpKr9hZdg1aptVAJHQT/mlLdaTeyl42OmEcuGyOaVEBG4nVDPht8gVKFDOY7Yb9GL1jU22mnRsla+I9DK8QORpE3nikNLJBz/JzuDOitBu2/VQzdw6m/VMRLY5fztZYYm/MjNLi9RrqS1qWwbonVUK6jtgKO5+Pp2AaUyM9NIBvpWsnnd3EImRNG9HDh6an5tmX7nJ6Unsys1B1VYLzTRupx44c/bcQzxz6w0NEpq2TkxEMAvyAhrtTaSZBNKQvQze8WVUiRuElNNRF7QdylptSLe/aUg1mju+koxSMncWVhYX+zdvM9053OHQvb9lIVz+h15ShyjUO5Czck9fV+fq5XJ7u9KpFDmfVKmRqpNjXMG4Z9BSWIkjApBQK7EZukjTPKWB7GpeVC65LF/eXLi7qn2XnD4tCxldm6oupr3BSdV8hk9Ya7AgnTIq8FSF40eO33j71YFCehMmc7B/0z8AEpks1rBiK+TCvws/Xar/8ucee3g/X0etZ3ua/POJXRUsZ645ZKQOvc8QlMVdZEZgtqDQG4WokQCnsoGp28mW9Ik1mmU0RDHz+Q5aNpUGtaNaLj/18PnvLN4lCc13rlSmt1Tn3GlX8+3DMzNPnD05lWnTp9KGMLRkxMSY1m7sGbUEOlqVF3zwVf1QoLStbpVOmfZGcydvvhCKOTviM+6EKMLojwuhUazM0P5Iy9DKPomuWJoqbSaTO876Gr5KXxZsCwH0CDo7bDWLHjsZYDDeQScmEvTklHM9yzY5dYLq+Se1mvOT5MFusSk3K8S1uqasS61CZSQLWOXFA72QMq66RkDItELqCcXabAwkEqJ3QZW/xFWKJa6p1Avs9LcqUtr6DJe0wanNmYEsd+OVkBm6nKqjE6Cm89CxW5s5qQo+DBm99PIT6q5yhen9R4rlCiNzKiKxbgMS0V6AS9iO/KqwREoFAwNZZBTR80sCShARecsOYyCHJ4+e5CXru4sLOgQdkZRWDnxdbmE6wIOaA8MBRKLJqY4Pokg6Fb1L5DTGfNmtiysKqo6maxih5yQKBOnFZDfaRm14wi+xYq6EL0eeaRyRWcrTWwzA+c5Nxt9gU5sBxAwAnYMvTSVpjUDiyTAkHoxTTDxFwBF/SIGSJDJ6gwdC6GHgsAq5YXriMLBYmegQQUuxh5ciptN2tBucbWk1OdO6uHALXVNRg7rHiES+yili0eraaouN1XmycWwCBMFjI5mYPEI+QcCJ7EZqhOOe9NUSRP2PECRH5MeTuwF42EtHyNJ5uqg8fsiPogKFmhUa6ZBRp8hTqK5vQaA7Yj0CmrYjvj+OAEdi4+OOgXRaxBrBYSYIxMUo2KnDuaaGft2b7+xv391Xylf0xQHN48iT2kU7UYvhiI71/t5a2ucX1KqwoYaPSxgpv7TMpLc8J3XYFG4VMMx/1aurR6Plk9asVVfHYfBox9bJJVZnXMSI3oEw9CVlqqRodCRGTy1nJqNRh7BVLPrlxAvIpLJYtWQNicinM5QR+kEVkFp9NRRqiXTFBM0iT92avdxtatGBF7rhXI+vc3FiQVz09pA4ixzUjaWCjMb04/fkSgQJjK8DJZZV+YKZ2JYM9ep1DA3rlADZIj7SKAyA3lBHdHOMh8DnHtLKBAPhzo2bHz3wwGmUSVoTRfyURG0p/Qhv8pElPv3Q1GRL/ZoyDoKrkK9SESxNTzFFzNlbdJn2MgtbXPwLvNLj8tfeqc21/o0bl3uFh557bvZXfv77X/966+YaXxgrtiehto/PLPH2/M2rq3du9c8/tO+xxyq/+HfK77578UffZ1XtVFVLujN1nR2cbOgtW+ohJcLYgLQ2iafFgpe6YugUWh1GP5OFXnttrfbT12fzmRNPPp5hOW5mqnh45shzj6OH+sLtCtJbS67iy/QPtJnBM4Ts/OPnn13/yeu3bt3+fkuLdzftaw1a1cny/TBXurhgcaQigONIML4/Vvfv575UPrtBKXrLoDIj0lpF1r681ARBx2aQrH0Efdo4H6mMqlPU6IBxG2WhxQKu4eoXr9xaZirBLm+ZdTyVlYrDTdgKWVKhc3ZIvWMJxI0oddYFcBYeDhMDlTmyKnkQ0ozNciFjViy1jzad9/WzFSbK9e3mBMxzmVJHMrCeqNLpc+MztxzIGPbtmwDC4ihpkRAd+miKR2XeLB/6Etu4G5yBnVmsvjmUmWwpPNlew19/9dvdffvyD57Tl9KpHWQ8p/Ezh37x+1nVGo0E5UyHpkmVVngG1Z8MFLxE22nYaHg81SiGP1uvN4gyZabSpvLoSKHULPOmBlRh+DYapGkRmreItqLI01NffNFz6BRUYOkS5awcxw0oZtVeqcbmB44b2wsCRtmsRGgQSdOxAya5dlkn/n74wx++9tprp86c5WzgiWPHn3v++RMnTrzyyisXr1xkM2Rh5S7VkpUbSDEVgxQtnPG1pUxr+vWFFHOw6PAen1pMMxfDpHrEWDoAwlg5vsuPZsgdSYCYYjLf+MY3oONJ3v7296kJje06kMmSDpByC4kndISfhS9FBSG1w4akXF1/YHLjN37jN44++ODm4uJ3vvMd65Y05dAEyByLvOSLwfF6cyuWV4hLshOlJb9sZFEKQFggY3Gf9oLk3tHFykmslKkdQiWljo44x0QhaAY0quL8VIXzs+wLIhtTMCDNfkfTCduoYKyrCqzGjZoqa3WdO1naQAJEQdAp40eXqF1RlUlVS/QEU8SWkHY6zMPQJ0AUmNBDDv47Hacc6acVA34Qg8AO1dgjd/ZhN4iIzesANBrSnDZxMc4ECKMWwjjlSyrBOTwUtoNAgC9OEzmrYhHuAQdCBFYoAQs5cu4oS1Gs3os5SzL2Wj0ILEBTRpPzWjzaynSwBy44YRc3O1GmG+D2KDTWs5OSQSpJ5+2JWFlWFKBsoBYganUHDgoIsH///K3FVZ1uS2lbsqSckQ7PQkswBRrGTCUaDSY0B5RjSKVvY7vRNCEjwQwsUzGRNM9/qds2fpguk1wFlDSPQjAHkH4Wn/rFkt8bb7zFPvxzjz/A3ma7ptuEc3ldaMDdGy+88ML5p5574YUb3/vh69TxtRVdzsWbfsRWK7rGfbulS7eNIKsZ9pkgsxwvcXXZJnOUwQXAd2HYbNBF3nwi9c4yWwucK0Se1ZV13kHhuztkkM9C4jMZ46UnDACOXHuFfljygAhti9NXWLnHIJUvHGF7DI0/RBwCZnSqd0l5ERvhBGCEj/QqC3OOAGXghD0haN6wOJqlUIaVNqE8CKTsjTMHSmLtDNQI2+sutD0FWlSGnM4IOjhi8RxirZOApOJs+JETZ7DVO8urHKOYnp2nqWQTBecSRjmVIHEAk2D6d0dgGmEQNnkGj3uEJLOVC0sS8GWoSlqW8tGbv+vDWAn7yXOJgkpOMqBu/ER5e9C+3yj4Qhyfj/di272NTWSYPXCAA4kM5YDTO4u76dw7amD3S30MT6SsyCBCLjjbwm1avMlhFqEOSvaQyt4TTzxx4NChMTICoCzp4q/JmRKkBytAAtJhZ2uLLaKta1fI5gFmGiyA+kR1l/Yq0WSoEdhqgZmxOSjTSrgtAQgjJDNsVz9pgXvuCeOUREUpGGZD6+FAfIOrbaevxbpYD6Vigma1T6VM2PpZbazSfNm4RXR49Fg97OKMSIhj7Pf4449Xqxo5WMWVwboA4m6zC29Y6PLTlB0HKlCLjiROVxBvH8BjS/XQIcaxTO+0vG5vDnlCKHNEiKNMQvccWd9q2Te9mO3ZowhDSj/DjrRowCjYGDVZajDOAZVYnEEGFIBEgb3gnH7aJz1oTgW4STJIFQVJovhVW+p+jCUAxB5DSyu8xDkaTwSM15CEHutRjhMhyBzlj0BRGBCIYLW6GrQhG5+ybHMVglwQwbDoPYHU69pQ16pD0ikMSERVZLN+bgi1s1GB32INO3GI5MqM4kWxHe6PltMkzU6/0ME8kIp+ge4g3sIxjhvpE+VhGEUggbQbTz4C2UOwGEUAmiS0gOnWRn2BVGIwQjBQOuMBJ7QMEs0hXsti7F9xADFi7j5b1p5BvtlFC9asb/GWyTijqARYB90mq4EuWESIATA96n6kdY5pvumwk4Lxx6K5N99YC9JoI0xH2KWzk85mmoKH05hOBB/iDjdfiOONg6DobVBZHRBbp/DIj4u6txgD7J1CLo/HjAgMMCXqQBraJRxVPt9peHWACHUHZ2uxpBI9Hl0889Xj2KICdEIJmthePcXIKxi+6Bjr0EIaZ4iIjiEJwdCdguDDziH4Sa0diiY5DFztorAjkqWAgpMGReFEEiKjDA42PQjHl5a1Q6t8hn42socpDqaBVoywotSKtwIp6HBwPFU6XrG75AWO8GUFALFJwoiIhpo7l6nvjBWxQYAgKNbZGxoe+WK8xdAGsHN3UuAzTGQ0kggwmtMoKgEONXOQ69Czj7z88svX/+BPYcQMi/6ItBgSK4fMrK9/8MGZdvtg9cVTTz99at/MjTffrH9wBb7lri4pVedlH7hCHs9CGCAm+YULc3UIkgQcxvnZN97gBf8Tp0517TXwXKNx6/r1zp07YEb9E0YYUuHT7j3/PB8k/E7/znqSqfALvkLDBeNAUbP5FLN4xwYSSwEcUooF6zo44ogNpRz6JlCIcTECv7EfMsX3esDhxCeRJw5MEyabpPX8qrWwfBFLlLjsOLwYo+wAE3PnOCNFFL+qfS4tPm4kAQiWR3mewHFIxOMY+kjqvR4Z8PB+1VqzzTC4sk+3KTCcN8FCri28F4W/XXFJdqwhUFEGp8rmOk0wfL09U81UjlQP6ra1Xm+7aFXRZjZhg40L2ZRSpcLsif+lsm7S7GU1HOemPZxtLmbW8yWqwbbtOq6yj9BufP+tV/9fP/z25DTX6RznLPOlS5eWlhaQoc2eTBl7Vm0kOb4vU/qysr8OTAcGJgcG8bVR4QulsiIb4CYWwXgXm0Uqsy7aKStU8xcmlQ03oLamzUku9lXh2C3ohc1N5vF8h8rql2fUzzVzugbHaS8U6BBf1hcUN9CqPw/56ch0uKuzSxkOJ8L39oRy9+fNha8eyB/+8Ma3/+RPsjdWDuZyk1yPly/U88r15kR+K9v+15ffWKmvLE3PoKiaNZjShmqFlZQOLve40pHhPFd5cucImtyq1eqNTU42ZXvCpaKrqeVcA+xNb1R8toE5MYfjvieVgm7+zXEYibwV7aacyZr2qQ52Mvuq5f/iqy8wtVst9Vm4L0zoNe1Gv0ubVanqFBV7mGpNrH3yRS4/6yfqSGnLXjT4/pj2yVHyKFE4AZ2CSD9kDYdR4dNUAXE6tiuV3TYJQ5Jku8IIKqPiJ6uh2zJYyHoI+09kFwO7wm2yNJR4/AEqgxwpGj2b74pH/EQUS+sZ1LxfmMqs9g0Jm1YGchuy4KqQTJ2lK26MgtXciQcIV/vNUqvKGqNxl9/vlrUkXcmzOdrnAFgpu1pv3ly7PbFvdubBY3xjgVRN7EErhuiJKtTnrkYZCHJQnMhpMiiAo4/B8z9AAKlfMsH81latXJk+euT44q1VLgybsqqyrv1z0I1EqtwlmBE0JXmsyGu8trsbH8hKp4kzE+ZBope4F56s+DaDkzehEdXXKRBArb85CWN550eO5TwdqdNwgWvLBWDnxhztA00Zkmf7zIQ7dzscM51c27r7b/7g291Wk0HJ3L48fUy+uynVdDdQyxw3z5+bu/DwLywuPv3OW1e4DZ/vFHITTL6cmeDG3YlKt1Tc7uo2sY6OBHsHb2JI2bRgtEdhwBSgyQ9S4dgXwgquf3SDu+kn37rIuybMn5996unt7QazaN1+2OvNThcPH57N9/k+sk4UWmkEzak2eYWyc2pO20tHObCMD4DsIQ/KigNeQeXoJuJ4IFZBNdFSOTu9UKX+K4lZr/buSwWafbMxS5YUfrKdLmBSqrKhAW/fb8IeoBmKMaeby4p5XtNoYkXEcL8f6cWZPkX79pwk1OJdNq+rJGqbzaMnjjz/0vPsvqys19glnZyaphQsyYCRRJAMiOZ5DDk1eMrry06CS7Jhjyn5kwYBxaapuM1je8jmSUMtsKaTCow0nD9FeaVup91obq9vsnTY3FwDRAmQSV5oghdoQjIS1ikk+hrJTSKm/zrHPFXdDy8YHaKoG+jN+1DYQNm+y8tmmLY0NmvXm6VShcE4L61PUgu4KU2nOZqWXXoa4XvtMzY+gaEoeErDXYZxH1VEo2LAcO3atbX1LRvvWmZcf9kMy1KPPPkksxAfn+yU0bSmx/l8Eggn7KIbbpGUeYfw8QiZYEZ9d6GzgYln6ms11kSuXuObg9x2RO6miGc5TacuKMaBnThxmFj1V13wdmlSN553KtlCq90qUROpuDl+3QQwUNVNMwQIaGYoqkbECVqYVpdyyUFhbYvDH7yCwflIxm3UC+724E2qTLPV4P2XkWvgKU1dBamNXo30qtWZRuNuPltgXIHmEXU3LasmUkOtqeddD8YJX/jcuQdPzOTbKxzHtJZN5kCPrg4IgTl8xeBD32ag1UV+/lSq1tR5p68+Qq9T+BiGVt5e45C10E2iTTOCB07MPHXhxKWr19mc2VajzbmFLAsNJ/fPf+7h08UcXy7pFmnd2fMjDc50BEn2eLyNw34TMLmDqN6ncwevosa65A0KzLUHuYc/MiOh/mjsGHTZ+NMhnjyS5RG41LeLowQlRIJAyJsQAwrqcfJNXCejuqBzOm4/gkkx2BoaT+xDp66UKdewcFTE+Go546NYSELg+pE5yelIij85f4cqLc5rNxFIxPkQGMGakqN/4848s1LwpDHw1bOpADm8LuLZjN6A7nBUjXmBmh/a9Om5qan5I6cevvBUyzZECiWdFcIg5O/iPC9JpJtQuk9JYpJfGMWcsKJBj/De+28x86dxAyVGDZNNEie/MTbiJzGfzS/0E8rSHtaFo14IvhsHqpYaYazIiizJizSi4uHdA0ogbb1qt3cjtgdcgn2ihHvQ/ARRrgjyQ4YbtS0OAy7eWag3twtaILIzaTsRRXhyTVrlnNrKcpTpgNoXh4g7pRvA0Gd0cDdiAuytSpd2uAfxOmsFndIn9hnp7xYAR3xjY2F4njX3Y0JvkcKj4Uf7GUkOjkGUPyth0xKeZYxRjHOMvrcVan/kdpB5nL6jRh9SHr4nZkwSAyNJ4mNCE6OgSCRV3t4n29dorayu729sg8l3u3k9jkUI/RXJYKISG8MYBWWXEtUig5yqj91tKs1IOW5fFkvvgqNqqdn1TbiQLcE11vJhsVZOZC2yPXnOVFoFDak8kNQsAdWui4XJwEzdH5JbdIMt2ZdgyKc0YO0q3bbCQJIhgR89scYBcuTXj8AagHcnyBx3nBKTR1ekIyVSwV2TJs5tIrb/WVzwLC8Iq0adNwIRPvQdSZkCEQ1yYZDE19jSrZc4AybKT6gPP6MBxEE4SqTL/Ozwwf2b66srd+/um5tjCiUKjGX45b7jhDWUxKJS5v3iXkd3y/ofC1SmQw5fdXTTL3qwjsU1WbLwRFuc8nY867H1xc5f3Fw/uG/2+MnTv/Z3WOStdnRt4/5+o1/MMtnlXtUTzc7WG2+t3r756LPPZp56/MTL8xsnT7PedfvqhxxtqXNF4mSx2srzLg9fBEGwCVs486sGevZZPH0eQmdiNTc7vLmR2dyo3fiwxsFk5jGM1Tigu7o6x7v9bIRnra+06RbWCGSWb5YVck/M5B566fHl776+tLKx3VTpL/NZGK3aa05ESlOt6gKlpjKRFWBOvTI375UqtIJWUCr2YDnEKcwZe6gpAbx4YYlYrys80mBSKDIDEZUT0GzVPmLGW5LMYbgyQGcZtzv5KzfuMJBjuppntGzOeOEZcUF8BupWIR9nOIojLAwzbH4FGnMBJxxEEb76PE5kKV0/xymmIquTElx1knhGhCxhKUv61IpuWWOYqgh0ha8hKFp2PvaL3sKTISQSWLvh56CtUiGgtL1/W9vtk82l/MpqY/b2DMsvh46Rk35xv615lyWFvafia2X2qhy8raSMQjjib/QTZp/Vr1f1+6EmeUIr5OjJjIynZH4nnNAiOU7Kt8XL1HMMYjxMEtjMoVLhgFPg8k3LUfOGLxAzdoqk2WkwEqqzzswdvnZJ4YJenOuud3Sr70ZRu0NbtAe82L5w+4MPPtAbd1o4kM7twhnVEFmvORLyS0+AD5w1R5oVIVhjyjTF0RRrxUDUAEJFslQREgNmi5KZ+31gwRfuSKj1D3Nwb/d4SQ22tiw1IBkJRFsfQD5lyHNnb1hyqK3zm7/5mz+qzkDzvG2OsbZL3qnvzoUto4XlpRLzQKuEnmubZQQEIaOKnHbzJia0pM5xSBYfmSUKbrWa/BKGF0RKdsGTKp86GhUx/4ilfsIRBHM6CUUAyNxc5ZlnniEAJFdJzENxOvUMnLTePVsLRQnaWS31Zfb+rKquazuULEl2cirBhl00QSAI4cRNVjxlxxyxqIqgb0ISUOxYI+XIRCGJh8d9pwnBSHwEB7jyKreTcYxgJ0aYJuiU8T2APKJleTFY8DyDNlxSr+mPYDo+GRCeaSZJLql8rKyb3/nQop19ptGkpFaX6zo/WGbftDG3r8nkilUb8Cf3z7FbC03LlCi4qC4EjzjjE/yknfWY4IPgUvGMALQGLIBe/uCKNwtDqMMPaS4wGERaMNHzAPyxQiTHobpkBGT50AJfkpeUXaUpe34pXjLl56B57RfTUnNhjnYs5JdqAn0zTzJer7dfffVVFPv0hRM8aohkTidGW1wMXeLVV06af/GLx7mv9p0Pbv7kJz95//KtjY1WpqLDdBQZfoM5uu4095K39CnFpOX0MHkMQH4ZEfZ6XDLFAjf7Q5TyI4+c53Do6toRTgfsr0xaq74lfJXY0MwKOsiMLSpHkWYgHX7QjKKsvR3B8aIMajF06KQNwLUaEZRcw1k5MFmDdgoSzTvapF54QpeAMOyDNPoJYUurYgBEK8QBf1o8yMIOn1jyhN9OVlfJKXgg0zZSXmxEc1hydl+bG9LpZtidZv+A4vDkTlbM1Czw+7GdU0AACIZ+jRHk4LTFzgTBjwlVtnDv9Tmd3VhdYenZ73ROLXgHIp7EH0L4kwmdCIXMPpJEkyLowzhTPBJi2zU6942N6qGDLO1lC3pnJToQ6HLSIsWo+w+4HnjhhleyVtc2LWEodydCneL7varYf2Oc8o3DxuWsq2q1aIG3+Njg2tr+uk5a+fCr27b+2sYzZr875MGIoEhraMxySn1t7QMHO/jWH3kYxp6EohsggGzy0DcCxA6xJTaowFGtMkeYYZc3dFBQyScy6ZIMazEwA8ZwoNPCgBMRnMI9ffA5zP7UU08RwEHBWwFrkFQ7cMgp/dhV454RcmRyhpM1nhZexEYfuUa4E8tYJVea4D7E5fUtmqO6vQvCXWfIwJYr+MoOTQRzT9PDgKBl3B+dLEw9YEAXQW0XNBRlBQ4CsUaK5wE+wJjciaT9yCUGiAUfEg4xX7qKRAi7g5czFb5bndU4DxNrEgZuiWBp5gobqRCIcZFXhCT0IyAEUqwHUU6TKJoNijJHw0oLbJ/3iHQI0AJ7GsKIB752CZmeTU5S3+cPH2Hn6dCpM4R7xSlqer0dbov2jJOWhOAPGFsISOQSoxziOhlPAloayLsdGCH9Zrx12mPHyTp9k2dggaDhAOI7ggfSvmchIqS5R5nHA6CRJEEO4RREKaI6YMGoXixSYvBorIUFHSwEG0oIjjO05DuAPxvQiOSfCVFo4iBFNmknb968SR6tPg2RdxxAnnd/HIStvSXW4FI4gbSWIG5kB9U8HeucBAnlP9CwFJ5UzEhTAWu3x4kMCX2vB5NWGSf3MIro42QdcwTuwJhq5BE4EJeT/DiaIKlwAJpWPcqJJGoYqg6R0f0HnNr94wd5UkLGtIgNNQbMDkFd0hjQJDtpTMKCW5QjuCQkUZ9lDrg073UtIQIaQXzPeSROIEExrZo6B6CEIPShSS2NXJwSmDEQOCZJElZ6FkFjE0rLujzgohnwE7SkIvho2UX25WPdhUVPrcZsaASu58RhGkbIiAbK4uA5ivkigECOFKNi7Ai+igPHJrsFDE38WGEA4pyJJ8x6OYuYnL9hfDUzP0NH46sB4pQ0BT5zoVWHjhZJzREvBQkSHGQ9RLxzMb5JtFUB4N/97nd/6Zd+aerYMY4c1b7+l8yjoYzrtDWDy5f16vlyt/vOq692l5f4VtbM6dPPstS4b/6jy5c5fyCO3t1ZRqAOX6SVKAahuH2O4NzxIU7u2lwZyVqHLRhx4RxC+qkRl1aq4FX1Xpd7bNfWOuye/uIv/uIf/MEf8K1ldOKkSB4Dg1ylQsxV6X8dDUwJlhibK8dN0ae+Ac2SpzEjPQOGJxLaq2jKAmEGeczUrl69ymCyXNXr8whPlHNJKAyal7GoBOXj/6IuhMBn1k1+uy3O5g05soww+FYKoYIj2N4zGuJJMkRopwfRseHQ1sYGltNtdDjCn5+Z96U5pXAMm21p6K8i24nQ32aYz4B2yEF1et/k1Nz7zNd77XdqdTCKdvBJfRogHVVhMUb7EpwvA8KREdTF1gs+554AcC8zYXaFKIw6Z0A4KVgo5kqVjh2z56OSKiTba/KrNlwI66u5k4gnLp12mJoFNqbYYdGiM2XA1q05j5Zvy82DRwCxz7dyJAoqGoAoeVi6RnaRAQh9NkB4ZPuTmt+rEG7bEe5c2YzS1vW9tRYF0RhzYe3fG7FBr5/gjaZwar5rtFXQOehuj3PQmeZMYeHGxcu1Jg3HI6efZla0sdXOl7hnEM3lN4v595bubFQnkHBlUhNCv3PRDyFy2BQ4X61HPm6smeCWyFxOq8/1DVafud8UIajidtqQFS5qVG+qWmV9Cu6vv/7a2lqjYpuFtOsqHpZ08bQllpnaVst1ot2fm5h47sih0p3FrazuxNliP5A1nYa9javNomzR9ixQNtKSR6fjeiZsW33abQOSaGavX3ZhPRp8MkuYAJIjoJoB/UHHmgZTBL2QU6Ycha9Y9VJWyNqHQQZ1QnjGn/ylaIIoB0RoYy4Ck4Bs27G80UwnTKICNScbScZHhBRLY0osR2Vhzvv0QFAWeWa/BhyTc2BUsMNeIa0rGrzj5M0DFkypnaYwrgmFGsqjf2K3st5p3ek2+Mvny3PHDxaqc9tlamC3MFets6BUnfKzip4Zbc/SIGItqs2mJq9Etm3gFueYfjwaFCsRKyBdp8ybSNto+BdffvpHP/rR5hIf38usVCRMutoEzfXYRwlaoVpH/aTP4/gAaBBldTn1KLUUEzkJc94Q3296qjRkAv/k/3iIIcLv/NYb/+7fXc42we1vcz2MOYopKSk9G69EIDu7zUCHBq/FmwaFSXrGFhcQ1GrSv+6lwuV//ue/9OhDT7Kw++ff/vY777xz6+7t//kP/rJZ/xwXDeUyG9AsZJqsX3OokHOOhT49zRafRT17ovLg6Se//MIDV67fff31199899rdu9wPnZmqlGZnp9HnRp1NI45/SSoE4o+gB4B4K6M42UDm2c8/x3tnvGR648Yt3kHr8SHEbIF3Hq5cufrRRzcYlp06efDk8aOn9s2WdBSN45Z93X3aYXVdSzBe3S07yrt2j0LNCjUOILHBT5WS7MM0H8/m+6P7KI7L0UxqJRcqnNyWOJJBQj9NaARZ/zFJ9ABa2hdrq/sABfeq6w/JJTwGV33UR8t7fdaOcbSKGszo8wUM1pQB3ReL3dvuXa3FRQhbXLuxvLrOKiqLcdduLHAZVLGkgRe7ZOxNsmxCLuy8AKk1WiQK3yUMInghJSpSWteJ9ZheVnw4TkB2+3hXqDrB2G5qUl9I++GlH4M/YuEqaNOJ3v/scaba9u6aHRZ5l5cWsA2u8ORqG1p/0vLmlokhe6Zs8YfqkcXt7aW575YWW8fibf1SFQqHXvAram7U32fazfbC0gYbCbN0XDPFqQpa4ksJ5LqljRjGlTqjFySxm7I5XhMe9/zRqEKbi4xa+489en757ip7A822UTNJ5mZmLzx6vsilFMqJpALZ/DR9h6chhvIpvDASsLFHMqYQF6qWsssxU/ymjs1kapvs/jUWr9bW16fam9NcXcLRHcpR7y1ocdqlGLMrgdmMIR658bVzm8uVe51Ctj+lutMpdlTuPb3WwrtZoqMzo0qFBUFZmiCVwT0W02UUxE1n3bWtbU7FdNTKafkJIH0J2/F06byFwTotho95kRYHX4jSp3P2GTOWMfD17um5fuZm6H8NjfYQOEEVtTRDWBSQhvpXtDd7ZgqtL37u/Mm5Gc6c0AaRa04gSA80IuhDg0heUYCVzRKTiu9kqYw4OJAKZzzJsnU9/oCoHOJnTGLFM1Munjp8+qEzJ7X0rF29LlWeadjJI0cqGW5G1B1/TMysKkkGaLhPQOpUl+oQsQtRbG0TxSE5iWhnyNjiRD82WbWNQxBFCovlOmHru3kMmkyziFkAiKORotQIjMCjSIY18BAIpmKmBk4ZtkM6QgCi1sD229CY7/ir6E0tlgDLUJadFwGO9ejVtSS/ouLNgSQaOKjFB6UKdS3CxHqgK8sLLTKaKufLtLEUAfr3c0AkhZr3QXnenMxnTh08xELzDB+jOHEif+wCW4P9yWnGmUynhdkrNJrtrUabth2rtgN/A2FUBIkjnJaTcBKrzJjYwU9ShF+K1GMRjYuDTx4/wdyYzwogDxMeYsFDz8k+5kjq0UdIOTX3iU49BsljVIwdKf00UZDHY4GE/CY1QklofLQmozqiYmQFBxgP2EtKenDUutiSgFJ9UhdzEQN7UBrPwh7IHzcKASh7ZZvPAq2uMmvkgzd3lxfoge+fFAbjZX3P7OyWlxE4j2lSaeO8h1Q6SGPz1nvgDUVH+tHynbuPENKoasqGK2y6QicyD2qZ8FGtDstqoGWyeaxXHIGdvni5yuVbgSQjJUcQ54DrgPvyESmy2C1BIvZo/EjaYobZT3aeqwyWlmbsxQptrGrJgnqj3iemj2ECniePcjj5ZxPVjkVqjYJW1lsiT099A9n0JQBJiCdE2408UBMRu8I+0QapdWYGHN7jSRouzNm6Emu0jTuJ4azURsZGAqZy9VwjLkikEawhh2g/10yWJAfdR2w2Oa6h0YOgHMM1XwJJ0B7nBoukEGPrJdWC2J9fGCgYZaTxj+llVI3SlqfVTDyExQjuwC1Kj0HRYoIQGojouDNOeZGK4cGEt91tl61Zhtrs3PTySvHO3cX5ffrQunpfCa1GWy5ZYfZKwToIooHBow8oCEdt079rpG2xJZs8kEflyFQ002J01H985Vbt678//Uv/sHrkocaXWxdfey1/Z4V2o2RfuKlyJ2mnc9yuCF9/9/3Nj240zp2ee+CBzPMvnDp/4c7VK3whfHNlmd5wX1utTdX0zLvmaEA3X+skNmeeaMq0Q1/RccHunC2cZXg3zcY80nOvaNljwEQzXkZmZNSCQZHcZKqbmXKn/cyBrfzpQ9ffvU0OeP0I+LbdFjLILNmynOpr1+g2m+fyg3ypjLoZ8CGPm0csSXtEP4wIUa4XmSiYhohUFmRLZuGwwwHAVyHmcywrdvk4Ua7U5rmtS2JXt9sffLR4/uzxSmUy32XFQCaJb0nlWX6hCSTMwmJUOpBOEuGSJDVoSeAuj9aS6Nf19mRdRzE6eo9cSyoIUODbE7luKd+t8J0ozbPVd0YWUn5KQicLUGMw/Uh4t+FwFtjqoPVMYMhN2O12Jd6T3u43a1vNK8Xpk+cy+/dn5/exoNbLTYl+X9/69vPjLLCRygdpjBUVFpnP3iH+xyfqssi6grP8hnBS9exxIPWuC9DkmZ3/bXsthdUpkvkCdNPuRbWVGW8IKBY+TUcFFilVFnyn7zo208FwqGC8vk4slDE4x8EI3UnRFqUmjoBXBipfYjehabOSpeqNKWhMX8bd0Zw4ZPdwjhMrJI9qnHZ3UN6R7I5AyKThHnbyIxmh+VAvY50EO10XmxdPnz6NumjIqLfQcYXgIypppflUO63kaIyb1EulCa6JZIHYPufoZ5/Jj0gbEc327AQTC2eeiiSlEt8kUK4pBdH3ssP61Z4JfnBmhikBRQlriBOAGGEKBBx92CxRi2tS7BKnesPZWFswhxhgCLojVRIc/XUZgIqL5dfDnhWIONx9pxkpI4P0Y30LBAQ3ni6bcyKhBywvHtzLj8QNSf2lY8fA2OOgsnlU5MgjqVwYlZqXi8kzQo2omE0Pq0QSVTudSNZJYQhQhhhwqLG7ePTo5NzcXGFGR9YrbM4Wi4xksCvKBQrWsvMrR4+IczoOib6AlmMnG+E8RnzV7paWNWH3la985b2fvLmwsGj9HcIohUkVwpwodiDw+MKOwjZoQFSEHyAk2haVXZxJItv2cm20MiePH+SLqxy4y7UevXHjv3v1+82JMvs9whnX8zhVz1qn2fTDvDrLbB8oYGkS5AP7D5w6dYoVN6bEHPpmwf3ApF44+Pf//htUJ14zx666+mKSmkiyAxqpqFrSvH2088KFQw888MBzL21w+cMrP720vLzW3l5Bh4VigQNYW9u69EaWO6guozJCm0kygrGsyck+llDXuF2lXvc3GJCflnxzY4lDQA//R59DgJx9DIGXzpEwk9NyPKWqgUuiaAlr4TB0tsKNwFH2pkYNkSgze2NDoURcNSJmvZ4KsjhIefmgkBFqJARhxAcnEhR+QpwgYkrDNlIELoPJsMjL7n2RUtiua9sMBHxYETtRkv23+zmW6W/dXmJJd3WjgeZPnT6HVNdvLXEfOgvQmC5Xd6eZWlhlEIEeMGnVFHsUS6MEYAFTd3yQFXlYa6alpRpSRqWyWk7fvgV53IlaUlNoZvmGEsc3a3eXyWbOPsrK4n0Uw5OHRzf6FEUXLAXYIWiy7wCPoBFewMchAGk08LlfQidQjhxEh2gCTHfJ5ASUT+JMJ30OQT/99NNcwHf1oxtA2ElFn2fOnOGkPxr+JHQ/izTYFkbtlFRNbKlRuziZzNbKCpbWurNI9ZzqbGIYGAOSd9kiws6toqEjT6s8Dg+709JZrNUOUyqGgWJTCakLZoRW3QhaLXPLVCow8dUdWkfDOuzGxqZaIW3eGdTg2BhTFIrSYQgMPowGj3YeBDgQPxnqMoz76VTptp2jQFxS75RJRYCRPQ7WOAJArCEJ1QpGRkpIBJ0REKdArMEHNcIfTWrVfbXY9rG7fKUCMuubqn1c0G9thfHSDI5U+CC4748iZVURds4XBMdxAQKcnwSBgAtgFFRUkSxhl9lJuQ/QCZofuMQorEj8zHnPG9M60HiZ2JYUoHOMaOMBcNJAHsllVCZ6I3aEjh4tTUjrMiccAQZ4QlePNlaHGpSdeKVQ1sAC87K+JmqGFhvI0WOHfvmXf/mB06doJBtcNcT3DHL7oEf/RyzUKLW+fTyWY2XYLc65JDzDbyQLUyuCUKaEgXgpjUibpjBcHBkOXLOtSwfKAjSpkCSh7/pIJ/WwdOs4Mc7ZRaYeSMsWo0YSRgrpgCNHOT2KRyk5EcozSxTIjh8DEWhw0mlsgRuyiTS/v5IwwkiSxI08JuB7/1qmhEaAwmLMhiroPWVCf4OdxE5l/7OVNBqDqyUSH1G4w12SqEaAZqihd+PRDYUtYXAwHXwsR/YzXIJgjkMciP/JnLG6R9L7wUmToNZgG/QRZMGc7BAgbrxGAPS04HgjCrsRjjxK4bbcFqICqpJawqQPS8vheg77hKmIYasQQaeWQolByewnvBKQ8McpJLH8Bnkcxwox5NFmK7aFrDEcdFTe+IkGUjRU0GQZUs6LR4XNpQMeDr43VQkaZElAVHAJdbFLRlYJLPwGpnn1tuJtpBgUsUDBW4y8wsjXbhjIgE350neMJPdEGgkkfd8IAgSZhdGAOGWPJRyVQBievNP27ne+w2Ej5oHP8cbnN/6ckRWrL4pjBZNP0bDUak6HXS5enFtaqp579NipUwcfe+wgi9HvvsMb7Z0lNVCMxcAHFxaexLXNxIxHCCItECQHA3zxjyMKDqWwv26vDnlmeU1IpHJZDpNxGSTaOLVVvHb9Gix2VIhzhKCKj3vhpqaYKkITaq4rUUsEi2HGay4DvlNI+y6tpxJZJ24JoMwcV8iWDkxa6evFHoN59n2Vx8Q40wRjOE0tAkcCrr0RoAkjfcasmFRaBGfeiK4sKtRQKBCLBhi064BfTDNCdPjRZRuG7fwUc0E0YSaPax99NMvHnM4VmC32Srq9AP2Ir/spHe5M8V5Qkbq3G+Ak+API3qm9iPfGSceOLEDTvAYz6h+YXWflIq9viPHpKny+M2cp00msOe7rhCZ1jVi/NY+2lLDf5ta2JtXhlYJOlhGmVfA9gX7el4G0Nw0ELtyIYXkWBbVJtsKpcHrRWXUkaMQ7BJ3+SCqA7zB4PsJuQ2gYEWmHHQNwYKybD62JhRdnzjgh5y2tnydyauHWFeMsymqOJSdrePhus2ntANzDef/GmFG5VlYzjbym0OVWI1/IfzShmv8f1q/N3974x0fP0WocrckQc/1yKT/FXbG0Dm32JqkhOgnXa9mCWk9LK33Oa0xOFNm42qpv1bZrdFm691knMU1ma7Gtv9M1zVQt2iM64Gad82q0EOp9ue2IGSM3O6LVIh/vyWZPdrtHZvf/J+fP0bhz5hkazdw2p4dYiIaqm50dcVUhAvE2zkuJ3sk4q4y6nJ6Ht7KrVPpJAiOPlJZIabzDrzBBiG1KRAZONBpwHD+JaTIooR/oEY/EsC1k1EznekzEEJY5S65QDDgOfgxYtA3CrFUF15JGb+QxwncIeGvrizUc6FSrZwoKJmjWRTukpsik5ThTkENMkEEeIdsn6rNXh+sWZRA6uamDuuRRL01PTRY5cmbRbIRqpQwHunyTlyiDmaphx0lS9v5Qr9mMdgc17hSONghNPyRssVfMrocvurF7yYnrntrxTP3OE48++uXHvkwvvlJdApCzk4+FfJclqo0tffBzae0WU4Nak482ZDa2+vi8bkFP3Oe4WifDCWD8FoWq41mcF+RNCBEOyrbK7wB25HF+Wf3hgwp78U7tz5w8eeeJs5v7K++//GLuwH/zxP/l//DT999vbFhOyS9VwHMdy1qJ3dmunWmHUbg6xWaLu0oYVfj1PKbJbJaBxd2MloyXl1Yny9V6fyJTyqxt3/hfvv4K7xax9l3JatGzKJ30c/ad5WRs2+pSCNlyNZ9/+PjUI//x1371F77IaejvfP+NW7dWmlu6ImLb8mjDsx0MFTMhqlwo03XxvUGywFInQ4det8jiEEDqtfTI4mCreWD/9NkHTnLRhbbKYWvdvLbarVqxrKRSDWXK8pXyTRTOgfi48CiYHBxxHkA/JOIxsWePFQX0hQZwHgUO28ykUsHS+lmbwC1zQLgtCwgBXDqALTpwxGf9kaaGPILMWUZYqOSz2VZ7k+tMthpemyhkHePgA4VssLMCsr65fXPhzs1bS1oO6edee/2NPJ9DpkXt2sdJ7Cu9szOcjGFNv0qm4Q199ARllwrpPYBfb2qTgIaUC/enOOFcLrMYyjIKvoZxJQUQzPClgXaru11rNmwhhpt3Y47cKvKWU8LwKrVaa0uL9ZV18ljptMmm40AL/khAWj+AQLsN/cSuIklHGTzuGEqn8kwlxRvQASrjNONWzeGMUfiWg08PQh20jyQUbNC/+dFCfqNRPTSHNpoFlWzDxtbtgt5O8DvR/CqtEB5hOSwoSrOSFfTRhx86fvTw1PQsbQjVjYH14eMn9h88wCtYEPQzMt6muT0PUxp/sgo2Dt4F4icRujneZ8CglBcugEQ5fpskH3NQVjstLnbprK5T/bdv3Mp2OvMdLXdWstq7ymbU7pEIRGZ5+Oq6YsOb2BXwxKnBpaSFo8mSFjgp7ArH4PN9tA2c3h+f99JEWS0l6IORiGByWA0bF5pvb9ZbKxs1bkmn1tOKEc1bkaDRVLRpb/tcF+QzABi7flSxYUEu6Efk57UpXqxMZHnFwSZpCRfJmQ5LQ1imznJxxLrFixovff7c8cO8EqI6yx3KqrJ2VAbl4BCU2srX4hka6Q0ggwDU+Wi+cqChKBzERT8pdRHhyARwUOWKd47FV8pag2akWeA4C8y6fOkgyyiqkGXYBYiToaqbaAH1KJumPiCmdXWtBBnNwg4ZYGFRHP3JMF5Cn8ALtG/ko9+lMlrBigwKp3VGWBdSVQclM2SSzDCRYkWTisTbAZSaEOGCb07EJVLUAJKAT9bhYkQUZcIoDSB+iFdeTCrTjcLCUfuAffgkCtb8iQbzVjLgDNV6W68pIqDrSJ8YoiVQSQka4wmYUC6eRCTsz0lIYENTrNCD87YCn7unOWLPFEAjdglhJm2ynX/kyJOPn+T1SZqKYk/tMN9qIj1XcooKbRuqIPPIwMgmmGWgv/cPWRhHQHUyBjRuBuZhR1P2pUONDfL5CheAXLl8cXV5uWo3QYNjeRTNQWZN85bcNeOx6CxYCxXeiaeTOOTT+CbqKAEswWqVS4JPFqMYsmZ3JKNEUCkDIIWHlQTOjnobZfbZPY9w5BHaH18G5dqqB81wo92s0+DdXrg+LKZrZgCLXGJgEDccAiGNg9m42CMUI04MQCYd5jFtcor1+mKlsIMqdrJhUvk8mgDOKSSGPSRRNHKKG+c9ftCwqtSQJTscswHuCZUmccFO7IBLOAdtLY9JAilrbZLMJqTCjE/tzahTHbxPhwIjwXSSCEQDlr905M5hPmRBW1LoNTjBmV9enm025zqaCPT4CJLaYz+jrLSRqWKNOoE03MP4SkXLhtroVAjFImM52hw40DPfQlbiNLU416RaeJWFv1Vj9s8jlTcUkHVFITxUZKJrPbhXZc6TkESaVf+SksQgdAP8ygbohAJJAMGF8jVV019iAEF4q490PWY8ZJKujNNITC5on8WKUvBjjtZx6QJb1Q4sNOpBvZuccfLmN5S+QUXT9EPJWKqkl0lEC7+0ZRoEgZXostPv8IohL4nzr1Iq88euIe22WSNFos6Wbp4RBIzcWgiE0izqumUvF9OGNEIsFzKrhrZMz3QWLFWbsNu8fcZqmN6y6B3YlsCTl96p3fyw+h/9p/tOnN/8XxXfffXVrYsf6siXzbNm2hr/zCAC773x/s/a0trC3TtzczOPPFg+fTrz9/7OgwuPr1/5iNPQ64tLdHrVTl2Ydh/0bJvZvF5eQfeIRP5BgC/vbBHWYWXOMttNyrkiV/GBqvcsG3avQG2yzPmA3OXb3C7RX9k+ODv7yw90/uz6h6v2crVm47u7XKE0MTWd40OJ9uKmjMiHYTKc4KRGunuqNMVk8wLVbRWdITCQsICGH8kQTgD0T4wuV841OrpghJEnb5pO2cc0bi6uZ4vLZ47t17oUY2yZHDkNZS0FWIkT8JJKl2YQy5lbzz4CiU2ZFzxdsyFoT0XD8Ty3M5SZymmcCgMb1RcyFENmIpeZKnBQi6BJgFAmhtP3ciEMfRnM7s7tzbTCmFh4aIw8Mm4kPNHTgsxEb7VfX+neuZ3nRuwzD3LJXX9Wp6H72QnptqeNedbclHF7izc9zhfFnZ3ndOe4naCei/FUKbgPwLzCmzV6fwE1yRaIjuE7PKTSw2CWEpIkP1Bh/WJkUEJkLMUEERXa4MaaJ5gP4Bby4vH9QI9yiGMmgioGveIz4iNg4yVpGkdu8OHiyRW2DAQ6Dv3Uvjh5FowUYWv2xzP0qTmNERjJCEashQb/zmkuxydQfrLwE3bY0AyrJSA7AgteoSwSsa0i6cvgU1MT+KyLcQYQbrRZPsREjzx6Npn9zc9zJZGOVzNNBhnK2uexK/YJO1r0JzIFZKhmOtCky2Z/Bjm8sDxDEsbWQkjiORIkTITEVFZCZfMqmpgJEEeGCPiRr2h6FCA5DWsiMgGLl+9KMIhkxlCU9pM6OJEUIV1Of9yVWDILcnl2QRttkqLApIK++6TlCZ/WHK0WrCNJmsghCnvyGhLBhccnL2Jk7Z8/stiHVQiYGA8pXf8EgOOGaNmDA/HTqRw/7UMHiWWf9R6LREdmT7LoMH1Ae8KzMxOci2QlFJHo4/C3WmtQ6+cn2Une2BLluyttWp5Cp8gm9o3FTWx+abnOmccm35PQdTTbxGr1jhX0tlZMaP3IBC9ocf6Sc9bcW/Ubv/FfsQ2eyzaoO4XWTW5WmpvLYuTZTPmhhx76p//0S//tf/t/u3075G/HnIa41I9nGWTslzCSMOhRjmZnuUv0wNwJTs4qcODA6todDSOqVV435mgtB+6efOQomKRCJ1yDQ0BmmmjeDh/rLna0wXLqiy+++ODDz7z//vtvvc0l+R/kfT3DJBkX1YmIbOJY60c9peI0APAZBoHDvi47ZU8++SQHRWlbvNqKH+PLrJoUkI3UoFo5PbKJcy5p7vTWPDocTHAcH4IEPIpYAlNT00bDa79OBICDrw+wsLjAxaudTqstUjgg9mmLUIUd4pTTC9DAozAkxxKCkHqFHMHIn/bVcKxlgOlSec0iLVZx/cbCnTt3XDAu+aGkGP9RdsXyDPjFshbpcJ4vklDeTsSFwY8CEOD8O3qm7FhsJQxfZdBusCWV797QbEKNmyOwQz4lggzrW5uU1I6ndo1hn7rDncGIUVD+tPYGL4pK8piuCAsQbGmHds/RosyfMgA14x9M123YIchhvMx+jA2PnPxdb9fQaml+LqB9Uglc+dDEQYMFfV5oIJCzodihY8f42LfLAAK8HA27VHX9TJ1TZjxuVMULR1gtAm9tt9v1Wm3rzqJu29/aBjLNqRRzJOT7d2BqyU2DTq8pQbggsAo0QPb4kVHp9Lctp4aaO7CKdMJI1gJSHebEaixDAxoKahsQouQYccvgtdNInRIAWeREzx71DOsAElijNay939IwA2dR4uKOx5gQiIc54fL44497gTpBj/LqBg5R1kooLcBAy5Ijm5W/GMHa6RswMHUWQEjlxEEDiMEYsslvoxTPCD4OZCMypHojhSdHWsi5nM4OiAvgjFCCyy9MSwIcB3HQPEyUO+IjxAPjEIeT3MsiIgB3gQkYH5HyWIh7Kufi4QhJPw7hW+vhEKdMcn/0JIQdBTZAKADQElZpqkGMIVDyAE1SoTraagzPijVI67xA4J4oWtHmtm7iTtLt/OtFQJzJ5lYnTOBwcYKe0nPkYXy4eDgGYtRIQAi2ZICovOF0/aMP6d8fvXAhjZZmBDxmJI0zEo5J7pnHkYQjj9BBQpwHiA0ByyCZdEamENOSqTQCPeA0ITKipcgr0EyoRfjPLhA5RhbjkBi1d8Dro3y7eX83ZLIfozzs/m46ceQ0TjpVOtbhQAgMeERmYwHPaTq/6fAY+gAwIJ80LIO44dDA6kILpGjKfxjrfp+gZudRmLdYixGs8X6Tg0cGaVvuP4HQU27kUTFD8QKMq55UA41Zo8FYzEkB91Z9vPRjkhgQ9dikJO0bCaEBNVulVdFLS4kbJEnkFF9vb823Cpu0YJbZmGOTEC9JabR4HJHHWQTBEuRxNEnkXYndxiO6IA/RjpRCYzJ4TkKeLZ6oZcl53CFLJzuhlzTKUVS0hM3Fx4Te6O94KTjGeEKXH58o+mLmXB/duMEHq4+fPEkSxdIAJh09ENAEtPVowow3NJ8j+agIg2ehp9cdvXM3+4ECY6r3fvjD5557bvrRB17I59fXt5m9djM6d0IqlOCzPA37bHjDjOOjt94qf/DB8S+8OH/06OyDDz7NYvS1j9YWFtaufsi8gIGPUlmPRgNNABWTFl6iaXBdSGsOTO3vqyA0tgSZpefDZ49neeVrrcXaDqkxcr6Swpj8tau36IUhYjkaZJCQKGc094EgR2ccAhrOooQMcf0kOiQAxGsZskVHEsOSF9MSdgQC6JyRMI+McPDBIgkB9FbsN+hzwTE5NZOKxOCFjBLIVuQ9CZgxQBhnaEPNmoutZIYspEBUWWNxCgDnhzSatfc4kcTE1hI5n0gjzLI1zvnG/IpOyqX5joiUwgpBCWMGFzHFwIqYhbP15eWVVpcXsDqZPPPK3ISNtEN99/bWcz1O+LOEIJHIuR8IG8TDKXhQp8FNUQMDCOl2+gkL0M4G9VIsudIks+gVborhcjxzKjxZiDLMnEX6CsStjO0MLNHSnsGhowIzJLZvoOFbY61sOHRpEKctalzvJspss8gZvuWGNTMhwVqjwWD3AlhD4SLE9ZmUKoyyaynIaZCUl25pmNkz7er60Upb2u7xjjov5jgZQ2XOwtNQKovtWh458MmTy6dPw46KYqi7eGbl4T5N1mbAqhW1KFHQp0azN6sQy/5lt/fae5f/yzPnJ2ZmV/Odw899LnfplTsbG9pyhRenmpgKFrMsPXFmf3p6KlfKrW+s17drSN1rtpmnekHa3YhMMlmy6T/99OOf+9zn3njjDU403126gxh2rz26bUP0YL3/2IVHfm7uCFcKtHp8TLf4D548v4/zp+vbB1r59V6HDataudfoslk3hQxMs1TirJvqveCQVSBqUXhWvVVJ0NZ4nIDmLF5h14Nd6jekwLTOQTMLESls0sLeTBgt8yLliCnDlUvZT8Jd0N2HX0ZKqZxmpCxiJomZpj8leR5EOVyICiVL1QSlFmlDOrFmkcCgM+BAGudrOdVOLCuFSmvyc1ZIzpYMXBL3WUoGU7ejJ/nUbq9XETDsm73s3KG4Hif1TWyYbhHSPJwfqp30wwIl57W49hZqbF1TrVr5DN866DZ8Gc60Z/XRzy1CEPI6N0cG2W7XTTDsEDPgYJNXu8aNem261l29cqtY3pg5eHBfZ41utZmtrfGuzb4WHWT5QFnLdvuqKGSiemh+jraIQ2XcQY48fCBui8+gco0HstEjdjoPdZr6sMN2vQCZWiNPV0rHyiLd4sIyc9r6dp9Fn6nJ5qFD1X/4nzV4HR9h6FMKmSoLsL0MfTAGo0b8i7/SaZRm/8//1/WbN7lwuaCvpWbKcOFMFo6AK5YADojvVU7YFgh70TYyyJZk7yqd+sb2wbkDp8+coZNg1bje4FxhnVXp7RY7p5mrt6/+6z/489rfe5YFsnJRh5+p1DAp0aImvNhlhQ6nGLkEud/mTrrMsdnS8Rceev6JMzdvXnj1ldd++tOP7nClNneqFajj/ULJrGUW5US7svqNjSkDatKbbZ3NtEaLn/zEZPXU0WOnTj/Y6M3IOtqbWphurWp1o+j2g3piBSFrIJndiooU4ppxHwhraQDpFYBEJ8yOemk7y0c+CGY3t7aNQlyF4ambaXrjavICgJDqsShhgUgSroonSlZtZmphIagcB3yxDWJCK6GeBGK5DFsRvE3CO2jdjo6rlxlaFJc3mzcXV+6srrMcz9W3rQ5HH8tkgxOIvUyhQ7WrTFWnp1kgzueKrBFzRn9mZm59fZWS5duN5AVGtG+oDp+zn8CrUzOsO9spaaSUJjmhyYCJ7PBKyeoqxLY3tmoMARmhYqurG+tY6eZWg/VokNH/r56Wxsgjj5z6w8+39EG/ztoG0cVsT9dX27la3slBAFv6lLaESYlZKUnR1EOcaen+Pe+vh/CtB3Q6A70bH2nAzkLAnYD+5FSyKNEeZEJuRmXrT3ubva3GYmlzU2v0s9JhnRutMcCc2iI/+0xA4V1EZ9dM1uixSSuKjJwEIBUqZG/71MnD5WpJ70cwkERJGoZIHEQUaUmogKluICEQ2xc3SYSGw7ZAsBz4SMCgPibR7YfasVds0b+84LW4ZdfMc3/c2hrTDko5v1Wb4659ayELPRW0kQleP6Pa6qchnJef/fdoX9qWqlNOxcvpYauhNOnEET/b6WAw87zc1Ok0sxozcL+5eOV0Cqaju9B51ATYNIGB6d5nRjuNdp2r1fioBpqFHZTVhtMgdTutDl/pZHai464du2IofdkL9TqvC5bl7JQTH/4oTUzPbW+tGkxeyJe1GBrWs51jYhe7apdOH+h97cVHTswUsj02G+vkizEPVZhKrNuwkQ7x1bQyBlEXT5nxx1K7hbniS8dwSWXNhQY/UbcE4qOHOeHMN0e0RKKJDsx1QI1cFnSSBfm7dLOURDFRtZNyIq5qIA4ksdsnDYoPaCVi2IDnqA9a0fcamEgxR0SlsNPZVywPcgBNLVgjSazEoRokJxbiOI2wyZU9WaJYvyzW4FIKWQZJba8MkRJRx2ujUFBIqMIGx3wbitJqACMD0ieo4NNP65iBmhNHlbQ0KDxKQmqbKV+yBelEAIbIzYgXa+F1EsVaj8N5aHQrMcRk4IyrhKF0KVOsNFOY4Abn7ToXjFuLnZCndDGqw/OTvcb6HBdTcXVvbpLfor0vxedFICrpzCFDp6vxuZ5oHFA+AiVyesE5pvtgSlRTiEESVPJj03hHi2Evcfn0JkhOwn6fXRM+F7y+uswqORbFPF91y1olJw4RT+ii8OaRP8Ida9MQKmbA+DrTkMQfPo7vTPdILgRbQKdeUeQUD5XL8fFxrihXDhYVqq0sFkWHkqQEwfw4cv114lolMNm9tnZ7G2ur1PhmY5ux6LAlm5w21nWJYyESiOGRqB3zNoIMjkPG4SPJgwGnoPdMksINXNKlo/DAtNO4Q2HQRhj52wy6y37gvO5aCzwADoVCjUufg7b4SN9lg1c0LU/vcMKikAgc+BlGWg5PMpJQacfMMpR+OsGe4XyGAXp21u59nlq/NV9f05qx7U2m9UM48lJ4mGbEBE6YNol6bkFlW/i0q7Q/49diBEJSAThCtSUwaFD5qLqW1tRDwyo6qs7UWhKM61Mj5JRkYipkOcnrPaaHTeFedkzxgBUY/tBQCN/GN8MZ5ICIhDBq8FXeXGCEsnmauhMBjY8Yal5gTxEcAsqaI9JHAQsCp0t+mPdOTwlxyx0CoRn+SdmMdpi0aoTGFI8h+szM9OLiEjNMlvCs9e232lrAJafyzMiCQHZOWYPG0EiHEqcwCUFNO6Y6Pd3ze2M1ftU4UBnQqnYuP23rHI/der/+h+83el+dP3Fi8td+ZfXttxfe/Cka2dfSiZ/pjkZlE/Zd9Ipmkd1ZHeRrrP7+/5Kdny8dPDx59Gjm7Nm5B85MPPsES+cfXbnMPOWA7v/ITrEEg8Qac7LUxIgpX1RGMx0WmzhiU5IkNQ7d93qbBS15V44/NPPww9kjpzcXFnIXr7QzpQp7vp3Owau3/vHDjy/e2fxwYbG+pR5qYdJH41KH7IqVdAYwHPgtV3K83Cbd0uvrVmp/d5kk0AdZ+Fp3Jo3Ggxqdktj+5OFkXTba0OhLEkLN4PrBaEirgshwxFwE8y0uYeZqQj6bk7271ujnl48enC6Xp7otfUme1FAzInrDAAhTRMgovTmoOYLzAsaaO5xMwoDjP5LKQi6DDsZLAA0S9C26IqNGjTYZS8OWUBl9sq6l4+ZKKJII41n0nA6Rl1qUL6R1kQw14JvNBO6WCkMEEy0bOtS5mrNNnrJ5Rqe5w1trma214vWrlcnJ7JmHMtVqZno/6Tr6GBUy61CFjb5IqNF1Mv4iCkpmIgZNe849Ddk9LDooUp5XGOkctzM8qNoxyT2IAd9nDOma7hDRcrkVig6FcHyvZucNHShtKkuhtBVIgkIwOKoATjAWuR5wqRwHiKCBAKWlsGGmYw3lZ+7BEQHIjIuxB78g204GF+WXZZpz5KiHPcjuHQUdRro0Xqx3sOj22muvvfzyy1geBy3/yc//k3/+z/85rRhRCA8O62i0lVPTWhxZ21hjgQMzAc5sFAQwkYcpmVHLHTo0x02anK987733WBnhPQvIUoBgkgcwCbOLeOzIsUcfffTWtTtwpB2nIWY9xXMHl5mZqs6zFCs0HPmKKGQq3LLIK0ziRa0CwtAWjlQ/fEpalE1LmkQlDshAY26+SRS/XnUjwDF5FHGZjVcGxXsUOY1h55WY4BBHJTCX4CTPyW8C51cJlTtzCdyfXAYPq+kJoR1+AneETFOArHSeWA7pUCy6AmLUnKCqq7feTthz6qTS4cjWgSOPXmUDPnpN1O71l1gXA5/tBK0VZ7OULIsVlkSpI9lARD9Wg2yAAgLZYREDMAbnu6zkBRsjvP+YjqGxYAFl1uBwrYUWy4L9svafuXSXZbx8WRcXzMzNY8mT+3kJWx84kELsr1wswWyqqo3ZTH7G9DNJkJdm8Htd3d80UVmGSznP9aYbfHkGOt1OU/Jk7BbmnqbB1YljX/3qVy9en/qX//JfrdzRZz8Zf8W8J9YiJjiHh5BtlWttxw5KsFsKnINRv/3bv338xEPk69IlbnBenp6ZoE5hL0hYLpeQ5M/+7M8Iv/jMg8iztblJvqK2oWCjSuSzOkKG6ZJZHNW0uaR7u05yYPDim5cXmQDf2dT2L6SI9dKUYPfnWBf7wQ9+0Fg/wSXRc9k+O/CljqbxUEM/rD0RwEGMrSgnySOiEh4owWxA+rRUNpDUUfcogtc+f3Hb03ryqF7xSpynIoq6AQybkd9T3rXUkjgtY+3pRC8g6Nc7J2CQwaiwn2xRVmdfaLzR4GtW3S4L4PioXBy1UiRnbHXvG7kDn7YOi+JtEojMzs9rmbla5VwAAcpRZ2vVZvoxzCwWzgiPhMt80HB9nc8/0gLXtvjy3Mrq+gbKJwqrqLd0KUqzJRkCu4cOSJLkHArW0lxbx0oLOqCqUTFhmh7jpXaSAHDU5GqT3KY9r8X++Gn8gS6HqXgJOoy8IAZKB+j46dh0OmLRDJ/iRp+FXhvt9dkpTQqX2BhOpxoJw24EEh89OYfZT6ysVOfnKVSPGqaMVfBHaQ06i0ghBswMdmbkXHRRQxz3wID8kyvOtG9tYCqb129QjhNsEPX7fHsBspSRYSW2GTmNBUTKXNSGQ+LjSCyP6B8LwLZxaBiIJ0FXNtPboTNCPGxpZXMFw0RPsijeKsX2jDsJoYAv5/wSmlG8GEjiw37MtmEi7TiCYwKHD6Jy9fMjjzzCHezw1WuE1s3BEMGAgAaEdsOsC4DvfSprHjb6SgUOfhTD4AMNACcWIjhhI5ihethjSc0jJqFYyzhKjQQJAPRi8VQRzRVEXoA4vgucJBErcDyxp/UWbCCM0Q3UjUQ6HMk68XQUECfiUTv6zpGoNB1SoeOIj3jEQhnnaARibEyraINa9y6PJ2Ak9yuo0kk8TFQEegjWnsoZ0aJirkKzk+lIKS593jRVM0vbqys5jRH4nsR9l4SExLKLAE1Xu5djepYRBUgHnEgaMhKOkrvAxEYIdZzXONhC46yGjU/msMZ0ck/i+MgOL4Rk05HhE/ml15DMSS+TTrhbGPx7CuxpDU3aCPhmk96fuhiQig40V6BDUhQ8KJ+o+2Q9SHOvkEtyL6xd4xFp17h0RAqLJJiZWxojtDSWCSMAOGgjZtYD7lOg6cd08hiOdgIEZGfuqRwnHU4jRwr3E0gXx/0Tiaa7o+oAupWmBYhJALJulI7akQgI6Qw6ThqSpiCaw4UozNB86icELY2HR/Dj424s0hSGWafMIokQNVuvAECYquolTpOiAwzDWRt/TMiEX0RK9KlOZyRWNmYgGEXhY3aIiYMKoyNpsUunY3KqMRnWUOAA3KLCoISweBkwekK15jthvYM2AjlTBRR8xq0lRVppI8QAnHB4cOUYGSJF3nziYeEuEvwZBeAIIycewy4GQHUi+Ty9CeOcpaUlxpw02sCRMGo1KS/Ln8E9SiM8y44RJ6jNb80IWhqcML6HL/9BDmiJ2dmjHlileXR7+/iTz1146SWuM2ONpbFRoyPwpTaGDd53iKbNOvnUD+a3ud3I37jRvHmTb5wcePjsw888kzn3UIYjhjdu0Xw1F+5C2UuWIa5UzSVW2tjWSgsAYouVSVZpZh95giFC5ujZzPR0pqks61NAtlOOz7uuvB7Lys/6t7+N/kIWLKtpD0yUlh7hwAUHPr5jEgYtnYpwRIhojk5japiDmuU77QkxRVJk+E6TDpd5dCHT4IVdLlUEHkV1M8OnPIVvQ3qPdaYRk9hQMRQacgmmCWzE1dxbBrV+xWk2lEbBIxInXUolhsmkd9lCWls/GSKaekC8ceWk4kPQRN25PjoXx2NszJxx+do1RiDZkyWKBjgIjIQIUOnvh1dg+TfvRzMlz23QLNOnygTHPJgiuLSxRMcDvtug6zzNlPD1nj/TA+uOWSoRcfPHTcEV741v8JM1e2OktLu5kUXJ3dB2hNu2GTKFygMvNkoAhv0/k98Thn7D6o7rgkbI8qh49svwOY6CbwegUaPgnurjrg+xfKvEwYlyz04tb9tG6lKpn58o/bjbunXzw889+9gv/ef/6IsPnLj03pXf+tY3WVhmZ4azPOUc15ZPMdHY2ljbbGyqf+Vl/0yO7TE78IO+NQmEsjeC3/72t2na2k2WBbk5lSKjtVLumiUy0quysba4crA3f2j22MSDh0gyVc9zP+/aRqPYgWanysUdrRbNMMsk1DeWSaiurDcrbHuSygLNhN04STuBnuGtKm0aS68pue2Av2NdjIOhaH5gppyVgrUdDBqBc6bTY2WMoS8RDuI43HxvEF3nDk/r32tEoAw+NRxqBFT6ScDoJP2Qt2LDbaUhDHu23wVImrEkSOjNRyIq/QsQcWQ5C15iLZOiRql02KEH02VwApEB8JBz+4WDCiSEd2ikjI4afYYa8tEe9HNaHuLIDkukrFHw1d1GW19xQWTA1BWxs9e0FZAEulCincm1OrxaS9/abWljVctmXV0djqj95mLxVnMju1bYt29i7kDhQHmKmw6YG9zdsqXhfJ1U7ezaRna9ld+EZm66NTdXyRxqYy20/lrvm9DCdJFrO8lStoI1FrKT6Chj5/5Kk5K/m5uc3Yd1bdsiVG9mH3KyQMxRWKQknqVGL2JmgrcmJjP/9H9/anJ65l/+d5urqxKW3PF5YfA4L4SPFOYPPOWKNwmKjCSCVikSLUSWs9c+vPzmTz9AtkqF7mHCp/ooDZrN4tFsKXtr5erv/ru/bLSaP/dzP1eq6iKdZlc33pSNF+0PWzt2fpYkbnXcFN3p9qUNRioXLhw7/fjjX/naCz94/X1uiH7/g+XJSXofl9AWfz3MLijF7aY9EDyEavX6u++/f/v6Ve4zee6Jhzmj3dm+Tlyzr4sCdNIQI6M/lwvDaFiwVErGOZwIDgHh2DeR+ZVNGi+uWwZO9lUsBX02gX0uIBxGtSR+pkOoamVVmyiCMFAWllp/q6pqgtRi8IlknbyQos23LKm/ZThIjSDCnIaEWpnSpaHJoNkktAdiJicr7WyFJeDN1S3WDj74aIV0Zu12gI/Jhg7/UJ24BjtHoWBps7PzBFZXdEqaXp/wg+eeZWB3YP8hCkI1UxKL/eZ2G4QtTkrXapxxZtF5ZXmNZf0t1iI3N7e3G1qaNKdKwaeQTFcwkxo4RcqhTmuvCs0WVa7Q6rD0vL5yF+RiR6eecZQEY14sQkWitWjYhjYNEujKz01LGkX476f1TcwdiCDPAGptoOSRlCojzCLGxgICgpTYeZ6PXrZZeGq1uRH7YJfh+HZJU4JWXu9bgBDT3n/AU2FhyHDr4pWJbvbcU+c1RGNYzkkKVwd6Ru/BZFJyjTEM71GZkXHqQWLYqSV2b2NYEAZFbAzwncn6Bjts9TvLWFexoxvAZ61p9A81F7wPsgs3nHI4NixaI07Zd9uwCNWZPRSSjqJpxR6n9NXu9jrCceKjVyIH/kUKVB85oWQcrRwWi7ma5WhSQiOno+x24QbWBNwdEsS0Hki0TUPBG4Gq3RAUMJevTOoVqIhPmCh/dI0XWXzr9ycz2yePnvzic48crPbznNjmO65cj8MZCb/t11afSaXmB/KANc8KWSA59YKBuDFVXyy0ZBIY2blUIkJzxMnrrI6Ea3DHMMgEVHekBozq3i9yMpXbrmwd2Ze+ydOONCHoGRR3NWpQz9DGgSw4RFQPGV2pFVUAhuoitcNEY0hyegR1hUw4k3EhWXOxRYEaLjogSjj9mHOm2IbMw24RRXwrGuerNpnmy/58fOJGD7ZySyEEChIAgFpjUYKfjdOsDeYDlColPHsVDJFZWEcU1QSNCcTQM63yhqF0y2tVtnAPMUnd0zaeNe7kW4YBLDrWk6FGH4AqYMGbJwwVvLmOOOiuXCpMVycZJxb7La5nncjU0OZUT7el1wu69zCbn1J2ZQXcZKMMdvhCvd4U1ttLkaBwdnKigJIT50Iqayl8Bw4gPFsqOHKUnsu73vnpmws3b+l2L6lR6kVf7iBMQjnFYPGdGzc/YumZ97ROnjjN6IWhVcJ89Nf5jkI//jPyRNUrs2YAlCN1h65OrxljouSIElGpmEKsJ9L4PMXOcp16TgV3E3U3eCrpICjZfpZO8pNrriukEWm3lpYWeNLF6XJswwTulBEDkSiMByhNkDw8EpWU+WDnKSIY5eCNpE1H7RHekdQe+B5FKlXT4RrndYESHYGPUPPYMb6D6rNj8nQqheluaQB9tGx1X62FjEktkvVryBE4R15I6DAoYJEjgvlce7faQhLcSBI9jsEiux2QmQXbXGa+0WHzeKaxRUtC40xtoKFJjTEHOiQvbraRO/UHyioB6UFtoGa+9DTWKPKSCI2BVm5tSG+YqmX0AIZvZmajC3oPCZ8aItC3qONSBSWJt1EqVc8ic0DPkR595O/VV+8POYpYiIs1Sn62nceoEEcXEYSRvPQHSqqROjMpKzY8NY0yhywDcsKUi3otKxhVKDkkAUNpdRTDnuGtQQW0/ayx4QUvqML6EVnLIE6yeawCgZJHmzhhLiBGipVDFuOifBHCDvlCEWG6MlY0eDdx+e4qR0AWbi+dfehBso/eUL6Xo5NwylK/PetlKbLPkoo9u3SM+QqVAt/cAs4X2xGaBRD1GyGJJLFFGklA+JGba/nFV5cX1vc/9ljmaz8/f+6BWz/4HgdQTtvVi9Ut7hXIVGU1mapdPFjgdbMMb+myuN3aWr/YvHh58dK7h48cyRw7meEj3i9/6QQ5vXuXMxarS7dZTy9Vq/RH/j026/6mDj50+tChQ5lDB6XQeiuztZWprWfuLmQ+uMk3D4+zoKO+UqempiG1ufnc/L7K8WM/vnWz2Wyt2VcWMD5w+AKY2wyYHLbROCllM2g5FpfQklIgoTtioUKUrSQYzIpSRMKTHcswZYUkZks+JmoWs/1SnlV5uNNSM/G8u8wqVvnE8f0M++i8gIu4iWStNAB9UoLZqtcIooxsEJNYHhOgMwx+sCg7u03hYd0MZDE7/ue4PrtS6rVQBuOczFQxN8lsluphr49bekmC854i0lUNGXYu6jBs8GRaccUMgB6CONsU+PRQ+EXMttGcbbL6ttS+frU/O1s8cpKbRnP7D7AAwevNOpyfK4PZtzVDfwc62HSgbYoe5fPxn0Nr4zl1mulcO8RbBiN+D3zhyCai89JiHs7kmXGmP8YijIGI7wFyPgIBcxw4gsPjbgTHMf/mQJS1pAP4K5NKPZm9DEIDdOzY3z105kxm/yxHklnfQAY6GLRNqRFmM40Zpuprr1eyCZJPhGJtAU77xUe9qa28EzlRUZOtuUEyIyIRvNQpWgExZZ3IFGl8NzZqLDDFIiOwvr7BUkK/NGFi6OolvSCCYzHQhnFp/ZBWPRWmosFhbMrSKDuH08jpcNrAHJ72ndYw/oB+ApcYiSjJrwlpCNaxmSWTp/EcGbmhVM4gLdiApYesQkIcavg4wFHz/gjEA+B4uUCQMEWgBJYk7ScsRCpIE3BCDMhRJAKD8WCS0n+dJv0cOJN8SY17bIt5sxNioDCorXpOSpCA5FeHLBPFElq+HGkIyIzkrNyxTNOoaxFw/mCbl1iLB3V2L6uNar6ipQ/HsfFIWhpQiHR6LT4fcSd7C8GgwHJeb2uTsPdjhbzOSbnPYjQUSnZlTXZyH/B8fxvKDEFE3/WBJBI36EaqsPYK0/3VX/3VxYtb/+P/+Ie82g0+WFFRw7rRk2iYbWjROdXZIDxwpprA/buOXmrgeiqq23y1SsX81re+TyV96ZnHmI6yvQDBPHcr6AKsHBB6WLGwYSh7D84OCjiWLzsFfXSUezy4QXvxzu+hqgTB8mWyISLIAT7aJIsOrfpKo/bqq69u3LnBfe7nj0yBzE4D2elpfKW3y4Ho0ZzSBIJqYRLlDJF2sArSziRCRKdBN7Ugu8pGWKPBUiy69WMIs7PTLBHOTOuuZL4BZqoTJzYgIt9kIzDkxeCWR9s8i90dcK8PQUgXNZa4aR8umbq+zbi8ts3yLov8WMh4440AUGPFmWthkJMww1ZygfAMHHmENsaJY12Rcrxzdw3kdTW0NZaqdd6Zi1dqfO1Vi87YEGVKCVJqnjbRm0RMhy1XVridztbSXTiWfVhvExgwEQxdo6V0KsvoX7NHvpCNEr+nHFFykqBASoHqQgvQL/qdvK6DPclYbd0DA/3QE/GaxdLWCqf7p/bN8e5OeXZa1Zx6SotnBpvm5APQNM1kjDy0HqHi449FZ76MjKvr9vmthQUMo7utU+ole4FXnzNiyOivIpq1oByIx0Gf23maXTpsRiLp0rqK4QhPQ2JyioA9H6KMiCoyUR52HMLmdPkVLTOOykglzRe0mcfWBlaKeCQETU2AL/sm54uhjPKIihydXBSGtNQyy6AM1QQYIEc0UhHL8WeuMiDAo6IMEZZIBV8HejNCGDg+DkzCyE8YMXgExzk6JOaUgBKAZjjk1KgF2UmoOYaa69DOE8Z5EnzFpzIrOuK2w0xDl2QlaT1Axi3vQQBLa4mtaPwRzCgqtB2Y4mhmahgxKtJEEk8bfcdxHzSIu7gpggNcC0l74IcythIXffWHQ8QjZZH1GJPWhXEijuP69jx7LHBcxIQyYXwKmC6erybQcqZjoeYaplugcXZ8Z4GfdpD1R+RnO4/HIvtM9qqBw0kLED1E+jF5TGuQ0YImoWOCFsNpmhBkM9G7MMYwJ8+chq+PKIgi7KliWmSgO+A+Ljprcg0cNyxDFG1gBgNQEvKMRLNJwINfyMYHDwdIoiti4esOasHWreiRP5080vEASfaIHUFOP5KQEk1D/lrCZiTqf+md6VijDFFLrtt0NqOeI05MFQNRJxE5RhEg1hHcH8JJLZx5EikqcY6fPP1MftPsYJBiLnY7CuDAdMJ02KUEAppMK2nVHeKxaX+ExTipNPJI+GMhx7Se6p7GyBbRcRtleUVGTuUlUrGASCVVeLeMACcp6FrHsnrHs6VDQRBIKlQIhAYBnMAqCUTIeICBYEA2+uIVIJKXzg6zj4yIIiwjHMlMJDEcAJlyxHfbIJGCgATV0IIVXgAwIF1AMnE8DAIcYZeIpPFA5OBAMGNsjPr0ARcAnz147xHwq1UGJtwD16N86XQ0sNeuNBJLBnf0fZ7WZQAYJLQ88gic7hGtQoomPTBKtZzkWtbiOjEqXM5HQiYdjKwOz7984JFHDuT6V958s/bOexTQfLECMqe2wCURcnJqQET01nG+Yu+S3l1eZt69cvEy3Uf5gbMcf6nOzR1+5JHDj557lNMPaFWClUisowAkqbABlMusrS4vLKx/dJP8thbWGKPu17WNmvL4TAS2LNfyyHCLt9DmtmtM9gF6Nl0D+AiDJKgLuBsOODHWA4oaA0Ycj/LSR9CIHBilBlTOwCubx6bJMtjmHPS++QmE0ZZuStQUmiwU7cE90E/kEP7YqCmJHPqV0dpWEBRwlARz+Qz3iCan6QmIYxgpySrStu20gCCJh/EVtvN/EIzA+wx47vDRu3xrTyYmJzBjKOBv3rhBOTbrDdYA+UIiVsS2N4y0vQK7PRl+XHl2L+f7zM090IYGT0xIWCKR9vm2Jve0WU5cAmljLGO+uadyVhsrTn5Ync0/wkzM8IuDQhFCdGFamOTPtB0jfyYBV7348mdDauY0OhJgMlidGq1pJsegGXWxSBRL0c/oeROfAEfxP35mRKGpV2yzOiaXza6VFa6p/SxerG/UKvlX3nj9j7733TJ367G7xSezOqz76MBdvlPH9nNsJGmPl/1cPtTu8liJWOVXjVWVyxTL7K1wIbTO8ACEOvCS1oIy+7mCo9mda7Dywm18Ssuk3a6yghpMafmoXsV2o5eZVmXjyKJo9gqaKIY9LucrlfrpD87dgNJkJ0fUQkcFUyEM/HCaXqgOtOrnYTBxKjA5JYSzwQKcH282HSOJckmSp+Ffb/7SSxIibaw94H6aUYoAYg6IRyFjIIXpQdsdkuA4uHi7huYVMJ4sW2hRoFDgTk+/4JH8S792BxE2IXY0NEZAYTZeLSyiifnql61B2WlqjWhcKphSXuz5Ues3t2ssE+eLpUmae1vvnmJrod7mpBgLk65Vbm6EsgoGgZPOWyWlFwoExYUOD3HAY1EVw8hnypVirje1eqd+93rvUnYxV+GQYrEwpTugWT/mlHN2al6ZnJuV5AcvHz3yhUNnrgNh1QdttNtb2DZvogCBA+FWc6XZ6TcaOp3N1eOmPZ0F9qFeoTdF75XvTuNTFehQC3w2WEvhWvjO59fwM73XSPXyC/uufpD51rd6yMdmDvLrPNuwcwsjCnzPJQRBgTVDk1KBpfqJVjtX396sVKfRJ5er4lgZMjJwz25m9ven9q3Wb379z9/odvLPPPPMZL7b7rTLGQ7GsmtPCdJdkXN6UxtB5jKlss7nQR9pGEzkqtrlvfjupT//82+XCtVMj6Wv4bPPYuaSuz2M5oILPdjR4Mq+1c3GX/74ytsf3D3/4Am+QXr2wTmt4XMReCHHF49FxYabnEojv55HAjg0YDmSR6Hiclm9LoGM61tbyysb3HGxcHuFwdPGVgPJ7eRujsuY0XY2u4r+WfViJDfH4YSZmYP7ZjhKtn92Ehso28cGsBdxCYw04OA6WlSuUwf8aC0xCIBlA7E6GyCSW2Lp0c/UcB6fj5xzDn9js3F7lbPMXEA0Sfl5PyVdMWDVvjZfACCbWdabedeOAStyliqVOu1Uvc5yw3f/8vsMAacmZymFrYaWUFfXtrDJekPnSest30JwvhqqIhg+UthmUxBPopEFGZj6RHcO6dxYYlGbvFfNemXP3vTJ1sLQNjlDY+ksq2q1Qp5VXrjkyWn/LHzqAdlRjuyHglKJ5OxjkoN8DnOm7RLAMlDWxwV69SUuKdmYPnGYZeh1vaPAyQvpJIx2rbVXksS5lpKnwW80BjSGATEqu/PBVn5zG9PaOnBg39GDBPIcYKDOlzkmkOewhBKboIO6gUVRl/H54gP5aemtRR3yxecVhHq9u7WFGejDu0w/7BWPHG+ocLUO72yQd8sdIxwTRt+3sLpHWZiWQpYwBZQgPXhJSYxRN5jGjMTEbELBzB/rkPjkGp/YcqbHSZpcp2GVh3un9UaXouCGQ+N2Npc+nDtidFqNL2zSXjotOglGGNQm3VKrmZCSmKGaGK4nK0Hj5bGOADLcs/ni1PQsUxa7UEb1zy3BcXShX0b361MrTh7Jvvi5E3Nl7hKscwWxksKOS+/bWhzXCUX0oxcDxDktCaT0BobKR01Q4sgjfwgslTs7pTQ5URGtk/o+dEBOIY0alGWFKTcjwnBEAnuePa3Tke9nmS0zIBM7iGKUm8nqALW1V2x38qdW375ihOQuPF9EUIOaF3mXilT0kkGbRpCoAXGTyYR0WeSDoIP4akoIIiwBTrZrTYH82IYhbQ4kMUkGCJLTRFZa0RPn4BgNQqGt7wzQ6GHFOOmkpdEf7z3peDbfFGubMbuaIKC1B9gbHYCkkbq5F07vV0FQCOTPsuiNlayI9k9QOX40osPwWDZuZnKbtW0qIsMYplVgqFczNPqCSrHEhmwTiZBFZ597lbbeLchy3VFi/eVCsVwt9qZlmWTBlB1Gs5Z5SWgER6eIYCZwKcbDIBN234HuYz/ge5S4gG1kjx8/evHixY8+vMppaC9JKbPTylv/5XT4/izoXAFGR1ivbYKJDWR5E4sPqSTOuqnkYfjXiQzDPvaTSx6SIaKbOKR18pl/Nu8xJYBJZl1l6TEwWQ46+jjMZaYD/KDwAeBnHIqtq9oVdrVaDa5fu3njGnXTOKfkUWsgTUglVJ5kDO+l7z5wqXF3mSPaCIrDR2iiTUiJYGKfPMSEzsWZ7UhWCSP2/QWc0YDdcCovazQ2QDAzd6wRXgOchIjXDhdVsVbBWQYiHOV3eJIiMAqWn2IQ8Ucwd3uM8Bj42KqxlHm+b5PNHqg1V++sTvWLhR4HHTlBS7tkU91IPQlE6/KmALCXH1OcBAXQIPsDoNoulb2fknZbC9SSYo1KIIDl6DExD8jz5+XlrFyHniSxIT8sKAs3EwttF90NpaC2ErhYMrq2h8RTOZpBgqYWEjaMtx1CPgEB40CZduH7bc34+IY6xEFRq65eO5BXl02PQGWyP9OD9XXkR1kxI3G2MgzruRMprNNP8huBwkkabQFNcsTxE68QkQCS28ZviI4RWgnQziIJk2f6lKYuxuvcvnmLownWYfWZ/ikV40wSEBYLy6faAZWTeJnKKEyRd8FyPc5Bd2rKNe07cHpAwioXdEiZ0Z1Z0oM1fd+PFdPOpcvX794+/fDDmeeeO/t3j9dOnXn77bf7i0ssr03y1XVez9IiDYdXdea+0kaS/kxTLdWEDS2O1DvdlcXWtVvr2ezWHAecqvlD+5g95R6+gKBagMY16nxnpn1nUWPU2wvMO6Y6/Ul9S0Yz4lIHnP5mLlOYnFzlPg6OSbW5JZu5sRa+/8FjF/74T/98cZvOK79cUYZ16FeOgU1hcmIGAWUxVlhAbbplOTQkQiFqMKLRGFcY0q7KBo8KhWewYHmRhGITUvarYkAaoP5pcYDbrcyt26vHj09OTuimWSsz4kNLjgCBhKV3eUYgCRPnMOQnryaYhHa8yVcSWLgoVcqFrSYFXS1mK9kuvG2gQofBYEhSu6YgHvUTSZtUwkFgLCE+RoTdAjuQQp84HfDL1tZXWFrRiLrRmO81Kc/KB8udy7n8oSO5fftyBw5rIlOeJEEv53dD+3hDoxGNGOVC+abCFrwvz9Oa5r3Kp8rdBqjIGsolRX8c3+k4S8VKPneW/6x2iszmRpTLY4I4+A2wJGpHnAH27qFx1e+Ou3PMbqzH4WkIfMV6h5ztzOWvGCrZUsIhOQtq3/zmN0+cOPEnr77C7UL5QoVGh/EVDVAv32Klp2xTYBJirBqhJwMCK9KkyaDU2TNhNsqX4hp9jgmyogdxrcrJ4qk5mGqWxan+pkyfWkRdIgp8ThMAwRmmJhvW6yiKMD6OfUhr6834TLduIGQGaUkLTqTgdETRkNSQJ8QDiyRHLoD7xkukjWFo2ng0l7R0zlUgL+ABmrNI4IYxQDYaw14UGDASpiJDGILgRHg6DDBhp0CM8jC+E3QNi4LhRDQAI2EemWymSDnfAXfnKDR1jUHbCtuyKQkdwXxtODG9Z+n5vQ8u0jWeOHUaSKPVYvnm7t0lOjwujgDzns4zYuWrmTlSOyMvccK8f+8QXq7gY3y1Wru9Rle9xXQUU20Xr2F+7VmW8zLzj2jnEY5oBgFwWmaR84kEt+Nyk672STgZDJQ1JPxaTRswfKiQnPYaOnZ988ZtLpwsMEhCHDMBLzov6lxm0WoBp4qf+ulPr1ODWh3tee7hTKV6lRjBYAImgxuml+iNhV3kZEWS5dR6q+E5jaQcGZHYc/7Wt76Fhp8+fwI6jF9Eqqtbj738RhKShJEfbXItm2X48r3v/eTOnbtZruMplba1NCDjGPg+RrOcRtbpAGJgr5QIYzhOBv3gBzfeeuutU6d1r/GBuWkWBGfndF0ak3zkwQzRDwFvFiIdIIRbXd2SvL62Ro4Wbt9l5ZkF6FqtoTeitCo1Qe6YloPcsUE5D6SCNQ1Is7ZFKt4lgtdctYylHZibYrA4P6drQClcK3HIoBydiO/ZreJRgPGAlBZyrR9/ICHai/Jb2Vm7ZOkZSKANVIhItEbAbI2xxqgLUQHiKE2EaX3wAbnjIw/klyP60HETYxFGNG0xi4BcEo4SCubW5kyFpEZSnK3giLVDwSILCrqDe4zyQKT2NzOAkOk8jggZspAogUfW5KinHG3Ahv2TGgFH09hB8zigk6QdQCzkqVAbqTBRqHGzC7WYYuLceuXObay6NGNX90zqc5FTk9PoNl8EN2y6ywBYNeSNfp2/VbvRaujeldb2ltYsrJi5Xxl4PryvIHwdG7YqgABYEbLoR0qQHnCSziCUM8EASdpDxe7koOxkd4ocwBwH5AgiT+SOD8eQCweaMBJDEjJ31YKgjp/LpnVjl0mYaBVtgGA4dvjZWpVE5sAkPsZA5A4E7lQ01A4Q8TDuGOsBmCLkk0+ef/DBB3N8D5aeyByxxllymp4w/tD7k4SomJziMBwpExbuIoKjgUCshyMajw7EV8CsheT+hFQgmAwDmZ2R08EHOYZjFEClNXN1akQhM+gE3BkLedAHkqbjBNPyE+ZUdWTkAU9LOBIk4I/4TtZ9R5HZJY60BJPlBuWOaR3IWk/A5u3Gf2hAkLd/8KkTyM9uJ/lydrTWIuIadQu3ASWpoIZZgR5zJ8KJnCTnkVgCINDEEmUX73fbhTzNrEcBjA5M3j7BFGlukdIp4IOAHwKp3HlCiMcSHFdvxHEiILiQ+Hsjeyy+Lqww+1FYKw0ZXq1ATl5D5NsMlalJMgLQfFBkJ4hERUPgxcVFagS55zwagyv6uMrEDAjuBuUUISnrTWAf4xe+I9hAfEHQ9Bd6nKBJAyHrSJKRR7DGyY7g/M18pIh53YNGgyKgsCggWfWYrK6ZWHDEx/wScF2NJdoB4KliWjCcZprgDsk+I9Aeoo5nIULS0iII8HtYQ0paJ+K+01Fya+g84/aoBA5PJf3YQecSk316gk4qksVCqLC0PEgu4c2L7HYLxOQg+BjAMWOViupVLTSoJVET5HpOtJ38WnowJUNSHDSESuvNYMo4nRc+saR3YQzTFl7ts5AkG+M1qAFp+RN2kgR4OsoaalUE640RXTRxCGmcgyAg+CMBYgkT73GEB1wN5PQFt3x5QmeNHx89+R6+Y+LT1SBPWganA4RWd7OmA0w0whzymJyecDSlojtE1WG8LT6Ws127BhJiJF1udLPuCQq4KB6x6IrjLAC9i2XYSCyd3duvvdbb2uLE8dTp089zNuLVHzP38YRJVxv6OwETkhBkiCEAkmazTJp4D3ht6db8/PzKbSXn2/EwLHF4sN+famvoNcMLnnIamfhVqDy4YFh4K8+ZNo30wKy1aoyNDx0+cmj/LMemgERHEgwaCqx3RyBECCsq5QAGyDDcUYglDQjuPLlzdwTgKWIhqFTGi6QeAI2yQ57jx/ZpvjDmHI29xBGbjIgJQQHGmQKB4QgcdipMO0vHTd2e0OiELMtyEmqe1rkAJ49pajwCVOxO+ZVM93LOFyyn48Q1PcFiechmGV2go+bqBoOTil0U2bU70KmLhmz1bwdl34vxX1W8rzlKQMTF9tiOcUNBfG873I/yJCslKhivML4JpmOQqImLCKnV9sET3eA93EArSeIS4mMKSrbUEsRdf02/MTapDwmAlRCCbhnCtD+aRo8HTjaRz4/LOXCYIJMJgUOP4Rjm01drQG700+2r21jQTwp/76DpcQcUFueA+qxWxyBxHFaaKl2+fee//mf/DZWk3C9wArrJrfLMidkY1N6RPgQLIocOmVxwyoUwy8Gxwrg2uLKD1UDI00+w0GdVXstqJAfh9CZD6szzk/NPT0xN3+UtNtaQ1EQyeaBaYvqg8ao6HFlL4MPv5aLW49qZHstJBXspEp0wI8UK4J7jo6KQteaeGQkq18fuVTm82ofiAAdknNueh92XtkOrJEAq7BbkdAbwdIkkdKTjyMKomBd4KswMQz8p5+Ue2XkAHyXEMOlSKXYNwjrhPsolRUrJyX65pB0gbwEJyOmQs3ZjU8qBr+2+YhRBDUPclchUSoAIf4zAiFoqTrJkc+vG2q1bt2YPzNCEMXRnMDZZmISXji5TxGx5D485nKZqvUKag8GLIDJpOYeSpQfk/QlzwO3wmhBA45KiElTtZVA1Fb0sZ4C3ap1acRab2cpsYUutbq1dPJJtL4tATovC/axOQmWy6mK5pqNYqeYmCsyv2ioKMqgm/sCcYjP9MlJl6tNAlq+82u3W9tWmldYXr/38aVYddrPczjTb+cLG4w8d/c/+wcnf+q1/27BejPusDd9LarR8PaehIOw0LSXFEKdUmEKgHp8W3lrNSYQ+S+kgF9kSQnCjuJ2fzUzN3q2t/da/+fr2L73IJRjdpvLFihDHDlAbYQ1ndZuawiz9os5ioYL//R+9/md/9mf13lQpP9/qFtpo104rD3391r+Em915Gb3b05o+r2xR0/v60m++UyjXs/l3L9ZyuXopv5zPX/cXkUylmWJJHxRlRRhHwGo0GcoyEmLU7kOiekNv9UMWON/qyBV4U4MnFio4T84FVoqamJjqdHPc+omj5PExFHzalMZ2f5WPmi7WSnpXPl8paRH/5IljTPKrZd3aUSnaIrjZWK6gfOmOPh230cCLWsJ/HsXSex+rC94ClwoctuQss75p2VukSSuwWCyDsdUnWSfnOGghtaas9o3apaGhGBYYraJzYpGHrY5uZwNK5JT72yR5Fk3qvkASUVjOUwEThIPthEGTYWrkDo7ow1B1yk4QCmLpgJb8zKfyEJzf2ad4JRo4nVTF2TsQDiWnRniA82lCJv4IT6c32mpFLhJHmifp6PIZ2Y1oVlHoSaX1Yofjgrn2VrN2c6l8uENZ8zVn6LR1z1pI45r05ERFOulAKHdr3HRdG6vQ2TY3fuosP3dor9Sam8XWUpdPaOu0Ku9A2OySJSu3ZMi6c5reivqJZrTKUNerl7pWWPj5CBOEVCpXmxhwhkJ9n9mzmyFmBL5aDWlTxllSh6SUJFT+h10cLciurJlVU+99ZwoTCCSsH3etyhfRbK/INlYhV6nrAv4t6/7tDjjMUQ6LA+4vZYgyd0dog9mWmaGgMYz0zgljQwbOwR4RTjtxt4omiuZirFbjC6XyxCTXoDNSIFL5ppSNfcnOEFUzW4889MjPPXd+qtDkLga2RBFCMmAQnT6fGCAbqlWa1sIaSRSLgw5toaT3sKkYVHfEws58ajXINtA3Woxl6MV0NRmVT3UElapAdG8zvROVXN9fRJ05bcMn64yQdYKwI6CwVWRYRAeQVEECrcJqmMxfh3ut9SqAUnlykghNLRZCquRl3xQL+cYxRnZNGZwwNiN0kwEluh6hRt74k02ZOBSPlzxH2hGcBkxlKTW4p4A5gYJ1WYGy9ByoMSKEohIKwpwJdZETviTNZrCSWO599M5FSUjFMS0aMtmNaQYIOoAhGVYxGpDyIwcYmz2RaR6Fz8ku8LET9nka3LTPNxasNIOUypoyzS6GxAPHagklAYOCjn+HmmjXPnuiId9HIJBU7ZCKgv1apvWIs8KHi+qlNKYsqyXnUYpInIeRgSjC/PcYKKNqgFxcc+jQkbWVdxdv3zrxwAPggGEeYyQlwW2trkGhxcS+26lvNe8uLtzm0yzHj6cXoBOG4ZdUhPCjSB4YQbvno1JZpqA1QOajvprlqDwoXJUXswQzFvCZJflEJuJHMYCkwxHhUwY+Wdb2Zupljb0JDevt9tA/w4/V1WVuRZMlYa6JThDATIDXXChiFWuaeHwkoPBQZBpxEDauZniRlJTtDqNLlYVMfUDRyz3B/Hi/OxYNhq1aYE5tSFIXAiTRAI8J65Qwg6CjBz/B1GM6HB/TOXIgEP88miwuWvV90BePlNuRHUCDu9YNe0jBggyLtANjEIrc6JvJzC1vHdpsTXIwgwrOwF1Jw2ankR54xnTw6CETZcCeplCso7E5Z2/SrTWUlVgB0aXBizc9nKwJbFZsbSOFaJTFBGsmVrWURtoJGkPK11LJ59XkKB59HGagopelkVYkoBOQU4XIXM9SyWDETmhiYAFKjk6a87JKzRke1GPLAGGUQiZBw7T5U32SraVOTogv41pVOpW/Hb8eqktiZZmxcTtKge/9OTD5U9tMjvBl52hGT8B1T0iXN2JhnO1wKoFJBDNcKPMu49mpBzXisY1eVQ4TgGSqouQlqTgeSHdSJA+VuFpg5pPvFukQmcu4wMqh6Q0q6KHQ7HEb6Zzd7DzJxfv9/ub7lzYuXi4//OD0uXPVL34xc/fuwkeXmWtMd/S5pImW1oUP2h2ILL1JHDOVouYa+YqODGX26arG/uFsptDaPJjX4iPftjFEZZuxK/5kb0Jh3hnlwihrCXmzEPprk1kdrjp6lANG3cVbzFX3L6xlG81T2+tfOjT/+ofteqO+WbC1XT/l1c/NTM8xCSKtZ9D9OFwJQNOe4UjCtDNdYTtKTl74c4ykHgg3EA8cZAlejoojd9rcpxHhbeUCQ6u7a9yrmeODhNl8E8HsXXAqmVoANyLJZhZJIhtuUCCp9sFoEmW/8pKwlSDVhTtjTTiNkDGQHJ+sb5eznWqJ6+3YdNCokWEdMpPQzSOhEEjyOKKuyIt8iWN8pnDtYUi5qVgPJrFBtxAnpHmkfdlFIuOYweeK83ZsrrOwmV38cLtSnd23r3jsKC+YZyp6kT2TZ6WO7Jm1MO1HTj/MFxiMlp1z38lP6XNIt07BYkOvl8g+oJKKDWkH1MIJaFffVHUKEUPds+MhAyL3HYKUU6MVuO9EnxZxxCAiOYfvFutyggwClhUbndgYRTrjgZB2LIvAd2M3TuR+IFFID/jQGVFZHGFVzaYFIoPMNvrXnXTMJ3F6V9MOMFrCVFfn56NttdBbXdAg68JQT2dmyk+cfyJqA7jlKMw0pCurhx7wVEnaQUVzPQQcGgToJi2aAQfpwHTkCEqz9goco9IB0TRH8jT804RdEgm0CxWP2S0+ipRObfmVqKgtDYeI40dqPFIWOCA4T2gz6KAGgCMUeDR1Dao0yR1HNpC6UMJ5pZNfvnyZEyKNbd1B7BvF07O8wdOfnpm+fv26C+P4Lkw67UgY++GddTj6iy3M7k0qYREguQZQTCbtNZ9ev8wCHzutROn4XLF49NRDnIHdmtVVG42ZtyI+KhOJtO+9F2MOLTpIQ+4rwJ+rh+llLsfB1Y2NRqdVRza6Lfn+VootQDMtY091ZXWVFdXZ2Rd//uef/IOvvwmR3XIqkSgX447YhD2DLNBDmaqHjxo5fZm3+yniPNbRwMdBxDH/5E++xf785x97kNhWI5wNB0GZpWdJ+jM0w/rZ97///W9+8x3BWTVmXTunRWR9X/HjOLJGy0AGEABG+DwC5LoJpOKTPSwra/xmDsL1Ro9T0gSk2MRPB4SYGjjypJzaZT6s/IqFLcYx+pG0ttxDAGrkUqxt2c7UqW8wkryxraHV0uJtymVmQh+fnKrkWKCcmijhHz48S1rGBxBhhG9Sya5ISyoVPZ5CuEEhMgDlfZE3Lq1K7cUCwrAlhmyYI3gADV8eZNls59UzYn3MSkAc3SRYKebub71+x+nv0EUSlbB0zjI+x0cwHFDDwUt6Va+t1g2DAD5TNWfkafHT3abjCOhm76GfjZ/mNc5BGRl2FDeSO2yntF4VB2n8GTMxUhoWry8tEZ2b57qhT+WkX9MwxiDLUt1Us8PqCn7PvqHjo6NBK5kwlDnRS1pBhgVoC/saLZ+nBNHG0vpNEiloWTabpxqoatvkIWWHSWs/qocBkVQIap4FNEqYNDym4keDjuJQshA22FJYIJB1lhrNV8tMpGgyUrdScwhAArS6NG9G8x58nQOYLh4+1NigipydiPNynVCRn3322bNnzyIn1ZUkOOcLa8enqkHHo7RGZg58bg2JOCQBxx1hEuKIJZx2EHEc5wKOBQyZ93XNOY4nxzfFSCQhmXOCjkbYucQo6IumFayHiTI5AwWSAIeV0/FHfKct34Ix1gOiaZpx35CDR1JieYj5JUzzha7UiFlLAk9i4Tsg66YXBv1KrsVnc6JrK808EcR+sQFeQxfcnWWP7sYIqpjgrV7bHFkDbv29KMKXZU0k5JdHUEBHbR5QUku1TROfHGY3QPBI6CYk+Y0vhSEKoZalcYfs07h4rPQgBY45B4IZYk1Xg0fDT9FxyQX1uazTI7+eLz73dO3DK4yO5g8dEkG0YAVNLFnDbW1u0gqRHcbnLIDSgzPEov86cESUnJEbnFMeYb1jFhwz4t8TJ41PWJk1nwAaxsdhakiOUhxBSRLlETsU9nYjEV6Yw07493IDmffEHaDdi+Ae8ZQFgxlyyttFWDXv3IwjSwW4VOtxP6xJMW5iBgxqdCIyY28ixhmnICCTNgUYDUaRrKRGYyPfdISXL5A05XQ4jRwpGKOdJUkLmQ5HOk7Efc+42PmoySwqcolJPkFAND+RS3N3fUZSBDhq4ED5SdE6ZA9ukcIIDglxVDB8jyIQBfD+jUaXKApUCNq/FKY9Bgj4qBEgC1WW1vGtZbZRK1FOloD3Qc7MGREFNTeDNHdnBMUQMKbOSHSGHaRwDhMpk8SBVC7r10IGHcejhmnc44kkUZJ7oO4UnU6rXPBF4aRuEiAFCDgG7ThuSWLB99iJ4zTLAImlr0oL4EBPmObmcKfmsVp12Q79GvCgI69uobzYz+eAsvZTeekMXRGgX7hx+fLR7e0Dzz5TPXnyoZNHHlpZWX73pxz8QngcWoWXs+ORJPTUPGoT3USFDoKDxvxaaKmeEXwgdOPKl/W0ngQiBDCG8+fPF86f0ycNX//xtUuXyIK/3MmXliYW9J0e0CDi2YSRjhLyOrK9ykMU8N2cM9otFgFwxEYt7Ya5I9yl4kortEfxkZGDh6dFzfTJR4TSqZCfISRRLtKI1J4L99OpBElQPSFcoONGjuVwEzTw+9RGmnIMSzDNDj6VtTs1iDgdfCCYCSMN5i4UmbOgyO4uLq5trHN7eG52HysqmcmyW6ASSnPaMhrXQ5T2/gMuw6cnpTUIZcZr7OQEB105BGbmCEx7XL6O7PzAdGPyMW0pDCztEFxiZSIYhrx7ZSdM6pJUKVTVhOBSxh+hMc9BEiNEOMI9rb+YH+gELhSBMgQmjT1lxpLZwAC17KM+YIxOoMGP365IUsJai0IbJmF6gkq3kZj0UEknQKVKh63bEXDElXR1QMh/kyUXhfV+H0tStSIGzfuMfT7j1itP5Dstths7nTYZ6OhC2V6+yX2AeoeDVEwvjLIybrwYNEuXmibk8u2+lj/mOB3ZzhzeYlkm9+v96ucf+fzZ7ExzmWuoNbXQ8hiqyWe6vINu51M4atnlpd9MrzpZ0cqNlvZ0sgwHJpf6Qp/Ziri0bEkrqxf5dfxVIoRFH7OoUDBgRgMj7NWJgJxzDLlwkHwYxYYtZSZDdAbYO4b8rI0pmR1UFwDfjD/ZbbaEyOO/5js36SQts3NgFcID7ktIZS2YS8yEw8lEghYSwXy7UVdT4Sd2taihrTeimWX6uRU6jGJxknfLmS3yjg+bdIrlyivjZaK6tSCdTh+rfcpO0KROlGZox+vNNm9tNDu6zXZufv/++eMbJZ33NLLFI4eOzs/uW17fQOLpqeKdxZsxjwT8PL5lMSm4foXssSzHS+zsVXIYr2dbWjo/6Es4LJSqL+5P2DlHPiGMtPStmE22V8aOq+ee4NXs2vRBBKjmOiwHH61k71zkzuQpzKkyYedep7RyXNex7N4Un1xgORkKqMU+FMDtVVhMpsjWaL9p34EtdefhuHrl9HvvvV/f4mwXlw+rBIslu0Cjokavzt0cmU59eo2F4OLR3stfenaqMPdv/+1fcICY2I6dS+Vjw9KtFX67oash9h86YOd/tXdNlDKWrzc6DdTOqeLJKvfOFllZo12kMhKpHSKS94ssquTZTKUKZ6scf6s1W3/09e811movvvji5IQ+D1ghL9zTyrsLWkzS55XamQkOGv/Pv/9nly5dWmnodDB31HGC2e5i47Ca7Vqnzc3Od9tJNGiMOuyDyiyoRrE0Bn50jAbRrIUlOGVTCFqOk3MrsmDKK1q9BkDuOOSM7wVtr15w85nZYbAOY8eAiczrHAfXF2gRX3vMnJE259d0qB6oPJWMdoqbv9a4aW6zbcuCy3TfGvZ1OwwIKDoGkTNTZVaWpybQcXamqi9mUlxojFPY+FpcZqyWrRLLJfndSuvIkZWNjYtYPqfKm2Q9OZ6vXMDWGhIGiVoSXduUgOaQnj/ujs5zmNLU4X24EhFj2nb1O1zlrDrCdZ9yirJo2kweMV18ZVD0VSWkFX0EUoccYEiU9R0KAEcPhDwJkIGzC1vU+/CnfjbpZYwhaCNJdDrEXBru7AY0HUEU93IxlQeCoiyXGMGOKZ1paBQDeUYLXM5NT8Edb+3m4gr7pdTBWrEE2QZFTsXRgE3Y8jWZlU5GHFHAnTLxBLhAmG6KkxuyLqXg7jYZGSpWlJ+mTJGSDikOGcPQ0hU8oczhFdJKVgagJgxIwrdYts6UWLEUk15OVStL0Gmaj9mT1ooxycLYuChoxml5YiowE0g1tSLu5WuBwNG6J9VVM09OKHG1Nv03JdDlhjhis0VtIvZZd5fT5Md6bVofq+FgI7zWHPl0RL6hS0sK3H/Oir1ahjAVcV7yHapisKGrIXTIKwfKTQZ9ZGJudt9H126QV+SFjjRmB0KL/RZ15+HTBy48eOQYs6xajRZDIwGauG6X+9NxDEfANzXzqwwjMHI7cfJh8kuTCIxjpskf6+perUBwTNOGWl1aem7lR0Kc1NDvIAP0QSO5oNiXpLc9TKuVGioNVxyvrWAnxGWK7iBSKU9ibvRvU1PTzNbYuFrd3MYAdDA+qeY0RhPlSquj0dNESf0vJGDCG9IIooLRo9HvcoTdxmTGDHyZi7EGp6Neg5GcznhhUDriBRHO2Nr6Ma9poHCGikpqSkMCTxsl99P96IP2n/bGDk3bqLCvVogT4DpEldXl0HxMlc0avs4A3OnQ9skCrXarTbIkUFYB9DADZtew5qp9dQSUsGcfn1KjUoBZtc9cW4ayrU5va1sfcsCRHWJVCtr865fp1s1BULluqYdtFdZgUepokajZZyrKPa1l8o5xSEJXHwE0Y6NKNOqMjFKwTzCdMj6x1UmrHWTcikDsDe5o8iN+0iU5AmkZMDPUfejsmXfffff6h5ceffRREeGlvpYuluGlH25+4GoqFgOOHTxYP3fu/Q/49kCj3dhYuPkh9wAx4fde25ob52zaDZ6JsqdnQiYJhzElXnTsvOF0wbq/KhQyZcmlOhBVNlYdYqJ0AJw0QU/iCKmwtxICB12r7vMEStB5mognH27xvO57jPxBUSUw3xRMnnb+lSmoEZcUNGpqVvRaeu/qtSuMcjBvT4YwQUlUXdUlTA09KLEiLDoKwFMMm6FBHEy14JZFO24/0DkKNyKG4+yCdizDfg5aSCRPdOQBR8b3XBh1g4lbiCRqoOskAQKZnlNiJlH8RuljWaQiB0FilVMJFgI8xrTg0do49h50IhGScviJFhLJErLKLwg7SwnpVJ+YWJEYplOkwybMELGgN4tIS26AgQcRJOGZxpTwvmYNP7e+XG23aFWp2ELViGKgafr6mMoJBYg9qBHDDVpuvV2hHkrFxsxN7XAiqMrK8uTDAZKpwYjmwzIj60JAeTnIm1FRtvbT7cpn00mfK2qyKB0AUV7EBl9JQLetOA2I1JJKFNM9OMjjbbIjknsr2yCjx4qa+m69FAUapa+xgumhbG0Jl9RxrR5ZBNhKbEOYfKmBcVeRgTsbPuEDOEZTg+dxx7hMuTATH0eQtEoji3D5afOhNvKmMhDeN8TxrhVrCxr1k8BSUqBomONNM9UqA3tyzmrvw488QqUHxctYtV9OXPSaccpRjl68QWPSlcbkNKo2tAkTfW/N1JSYKijQJhdGwkFfFLIZWrc7V1MWJpq97spHNxdWTp05k3n6kcyR4/vPPbJ/a+vSlZ9e+/DD1Vt3wdm/qZmX8iKZZD/+FY1Jm1SX+FhDPjOpAqGV2wKHuQO+8o8eylp76fT0CfTNkgYY16Z4VX168ld+NQ/HUqHBGm4328qVmA/3MpV5XqedmvtHJ069urqc2WAYQUOo1vjmdPbg4UM2YiSjynXbvmPBAJFY2I24CPRiIhadGKJlw18nkoJNy8OJDSOUAWWLAFidMkl6jXb024Acn6jI5hdWtzlTSR/aJu/Amcjx7iSXzyXvMxGg1BADkbx4ZMp6GOZqGkuBTDjqrpWveDe28q36TK5faTeLvMHPC9UtbWcypGQMF/oST59qKxBVGnCIlrnskR8yIsE0h3BrSVWalBTghyeXRw8B3+C2jiAkrzIWrSLRG3xqSXSXNwskyFBab2TW19rl25u0afsOcptKvzpb5IyIn4nmPQ/GWzY+5l1JFZ9J64erwhvV/i41A36ETt3SHOqN4bM2aHIhwkAil3mgh1SZj9RcpTVdyeJJgCMDzO0JDFVEIQZVEmVPDhrAXXVDUgzwkmJIQe4zqIzt4tJRu2PtkFhFRRNvGTGzsLDzkc3vxDGBkcoRhvSwA5MASlNLh3dPcY8Y+OJoWaBGDgjwigJVjnE5A64dWYAf4QSSrIgRCaGAr30e7m5FMxkdZXry3AWWA1qrmh16E4xhgG9HymTfnjZWdd3aa7Eib2LA0omLo60EeCqhJdMztySHu1zj4ZgqBqDw2TooB+cvLlnugMDFV3tdNilHyhso08UgmzvK46k8Kgmn6o5VqAQuLOfo+NAkiqIBSJjCZYKJAPYylC4IIJY+xjSpU5ntlt4wcoV7AAScUQ1M6YA5DnzyxAP0wXdX1ljY5UQqJ0OPHD3Ogu+N27co4qnpChAO+FDufi8tvFgUtpsGhoR3OdO+c3Rp6bg9O/geMGFUuXCa+tOk8b0PnO3Z0ke6AeMzncZntZH7zTeaPyLMAjQ+14Ngmcv5OndgTZX0ncPKxJQwOVmLY5mIBUpzsOCXNUOtW3T4eFhmebnOkVbaOqy4XGF1I5OfyHDDVbPESf/Jo0ePckBvlVa7VHrsscc48/sfvve2i+0Eo0/u6vVGqVYDE54s/YuzVjI0BCSMPFIXH6CYmmJnsmWrnG45Xu8oU9BcQshyVvpb3/oWefnC5x8RnVSzCwvQtrabf/RHf/TBBzcpkTKfFzAHLxsNhgFaFO/TBCAZpYIOMkueYbN0+mm0EY6Sy5wH0phAiMNzyqxpEAsLtBEgiVqAu67AiBxJh2JZgMav9XUi74621egGdW56omwLEywzsphdqqA6zjij1W6/CgUWLLBw7JlYthuBmCyi7YH4iCQujOddGOZAwLk4BMDhiRjCAYOwhYjyautRESFJxa8nB90HvpHAIBBTAXJ8jxuRapDgMwql+Y6TjMUyHgVEaZPy+v+R9ydOliXZeSf29tgjcs+sfa/q6uqu6m70DhAACXBIkCJnNBjIJDOZSTTTmPhHyUw2NI5xTEZS0FDSkBwCBNDo6cbSK7qreq2uvSr3iIyM7S3x9PvO5+7P3xKRUdUFkGPyjPTn9/jZ/Pjx5fr163chwgwwZMlm0NE1He/I2XpXrtJoeIxQl9qEM7otVAb7AAczB2lFdRgYNTupr8xWv0CNYyAxEwFooYeKSzjrchJCSh4LyCq+EWn7bfJeE1b8az4Tjk6BRqKWVTGcRS6YpqLzoZcwECoCO2jCIYkUyAKTgPPqJwRhn+gmY9k6mqQWZXJVCjkX3MCSFRKE6aDnQzkAka8yXIVEWuLLL7/8JLdAw9tQGR/TIpoQ/JNEZ6Gq4TRYOGQccTdnw0GD1nEwEYIDCHaGDJj6rZnAvFgGpMLTCetTE4PPZfG0bNFkJdTwVmCTyAgZA11JJ22Zd0cItZOfmGQmdgFDJrdmmgy454/Fd8ou9+Z2WGzjVgGFzQFCpx2HIBYIksVUCkDh4fGrCCbQ4hslTTUASXygjiJA64TxiUEAArxkcUmQlcLZjYl/gomLsuUQtuqfdW8rZyTmknV8OAiZu7xwTnEJzsRACDJpLqMhjoWXVa3hpMlxESaYwk7lNbBGKGgul7OI0dMVCgJTEQKboJkvAVRZop8RWg6I4Bsq719/U88mdvVQk33Q7EhaXtmCA+ccEoNrcXUMIXDikxCAG6emMjAgU1WPPUEmRp6oIi7MEVQKNcPtpEvTnpQL/IEINe1CC9QIHyFNYfUWWqPBjNf1MsMEDe2rNk7JtTKOF5YCILnOojUtNJ1zQYM5CMYvVK5v8wFYRJfEQmDJPT2BxHkE61PgFl0unSjAkihwJRaomWq5lA6sGVpz+FhiOH8sfGaYoDzdUTGaLB814kKBHHJnC29lClVGy7xd7/EgJM3S4/7XzS4jpV+5R5TMfkIHX0pqHbiUVrlNkbZTAZECMY6DEyENLsE6enKDK5dAFDD3A+YZfYP6BAdyS9oIQEpWNBlxcNnp30loFTyCGlpqHcJh7iHCYEfPbyYfNoaPi18IgZS0E4KwbMG4wBiYQyEkwcyEWqaLJpOu+P333rvy6MNCzK2vIAcsGUBsTw5Jq2mUGVVnqMlFSQLK/OwnP7m+/T4fNr/w4ie22A39xS8++7nPNX7yxu1f/OLgNd3r8ZwWe/oeARcpnK0VPY+Yh3Sts2YPYSIHGMODxs0n8bVrjz/50kuNF17g7pjvMH7nO9+59Oab4DDNhgoGbHvinpd+8jv3DtmAiFAMxfSJ0S0U0AhloSSywST8jAFaGwlPoewPpDJ6UKnUtUicDW1v395Dw6uXt6ReQhFalqMGghTLqskB1pcZXxpNV6MgVAEIeE5MWmRVQswqlUDEKWUhyzoItQoGlgXlUzhAdLoIK19Kjf4BgYg1DhmF+2CYcG9MRe9vb3PzO17fY02vtTFUoZb1kjIzfk2k45GDTZAevCXvSkNJbbSqNEqWIlgfx8Dp92YwT7/MvQPbU5d1iIxYRz8Tz9YmtAjIT8/kSfV6trvRJFir2izmVYSpUU8gZ0nVJa/Tpp2GpAJPA10rC0RZW/wRC7KPx54Bnh5vLQrlCTCZmEd2CCvVxXK6KDCfCNqaYiKpOOUEdGpqorAGAy3HII44f2+dK/QDQDXpFNqFzCgFLsgK0Abi++OL9/lpvtQY/NYXfv254Wp/p9/Z67O5ZSme98Sd7PHRCkds8gJIPJbpdnwkH/zRh+7Az37je6Hs8JlMr0Gwa4bdGCVYEfCNWQyi4ayltw9t61qo07PlMNtZ6MnXxW7zKJGlB70YS3pG7JlD2Uef7Jhr30zMM0yduNrgjg1K6aiIGZ1nLsEHOYYGjtfGbqy5Rb9JDbMFPsZ0+kRpSNWznMYO+G6XBWggAoYIq0Tn4poHDluA5LLWg6q8ncEy7s59neRLFl8DIwb43AuPsRqLwOu373AsBF3V0eEuf8tscg+DEFsECQcuWSoPu+npK6dFLHXaRxyIyYsFUe9lNy6X8aljdhOHrq0m2u8ead/QL958d+3RJ/dZyOV0hfHand2d3Vvt/XssYJxjabjb1pL0zTf1AuxOq7O8PFjTeVWD7soePNutAwbL1aUjbl+753U4Q3t9hRXt9eEFOK+0zukLhOyWjqeHjM08OtVSybBxf7vRvrZ14crT3dVLNBKe0e/f3x2trH7ptz7PawB86++Yg5Z5kK+2ZUNSYgaexv7OXmezzRIz4z39uwbC2FHIoKVW0NBCNpXCAcpsKJPZ2ZbDmMSP6pV+keKvcXs+ZB/xceODWzvf+t6rzz1zhVvTQezRjm9XNPqH7bfeeusP/vC111+/ccAp0s2lgc6nazBxYL6VHr6nSvhlf3L/47aWWm5M56pOPISEn4OWW2XsyuECpexmjm0w0jxAF12a1gY3vFhrT3ydiY6D6satdSBGm8feCqT1zBqiZuwecJr1CrilD1F6uhkY3ht1H0Nyjnh6bAriIaejKox3iBiUqQUOIh0srQ6O9wKe9VdB6jKytjKk6lRTeZ4tDhoPpyDyeNGClVnFL0g4Ek+kJTxnmZUvKSFZuIrlcilbBS0IpMsQxGVmLZXPHubp5iFwWwi0FLKk1cnBuS5LwTuFIZxKuUhTgcQjHSCT9kEPdvcGw+Ol1ZX1bncvxhVewwKn3ovEZQmWpX1G2Uj+ZQcMWQxSJNgELT15KSIe7OACLpfVtj7JwlHdhTlUxrQU4PKfKKcLC60tYDQQjAkWzFM6bOgZkRjGZFkiir10oZBmI5FOtFLgGC8MWIpCljTxNbd4gUwJ+OJ5fK17qTvY5zRB+rghLzCwlIcf0kdlCusscj2cow2yxYmNm0POZKarY98MOdzTggBPYtmfmzorEdJlFpFLMYo5pV5vdfO4RbfcR0NaMjh0ZuiwOh5+4slnX3nxyeWWxppMriagzpMlUfozVwNmVa3oySsfrBO83dLieDx4NSHmJcieEZBllaQ80OizGPf0x+RVxSFDnCB3jdOS9ekAMnALzpGPSV+UyKxU/BS0Jy2aJ8Q86mL3ihZ74cjyNvN7LICRWH1MGzhQDH1CI7bdSBwHjxNrpzhb0uxF6hi4odS4YM1T9Yi/GoVDFJD5tYoZ95PSnzu48ELVIA4IA1mJoSCG32Ec45PcTI4vFyJGEDEoUMkLI4hDBD0phpG6UfwJ5Ojp1HAsRSpxNGXB16VyQrEYycJJdW+pBVgprHqAN8M7QrC8gkblcCc+JjLc5xufsKGr61IvZGrX4HhjbePCuU1YEyhRBJ2k3xuxc43Bj68UtHabW3h0LLVrsmkkxxKSr+ULTvP2EfVU3xCGkVX37GPSK9sUJ+O6wU48nHLmbhBrICAWoOPGDYs1H37s0e27N3/62g8+8eJLTGk5blgmbozardEyR2lzsjk3sceDJx57/PbNW0cH92kNd1eWbm4uP/b4OpWCijyvtmw2zUPpNCZTCLMqTokMTE4ifNnYuZG5ONLr2lQx5cTGemUM/XUdxYRB8JBIIObmrOAGGvKLXRdLAJprwlqFGU7Encp4sP5T6A++SD2J9vTzEtKIs7wwtRwgSNGMolIR2AI4ofRvZl2McIokcMQnW36GZOYSEUiHm4iiVXpEAxRsZuXAdobDLMbJ1ypODraDa8I8nWOh9SNGw90iCo5Iwg/xv6JnSQgtxoXkq5XzkCO0KeNIK/qVYjGRC1IFWTSFSapAqlxg8J9mNcUpE/HrhpR9E4lqxNxj6DyEtt7WbFw+POBBxepA30Slt+XP56tWTHSri0oa7aJQki5WSagKO/F/0dGbEjMGE6f1hMBlpqvs0lbiIgAwRq8Y1KL/TCyj17VQj1zIdZG4mZFPsU40VOdGXaOPykOImAZOUsOfevdIB8TdKsIMRHlxhkRFU48Uo7+FQKU8FUgbg5lsyBPA1X8d9BQ9iqyivb8aS/mLmbbe8xzSr3guJKU8mkQilUBQBRWdH7eRgKQIKWgg7h656jzZkD+pazBVonsKTKzpMxx5e1X6s49YgX2sy5wyqJM3Do72+TbsQ48+Rufc7OmBKCrDhKGSOHSZlpTHDvThNg+MqAv4YgTuBpMCog068VpQxwHlsSU3843GxQP55Oj1G+O37+78+M3OxYvNL3xy9fHHG6/8ysXPfqGxs//BW2+9//rPmPh0hvtshxreuUN9bR6Kal3LOeigmFOoif19hbhx5HU/zQl39AZt88bKOsduPPb5rzbOn2/c32ncPOgMxs9tnt/prbAIcLSsfT+cZD0YNs7f2/lvvvD5u9/64RvX37g33OToosOrV1bihiv0Rm44PRdVeSMrRYwi4RVhyTCEqk2+Ij3xOmqEUUSeqEKkUJLgRCVrugBN9BVRf2p0KqM8jhB7wPePGu/f2KM2WU4dNHZZGTgea7GV+YbqKKRTR5QOpwLCvUY4LH4R5ZitY+kclSkJBG5PmTT3Rv2l5vFWh1e0B3yCJOBElFP0TPqkaBpVpKHyQsvQVtMhFACoIuUsfpUWVAEE0nOeL6vgjPJHkebgcb+GJLZRusDSI200gSvNoXPIjGs13lRe3d+lfzu6f+/4Zru/tNzd2Nhf9ofZN5pra8xCRKW5GJ0QuxJZ8VB67HegQ27eGS3deEgmaRGXshhStEOPqH2Bo0uJRE6nNeSY86i2J/ureU9wZSXjhoFw9TCr45L1N5BwpSJoPlGkl6zpuir5D07AgeDJscuY/GaatFgWcDhbyp6CBwxuziuJhZcA5zzPiGeKUdX8rS1pAk2Rl82LuOgop9Wd5g0JOqjscInWwmLfb33+CyxEHr27SwfNomPkiswIJOgWzYZlGifMB1apjUgXmRQIAZzohXInEhXqQdpDZnEtqEgTSJizL0kXiOEfb+yiIQu2dVyk1CoV4EzCTFxeZ51E5bIsFFQI3fWAqfXMagoIhIAs1UK7y27Q1e4SrFjx9EooWegATlYvutcgob44wv+tN9+zYmzGefu9dyG8eu1h4meffZa9POcvrrAkfePOXTgAhAmC4GltAZYEWUVKjBGIlC/xqBtCDoYkV1OjCFkZ/c5Arl27xsLrD1pbHDQx3pBHUWQGyHt3b3NISLv71lNPPXV3f/vWrVsMS3j40rVNVNroLSOru6KFws2NFjuqui2dHN3c0FBxvLQBfOmAs0Uuspv7mWeeHv34XczCDBDpvGqHn/NtIzisXr0Czv1cLjgPxgNeV/lbf+tv8czwf/nLn1MUJkJwgxDOKECCtoLlL57bvHz5MpugeagOIUstIJBYXltFEzCRMhjq+8uIlnv4rQsGCAZ+UHOg/aE/DGl3htmL3n7rbfY+37qtrdZYUhzsn0mZUsWZ0Rl+VfyJb5xG4PLOY1A7cCj6yzeiSolALlSTRB5wi9xJVigDB0xCGeGsOornt5bLHTpUDmSRyMzkJLpUyGbBIU0Wngm6rjx8xhIM6EjJKLOqFq1gS92BhmJIMb5zMw6/hOQShSEJoJC4pgocIBACCQPryySiK3GU0Ti5aKnXrQkLW+HHYzxDwDFVQS5wEsUyhfzDJopK4haWt5dyWUR/WJ6mhUEktA/6gEP6HtbmFMzCgwnBc5XXCgBfGMBBK9FGsM0xvFRNttcPcJEHxKWw0YoIXVb4hpcK4tL4NmumDZZR16QqtqoXcEosvDCaE3Ex6UsNNE/7y0RcoHI5Icwp4/C8EIBpMQJBN4kKCa/WASiWCazyMo2+vhJB+hAmlMGAS4DmRdrBEMpLt09oDNTdiTgGL5rS1vrWZz7zGbaCGh9ymiFpOkl6S3MDGQjvXZLAzsB1Ga2VS0JBg7nJDSdWXfE/WwUE4xCbLbQkiEGLTEVcii09SlhHmIHApXmKf3wLPvMRpnNBtm5c0jUXPS0iyMmRDzjL0sVQQcqgMqmkeYzvWX14KIg+dLNEFAcIBbRhKqbtsljOjbKE55MthZMbZCdWxQXfHEGYk+lXvMLpcQnszwI+GaQBs15vJGkfQcgRuAqVPFFRHpfEoYNy9BQggEw/YAscOpMIO5xBX7lq8CrSJiNmmEXGAZJfNpc1gECLzwD38rRXTi0O4CmhqA0OrEpMAnJGV/Ov4aQJZk5cG0uXY50XhG8zeeBAMyYqTGOwVdAkE6GtH2QyrWKG8JOfvMrMin3QzLtWVi/Hp54nI0JIm4oQYUFFjansKAiWNtpMli9NTtmlGHg5kEvS9sQ/QTOE4i7kA7JxFubOAEGegZxyeXa2pzCZz5IOuZaxfI1QJNqxscLiMtc0J6SjpMmMMyjFCEVcQTDVPHwaoVwpAYnxqbLkW3l8qQAlZ4rWF9anaFUDT9LEQkWyqD6Vm+UYk6tCUrSlCRR4nTCpmOdwkho5f/Jrqpp2kndyqvB3wrGZMOko3KxwQYZfnebSmMXshbBkAbFL1SKsF75CwiIMIa7503HzhIs+N8FTwyy4SlgiVKm3iS5aw0l2+IwDAKhYKUV9xUKPWEASlQqQtEahGBYjHRfmlsdBcg2NiS1JzwpSpyHi4IZNlAxppNGQayXy2CHBZwi1QWp0mM9n1UAMG0/kJzMByI2ArkyN/HoNX4vmlu0Xv/gFG606fNoWnYsr1/JOTaOJblQ8KgdmcYlT6aYydROqr+wcvvPOOx809h56553Vp55mHOlcffTapz997aUXuS1s7NxkrzJ3m0fb24N373IX3NzZ1yDIA3psqwXMVL/2rmG8G9rqtrgzffnLv9p45JHG0lr/3Xe/89q3GXdefOwp+F/6zGcObtzYe+2nhXY0GDBz+zt/5+98/etf/96N9xm2sBWQo3CM0kopNXf6U2WIC9kwB1DsDxkg9VI6SFF7pgkUTCckJJaPqW9BHE8jMZLyPUmU7MRBYfCUFLZ4BbJjIDOygKD/RJ/EM6sXlgyBTfgvHR/HEwtrGzebeIpYRzStj6/ImGM+iwfCRB4SPQud6xZMhv5OnG6xguxOWQ2BwAOZaIZSWRtONJ1jAnY4PqR0vEwN2hBHYZayucU0pre+BQ63lPQsPIiDiphc13d6kBa6wzt4WqziUqaSYNF5BmeCvSiVdkC3u0t8y5wbaKTaNBSpMOU2BXgescM0yZZK43eoW5gHlZUvsI+ScDFKYUrCvGqJAZkowGVUgMC5CKk6AzNFMPRqfdkIMOHp9dRA5FlHZpK6bztHWuQNnKlZapYx4ZYhH9cvmhPQisBo5KkkHkazXN/QftXdu3vE9CEgWKgf3tUKXOhjsfGFfa0ifbnR+Fuf+uwrjfWj63tru5ro93gaFL6gwqblG85WO9IzDTq/rs6AlkfEGyKhzmRAtVCA0qEWiT7qL6IuYjGOzISW9TRtzaFmUKdL0Wrgg9N+9pLxWLQvroK9BC6ahO7R7QK3d0nznB8soox4AnWdnqa6RNnyqqZ4Hj5tiKA9IdIuH92baQ8UulFMYmq5OV7W8VvNHn+MQ8A5A5ysXlenA3MJDh/EixU07R2jRdoDSXMmF1ux3n7zLXYHc84jyJxiyXIPQ/MTTzx16eoVOtyDwfa9+3YbepkjaFkXFpPQk3Eh13I+BSlOK/aqHjMTHpxhmR6dG4d20OUNpDlBVKZVh5LKrDPNV1fOPfMcGneON3Rwc3eZIm3fub9z/7g3fgjMYX/l4PCJA9Zn9+8cMx6Ox6urj+zcufPe3XsMkMPjAevLu2sNnhIfN49WV7c2rupQjlFnmXitsXy0u/Tdv3j1jTe2H+8011fWMCSKLC/h161xt33x6tXeQ+c5V/m4pY8gsgLa7XW6LU5Ev3t+6+J/+Y9+jdPOf/CD1ztLbCZkoYEVk1FrKbYs0RyOh/TjWBI1mMJ2el06cSDvvvuui0eapef1zQ32Yd7f0228X9h0j8G8jCZHJcowjcad+wfaq8AW7OGQW9Pd/ZVvf/vb/59/+93d3ea4p1vEPvuD9Sg2+hg1Sf7caS/o05J9P9IPes6018ImstIVX0B0CvGx/QE/Vb2OY4v+wp4QcjxEVDGQm9w8qWfWnCgm/tY+Hshjo+8FEt2aHmWNR+lWWbnhj54MDZt6aGEX8wAsOxLcxtU4jnFWCWcOrh7Q/SGqoIf8M6bKKJZatw6RbndFFdN36xm7+D2Xln2CVgnnOoaVeUp0mqZLrjSAWyyyWCudtDoiUlvFSchtXBIPElE6UdWcSxq4HaZA6sQ81Tykxl+YPp3kFOlwC7sv5Cqgi2QcS/GJh0OdZ86Xu7XfV91Po7X+8DXwt/s60awf+6DVgz4ouAWx8ZWtMKQxr44JHo3oK6lamh/Kx+uiSRf6MrGMSd6M2QMThdMMoNjEFRSXoiVRDFJwgANUncpR8baEZuRAW1AWkxduEEaAA4Km8GtBGQ3f1QhO38Qf/QTP2OjwwYyV+MSBMqKVRejmhTcyhiwXjznxl35aCktzSgSfFveoDEFuYkWKEyoVBSfGq2Oya3in09vcPLd/6z6X1rt7zGfEWy8+9dDnPvnEkj7Go2czVgD14slmcfUYWZgVoDknGMadlRU2FTwhVMBpYoKt7b8xvIR90gxZGnM8dARIsAD2FxUZkXBWAGL7EF6BrFh3x4Sk0RAqIhItXkGyk0QJZU8vMuqdDXbFqEvXK5ocG6Vtsama7IcyQixAw4G3ejAW4zI8OcEfiAqqLkKyoop5Tw2Tas4PJIBW3wM6j3PD6ioEaGoMSNcGd7pdzEU9q6xSgDiMLHwZK5RCIsc7S+6ijiWcH3yQozsHV+O4KNkJzW/qtgVJ8MjENEkinGl1kiihukARttAAgbnk6o81bXXKPGplcGT840MzZDIpAROdz22uL/c6jKvSPwT4II5OU3OV7vCgy9dOmMFQ1LgJYZcPtamJ6XRA3DRg+irGheHgECjb2OVjnpup9+XLKTHLMofQ3GXRziIByR0e7R8wW6Au6WVYXNbywTtvXtxa5xx2yrXSW9IjZxUc66me+RrKE48+9tYbPzo4uH/rZp/N0RfP3by4tarpEl7gbRz5kS4lLY3OxSDWnywdRY1EoOWS517I+C6tqfAvrdbjJ8OBK4hcaBWY2ePGla0wKld4JDiquUkQrGBCOsmZpFT7J2RNkOpUYVgDP5Y0auzv8BXo45sfvH9wfzeGYvmiCh1NwI1FDT/aLEDqPlpBkm/MU5RZiDAPBAITmJuVnE29SjTSsPy0nRcInOdppMLTrGf4iCpXh3VYwDpAhX9JGNOGIi1GORiIK0YXpKLZLbMjZrxs6nItHYKPlUnmKNlZ1QSofNL4BXHmssDPnlB3FJ1YN/YXXzna2717cyn6HL4Sob9YxnIbrMVFl5PmA5HW5LHIlR18xfwyoLQkfqMe1FdjutKqIi0klocB6xIPVBRGYtcODU73S2LpXpThGnz35q4Fu664pG4BXI08uo6YYSAo1F3K4aLUuJ4Q9Aoy3JUm21JLYWPGIjwgaAhxdCHqSDTcWERwEbkY64KE0vFr5wSMlHZ8cJ4sSZwLC4EFyzbRYAJfj31Iy3xSLQRX7s+Y+8hK+kyV2nL2W21tGQ80F1pfXjnYvb/WXemP+3dub1+6eKWj26+wrCTIwgxKStraWV/5jAZVD9aRjw5ypDS7g05NWzmaiUXxZ1pk5EmGsFb1XKCxfKDDA4+6MunGG9ePX3//8M/+6s7m5vixJ69eu9Z45hmdEbl5nlWARk+vvi59ob0OmcXcuX3r5s3rNz9gN7fkHR/bn1sXLnI+8vlrj1594olGu9e4d+/GX3ztJz/5SfvmXXaZNW7t3lv9We/hJ1auXOlubbKGu/f2LaSvHPMxieNLxzu//fJTr/35/vUb11e6a+ePxjdWtGRwpFGE5TEGi6iGqFYVoAr2XBpPwKLCkqIJCSnydsZctY+YGSvHhVGKWR2xJPAKarQF5kyycKSFQTB7PpbR7m7vHb1zfffxRzapwWHriMWQ7ohjGFkykXHlldlPYEi6xMFoErlNuYUyowCzy2A56nf7B1uclKWPQWmUJLAzSq1CNc80DdUIKilZkV4ckYvohXmGE5NNlPh4K7ts5NKqH5iYaY5R9nO9LBhTMB5ghThNbzE3XAm8idXU/n9WP+IRvhaimZzEuyCj7Zs7b/1syOLv0tK5h7QZsc050XgLX3HD93ipEU3wJdhZJeYqSHChY95ChvRKtR8qTpXY6rt3ih0nvrM2TrQ1PQiiwKhArE5HczHNfT14u9QMniTc7ElQmGTYbH+ogBfDFZuC6aoyn48cB58J/4/MpxCaIZcqS4GekJggB4L6tbi9KejzLApJwXECwhnIh72EM3aOibq6RhgScB1WnzkZgAGGWT44zoJ51mSB3Kjr0cbKxlc/+QpPBY8+2PZdluoxRi5XK+V1zXLjaG1LRcPcClAsJ+DA+5fmI0NFc6VPkD68PhQBJrkZp9LDh+AL+JS0MH9piyUZJ/y4LOiFUIu2xKRDNtu8GgW/MA6S3CQKtErMM6kylSwIJLBeMVdSJh7n8vh07+YtcvtjPa/jeGP27VL1LCIfHuoZKW0Xf6C9Toqm26fR448/jpNwwBTIKxtbpHvLKyx6dpZ6bORpdrl70roPojE5tDDnckZDLslS2XMvCUSDf+z6QQoHIgLhI8AoT6Jo7kS5RGdy8aj9w32A3V4PfFZ12am9PNTGhF5vk25x7/iePIq+MI5F5oYfhYHv7t0jzVEhlPe4OeBlunfvvs2z36VN5a6vrXz3u999++3r2qB0TtoyPsGTLG4gWZ3ChjzqRSIKaBUh9r26FwWCfX7jN36DQfrunqYvOqw6RjXSFBOqWJrXLidsyDj5a7/2a5juX//rf11KB6butFstbEx7jEeSdJ/urOXeBRNZKIOd2QTNcvYf/dGfffOb3zw42AB470iHl7kpZTfEZh89oBXERXTNyFkFMnNZ4CafyYVhgZREkCzQehphwphiYlgsFm+4FyXVCmhRslf0aXpomIvghW+6EoIl0ceQnjBdlEKB0EEUkZggYW1fAEec004YYvyaqqRRvqTNGYgDjMgyPyA4Dx8axT0IuM344rUiC0IQFhah4AQrdadWr8S19AIkUeCFwwywIBQqi/BlTVUQPq5EFs0vUw4d0jdYWYq3AWT8LHqBF5GbaSe6gG8SZekmC0JtF9IJO8I3pmrWtMIKpyKj2JwsMwmSlDawVI3IE3/xzJ6itINrxzNV09ZswRGHSQETVf2TZSVti0rGMXnBdy590XF8n50ig1DP30CggNaKGARGCXpOVqpJwyc4qO3EU2ddikEyTlJVPLPOSkcRTEv/yeizHwoFK3Ggo37lFc0rjo9vIxT8GFzU5RIKuTlYlvXxoINioJVcgK4j4FGENPMJmYrgAA6aWAHpF/VbECA3ZyDKC4y4tVIStnkKoww8pvAJ3dNwBv9iSZmLR7WHWjZNPFPN+uGZvI4ATojiZs5NO8HJMjxN6APZQHLgmULcW/hmw6x0LAmBJ35ZZzhbW8izlMROZXFtxjIEdFnO5DepgdTQCNaR51bjtDJMYG4lTcKibcm4N0eS8oE7ywXBXPR86Ex7kQ0DgQSc9Z0czk7ioP/QASpOlHMWcMiBuHMmYU2I0db8JawK5MzDCxVdrpi042WXGCysHh5g45jW0oUZgZkC7QUh4FAEcummeIR/6/oHzBNWNzSZARH3IIaKmZg1ol089thjfNOCSwrIhIQR/8IVGkVZajeiYtOWRLlMEKE8OFh/aF2uqFJVLJQACSTAoQgEzWweFExyCtYDEQqtdSuXH3uCOrKPcbBprVWRC1DwbITSIVgTssAUQlUX80oG2jw4QUxuiWZIBnWBtfk1/6LPDJf53MJhBvPsl9ZnIX5hXhKgOT1DVS5LYiHDAszlFaAwPKnUhcqY5dKJeYkwzzglIUBYOOfk39qkhRVAWrE7HBC5JDCiwOGUBmG5UjKYZ26pBcHBswIhZC9iCcv4cBZCDiVNwmmaa2aYkapfZzn2myVOQxNYXC1oyHCehy+GiAFBlUUxIymnxagaZWIygSBlRSkWCItcr0tQzpmWVRVFSRe5Bs5DpM3M0FgTzKWFPzFwyrYF6HKpa7Y/0z/wCJQHh8+/+Ik5BmcCwJAg1FTLiSpsNeWNp7CLoikfVvgFo8ao3dFLgG+/zTam9199lUMyj5eHDDEb5zeI11Y3OpubDQ6fpA+5fPkS4cXnX2J/NBWEMjwOJT5/QbndFV4nvPX66z/+8Y8Pf6yvGW3Gmy5UIvfI9w5H3F+fu3b1saeealx45N3XXz+6c5sBkX1rmOhzn/vct771rVuxs4qRAd20/pyDSz3TxFTqCUpGjd+6jE6DCH7hIP7Zac18ij76DSCqVRs8ssWh2WQ/+Hh4V99gYMJKLvuY2HKX7t6ixcXcNZqzHFaC5vRM0lNlqs0iAf4kWG0YD3RgKToWrWo1CrAkyIWhYwhlmajfBYKDxtLhbipgrI9ZRDFRYe6EeRaguyEjOwYhsaUVa6KFdyXriTP/s82NBquANfEBpjGkMaKmuGvr2mC3urnKKg3rFlDhRVoLUmXMKCNWuR7J4lYcPknJ9IhHpkCjAE7sCQxMTo6Bc7e3wrMZbmt168IUNpiyEJ2wrW6pC/iXDLGOYlhkWgnwhaSS+YAVgYybf0Npl6GOS61MihcUjBqZMv1SMWmICN1Krl2BiSYc2EoK3CaxztjZxRR+FHxGEGBDQCMBfUEolplwqHJngPWlZD0oSFap0TzKFiDcCBhnaW1V61xHx0eHnNrkkk00zGW1ME36L+1zVurxrzTaX/70y19Zv7J/fXtrF6c9XsaeOqFTPcWga9tiTjGkFzN9MS84sFLD0OxKrZfAkiBwNCSLrSaCxwKlni6yG2fkI/A1Axa3KFq406Q/glBFZpW6KriQP1pIzWCWGFUBScmISyL1qbE3J55Vky9VrbBV0gI6SsaIR4+luz42zIhVFCXtrAk/qhqkReS6WFy6fJJK2HAoK3Hzg6m0QUor+Hx6vtnu6Z1W2ifneC61mxzjsNRb4VZnfe0cdUQWYf9gn66EJVfdFMkR2ixAq2NlHOvx1gVfg1+FD2+G7t67panYsqYa3TavUfPx3IvM422W47FerGYeBYs4aYgqiyavUc/rFWEdOo8GZ7eOVpa6Ol9xIFfheRmLP7YblzJdtDv2865unRu0GvwtNVfbox77TlmPY2fOyvpa+3gL/durXU6L3Bt3Vi4+fGVDx9N32stHe7eXNmTh4WBja13f6l1b3m509Hm6Qevo2kMvbN8/WFs5f+v67fs7Fz7z2Ue/8Y1vtFt9do5zEjSPSDurPCUftzd7nfNsuzym2x02l4ZoHh7CFiukcO9L9PxzW//k//wP/m//7N/evbt38XKbZZQ3397GJuOehhZOebx/+zZr5BTtzvad/+6/+7+3OPWZDnokDrGFvTHaP2JQ7/a0WoDLy2R+Uq7eFbt4G3vj5c996dpjj6DJ3e3mH/zBn/yHP/j+ysoqdcy5Hs3OCqyG4VFp8TqeFSf/ccMJ78UgH2NIDSEPInDOfjsrRI8ko5kXvz+xvc7piaEKu8IfD8S8yhqn1aKYb6SHueDLEyOXOD/E4vEvnhmdyVg+7NYaLVF81AjVQQdCEelChQruvdlpSCXCkxMCOUdYiLEVpVCEkvI9ZeQgNSIYoKQ2qOihf9xR6M0hjTvMEkf6lCKtjT37OBI1PhjSy9IFac8vwd1RnRCzHOp0hp34WyNbQ1BrYKFcCJSFp8M8ZDr/AVez7DJ6l+8F0JbbWtnh5QV6uN0PrvcGw5VL53le5DFlUD0Cm5ldZDb6RUPszkk9o8Eh4nTP1GjRRbKllwx5TUymw2dkEkjkvWpcoV2MBe6psIkwPAYlu6nzrahMSP+WWEgB/gdysVWwVCEqhgkncKNGomueIeQyWpZPYjbubPWViuMdDTDYu7HaaXaO9WZM8/gIZeAgm5CyiwYbcjllvs/7I/GHT5q7dKatRJGL/piAhudNFc4CmQQxOE4kcnrW9Y274TaQ4MkXVnqfeOLRlz7B+ycHLfZgBh5wNi9zE1jTmhvNk5YbjVdtHIShNpqKDFmp1Yd0gLwkVELRloVhdskyesbGVengY2rcuhm/GInQmj+8yOTBXcMYWjG2UnrJjU307nHUe0ioOiX16lKEfVIaAhhVCeQyVEElVjnI5nmHqdVjjs1kC7EQYvF4uK/xEkwoTU5ObVUu+2M+z6GZGIzTJiu+H6NN/hqxeFuHNNojXP2fRlsiTX2ZB/rzBdbInHMHGDZ1hudFU7pLGU6MivyUYa2IuR8FbushhzR4WieWWFkJFKUIJaEL2h53GFqZperBWWqx2YynwOzuYsykrptba8t8NTh10MFDbVbDNl+6GC43+8vMhnSuNL7OU2e8NkaB8FhJCCchtiWlafbSAjTEsYBMpeguon4xmN6aiL3kwUpzO/4Rj4aqeuEP9cAm9hTrSTZO1emuPPzIE++/8/brr79+8fwWE61+vINIgVDgqK8POnGfAS3PxVny2N3fo+9f6t3gLbGNrXPM1nBxidMxjGWGKs/nEvjpoVibgib84OE0TMQn9vVjFMzBGOmCKPZNKfu6MABjnJwo2f50oTO5Z6dBK/SZIf/YL7FJv6+PFQ+PDlltoVwUC/dUg8u+QaNhfKb6sAHzvDiiXRYINFlSdjvZ/ipF5J6OVhfNVjJ/wamL0/nXxFV62toqUQlWRjE2loqpIYAQVwVxQSJoBTdm8Z+FVfZAbvChf8B91QdOhwdYNZDnXeQsEqflTF25FGLCKNw85qUJAqfxUh3rd69f3N9ejpdx9NEALKAGo0FCfVuMEdiztgNUaixROni6RtTzViGKSaS7QeGTxRgaHkhnCdyW0XAbWS4ysccCECSTfp5l30Bxf2sh5CG3SFQab9bubbNLesirNQAqW6p4OYfywXyqYoKrCkLfMbG9JtZcKVMSCGSjhkKCRzr3ur4wptqc+mZfhcRoUKZ2KRIxrhIrAGAXSJ0uwMQfLP7MaCZPl1jaGOnxJ3cEcJO9xb9Jf8sNpsbs4ejW9Rusw2qRV5lNPodL4LUJ/UwH0XqM4FcFUblS6QKCOvzZKyiQqV2uDCwcxZ9Xd4lt7VG44/kD1dFmeMj64T78L3eX2jc/OGrosxl8Uel2p3N/eV0bsJbWGS/3eY2GYxOuXGxduNBY6mq/6nIP5Rp37mrXarvPbqz7v3h7efdg5fwTjFn77dtMUQ53thmPLuzsXL91q3/zpvZZv/zpR55/rLG999r3v9++8X5zvPS3L2xtXVz/097W5qh3a00DH3M/yqLZAIVMY1QqIzAHmSiCLJNqxziTmYa8jprQy0SpJcSb1Zq1QEGrmGEluWo14enK5c28wNGrRaOWJgLjd96/3+4dXLvIpK83ijuIvl7/ZuKleUhUk9oA+sfTeSATfYrCYqR3zGioI4rZOtinc7iw1Fk5HnBDDxu+PlR0Q4P4cw0avCC2dLFc4FAL8AGhbYlJ2HPgMIMNhKndDLC4mfFLdWjFgN6hLnS8o2y0dlNvxZnVuBEP/o+04MMnq4D377b5+MZuo3mn2bx47WHMetzBfbq0IjYCMPWRW+B48reemMBJpbWtrPasntR/iFMX1ugP+A4MH8FEeTVJ3Br/jmzZAuiMRUQzF2xowE4QQ1iMZg+YIzor4CRypMywmIdQROxTKmYGn4Nl2XDRbunrqK/99GczufOXNX+EF8Vm9QjKaWShFHyn5/mfHQIrkBHhUMrIpbqn5lAP/zlY3u1EhlqoowRCwkRsrbf2+U99ig0ah+/fo5NiyJN3RrtRIgKYTiAdk/qyxE5IE1Jxoyglmd3HKr8Exd0JessOwQh/028M+SZ3uRwHitSTApqq5YHUGTkuyPO1XLIyLkrMNgayLJoEzl/S0MJQ3aJKIQtkbrN9QT2Og2M9i8RMlQDkykR5mlujFdEFSAKVzAEqMyfhnoTHpNhW22P5tmnVKrWWGvc/3AiRS9bm1jqYvj2m+2cxlGM34LO0vA7O4HjAA+G92wesiGEAOpfSwqHlEm0ZsHWbpM+FTdyY3FIQKZDdAxyYozn3U8Jh4SdciJgACSUVfg7s19uLWkAEZyk+fGkDxxjtD9Gn4w8LdLyPWzVF6SjLxvoSHs4XJeBmI+zv36ewxy3lPnTlAvxhCz4rfTzBYzPcZz/72YOffheZKEYbgY6iLcUnKeBpfcAvhQCC7igMCev1v/M7v/Onf/qnn/vVV5555pn/9//3a9/5zl+tbKi3HB1q8Zr9q2gCZilWkGvwBAiYHoZDPHxrioY2gkyJw7Q7VM3W6jKvTWFnMP/wD7/+53/+7aWlNTOZ6BS8PsYI/nA7ib9zLa5OFwUWAs2QLNiehFA41MyLGiVBrjgwX8h9iJlTKeAkYEzuPeu2xNS5TPwLIlWNp+yTGjLzHINQAvzxQEQ71PoUHCcscQZYX1LRzBMUpyFZmdQyS89s8qXl0iwFOrF7ViZq6Gc6PFA06POEZ6GalpOubATHgEqiRkbcQniNc3raHIipXI64w0orq0u0XxcEM5ydf8GElkAVENwcuaxzF6aLnoGsK1MVWrjRl0ObyHMVzaAVPiSkQB5MQauznC7M66wZzMCp8xek6WdYDgXTwSNswZPmIR0jE2K4Z8lV+ahH4C4zShf9dh4wYVXcVOncUZIGGdpAUO/KnAqbcOmYXp0NNRw76AEITMO5LBDJDrikR+2jGKwAFjSyIHQMHARpGRNUixaLzAQ0By1RTgfYEsgFDKEDnAmkzRA2OZGIuTQ+ckGDHHxDLAgg7To4EKXKDa6KEpciMRtBqIEbCXdjCTdlhI6UVXKTzkLQqoRC7H2W4kmkf5P4WGgAGaB1IA7MnJ9EPfhHZeA+Js+lvWhCqQSNIINiE/e01SKm9QlyoYMLhDplmMNLe7FkDw9qhDeSQGMQJ45XfeEim4ttBAhdC5gCIOvR5lZiEiVAUdIkVOpcKb4sufAE2QgoxiXBuQgiAZB8Egz0mg6FR1IBkFgx0PB5tqf97Gc/u33zOrqRx/TDC9Csn4PZ62kmNm7ofRdmbojodtaYh9DFgd9eUhkpHunSspBoEVZG1DnU6Xm06aKrpei/PrAmDoiG3HFUYDJUKXUWcuKvpc9Y2GqcSBMZJpknPJ3qw+aiHkWiOARMjcFnJHIJDsGcsXlYXUAgUOGNpGuqckkCBBFECBKiBKxJjFBiZxVC4KcgFyo4n44WChR0JaQNBZk4SwJOIZ3hAiYWLW5nCAX/DLgLUOalFMjpFljA61RQ4Vb4c/cDhS+peiPUaEqHEUgYDR8oQkpuYVhlpSZMFkYsPJO43FDFttCcISFmQUCi8AwJpxELOfJJENI6ck2RS2cYZfTcNXZQ6IrgPrwuPsjWwbGQUkc9NcKC5iywSRS1a/klfXpuQftQCZoefSAloIrpxpnhc38K5L333tOtYk93c2dkKPVkCoU6PWOW0thJzGRBiBEUR0U6DTMlYpy1H7KOCsSK+SkH97OEQVMLhXu9Fodv7P9MHjtY0uh5xI4vbXvSV9yuPfYsd5RPvvTSk6DySBePHdxjT3Tj7fevv/de68Y2bs89CPfRt/buP/nkk5evPvriK6/c+IsDgOPeOp/r+FbcQSM91CNKtQ8/FS0ZgCsFIKzryhpVCJIJkEsjGI57QTVDAvU8xCxNnmhjEwnNBmQCr7msdDYZgjs6RFOjNjGvOpNF2oRUgZTMrcA869icIQETcgq+xJ6+HEqN1yRnTCMXnlkZ65wcYCEHa1Ky5Dz5UI4CNM9yOZMgFwgSg1VyP7NVHOtgVgkUkK0bBOQCJ+akVzjwSIYsPb9tNDCyIG21lOOuDi7jdXXS7eVlzYs6cYRpj8cA2mQAFU/PwGdEjViRFegPDhEx1KpJf3hvV9++4kaY+hpxZOrGhvHAIHA6IN6HdxVitWBf80MJq1iXenCYfBByWwG4Bc+OikF7aiRZ8DO5Mbk0Z4BZVOKhh5simBSYiTJoqAEJ2OIT5/tgpC984QucyM4r9Y8/8ThNsV6AbsVurHhUQyVAJDWwanrmkPkXxeolyNAg6aOHjMkYLkiCwzC3U8HNP/Jq6oTMT2YygVAD8Gi1GDiP45tDWtTACVbYdNod3N3l4AJOh9UtaFefOhtz9C+hM1a3xVZa4hW2njab6wd68vDZRuOzzz/721vX+h/sbN7Jm5LYv2xzht2UDDvwyy6hw6P9/urxI488tMM+Wt4oXOqcv7C1NMItu+1V7iyWumvab+unpcmS4SuojVZM86kUDpxBBwIciIETSOOLJAwXWswJuATuy8rFg2l0RhDWwayAUMAJ3I+A47ogONfiCiaIpBl6iZ3WdpEIhfBowMbcpHn2AdspaOMJFeUwFfw9y6AIphIgisn9l3hm3Qp/Oa3gQqNdH7cZJbUcvD/od9st3iDi7mW1pwWa/p5uacZNNjgzEqgH2dvT3BtDIW65x0r02tK6rE0FsSLAfRaN/7C/vbN7xP2Ultv4tO4q52dhfA7uEBVOoyfDw0GvA/nyvXvbTb7dkLoqWggFo+Z0cjbqq0RepucmlAaiLmu83uZxWbPTXb178wMKHeWn3tnsLAv0wmS4SG/c22mt7LETT6NY6+6dA3K7qxsPPbLZ7ayoY+rps1pXouyHO7so3N5Y5byRtTF3oZzt29s74pcjOHWPzqr0+tplbM/JGsud5eH++8eH3e7m/sNXt66/t4L6KhgaN/tsxhq1+ruH2wdsZqaL4JEfbaOn76+2+HwzFmjdxfqt4TKQr7589asv/26zIU3WW701eqF9duZx0CMvQ/VkB316V08Omdhpq0JLvpEH6faRHmivYPxzayvce/eHPCQYNFpqfUN2LbSOvvKZ5196+vLeYPn3f//3v/EXdPSPjOI5oP0qfdU3Ntxlpn4MoQE1+V7uUyRXjVWVUhJUvS/n44LpLF+qmWbt/TuDJs5ufDgA87FEPGGPVgkYNplkRIqCFwimJm3+bhRcltbhLHIhyZh6cIVvA4SUeYw2MFHA6G951kuuRyZ3DOJubWUEPVNZWl3TI7qmHqjQXCI7dLCR1OamzYVXa4y2zT0hMw60pZQSwh9jd2r1iKcF81+dkJZL0J+xZnA42N3euXd3G1T3CezVkg5BPCvaGdOxcU7CBA5nKEpimvqsVzP8Zy7NpVTKWZmeiteJkXfckZ2X+dr1/tHhjVtXObc9HmtSzVjIu1Syd4udFUuWjNE5uhrvvaJ22Xyk44xVhzEchLsVPdKyTCld8svwT/sbqKqpyiXqS5vaDrcILkFoThZ7UeFKv1KzMnNiL+oJey7IyOGPJswSp/DI4ushgHo6iPh4pTHmdJdOuwdyv71eGrO23Kqf0jIaG+8PmQzS36udaG1XLZrlPynYYr0KpXXqvOQpw+2olmpNND6yeYS2qL/ucnetO9anCJYGNxH/uU889JVXHlke3WWbP+dBi5yv02rh2zNdtylzxfaU1UM2NlPnRv8vUASpFfuaUQggAQSUlFtE7QiBwIcOui19NI8OISZw3sgLMpk8DxUPL5WKT+yxCXIzBK3M+szQ0gGjsl+JAK5iaJ7i2uRbC8cDTRC1M4VYcwfZUvrQEwa+y6WZm7tWEbMXWJeoyl+yc/h5dofACZuoH1GIuZz7Hc6M5g+JMBzF/ITiMMZFXaGhArnooGrPJio9VkACDckRpEIVOLoTHGBYRtvGaUThiOEo9IiuF3HAniBaRFSIIKG7clVDCkC0pYmu+6ErVz/14idv3rnNHIZ5FFtfWH9m3rixwm0jpyerDNzowFD1y0sw4xH34t3GPg/Dl4/PAe53L8KRe15xDZcNESlCdJCm4pCuc+s0PbM759BW/uZ99eDMWINrVI05lfZqaejm+YbeJNDmnceefIpJz/2dO8yssAxvr1oo9c2Ojv7RNlN0HINHzDqUfnB0f2+bz7R88P7bTFPOsXONmTkug4HZVaRaS7vIo0LghP5IVLaUj918MdxhnkndlWI6ISPoRpIWxNtTeq/VkwXgWlSCJY6RZnTiSigcwFHjUZht9+k6axY4c7Yy9GONS0lnuBadgYND4Dy2vXu7vGO5fff2/t49Rny/vWFC7XMHMS6gJSx1ur/5m79pe8Yt45TzgAgObInNwXGCqPvDwVUvhsxgzlzWHJwubMEskJKewa+4pV5xHqFAzKSwKomMMFUcgBVzocxcGqIMsoLUDkKaIszy0qgHXMj0HuqCMCPlo4zuQ4K/siPMk+ccqVHSD0xY0Bwatko1GDWJa6u/vRBH/LXu3NnkNB7ujIE1l2mkzBVQPsaI5Al1edVwvFGaRSWaFI5RyUs2cf+Tu9piHroy3SglAtWgs2RAL/TwYRjdQKlbIxY6KoStwLYhiN0GkYV09b3BSFGlCe2dq2I88adfoYjBBRrVS6RtNFip94vDuPg2GeMY2AzVkqolOdB5r0pjnsUgGZ4hmV8hZeGaU8EqckFPG8oNsYZGhT6T6FeY0xADifMdkNAcVGZRm8TVxMSiQe9pJmjrVbvY7K0RP4YkPunAqf/tUX/Qa3Hb27p76/bhtYfWO+coQXSwmDaMIjbT6sWYrps7gmTzV4osYGiT1MOkIKYLfpCdpzESwKysG64TVDHhhZdKoWH2mG9wkW5sxNYoZICfldGdgu9E2AGdgRM5YO7LtcdHP3r7TrN5sLm5tbXV2dhq87XDhx/hQMzGY89effipo5u7N3/+84N3bu4Ojje3R7e/87NR72cs4F5Z2+wd9Hdu31kejz+xssuYdXu4QcUddze5b20ecePbPOzyZqEdcCJXWsU6m0H2qzy3S8hhTpU6vnMHD9mnWHCmLGok2X6QgAk3LG5YzGjkWFD1Wt3+0fCtd++NGhsXz2GTDgsDHMHGfAEH5mVkqwQmVVD6hywu5Mc9ID7M33JDJ4C1DnXsBp9qaHMieLxxlfCnXQLOLqnrzoKsbUqr/coRLFc+gIwojhnWyCapY3Lxc7lapXnpbiY8Kxq1a9/Sohter2Y+lC3VYDRXEm60XO6qSDJy6T2y8Oo084zK9dpXIz7hJQWYysb6Bis24hN9vjpRHns027yozWNbHfgbOxFUWuYV+rIHgUIo2P+JB3vqZ1gB0QJ0HEHJW2WE2FodGx5Bogw4nGnC/6W34PxUwSYAkBubCokwxFZYv1RyhpsvUQTRM1nzYkCogWEMnb/21d/8TV5eeOutt3hGBAIPwQqaSGKPBrNLr9/ZDuwbtcSao4s5I6Ww+utLWCJxUYC5+6QuKv8+SQcIVZt8ua7X++Rjzz7//PP3374Dk5qnaedLB5WBTOzoCmHFkeXEDlDZblzK76ergFzBA8duaikAwXSgIIggBuiY+b0RiAmQGIGESHQXUFeLWBZISQga8wUlKgRfEpuzs1K3Ug3SunWvpJBGtxkIXItxwA/OqfMVPuagG42B2bRIDA5EIDueDFQBDB7TEVRI4T92JoFnDvbjmRJLqyyqxjI9VWNZxKy1MZDYhtCS4FNRYGpeEP0CuXgSUDodglZ4eRo8brC0/e57v8Ar4AAfEIjhQHCCUpAopgdOmljMcU4NSdpMSkxfYyrwuURtEiWgAM9dh+sXOUvxtTfeYO28tbxGEZaXWDoZcz41TB66dkGsBpoHwYpdyUvtMZgreheky2oKT9JWNlb5MD0r7GykOjoaccwinNmdxMfqafVuI53WEJ/vrMQ+6FA1aQvTXC7W7aWbSqkQGVHkAKESrDYvXObmuX+oPVN+ycuIlA783B+KHHxiW4BHM6jH9A7FRupiB95RhdpU3Ks/fvWHP/xho6Fb6zMG6RZ6WspJVM5NqCchZVXJr7nV6briStoIxNlgJwtYlGM+2K3IRU9xc+vIvVzJNT6xHSnQ5BIeVtlRrvTEKyciQ0/VBQ5DGB3p1bb5AFv5f1SniwZDybKh5wlOgIRiMCjtQ20ftXFU/JYVOJhCKoS5Ms6wLGoYzmWUcQZrcmnRXJdEyZuHlKyTEpAQSm6dBjhffQXzoyWyxWS3AzZo3Lq1/LBOSqXDDYYTTU7nj56wKgEOdDrhM+q6sQ1ZM2WZgUABZgGSgKoWKkgAahwhRSiYQZjaVJQD6ROvMJo51HEhP3vCgtCZToZHHb4s5Fwyr8MJWRGTPSOQG2jSuBTO6gtyQgiSlCf3DR/WT5x+S/dIf0v66aef/vKXv6Q3Tvox2Q2GwJFOUyBRs7c459IAuURPBiMgoY/aoPGN4yIUDgUHtBrTbI1GmqyASHStAGlCMksax+UtJicLfHIJ4UTSJNIaFsGxnuYJsqmI4eBLYkISGvMi28x+WFsCNDhDCjIqSiTVJr8R28QklDeV+JKKuYpIchAwXBFanzCQc/SLYnhCDSlppBC4dMJxveXJR5rEOlIi8mYLMBFqWhxcl5FvTZCIJtyiMDq/+OKL7LriARMnJOzuqT/krGTQuFsOcZpDZurkXvPqWUPDS2EnCkRGUibKUjAL0BBiqAgYhNgX88hQeVwgi4IUKpE0GozgvEHYePgqadavzIosMCPIja/fvM3JnodH92EFAgPBrfYt7kTWN7ZknLA30q0e+LWe85eUCVkE0Jxb4xgIpA4g+xKVTCj6KI6E5fYODkDiAgmsBZExF2RUoAcyqXA/niRzLWZWTIW5xauHclqzFab4TqDbc88998zjT3z605/G/yQ+qozqqNW2KUoMOQjWdSHwoxUDidbK8Qz/oo8lLhQxQ7gQpwBP4VNwZhJFw6SMLTaD9KDLWskH4X6c+XV5rQOloN9mqHJxUqGqvuJDiXebKqvLiJi6CzCvPKIii1xLxJfwPHlU9PkkzEocIrjREsMDQInLgD3RPGtsNK7IKgzdY9DKnQvc97kmj/NWU+/nBqICKOgtED1m8UWICHDSXxi5L8ry9QscWbBSr5820tb5KR3kE3gpywR0hlTNhLRspG7Nv4ledhjrbpH5CWPQYV/3uTwB5tDbZze3QIIOXec2myby+R/LyVU6my/rJkWUJenR6xqPrDp3ljiuC0Jtkzo9T0Wu7169zYshhnlgv30T+OFPf0bcvXBFJ0c/+uwnP//5xsO379y8+fabPwfO+9Tc5nf27yN0ZW0LCFaCP2oTc6IZ8BPmCxMtShk9ktSWWVgWUyKrsHBa8VTVlfzZBGypZfBpxdRjr73Ftrk4Yy1xKHKhBK3Ub4ZLjMV7pACBQG68jKVtZKSt1azs6evMcBo6fYV9WBOHm8qXfcPMy+U0RboiF61sEx8TVNBcQVwKIfcPzq0neFlaoivinCCXBFFkK+mb+QyxERJt+Qk8HmbHe4pRiYkq+hxmd4GZbjRA5hIl2yMtM7JuIvV0vh4r5FpF4fTxDcRSUjL4BCRDuPrIacUtIJbOxZz6pPxYMHW7PC+IasbGIftji1ADKSWGLxojulYvG8tGlGhVSK6YeABAl9965PEnPv/FL+Nejz/59Kuvvvq1r3+NhapeT/scteofirP6zCXM/W4d98AcCMDWDW6laIzKYrdkLqMT7srrfrgMD+CfElyuUxBmslzkMAVR8hhwqDXqTZ9vaXFezXiZUwPZ6dzXC2iHsW3CZ2j24qXU1b5K96XjxsuPP/9fPfLc4Mbexl7reHfyttopWg3Yq9E6HnYHrbWmunBCj7VLbS5i6m0AjSTaSSwMYQjqLgyrZy3co0ZV+nksSpIbDoPCZOrWqzQq5wIlwJlLRIiVn9YGge9yQFDuSSHt1pEspqcnYVVw+07cZxo6xx5WBDKlXG66dNd4KkA+Glpxk7vaBEKO3qR4r073FRdFoOkneNYJSLiMEyqVoKZpcbwAwY4svtKztrF52L9NE9Ee4HjWCgLmQj0I0Qa2khqBLH71Xmk8rcKQ9N10ALDtj7ScvbS+Cg43VLwtSoJxaNDfZ59OI54EgqZ5vIpI8M5Nr9ZJsdwK1B80Y5mj21la7nbYONbhJAo+LMsRHzpn87g9kFPymKyrr/u2WVgerjV7V9Y7OytXL18ctzscUMBnEnVjr33Zndt37nHzxo49ODSOB6h3fmtr3OsMx/3j4T7HQXXOrTZXuoecmXx4fHvn9uhwwIEej1/QEvbB3p6sMRp2OB00tsCxQ4kzs9jv3F7iT/sEB5zS2GIrdCyyhzVww6hf1ScqKA1Ws/nwlUtv/rx75eKl85tb3/nRm7ENXDfjzdj0hMmxN5vs4Ok+R3ZS9XKMZH98qB24XKIPa9B0v6BRId97887wWz957aevv70zXNe4710N6cl/4pOfoyo7h3CYdC+UYb/Ub6rZ9BC18v/gmrwz0mheJDldQ5yV/KHg5QTOX1zdMF+64U/xqaRkasmNqlHTExCPxsLRz3BBlmfWLkvSMnbomwPG5yU1FgJ8OROrXXCEORYIP1fTZk6qI0rNKfObVoxcQm5o0ScYpAHaT76kDmfuclzMwcEeGIJr4zwC9M1n0K2Ji+Z0AXLpdMnlsqTFKrqdgs9ljV/gBdP8Hde5Mwi1iJmsmgMFrzvtOuvsaXcq3djkPGjTtzS7o87B3Xu9tRUGYp+mcBgHX9Q83f8bUvsb9Uj3gcdqU6oe2KlTLoEi20RRQDJUOeQaXvwcyq5uCxGiscyGCkunGQhzBuDIDUILILmgKi0asWgSx+kVXZSQc8gJpoC+gBt7GOiMuAkoOszjxQ5r+lWda7m50rzXOGwfqysZj9eI6UPYzcSn4LXyq4mbFCZQQLHSllMXCwIBeGxICQ2zVeveTBg5aNbEiUbw05xS/Vubw+v7gwvN0RdfeOrTT19ca+zwPXF6PRbmJDGmncxV6PkyjyQcZXzTqPGDs3gjYBR1xSjRZBMT79wgx7tUpSgMUZKhjjStvsP2Zz6Qrmut8cVdiLpkG5ZHS1zoOgghtgJwCNFSD4iUUCItODoNkNKp7hQ0TACR6Li15uYt0cLUfsyDLj7SgMuEDOwtk6MQf9pcCpJyTJUSZGEfVQZdj3yJSRyY1Jg+f+syRHljaIFDUjgkTCLzdN2RdsLZSodCcOe3+HnJte6gOVCbmFFMNDGjEiK2JtGcgEtcYMNEv65WfgWXhKgdhnRNOMCmA1QNjka9VuPKhc3LF7X8ijWBMHtixJcUZMbx2WJCiNJ3R7y9xO76PSq3PT6HKPoIasFL4X5RVMhzAcw5GICJ9XAu6px1Y9eHChFySRBMmzQBLUiJcGfwSTCU80WJi1sXGeVB0x0VPc9QB3MRgAyGekMRZ3n6ySd/8Or9dkvnbrHs3mncuX3j/eXlHsdDq/XDnDex2FgQ2/9ruahhzrKVLR+KFfWMXOKkLZ2m/vi8BVuhZWK1K21jVNOL7Zja7FkCVMFQaqsHkDfGeJoxEtt8efbfk/Q8O4ezYFq94cFRg7nmwSFnvK4s433qPCg2/3FBbewkEY33xWef/4f/4B8+fe1R7uy+vX1XxdeRKbSLqeFsXnnso+4mzBVC5QYAqR2QDZ9ROCpuBqbLIFcCQtPqIgcgOSnMuFRRZkJhYrjWKuRPE+YzCEYzcJGIxD5LnJGWcyeqJUjNSh02NpnCcX/jUXIBT1k1h5PMVeNk3Af/luKrfbI3Er/mTPZ+nwdgq+x3IWiWp1cvaN4uhTRnXMDUVAE+Q01F35k6v5CZcISU3snLueIfnNK9EncEXOIfQVdFeIutVBkKdXgHAoWAqaOmFw3vpezca6G/cZWo3IO0hgsJDjNObImrqn+TRlEaYYYPubBBo9PtASKW3jfkuZ9g/yTKqNugVbA53ISUBc2EL4Zw018wJyEVXE2MjsCl9vQBF9ZfeKeGoqexii9RaOprJrhP496SeuSND2ilJNM/XmnXdwPYcaWX21n1ai73OmsrraPdXrvDC773tnfZB82DQN+722zR+U3MGwUlH37OV6/uUiRbISzqS2LDJqq3bGdDkkszm4PRnP5SOILQ5nLhMBPi4OgpPCvEscWwWY17c9VkeBfaHvV18Eh/f6//9uvvvvEGm72ajzxx4drTvUeusHR79IvXGH/PNYadbqd1cIgOT+1xOO3oxrLWB/bWVoeN4d0VLRfOP8y2Yq4RJmpM0KyJ38nT/FAGsd20VsAcgjjWDOktsVKqTVKgphYSBzjQD6sVCZc82dxTRrcy+0M/LpZbS3v7/Q9u7XeWL5zb0CvdTDPR1sd4pn3QsWOapkzpzME6m08nararV8AHK6P+Rd6LGezTI6jJqelr5hMFgTTqPmY9xSfJOktANFqF3MnIK18K5nAgvZAPcDuN5u45yFWifuGJmYI2NW3giWe02Zqp6yJ1TBoYCeIT5JRT75XRWHyZRSFGPI7DqvZ9I2AaBLHhnJg0QOu3yiyXdIKgdUye9RGUWDVCYS1As5IY9uD9fQoDAV5IYPVZ3GXpWvPULmqYqNV759KGvlChjbMmBfioKXMrcWEjo1chKZocXRlT2YHJatpv/uZvsvrMmvJf/MVfcIYObY+bWzJV0uhRWKCkybEX40tf+tInP/lJnqiw1fHf/tt/+9Y772IW5pFamu+qgA7B+EyRRCwK8AF8QuYCgpmC1xiIcAAIW7u7SlYaTxbDkPDSMy9QwMEt7ZBdiw0YZ+n4KL6ZY8x2W51drMtLBAG2CHUaFQIzNIkHi/gmWUnDcE0jmyGxfEktPTV1EkDgSQK3FJz/yVxiA8lCnVNWblRpSMYlwl1FlO0gDtWl4SHBOYtj0KI/lAIkeWJhQsHDiyiK9ZzQVxJRQ4jTYsyh4J9yyd0Lgwk7lO/dvccTzkvnLuKx40OdgOwCQgsOlyQYMBz8SBNjEsQ8KgJ8qo/7aZTpMWWNLWw0EPyfyRnKwJlxC5z+IN1jS8NUxlkz5qqJ2ZhGZj02oH3xVV94Suh0AB8FeCDNgnLjwgUQWBlEz1ZviUR7vAIENSkIRQBnfeMa6WGfV4s2qUvIuc1g9/Tuzj0QOvFSUn90U5ugu8vYZzTaRXlkWrRwOtqUzSfJUMwDoTUiC26gOUGsJwYKUY+R1NvdcST0H//xH/M1Ie1ljuFkukzpCkrEliw4ozlfzaKtmS+GBQACmN/73o9huLN3qL4o+udC+DEmwpi55j4S31JeEjbpR2JzIhHOQB7MiYu2RZAhGtSi4WA9VU8gk0ho4WPOgpf0DAtbZGYltzQTqigDhQIQ9yDX+I4BEmrITLpwKIkZhHLJ4MK6g4oZCstF5hqFkWu409aBNAnHhW1JFBwgRnMWabrOmmchmcGcgTvXQDOvEf4G0rQXniTR1tI6UzwJOIsmxilFJkGnC7DUZG3DgnbGEom2dIKZxgxrtjln6hcEHMB+a4cnW466iOcU5cneAprZwog0na34R/djEYwEJGJ8UBuLdNoHPSMiGSjaBTwJMwgzlyDADZ6Bq5algenevccfe/yll16iz2bC4MIiNXC0+LiQrTmAjA6MXwQgqrGoM2JTefe005Gptkkiph5ptlB1vcoyApzdtAsrw4nhRiHM02rSsxTmzrXaAOEDhEDC6bpEzoInWcQOBhJLmdCHdEh0RyoNgQg55kX8KjfarGmVVQWRp6l+ghZxRe0KfZIkNwnKMKlEsPAg5spMHHPJjaVCTG7iRjsoKA7rEtFp6zoCJIU/6QJ0gqywVYJziVg4YCnYczIgaRiY0HwcS7p3hZvRohhMSy/xDJZZAYwEkRsF580IseSSntccoGokCksJhc81IRVF5G400Oo2IW60Ck8Q6cTY9/3+df6931tTuxh0Bmx/WVnf4PVnegScU0xg4wqxtyApAvCiVYad9itGsaxPrLR1dipYwW2eodFqvoLkeqzhZ0kX/vNsS9ZZ+JwdB+/SC0ajEWdWFhEkgPBozGpwKWcbDnkFltkm+MAJwB0jjvoq0wCAQEpuwbFWkfshqgY+C+1pKTMlLbJKYgaBy4WE82gnQUwurSLMCJq5rJkUkhpI+iT4DNr8JZUyD6whVEp9+ZHTFMqydN+Um5XVVpxNcTp/OOBUp+PA3NyUcEcXY4eocj8f6dyJ6EIh2dBjTRAafkpsEUZQoSrHwG51rvnPQGrOZDkArGlLzZJbp8EppgBOblEj0sC84lcLUbrmM5uXr4ugDFjwK5w8k5fE3H+CSpct6Vklao1Aq+93DugBvJ7DqxIX+JRfFSgRaIWqylHS8Do2glWtFa7TNWHNeQbHrH6ZuDDHJLVKlJePvsF5d3ubRa2l2zu8tbN+7fHnLl5sbPZuvvPO8IN3qUeKDQ7FpwPkxh9uMlkEuC2uSLlz8mGokjNEO0rQuTZVlHRJixFKwjo494ExVNy4Me3kZN2lzgZDLXKlbCybYgdxiHIBpHJTKWIGxTI9hYt8fa6JoCWIHPCgeekT95gr1zzyAyHSM/snacIpJAUTHNRQHOg13OQJkjnP8JzB12Ul17k1Drt5uKRbCeCko8aygocU5/pewF8I9+MHbndBcOd9zGHAOcQZZpoQUhBB+WHxmetxWqVWm04nhkQhu9Hfxiw0nkpQJvHiHNgY2qteaaq03n2WrDpRfQpnUTUXBGRQK8SGOG3gBCc52aTyEnZgoDPN71MvPc/S1Xf/6vtf//rXOYgTCJfwORxotwIJ4kevPc73Cb/61S+z9Pzqj3+ytXX+y1/+6uOPP/n7/+P/ixfkWZXDM9kKyXRYixjy3ImD5sY5gUy7b60RapVL28RUs/apSy2abAS2mOIwcUmkPtcx75aDduF+49GHH/2Va5f4Nvdb23eA3Ivvj9u5nx8MX3rq2f/tw88fvruzfn+8xkMh0eenT2DrCdLiwNuS6D3sjI/53qFuFRo9dh2xwULrNh2VIZpuCFJdgCAjhXNzi4iSNBj8DHXJlc76camFVO4y4AChZAROMAzcQpg6F3U0yqgCkKmdztVjCaab5JZgEVxW1NSoq2YyyjqXPSMkQp/QCt1izws9AWCyxMbqxS83xYkQYMB9aXGp7AFyuoYYc0Ieco2gFts6ZrHyaH9w9+7OhY2LFy5cuv3Ou2gjU6MTH43X3EhDL50r2u3v4+x9Tr9ixOW7pXSy7Z6eiHKNz2tjiBrA8aVLFzfPXUTK3v3b9+/d2dpap09n+ww7j1q835AtH/tE1BmhFJGkROxathHYaox5uuPh1krvg8ERzaTZW8Jll7XJh+fU8pROr3XzzvXBhc4Tjz06fuoRVppaS+1hc7R3X6+prvOUun/EASOUtNVdWVpdaSyzNNjurnaave7RqE1VdlZYbF5ZPb8FN57cMaHcvbN94dzFCyvdn/zkJxevLnV4yt0+Ynn3IsVlDx6e2O20N1ZaHNzBg1EcjHobN7qj42WVQ9aLhq1CMKbqDluPA2MNJaYyn3jx8suvPPKnf/om1mMdGyRMDeV8eyn7oGU33aiz7KJ6GfgDSngv/6LZtpY7u0fabC5LyqpTe5997u0CAUL8cAFNEHESDVnSKaoy6lp1hE1c76aauSszkMbgRLpUMYBoThK0Vf+WxgIN+XhcTVXSwOXG4fAzuob++CGZ8WCa67iQQFw0OmOVIpHprXUqWpzdA1Rq8hQC2mjpVLI0MVWvx5EvS3RP4hn6s7WBLC9SuHbEMAcrIFa5OKyx+JKEqYpvsJzq/kejBnUx1nQwekT1J0iMy0rLLOVD/coC2SxOPJDcoufRToLPY35cEFed90GP29pzcXR3uN2+wZsRmg3HCDPM/SpCT6oXNqDS1PRak2o5tjapnqlIJqD4w2xDcM2am8uSvDa8kZoJS6YKqs0i/lFx0JJ284rEpBbmjVO8ZT5rHiIRqJF2f7vVWLsKF9H0qOx/4SRLvkPTanT6mjKymVnkWi5DPZWd3VVwY4LoOWLFokrSTrVDEjvltpPOoK9wYqjX2RDa/aN9E43mUbszai0dL290fvUzn3j+kSu9/jbV0I3Oa3jMF+ZZWWaGiUbzTu5+Ji0NoOExr1ppRFGvFPMA+Mf9MFuCozBSJa0S64EiFuENFzZAS+1QS51X9CTUF71xDEkkadjcdKCCuUqZEmAZdkqMuSSL9stLPCS4hNpAFNRg2mrFPcsARxOhKoGHl7yLFILUESqwiUyHRMvzfMMgVsCJnSCtVdgKqAu8W1vGtS9NUiMEla+KD8DHXpHUy7gLf8FMyBqhkJj7/IJto8kDkIsXYH/+x/AHjj08IacS6AoqF5CSl6IFvkyhrbiqFw5319zSQoltTfpDSAajQVSJNtx59xaeFYKkMPvY4MN5LsRLEfMNBsjTpybDJNR3SBSRK8qpok8kxE2TKM35Va20k8C3VadsSC5OqFi4EKovNxWTWOAqW7YIlwpxvAzf4uFGAZMAYCsZtmSCfG7rwiee/8SND27gFvyxM3p7587KzZVzG+vnL13mhTDeVg9+mmimSgqXKCLQ2bWTRzSXN2muwkYAimA3crQvjkKmSk2jyv4pB5DvFQmJQ+FDAoTF2VO4UxcL8eFTIy3EqRE+bJqa4r079k9R5A/ee4edjrYiVaJJQTws4ZMlvCwy7vf5CuSF3upw+/5uY48X68bnNifiorxF24V62tVdF9r2J4/WmXhAanzSvpxU3ETMxNNqWSVdIaZkZE3qYiEmQLXqUwPK4F2oWsY7UaVbL3msdTaPIqUGzrM/PXce/8NCQuUPSzSLr/s16qkx5O2Urf2793ZurMWKW5tXd5o9eh+mAbNtaZqHO2JbmCJjnDRz0D5h9RSgq6crIZI2Dk1evWg8wcPKxWIkqY/ougqZGp3vy9ynRe+LevShaUc2qEl6NPDCjZYvTdT+UxdC83fLI7ZmpU6LPHwBC+u1sWO9HlsQ1DPTxcpb6KyT4xTORtN4HLLCIJ4NSTdrGNqpBwIyURKGoQq6Qov0osnpCXOASoUk5Fhvjag96QyNeBVftSBkiizBwkV/ugdiZHd5UxxPYKlr1DgcHt29vX3l4Yc4mHbEqbbRn89rJSX1fFTWhaXrvXiCBJARtCHXlk52KLka5slxfQS+Pc6EesgaxTD+KXGPzd45TLf3KSNLUpR9pa+R67ip4zTXBwfEh2/eu/vOT8dXn7nG4VGPPXz5sYd3X3+NT+mu7e4vdZauxsc5n9vt6kCjVVZT+v1V9W8HrbifSqIX1xqTMdknRjBVgCzvCgg3iLdqMD4TIu2S1n0W2bn+olht7VtPpsCJybYkl9RuTTcOZ6iJB1HX7XHn5q3tTnN8lU/HxBK0t//G6WVMYSZ7uxASdSRa+SELKgwMSDnYXxqNzvNxLT6O5bqIbStkUSKQCaa1exSgs2biRDADXXTp2ifHDH15EnPg9r3kRVNipi7goxvx6TA9HVCe5u55zpb9GSGphVFSIbH9LiDURXR1WFxgYzG/UWDi68k9XUa+QxeO5n2utOghYyYFombE2rtJa2LqE0+xiFmpgpV5hwClXQrAtaVKWsh/DQE1ioikUvhNSU8hJB0neljnwoHjBTjqi1Ue3uXn8fja8grthGYFk8OjPvCHHnroi1/84nNPvfDMM89sbKzdvn37hRdeYCsoWc8+++w//af/9Jvf/OYf//Efv/baa0xdaR6F80TkolTRdlGm7anuDG6nYxZy0OYxawiV+NDVR1555ZVrzRGFuvMf/+f7+3rS616exItPPPfZz362/+4OmKlObawi4+SEF1C0+FaFgm6YLxHEpYXWCNmbBAOhZC1MmAlxnculIDh8VITScwENJzAvP8U1tTmB5xQcZjSJy4luzq09KuFnFCkUajjhxfzc6KKYqc+QSGiLWaIKUu0nnqGVuWUFp361iBlPMnnQxw4a1m3ZysFpx1QNOXBeWlphqYum7GqyStwnjzjyihfQ9DaonI2AE9MKuJGyVhgH5rxQfP3GB+SurV3lXQEIWfqxbgDBFCUhbma4VFmy95YiAAQFbigJDsdRXVyfTGuAOIDGEeT0OR8Mh+wDaq2qCzvX7AGhX0S3/YG2P8MWQ6EJ2+uOBxoNh0wh2NEcb0MzwOjzbviydF7D5/dvX3dFI31/e78oj+HhSXEIvAECPlnEMEduJz4BZGApyHzit37rt95++w94bM4XD+F2fKgD9XOBplzatBYBgus6uuiEjhFpAUiEA99NpICcYPWfMBRVJ6VOU7ykVIHjBSU9NWmPFu1mbUvm4qhgQPJluE1cYJYCdAIIlUu6xrc48zQcSEGgOzCVILlXIY0jla7AprW4mjZB4i0NkNUiYtwuzK0Vl0gppS65JOCg3AgTZLzLFxHLacvYWsFLEuqSnpdSss6SKKUzH3MuwDNyKDqAX6fPQv4x4iCax2CjtRXtE9R9mjqWB+oDQuCoSrScpWmuShHeUVfLrKZmTly8CAwDcyKRyN9ifazkOmP+MgiVGSpNuBn/pHiGD2hAiAI/uUphCO+aD2+QNPwl1HBObgbo37AeHAho7lCT1OlABiX6xviQHRc1gtMCVg+o4EztML48+uijzLXoqxuDbTDD7EmuOc+zUnZUK8iwBa3guIxmEuQaVtw/GIcsAkvQtFy7R6ElQRaxs2p4SSMXiQQgxG6F8DGhgcBrlULZsTsW4MpqS21ISJMLVSHnEtsXoHtLiys6kAChkIQacAGmUKMtTEM4jwakMFxIZWDCibk9porLpH/N9iysMBq9tu1QJEKI8X1J/kzBzRaxKmcoTAwy0YzEmcvCvyQgVEVkS4JvhoZUaAmH3NQ4Iq/UeKEyCZfmIHTXSDgD3AsCsgg4oOYtoUA8EFERIKJFMLJjGLKuXr3K1hambcxPQEZhxgUdBbCxiQK+YcZfIbQoJXKJYOVLy61jCyoQqZqDTJnnHuZJKZBFpYiq0JyagNmp+Q/IXEhu4EklegDHE7KZB2JSZpu8GFc4I4jymgIgdcG30vkQ/ec//3k2N3QGQ5Bn+EGCo5qqKF8zpIYgMU4uyIQHkII8gUYqsmZgyU+AFlkFY57VPE6NPJ9eiE/RpHxadBZRLahOzzMskDpRSOpEbaUamXSxD/gzWTOXMz3GTO4ZLy3Osqwh7Y67JOBJ4ajQolXN1jiGGIG6L2qXXPEJpMSwZnFyWnxMVtlkqnZsn9SFgo5riV0meoD1LLkulyTmENzECSMXeJ1wOpOL0JCMk7Tg0pBijUwizgTmCcSlGWb5p/2aYY1ReNbAJ04zhAABAABJREFUU9KhlHQuokkApOvjPpHuF33UDQ6P+RLYhUsXzb8UpFAVEa6XcglmrgXBinriUBm54NcJ0eZg+IcyTs1qJm39AzjphRLQrhN5CMcCt2/cYHa9snPpypUrG1eufPbSpcbb19956y07GTMculNiTGFlUbLiPyn9JJV7EheHubekRbZNAp+Qrwi2ZW5geM5VreV0QV+cMBp6xm6yBpugGXDbF7j118rDTLDykJAgFBGkwXSNszhA2jgz5B/jpSXOMEQowcAqOcFaSDXJnk4F8sTa05m6StzCGiUXoGwR1jAQQKwUC6x0LP1n108DqzGL8nPLsSIkmAO/mJpLYmPiZ9ptpINKY54kVKvBFCU/G8WNYOAJtFzKRZO9NL8UYoalrKlH79bwDHG1XGiNJ6UqJstsQHAu6mZY/o2KNAee4nCkBsev05ZYU/PiFOXlppQl5s8898Kv/Mqv8H4cZ7GtdNcYluiPWNfjXOxzG5u4I1Tdpd4//If/8Mtf/vL3vve97//wB2+88cYPf/hX0MaWCIrt27M6jooRXHuESvDDi9A5zIe506RfKDZ5QXYCNd2YxUe7USZobJjgcNmoFgoqMXSrxC+vrz/f6TyyrSnmP3joyZ/9/Gc3h6O1xsp6Y/TwtYd/77mXj27eb+0Pl8l2JU6LNKtpWLraObo/ah/31pbYz6TTpjlCgWe6PEWkmHxTMzfs4EBXpb0sUj6qKbHl3FxG0+wblM7e4lwr5PIikkvDcXdludcOO1RGXajpYqA7l5k86RAhaZj3gk+sE60NyxfFEn7siS7c4ENtwoRaJoq+GjVj6E0ri5N5cGEOEcilpIVbJKwYZuauScWmLRqTc8lXlvSxPm5muNW/euURhpPDo32m4AARypZntAWZQGcCN452J00u/o+eBCqCjpsETYODROi7Dwd7eDurCue2tg4Od+7t3ur22FdyONkBzUqfnlnpNKDgwWmdHKqnBxIhRTCSHNWswzOOeTWmdf7KJV75uXc4YFlkpXEg/w0zcJb6cXPQHPWX2hzALAuMtG9o6SgWgq3b6PACVYZ5Vu+vNDjwem1N+xgVuppADPUslwkEpW62Ytn6eMw+ac6W7g6POqPB/dvbR3feZTxh7xSbksZL3e7qWmupq9MQ2Q/dYem8T2Gpxnaz09NXlKktnqE29kMfNI3i0KbAjBvX4a3HH137vf/9l/7Vv3rnp29i+ZVB/xAr8xTR1g58GYE6dbqu3EgDNmMZiqDtenIArZ5IBpCAe+9zABLE6RIbrVx+LAksW/Nx31JBVCgC7TIc0leTWAetKUPlMCvHlC+Qwoau/qqfn9DPpIwT+LZnzteu8zAwUvRgg+MzEeh2zPtYlSwtCXmWo70PVZBiMRDWBebNAD7gOz5SMaNSRMMf6qeSVxyMMw3I9e49mNBKJzGgCTCycAgltUwHoeJYH06om7b5QraWUpt0Ri5sgYS4mZyoiNDe5GRLehUKvIL9p0y6RmIftErFy64HO9vrve5Ka43r4y4jht6kkIqpTqPsQebRFovTWespO/cbaQlVNx4uFTjgeu7jsqv9xSK16GLSDCUQegbt1AjO2oiKtwWL5Bv6+gGiYy4VlYhTggOKESyuxDZ77OegBNKxDmoigKdqJuVDOIyTSemrpkhUj8FnzDH77P1RD8/twnqvt9S6Tw/pwcXTR8YZLongQG+v3iO4WeHCFlmUXU9LOJxcgfZFyWu1rHks0erWUrUhxPhOwIWLq3wk9rlrFxv9ve5w3FGr0w2MisD2qkXPYMiCO5F9GDRWccUvAgkGERmZF39irwXcjEmnzB9Pjrj34EZS+58xK/pHR8DxU2jm0lFLqqgIIW2Slk2mJgDehZoIpVtMlIkRCjQA4QnxHeD+cKDOAWvKmGElIeo74IIJVDhIaOLAqBNvXgSCyaJVQiJJbMiJf7oInwp96VSiOCEllSAYBlsNUnMB+hGew5PX6SzxCTauWG03imGZVdQQIWwSTmPGaeqUCwYlxXrExoeN08Wk5mAvNZCN6mlFXnzlS+igv9wNgsZcEj4MjAGk1Amzc6w1o/ZIfk6/ECpKmZGqXYE0sVkRG+JLskIBe2/VVUZLlHtN1uNUXlvGtlabwAOZbccDBokIQULLaluuljTUz0gfG4cZF1R2sZXltRc/8dJffufP+RIAGwVYBWPOtnt/Z3/3Hl/L4HMYeJK2iOE/8ekn+Jdg/o1jrZswYYnLsJ53G6FJ8j9RIJF7A/2pn+NdHOmj3pBAa5H12EPG0aj2giLkryVRKqJwRwvbrSRK1kdO4AKHe/vjQf/WB+/TJzBnpnDqAfDJZgufk6x+f5PV50+98vd/87efe/gR7M8yxble7/V7O9hcFpRBUA26qVZmrYq2OBYQmTEHsnLyl/r1uGMWbs8WWkSfxF0IZ1PBLVTxyfgPFHeSGjW8MHHDRyJds2KMfDZzmbDm+ZHTSKTZEHcHR0utxgVa3fYOZ+YiotXWWhXGwAesl+/Tq/YksfbYWgEgLgj9Nfl+wqtaAyonmjiT/Cl6S/sM4xTy1IlAFrMRz0kSKV15XrHVQC1vVBAFse6n0zxgvsZBEE/a9/TTvplLq21kKRGlo4+S3tFP0rNYPecKIXpp97PgiIPej5mtyOA55VhhYd3jEKR/xIUtCSHI8olKODnbQCBhKq1+5KQwCkOja+7nzSORF5jUBryVHx9vYKKiEnZXOu0DNnzr1qDX0gYmTmTl6aD5qCYtUOV0K4wcenTqRXIVxV+iiB/rqD7ZY7HG3iq4LMROOCcpl9Hq0mXYgl/mtBNotc01urCUo+pj0hXq+4sg7YEuegPRjloaSc8dDca7/Z2b79xgLfjq5sPshr5w9dFLl+79/EdsRHt6VzvSWg26yf52V/s1317R4ux80GSyhDAdlUkxaVBUbsqNRuJKjmetcikpGX8uT5qlcKqw2kt4flg55Ra3AGiJIYVRGeH9mC00x70Pbux0262LnY1eHBKrM8EbvDwZjUguktwmtOJ9CDHqjYY8mFwaHnF+3RJHHLCjnqPi87SwlOzDJWrPCcqPwJA68n2K++razrUX1W5TGWmxvkEoT8C+xObjWPzDsEVVSQcS7Y7ZN+3CNeh90MaGKoRG04iG7LfkRRaVrN9wiJi/x1uYEqsQO8fiCI6YJKVpHBkIRQn3C1PFm+s+Fpfy44NKyxMCijnXXfMJWCqIdokeHX3/+9//xS9+ARrd1K/92q899ewzdDpPPfUMK2OwAuH6uzfB+e53v80O6PX1TT7Y/Wu/8evE3OXAhCM72Fn85DNP62iLt97gbsrumwx9kvhfGl7sT2FdXlgqxR0J9Z033aBhtMQGi+yQ7O/r82tsR7p27drP3rtD/T52bpN19qO7u3Ql7N6kyXG7WhieRU2YgOYVfCkQwYRILMHqoRsdNoFLYo+pTttxoTatgeYGJgUBoqJlBOORBSQaiOgSdw8VApwQCsEJ+fA5ISeD57oSF0ozmipg7Whg1Et5GQHFwwRJSZUI2kQeaaRTXsNLeQtXIAaWhErdaLKetXZ1g8VZTt9n7Nxr7fA4pMfLMxFA5nGSmZBGOJjldk3mjf2e1Ca5eDVPC29v3wVOQ8A3gLDf8O13fk4aVcGBVUmQFgc6nVw7RduCBjJ68mULIHzxAG354r3WnyO4+MQM/Jxsw6E33a2r8GTd2Cq5RuAf93IN1GNgOBjonGukotXSkhY10EOsxmMtUrM+TeC8DDZK8wDm+ecb2z9hdZ6nLNaKGNugw1EchcFqs5UUn7hhc3Hs4Q3uBhcFcFDmU5/6FH3FP/9//Mc7d+6y1KOlmlS/i2gyjEJBzq0gtW1Y+glPdpEz7n9GvwuLVhe3bgM5nX9zOWBC2UsMuC67qyDjPuDX1VSQ1NjQJnmpuGLgkourOh2lUNqaYe1U0dGRRq4Qi+OZyjqTnhHqXMeqzirUWTUCGw3wTCA1K6WjHZEoOsxzOAUihWeNvQAd5rVcy6ohMzQfTZkZJr/8pRwjRl6am3q8pZ6HHnO29RZKIQvL5FKfwUALuSwC2mg4nDPR0KNHbUzSFr3QjInDGZSqeSJO1ogbD3vpjHZCDl/CsXUM9F29bgmQAGEEdz2CgEMMQtEwJWJCn0mA6aZXfCbtNa5EKOYuhAa+mBiAzwZP5ksswQERPQ/n1E8TJJQfa2XRcCCYo5FBKGiGOAaNblZsos8ndv06l7T6/rpXiqIZQmzkJDoJVYsIfqn9kgaBYJ4krBv6ELi0CFNpU4K+qatzA4U2XZtT4qLi4KAFkNAw4ig1lAoSWgdDgReV6tw6DasF9DXGCWmoUFmauOfRnYWu9D/XdaSn6IUS9V5DKWy5LCQzwPkyFhIngnMyotNhwynTmLmNA5ULTuUBT+u7biPBEXJsbjVcfVliquV8OfmdsTauX0pBlmowt5oJTaiRqiCWTbKSQjFDJifMWJwmlxn4ux+8zbFgMKRbYwrEnIRNu0y6Wl15NQWDVhWkupgKEhTT46LYVHZcCIf/+HY0N+YcBUdZ2b3nmaPhKWwLk4+QsNyTCItWJyGcHa7GeHzMrA+eBPszJSUNExJM3p544gmOp6dS2MNBvLK6Slse72y7+MUskBSDBCsxcW5JwHOGauYShBp5piBkGQJbgi+dIK6RT2FitMKqpvpl0jMKfChWtbZ1OoyzeF5t/iB8KEEzyCYvPlyLNmZtpdRvR4ZJSpFrtBkRJ12ahIYXOqgdGVJ4QgiQy0lrnOYVxpl0pC6L4uwkkHumWegkIvukgfCnCwdey7VoxzC0YsavMZUOd/XZrDSXGUyrBFDBXUzWDW7AZoRaRE1lSC1anDwMVXkCTvt/lbk4uVB68En46BZc+dVkgDdU6Ar2DvUgk3UJ+gG+B8asaeWc3g9eLOPDQF2ummIGYil1XCOflC5Mai+aUtc1MleEQghnpx17O0K3p5nMzv37P3711ZVLO2wg2HzoobV79/Z31GdiHHI98TbVjHrFYnVuAsaDkDI7whmA12hOG1lxNeYmDjPC5i7hwEgKMssi1Cwc6Nhv93RC2qXzWtYDwUGk2d+A2G5KxJBq3fAKzkRgpkfXLto5cR8B4CYgwvyEG6G2iRIolZki0UknfCU1XK0ZTSTTEF9m6gpvOjlDVfjMwIvCgqf5XuiQ0xBmVaWw5VojSBRyP0PSUgLKRdwFxF0MUgjwpfo6OqONh5PxnNyo5EHJJ7jVb/KcIId4Uod0nbVHQD4Pm2UjnsMLEhZ05SZjqts1vbXJnPxbDTmxE2FSG8oPHaphKbwl8eXZlHp8ux1kWCfnQMmx2Wjuo/eef+7Zq5cu3759/Zv/y5/wkJwXr/53/9U//sQnPsG7V09fubY8buzdvPXO2x/84Ac/+Oa3voX7jo7VDV08/9jli4997Q//7Bt/8q0v/toXWMn90Y9/8Oqrr7IGfWHr3Kgfe9liz6LajXwiyhKlaDf9BqzHocm44n1PYMNf5cN66mxJ69EMzUjGr0oBgnZG6lmjoFTK4OiA6WxjVZx7aQGajUWjDkdjspw8Yqmxe7d9+Oev/9V/efHRfn/vxZsr3e7alx86JymMPjf2W5yNOWp3huMe1cpaWKmVytErjdM02ucmHvEgiea63DrscDYwK8VjztNiebbwQEmvWnaWen1OlIolSB7NQ+Uws2iLeHmQA1XJVTKISitwLP5iE8aNQBaBWSWq6gd4dVWS4X32roCBJjNXyIzzXLKWg88gxs4PDgEK4EbmMvlrKOlFT9cpRYUqKMSaM/+4RBue+YccKcY+YTcQ0NQmBJud9EPlIFkYHZ5YV5tW4llip8n23l48NuCthStXLm03dRryvb1bO/duwo49wo8/9vyjj1xhvYajZjps6GV6zeEb7JSPUuP88DzigwSo0mmwe5qPDHIW4cHhPpz7gyOtR6/0eC10Z+c2hDbSOA79kCb8YUl9pY8iwEOhNTrSzTlWij8gndao22os6aFpv7V28NTTm7ff33333o1r3Q0GBjbfgM9+Y95AHd/4xes3fvHMl7rI3dWhjo378QH5Ph8/1BPkJU7Y48uD2qjArkfZa9jB5djNze1l7LftELM5Tp1jo9vXcsClzv31xuHOne31e/c5oxyeR+yG7LRX13rDNntQ9RLJuNtgLz8HsukhAi6sY831tqye+DUay9lbVFPaYSTrAd9c7u3e/mDjYuNXXn4KT/nn//xfvn/7aGWJ75J2ocMFZGQhNobRP8pK6CyInp4PBgLzJxnZ7/F+te7YycsRKpETkZF8HfBgmfJRDAXYnsl1jTh1Ge0uEWQ6F2QamK5CcdKVDlzUKgVi+KRcPeHFYxgVCo9NLLQYxKXQsyVl5TANtRpZqVWKTxYB55CQGmAur4DG0ZsAEWBtCHufkeMOBoVUOi9Jh3bxGIauEnAKGCsn1R8igsrH+upnGMM440z77vSIQAegqtJdP7k4Sf7in8CXHdCNYRXpSnPg2tER/fY+h5vrTDE483AIe8mfZytvMeO/Rqh1Pl0ApTgdrVTc6XwW5pbaJ3eGD/bp0VzZncH7FEf3xhtbS72VI1UWRlZ/exwPirL/yx/Tzmid4MOJgOoNOBgUl9CZcRg/RkxcsWhSilYSJUv6xAWe6lzi8AfcjZTcTTjhtC5F7GanW80aZV5uFxrdHhTURHDHacxifPhIUJWbXC78yPy7w0MUWGV7o86200O7UVNH+PPsTrezsZJLKeTa0RTgBhOz5GWWwfB4d1dvsjfVQzMm8nYTttPOF5di4BP8G/raBB25FOJs3sZw4/iI5G89de6F881O/71lfe41jHOsh4XD4RE8owdAcDRIRKLVUJutub0RG7p63lbLHzoO/eiTGTZZ4E6rz66IpDrjLIGJULuzzKs6FImGG41a3KKExuSSBEHl1yyDMistF8pzZY1qWCgsnKzaYpBJY7pFMWyBDx+9J8QOqliXNFvFPkUUdbmBUbvXSMkIeKBv0rEhWm0fyZKLLKWkkEA5YOHIFwRPoHvX0VnCUcglgjMQdYWBH3kRCSHoM+Yky6mwP+S5G808U3lDUKHFaN2WPqdh8SoPf7IcJVEp4AmyE7QuiZ5imDpztKWlgAYtcTodMhNKMTrgcWO5txSyrBvSpvTU3QWfKR7dA73XuoMOnWM9z0Yd/Mq3durIERABqwFPz7xj2ABcilaNSsEiXKU4iasYfDggNwJlScVRHy6lFXjXp/AEDYiRSLEtizLDSupRGAyk261Wr7P04gsv3rx+czA46vC+F5O37W0mHiurS5d6fNmbtxO0sZA2hWS3SwrLX5hJQk8J6Q0JzsimyGwBVsGleSoD7SNUR2cC5Y3pufgl5cv4GzKogP9cglrTgkBrB+rOi+9/NAaj5rC/c4v9fJiMbluz3C7tiMPHOdzz/Plf/9wXOXnjscuX6WR4pMnk+2B4rPfxD45YZ6Ezk2+LY1QYPyGX1xVmZGMuWbDyqPAElIxZTWBLY6NNE5u2hi2ECCHcsmBGHVlugSkh8inAgy/mCqSGHGS2c3L1BzM6AWO+RCcgToFxSDe9KegvcREWS0UjzRZM+F9m/0qzefD+e2uDdHQed7LTlp4RiU2SAyQ8tfQCUePy7N39auy0ldAIqi/d66gFy8K2sjcDwYJcM+Iuv0jVnFHdbAlCRCLLpoyVdc8vmhj0cAIKwZVcgUQ07GxPsPibrVPXkU3EZ094N2nAZlE9wRFTw5WCWyorVzBKCjuroCkvB5E8KNAR1Viud/XAAZ2ntwqmmvde4AwPznWnKZOxUqGZRjTr0Af+aI8ND9jF1NILt0Mw9DoYPcF4d3dn/fz5uF9WKwYsosn9C8ad1FHwm4psTyykRJhsul3qXt5jgcqIcsQoFMeZMtJUlpxImaSmRE1d1I2fHn8qj4u4R2YYVzIvgJLWduTxeLWxjdyVQxFd1ISu0dx/e/TW263LGxxV+dBQ27l6HEnI/O1+Q0fhXbgA5Drv0tO34qLqZDmLjFmm5KrYlDmqSnMs986qCXGuAzVFYAhi4KbTFZGrXtMIYJDCTEidiIMlWfp12pNg8WHVS0sc2tpFR0+P3+p179w7bHTu9TotnVLQ0IffWQ2Eltt7hdgrLVV5dC2wvla1v3dvdXy8xYexeK8Rq3ktNMzkAqi+IsWPS2Y/dFnUMmRhIrIn9SabCDiBBMbiCJVsK+bqYNgCSmioEcm0REFKCPVUPLyKOO47U6a1kjdGEQqJxAkzWKM8wfqbQ7SpyE29QcGEKipKrQugzjfkh/kGrFRgfXXBBQHsVRoWPsjDy8Hl5XNaAjfBbSqtP+oym067S8LoqFFUnE9Qziiqckioy3hQqPV+EO4kP9SIzj3bKPhU9xVRRIxXFNbbmDmAHHqO2WXAIc7sO8A5AfKIg8BBz/QFbEMg3ep0//RP//QXr7/Ngh3fRsWbaQHQ/u2//bfZy/Mv/+W//O53v/vjX/zo937v9zij4zd+4zcg/P3f/30spilLBBcw1V2G2ErEuX6zZnMIUxmLLmASfJKjGCUgWvDi0zt0Ci4vslwjlJqgG6EIaRIc40qxEbSYtgjMHCbu6CyXDmPDxDjqx8PzuCSAhjWwGxUBDjcYIKSsbJR0qaqENEOTbHEw0LGLIDwwozHnhpEI5n+EeeZgnR2b0BLVr3A7GtP1mtk88wKh4Ew9YeXOEStFlpSBJ9y4LGXnGwhmGzgutSSW8pJLlREgsbWlT9zGkADOF+yIPZdgPPBWZeHH+YOwBY3jifm+LbNt4LduvAeQXbrciVkfnJbQXV4C83DAsdEj1prxH6qPsrAQQJd9d/s2W3JqC4DMJZoUYBQhXfm93RpCGnGtTg9CHtSgZ3vUf/fdm6yVc5O5ts6n3jpxaGd7oGPYx5xsw57r9Sc+cfHiRejQnNIqI4ZPlJT08FUBAcf5iXxxBkEsgSDFHgItEHYV8UD76NYtMHksAzdWMTDIyuYGfFi6Bo01LeB2Wh4XEAAS4A0HQilpGU4AYgWsxxYb9OROhir4F//qT3Z3D9ta/UgkdB7IDWvh62ITl0ogEQiLKe6dBYpcaKkCpJ9tnDLdibFUycqIZxUKvIIpOQ2fkCwcCUCmfK6Ums80k8Rz4jEhxaYIDnVOzWZx2h5YD+SGxOiutgbnmBHBOxkdRhrzGOoCYlPoxfxwzlIpCT/E6glEbnEASlZBnk9YXWOWXAOJ5Zg8Uj88RA3SWBYce3LB+WUS4jm55/1wnErparKFQBDQ+qSsmvwjpCkCVDOOOsPHhqW58cAsTa1i9n6SVqhKgCe9DZ0H7Q7MCCoINSxx0TODBoQYiSVh6VySIEtGDgcraODWhKDVpSiEMwzNllgIWeh8wUVVdxDGjxitpUx1I2ENa84WysSGPhxkLhEWCcnFDwkhnyKopbtQ1gonDYupv3UBoXVCCPwPNWbKLhFhK2qH5/QMIjwrJAS6hBKSJrnUQGAXmiQjg6/ZQ3UnBoTuHPtQiMiZTD/gJgEhgrjYUGwjRGZSgFyCka2/Y6mUtBOVaU1oZCAO5OJCMCHhNB0FEEZMmAABX7mRjk5ebICXUsNHhzyAExxdLa4FVzWYhT+EwqrIgRg4o5iMuChADaZj5wc/TU+4LDobaARxztZbxHIBrIio+dR41laQirORS1xwgEQ61doMH8pJLvovlGUmtoXTkFMRhUkRB6QglFwnbBbSIDsonedsBRlycsvlSekZPlxSv34nm8kDzeTnP/8pEmluTIqYg7EzYGPrIvMQ3dvJkTTfCycqopRAHAqAIG65lqcwKv2lWzQ9bEraBYQttCcZYYbV39ily/KRxUEeH0EZ8VEUF5MC2gE4N4XyvvzyS1/5yldeeeYF5m/sPAfCRFdVOWLhadfGwSokXLe1pxkk5Ll6txmBO7euLwNLiVxlvpzJAlhzLiTzaAlz4n0Fd3FiIdvFqBXUhaoAU01mIc95EqGFORaWAuaFpGZIvViuK7HWoU7XJBivzmKk0FFMVZhRALlMtkGzAkUilyF0ipvZFFUrrpMkuRTUKhVuJXuKdqJ3+JI9KmKXF3ISlZclNplJjDKxMOpClXlCRihilSg2JJHtOoXAhQnhS7poFzq4u0i9MWguGjgFzSLYqFFDLMDIwXxiT9BqPctlSZh2Jq5J6vQMGpfmU2JBYuZjP+QyzJv6TzoH9w/e23s0OOQIyquPqk+grHTLepw8NzUVZMEAlXRBdEpVxiwQEpZIotgniaiRqnTNsAIrWUwxjTNl4RmSmctpwpQJEM7MtFF12FsnbQ2ZT1qoRqgcQMbC0ZQW+EBoODFIJkqaWzrMy6lf2IRmFGhRigWkhUfyVfs/ghTC+LCFZ6/b4QyD7lhHdK6t6t1oiyMGEaASEVsIRQDIFnhiV43xufzYQ+FcEmcREeWT8gupXKhT+ATVlOOaT+HmWi4cgGOZklvgCxOgQS4ShaCqBkJnea89aYJ2eMWCDTGUtDWWXGlXrKHRDSFXvCzJfOLpbtGeSrefAJnq6OeUS2OJ4QhzwhU8hZye24CScJwb5ZEm1idiwYCExSOrYuRHVuhvEoYZFuU59fiTLz5/POq/99ab97fv9hpso9o8f57F5Mtra1ucAPA//8F/YCX63IUrTz/3/MbWJg2s21tjgvi5X/kM5+D84Ec/2O/v/9V3f/TYw9/6P/6ffo/zBP7DH/37b3/v29Fjs7hQidcRKIRUhNBTgxwLZNaZvPSAI/BsUBhEWeIqVlCowchPDZU0O9C53W5qVyVLpPGIaah2QucAZ1oXOnOGDZXJmSBsB+q3G9v9/fd3tjlgZMCRtm3O/RmCJhlw0Fb3Kd/C7Ce1OvhTRuJB67gfX6Zv9zqj1d4R37ZnLYcv2i11uZvX8YvdOLYP1HBH9eDI0jYE3cKqRH48oJauxySE3GxiNhyFjkzKq1xZS386SiJBkp9EZpDDIewAO5IppOyz/oQwO1U0Irckd2d0RxhnFDspONbRLBGDnsR65kodRH2RoLw2rD/3R6b9OeBqC/YMY3IlDhGK2xtS4IEjI3j2AJ9uV69zsgGJHvNwcEjZN5bXqdn20hrLXOCTZFvxL9748e79KyxDb64/ybwc1WgLfiZx4+YHetVone8/6xgfRB8exPuhbQbd4dWLV2iC9+7dQRYbQ1jC0DnJUgiA/AB9UZoatuaOWTEGw0cw2ZGQqG0jPJxc7h6PO82lRnf5UnO1u/8OO7P3rr/HDUBja5OjMtbxHbhurjQP7t2494M7t5eWVi/okwgXNjX4jZoa9oYNvQTE43l01sF+ulCatoA4jqzWM4+oneP+AY+R3vvgx5R6gyOv2iilemheaLUu9w7ah7QUfaqx3WUbX4+t1LIdfhzHd7TYK8PNmTy2qgIuJv1YN07Q2Fjq7G/fXNka/60vv8T05b//7//NvX15Tacbn7CLwY6zA7Bt2ExRMKSZaEUeO+eaT1ZUiWJtKLZrJ+D8j1tNgnsoCuetMbPmbtHS3JC8iycNwDWJ0gInb3QW+pAwyGnDBRRUlovnzew0jeuWnj+5ndoHMn54fnyDGD4hRS2FhNhq0xotQe0ef4EQJplw0e90DwAGj86JTeV6Yg+sCuMTxOgtUS+sRG0IP/qThYJg0ulpCzwJB7xF01BUnLPzIuUEQ5zNJbmhGB+ppB3pSZUmPVG64Oan6IFyapFPkpThqEpy2uY578P8WuFTKFxrpyD8MlkuxTyHVLoYg47u3BusbnYvb4A21rnA0SHhRbJ0GlOUUi3rPPdx65C607sjuAm9gCB4qHrsMJeqwYUqRSOhL6ozquYqJw0HHqsyfCkdVZwIYaun/fIxvNchUFJ65qd+jMozQ6mZ/fmBxi/M5zDFZ9ThodZQ74Kwm2M0oG2eW9+4c+NmY9zHnYetZW1OGPGR+7G2s7CpVm/j0HDDcFEiConfk8MnixkY5PkRaEssneJeKMB0gxgziC6sQQk0jvQ1zK+M9j75+HNX15c4/ZkOVRMWD6XRs1GP8LNE9xIi1FSBJqMuFwR2ObOiqx2mgYdoCkIWaEYmjRJayWUNTfMqdxrSjmGKGsuVkDo9CDv6NEVqlXAFQrC4SMoxKK+0Ux3iIBYuoZAFjvRBk1BGFmOjAx8FwYWsldhihyggQF2FN0DLgj448ewJ72PwIE8r0ZRa/NFZIRYdRIORVZuRxWNtKcazSthpVVLI0hxZGgiiqGFRMYFE/Zy81zwFi71FqbAxiiVM8hRiJ6/2DS0KaAO55p1JVEJSYZEF1zB+2JIsVynIqgVYCySFEpV/ISQwjCs3dFY2aEIlQDWjjOZImleoneBw2hkCB17YlPRAlkRgKR18pZr7BFxJ70FGK8uqWKLEpZCEx1VMqKH2X1qiMh5o9ACq5PB/FQQppRSJWfqxY8An3J7yymKqdZkTw/aefOyZd955CyZ8q+MwNqCxD3rn9vWNlUcbo/hMtPZW5znzNHOotAdBx+ni2gQZDfu4IOTy6oNevIMDf8yRdNOQAphsVmi0fSSmSyAWsyHfEczCP9I1iiH+gaTg1HXxQPyCQAH39w+O9Vbc8c0P3mke9/lmCU+I2IWOzZ+8dOUzn/nMl195hWMYNzod3ZjEm0lqU5qxq47o33WOSzR+z/fcC0WVSY7WRhAT1Y2S8jpVqlocfQc3IVxHBfAtLJmYtMgCn9jlkqDoSA0HSCiWoRZtgQIEIrZi8yFCYVgSpxMjbg7BMuVXM7kn8bTmM3wKsCRyA0++anyLmOFcDFWqYIb5KZe0PngWDmCaOQ8MGU4uHepWaPVoDyD9CL167ICma1F5o02r1DP6WNxJ8JQbJ7Or1mRScdNIoL/ouIDlu0WYS8PYXqON2Tm4sDP1IaFyVboxbZ+wF9KLwgSHIjdGaHV5ZLnm2NcJPu9DVNabSku1mCFDLT2HupkKhqJQgtE7ZqvwRAAg0IGDj2FpO4xiTCOQoogQRfAMB5b8afQByBFq8Q4pSVjXlRIUIlYJIgYyc2kc4tKgSEtKtlGor1KjBltXGdNanG+MaUMHel5Kw307IjTzV5E1W0ATHiH3lpnw8CgCmXIFxuof/eBnnK/YXsIWkMI1iwm7MpoAALdodUpCAqcDnqaeI0Jh4sTpHKtKLBwXWLLk1QnLy+IkZ16xGp80yMNR7/BouHF0JCsdaZ2hPdpG/zucs394eHBOO6O3e1quZTSWw/ilvyiGe0V5gCpF8uhj+bVvkFCI+z5Vjvbzy4nCj2XuUE9NIibswSHwVYSYq9Cnow9TIwXNdzT7ItAng8MlevZ5la/b++DO/qC5/dIzjw4HvHLQBz4M+8MdNPwEKt+Rjffvb7BdusfJz4M0I3rQzLxUSl2ulA47SCcKmr0gV8GUHSpEo0/FLk4NEn8MW3kgBakQVNs1T/DDbrUnB6SiIUlZvDveSmqyUnEFyOwisgKaskIWUCkjkHAknDTVEAKYfdAgY4Q1Q9JxqTGXrpib4uV+Z/fOQbrZgBcZwVH0Uba6OILQrp01XfKQNx3BahrwgKsa3zoQlwTEkZY+iC7pKf1SxSQcGDL1Z7sBq1Hf/OY3//Iv/5Jmwxfa2I/JGhwcWH3+d//u331w4zoM+Szyb//2b1+5pnPoO13yORqsBwJ7E9jXw4kFPBSChOb3ox/9iAWF9MrnVHU/oIChs2vmNMxcuoTDpcYvlmbUUifFVacQDwC4V6SbGMfBBWAAh4KqRfOHHnqIVmdGWMOLa1zCp9YDknmFiiwSBHh6XGfFbbyyYiBppEPe6zVJL68so8mwpeGfHgWJ/XiBFJcSJPwh6aPhIalQOuV5HeYhFVVdgollUMxUBTMzmZguQ6Z+IYwZZjh56BRllOV9O8HwA8/ENizmEhhC20tZcA1RU5pUwgsctlMaJA+XArnW1BgxYyExHASAYHGGzGhFt1gXtvTZ1+3tO2CSC0RbgI+OHn3oGtuBj44O8FvgBA6z4LFK4+4dpK9txBPO6PwRw1nhq2srfIGzaAUf6s2X0E7DJ5fBmNszrWhAgg64QSxhjEisLWvSz5k47EFunRvpPOi7++w62dneu3VrF65LHNXxvoa3YUtUndt3Oah9aX2VEq1tXWHvdntJp103umxkZjlb+pBAFidzkMYUlPH+ffHcu3MHWbwMDi1tAE1cL+x2geTomIM9Ois9bQan0NoLE47fascCNBrwpDSKVRc2T+pcei3xs9hNwLyA2FZDb/DP/4ev9/u8vK7ycvdHLlnBJNhJD+qFqY82d/Oc9WjAi+dT3WMgTExqYWeMEQT5GZEXok2XVyiGWKE6twgSUK/Gy4cpFz5PnJ1lVghZ4bHSs+YGXoEgqzA3vO5cZ6hmBcQ1Ivhl0EWc2Xq0ysgyUYyR2okgnXOQelEKXAJCAqyITxJKFqQW57Rj8Z/OAoeumNZnhlngR/yFOSpZxIxuVuYkvkW9hQgzrBbiGLhQ9Cn4v2RWlDc8JBjhbPR4axdWqb6izynKu4phQuB+CUzmAwbKXOHcAMmtmXAJc8eGzyDMFwoEiECGLfx1EWGeeU0LWtWn1jlTaXOrNZzKzhdFVQPoiCC01zn2paaE0lXamgR8IMDiIaUOxIjDN+TeNYLZCs/Gya4IBEyAFy5c4LF9o7FD2kY2JrUGT+PA0BwAFkJLAYHgXAgBUsvGhzzwxZY+BjTgBPcPoBEsi5hcIYVljUUMW8fmb0FWoEi00EJbGJKQ2Aiw4RfbIhGG8CF3JgAHByCcp4IBXrKPpQG6TDhw+wYJ+CT96Tl7D0UGAgeh5CCGgWnOmCkAyibhMpI2Q0NMmtCCdSFxlpAtMq5N6yzimlWkk7bKchNKVCpy7v+jaQW8RC5LLbqoVwMLfklYgaIVCUJNopqJG8t6UmscRvnCZyYBAhBIbV4Yimc8MFBCBU9z6UhLKIOc2U6RTPM1LbHKG05Ovi8LIpc8YufGhD13KMCMhXe0mBfxZhX3JiubfEc5DnBDYvVOZyE3Q+tvcU4XBItTN4RLZChpksLkf/Y3DJDzZ3/RH91moWe7ltwQcTb0XxYLcczB3KUwfUU0BqQ3Y1WEgxZ/41e+8OlPf/rc0hJimLNRqKXlZRKs4AGxqtCqsG5ref6gXD8Mc8cepoQ5JG4ujkHDVkg0K9mN9a88EBgfHBtE8DR2AUvSnTCOmSgvgkSFJWfgOX/yK8wqzFySY5UqlMVJ6+m8Or0QUktZqOEM8BQdiqEWq1VBi9CcUMFnVCULWcDnfRhMz8dMAsKke62kfKgkrOBjfdzigBBQC7gSEYTgJaHUg6X+BIRcloRnEukWpeBSkJjDwDGA4pkZm/1psfkECYTihkT9BAvSIHiYdp9R5IokGBuZGOQ4a1AA0li/6HOSBoGVMqEqaMDNxDF8KF0NLJinJwoJaMzw4ePG4Ls/LlULmYUVoMFyk0g/wIdg3fzBicv+ypIfASaCKJ3SZ+wNVdhojAhyyJIBp1t+4EI7NTwQYSH1HNWslBkEl46yF25ACEDAxCUGTb2bS3cKcPPyBSBcYr2CT4IsSmSI+ScpxejJSWoipWFVCGfz4tp8YEOCegUWv1qdhtaX2UOnGFCnPdYSbt9+f7XNRkzmUkzbrLzxzNmMGYJ5A5tRl6zT9ZmSceaLLOvMBKcinlFDhM7WfZS28HY+dQdkEa4QDT8p1wgxSxUK/0tMlueHAlW1jzieXBGgIkmLY2VVm9eMF7HqVc8WxJ52qx0EwHmiFe4UnupU5bXgnhTsKOQi5SQcZ1kHcFJCTyFIRq8d6sAjct1aVDD+g+AqiSPPkoTDQ7nU7/zX/5gvTqyuNj947/27t25zdvPh/eZzzzzPWhi3sn/2zb94/73ry6trLBjB5Cc/+ckbb74N/craxnPPPcei1auv/kDSee9+aeXZ5164dOnSzZs3QWNzqJ+Qe+IrNVi+ZGOxVBV+6auACHhKoIzY9dgnvqlmwC0eRkIhhoDgREQXxpNTNqcwO1W9MKHh9F7eOqAu+cwpB3r3W81ha3zn4KjPctiyhqvlvliY+bwutmaBl2pK+KESq7N7h3vsgx73WuMeW65CMEdJt1jN0YSP3pxVexIcrQsh1QUfPj5NmuFKcVSlp4k80LRXqHD5BizS0iIn7C3hjVHvevKqkLwoo1H7gmqjW6Ylq+Kv3IUhFdCeGRdE8cvTMT3PQW9uSThlAyCaw4SEmcOfQHmthndx0l4oNVTgaU1OitkrIg5B5m99TFtDAi4GDnBzrmPjx6ZmFRqbg0AYHvXPbWwS89RhScOn9vju3rn1XuwAotuFsNtDWa27EdN7s2hLJbF5HiVhzjovNz/b924M+vt4F0dxjo6P+GA95YM/nT04MCGgGF05MWMSMRCaD2yZ9pOOBOKbnV6HHoZ89vvQdww5/hgdLrYvXbxwqXEJwv7RMQU52B9o+XjnPsn+IQfmsDB9B4n9G7GHUc/JJIKgw8ZRvsfhVDzviBVeN7XYRwAaYWVZq8srPWSxzVnnXnGxvLq0dnkNI/A0t9lprS2toTnPS5BCScHhBGieNmEXpASgeKB4Il//7WTRsLkfARKGOR4O7vxv/t5X794b/cEf/MnOXVbesVSj1+RripyKgxep2+JBkazHU9lWs7vUZsF9cEcP3HERnuAgvniRpJ05ZJVEUKdnGYS2LgVZrscZHIDzTABmN6zR1R5ZEaIStjbOY7jbN6/Lam59VV3UNOhH3WHw0JM6hXU8pgqnUluhaWCgmJEoHZiYrqhQErMlTaWb9GF6gq0jydTFoCk8OYcFnkz5iN0mYUJAJSDlsRMiul1uUwFGD6a8CVuuTgrWLWuI7uqiKSAQAo5HAIB8y/J+vYlxw8pgwh+tTpJydrhZFXwuPwamhV1VKR+LthXjE5Pqizklttno37vXu3eOrsonEXu3Wn5Da1JZWBpe3OuP2x2OGWZnbVe7BWUJ/tjdrGybPUxTjC+MqgoMl+tEdeIw2g6sILSCHGi6JEO9SvAkMoLhs2wrKXVWMJ+4vS8lLxQzK5wJTVAlCBFqBux0pjHoBTdaEk/oGsfD3pAdyseNJb03MtBWX71Fgmv6JYGaeazxjvYP+w0MFQsuWSLlogfTe0ipIHEPBQueOHpPUOdIe64//eilh1dGqwfaaWLO4BMYN7EJkFLMGE7jLjEKBZydSfENBRATJp0xjY/+GXICfGBgtip7NEzVe0sTIdACQRNVXoihOyqNOka8qO1cOy6INAuIeTqWJhG8H4QCWx/EuYsGgDDSnjE6d8KQCkkvhOAfDZ5HYipPe8D06eTMhXAQHTsZnoQpRCTTJo8St/BeZBUNTVt25YPDsrX+7I0aGF3GVPJMKIuZMwoIRwXSkJQRyGdkz5aNOhKjHGwNc6D7grDKVX8qhKjOTKFfG7+GIAJVqZ1adCinUUA8KL42xYAYjYhvqzDcY4hoJiAgCYbMklFiqXEAWqdxBFW/uRaC5DbWTgqRUiPkzRpRiTzbQZZT4/UDSNKqUGwS+VSSHlrTV4MPmaaxuu2ReIYTfpUbYT5hKWTCrc4tabK8uyoaUIPbkMN9PkK4i3JMSPRIns9u3L792NomO+zxE2zd0u49BRUgx5GsazCyooykEKeegZu8EW9Moks8hg9P9qMOFUPI8iYKLw+M7jAzMHvFENuvJqAzp1CjGGQh0QMRFlJNgDHu2/d4M5Np+niw/84brzeO+zROeo2rFy++8uwzX/rSl1587HFK0RlqNtiLvrHDEMBA3xowLmuln/rSZOy4yRHSbmMxQkd7CaPL+5psfxQk5OLHMiK+IHNLKfoougVbjAPxwXTf5mJqJih/Cj7cWpDr0ajyqFI0SEr6LIl5/HkIitExRtuaYnlqHVEw/MQta4qKC4uQDSptC9DY7gfsdWSdKmuW/8LreVkFbZ6/IRQcHGvCMgeWv7S/N7p7pxdH3g20mYTT+d1dQKEO6kMFdrJApRcNZCx5hO8O6Lmyi6CAOjZXavqNHkYzwgiSG5ZkDYGEOugISfm8DM1ijDxIuNznq1zcahK7dCYhhpsDfFS0JET51pZc0uraFCQMIzHGCpu/LD1yU4RI3iblIsSiAel4hK81FzprLufIaBgyDsxZH1Cu5dZsIQNImYltAXqsaDvp0kBiTVoINXGkzVOqKSUcShnKJL81AriyndQUj1XedCQ0+YYId4QaO+MtKPr/zus/f+eZTzzi8Z1infQIE4mpRudUCj0lRYlK5dAkOosgcU5Rz2ziEi0fEFJZprHsLdOwqaso6BRENggAHSC/QtB6Umsr/LMdd1LLjV1KcSneYf38Wqs/6usracfHf7Wuu9o7PUaoMfvC5Dd+DBx2KQ3JBZRuSdLEAgv0UaOhtYT1eDWAYSv6H3WW+GbAUVFw8IDGfIQeBuVjvoDPqSD4EzoP6dQ73Z+9e723dXGL9bFDumjt4+S2ABwYwofPVPHTG/U3Oty09zXldK7eQEINKw36xxCSrU/g5NyPSx5Fm5dTA52mXpjrOk2cai1IdSn7T+pLbxdMhyAAmNoambJqxoF3cNBMVVkEFkR0Zm0sE/QbrBUe3rk33tNqooK8JBAzh6lf6kNVEoMoCXtPwGaNBith5pDYLr4MiR7UQwew+M24AUqXE4bONZpRSSOxEJLm7f5f/dVf5ZxWFkp4ssV2A60CqFPo8B1kpgtsN2CXKOtQFIT539e+9rU/+7M/O+rrZOfzFy//k3/yT5hPvPnmm5DweISls6effhpkpom84x986qKHbkXrKEJtASsckImSBlZZNWCShqosc7tGSx4aulB0l3yZhUI1aULqTmVDsojJonTRYKUwWaniXNfhGbA1sHA2B9uz2J0Fbu72JFQjlkJB82XClxwGDN0eSzGG35ijuYGBo6COJjFmaKvlOg1K3fjV2ajuii6TNEDD6TidCMAUfsBTIwnOEiI1wrWIXXyXCGQgGuNlK31uiAnrDD6XRYqzQgQ8ZV4qAqDhyMmJAE2XwgqkjPgxH5IoQ64Dl9YKbiQ82TUw2OvDrzg8K8ixNq17ft1gxdNFnBYmxL4pp/qAaziIJswzFfYVg89HI9kXwPo1bB1wm0DW/TYvoAO0biXGME5bkF4IpS5j9QG7KR2bPZh8kZYVFbQMbVZ6y4DNwBc4E4MvFnYRd7jfQId7Q+1n2TkYsjC9d58H1Arksj7NthW8CAn6PJZu0KJ2IrIO+3uqtV5DO6DXl7VMwFo5zz/Z7YxcTr4ONJ3jXNSQMkAjAExdMNBJSF5nkgCrRKZiHzPt4nd/93d5oPWnf/JDVOVwAOLmsvbS0nQV5yKThhyVeADG3ebRUPtxNMcJBNK0WKcnwj9MStwiFIlcud5ryDzLcrzMTFZqM1NQiTh37sILL7xw+bI61e986y+uX78+hbLoAgVQz02MfC5tmRIbqCYUQbZKSQNSbPwp0AkXRRalQHQSFKKhsD4hRTZHFLG9l1x8MqoiNfzkAXOCRB7BCWIHSgEHZ7kfLsrM8ZgCQM41mkxBo7MyEISSAHchZqE1t3J5UmIebZ7tQtp5woVoZwGWQs0jk2VB2JB61CboNVadJqucWGGeCgjIUaEa96EVHw7TUN8YnT/T0/AKcosI0sjyJTGXhvgHJszOAxZomuhSX6oya2gS6wm08CmsTGv8eWDJ/VAJiY7GpXvI0AQIOlglQ+IyZgh261y0go9Z6GNBQysCl8TBgQgB4jwT5OFqNyo7b888/vjDLrgJAbIAi/Pb/0EDbg5lYDOtxcFNDAMNiIOYRDCc+qImnVUgVDEQs0KVwgFa4JA4FzYJJ5vIl6CVu4yCPMkKfRARogFrsPGkkXJZYUGTA0gNq4e4UH5SERIkNJmrVkbEEYBDr+LmyyART2BFASMbmFQPhvMI4Ihnbj6JJDRwFjH6RjwVVXYTOcF4OSlkczZc17lyAyL8UF45BNK+dGxgiWugZZUsEiE0/CzSBbkkamSnTUJs+1B9pabMf95WcJtnCIRqhCccVAVKKgRuigwpMfxhThU623DSmUoAcODGtIRx88c/vs1shEvmDEwJmBFtbJ5n60xzadlU5vCh4iQ6fkyYIDHrRjohF+UBjFHsARg5e6FJyUSW449cnCxhwS/MVbOxkYqqunXrFmrQMD/54nOf+9znvvjSJ5kSxy4dFVfFjsD9VOlq0Ap8iknsh1W2FdzAnREpa8S9KlJAM7dSLngmP4l5ju/awAmEUCDGCyDwYXIMczOxlHmGM9IDYQa2+DILneSqdhZVea0A2BBOaE5NGRPyeawCLNwspZZVp+c5FEhxv8LTWTOXBVgkngQBgYpmxg4HAmYBQigSP2wCJpA4nqc1/3k4EPlATNdr2sQtE1ixYgSDDbTdSdfkIMxcooB7LTMhtyCQmJAHvGRZkGBzTmPpcC0MY5vRlCdn9Se/Zm/aCTSnyD0pK6Oc9bfojFZR9KidsLaOk+LenKXSNFnSZIO+d7TsHfESQfvVvefODge0liGjli2T5ACfclmnyZ8vTo1pBKmaq4CE7YmGmf3kt1h7AooS+RLaGj4vuuQWR7KUclkj+DmrmejJZPQbUjRUZXmBnWrLvWXu05kIMWMsBRdG9hYlp4OKwDpkFUKHqT4WffygosKaJK2tpARzBADRDXbUQl1qEGjPiqOn5a3qn//855978ZmQKMUgJI22VDF9MTHLBVS9nhLmUDPMsPQbGkwKmFP6Temq+Dk39TBWfoahL+clzkMWEs4DQ8psn1aLrtMmL5D5BAgAZ5QxGgVVLaQgtFze6N944BVBOJFpXNcOdqcfZuUK+2sXXiBou0YuD8Q0pLiMBURu6GhZzqX+QiGi2XJm8g/3a0Wtf4ljaT7rEwO/9FQIV7ZkxwmehMJhZ/feaz/+0Usvffrxxx+/c/PW6z/92eH+AU72+LVnP/WJT3G39fbbb97fuwdB87jDoQR8MO3+7n5vWS9q0Qdhl/c+uHXj1na3t8qi4tbWxYceeqw/Gr53/YNY44uFKsoe0lmVhWp4pBvdElwKLp3AUJHIxQk8ASOBPRf2d4Vb8OG5UxIBKyqVezE9sRkda59qj+6i3RjoeR5Z3DDxfB+CXIXiBJUkxg4h2gqX1CJwBkJxqwJZDlabeNA4PmK75/J4oGNyRoecOR0HLbH3QEetDdly1W1xIAcbfaJTEFs+f7ek7zSylkNcP/+hhyDXOLlOudJ2IWJEK879AbrRW2SXSwtqwp4OZgisJEglFEDTK4uImKBlEvVBk6DG41O6tBdXoR4bZC4QqDVi2TIXjyv8gd5WNz/xRFmksZzNXQmlEHouI0XTpX07gJHJY6Ja1qREYh53U8S1/jzLvX7r+kOXH3vm6Rdu3PhAU+rhqNvhrZPlXm+F0YKhQhWk4YTqb6+sMPL2VtZWWbZmIAF/f9DndmjIrhmO6dATl1w7bDUCQSonc9qo2nmWcVw1VkkiVFwI/MeoftxewhCxl6enGzlshWfm3n447nAy6WF7qbGxdLwyHG8t83X7lcOxTrvmUGuVRd9o5qzEfRrm/r72Sg/7erjCV0XFTesAA2+wx8u0qr2scl3Y2oxd+cvr59fvd0asRXOiJhXgBQxYxr4Erc6rJBFQ3neOUdjsPLnTq4DykxZnsjeby7Q/FtC7d/+v/5d//NDVy//m3/whn0CgUWifoQLWkOvJ31mIxyuGmgctLXeW+71GX7UZbEFxj8rVhws2eE2DVhhfsY48S/XoxDyyCaXgorAIKhhLMNQF5qWEjz/+5I0bt2IyT61P9YETlql/TnWOMvJ6xdEjpcYYVvX9okxHq5owsP6+Vpa8cZF2E4pJSs7onRv4R4xccMAUbDTDtbiHNTcYshWehi3ZEgHgrCLgECRpPFZ/EHewAEmQy/4jdQhRJNgCd0zWSZVC1sJgWmeF0MpMFUFkVdf/eSdP1tYDk3qVHm3uuNHf3huv77WXNoEw4ikOy9IHKp1qTBnHHKlOd8s8mffvNOeU2dVjaB+0aFQ1J7zknmc74mkNQhRXCpCXSszjl7GUpWGZQdB1HciQ1L5EGmycq+ZZI7gUM6tE0jk3CWnuntZI6lz408Y67WhEvcYhf8sjHanBWfdADsbqxn2DoVEgnBDFGKg4POioP+ZPDw1jhAr2oGi0IsjEYAYfukh5bOxPYY8MuedGdz7z2CevduiQ99uNQ+YkfPgLOEpKT4ZviENbftGH2AEclxo00uVSgqOdQq3VIaYF2FRH97nzZr6lrlTv2fDxiehCaV3oGFYVbfCPKo8mLDWiIhBh0QzXpLU1I9sNyyGXYATHVgkRNoueqcYxUaBpYMp80R58j++FA7QEUOhfNLE2TnxQUfVDiMhw7wQGW3hRp1GbTMzyOCkCPcTlOG8t/Gu7MDUexaEI/PNtVTYp2AhFGfsY5WXcddrzMQoKN+/JtT9qHhIWxvbh9drLr6c1YT15NFL0pwC5xOoaKWpnARZPJ4JDTqpm+CMr1bgIK1ULnhMUzzbgUjvcJVphwG+zudrgjMfjpdgBfdiOhdpoyzodmkBdoSvOwWEpoQu7IeSwmE1BrLyvKi5jaKCvRhlN2oLA4lyOGLlyoUTLcK96D5s4zv6W+JUfjJ8JKbl2Y3uSqCm4/rf4osy7b70ZD36YxIx54M7rqLdufnBua53PdoDCS26F20mJ4m8g0D9STJU03r8pJEUNqUpDckcRVVBwfskEplhoh1Nq+ZSsGWVsN43jhOjn7fgYCGD/3m6nPzjYvXu0fWu1175w4fJXP/Xio1cubrU5/H5It4SJ2BxBW8TmOMeY18rpAd0v6YapyxySLkeVGp6Ar7D3HH9AmvCT94X0iECDLDwqGkFUJ5rhPtFnikCbLdQRKR1tBQ9TD+NSs+VQ5sqOPWF9eiqWz2qUUrM18COnza32KCmtoP7zw4akW/SoLjUcZOHohsJWMFdf/aFKcQpykVJUTZDwmaWRvoi0ub937/7uKpV7PGp3luQI1INaJP2DPaxQK+EqmwJNXUwsAydycrlEiN9G32gcldQ+zBsw4hEe5jFNI1H4iRSWF2mInimOL+nM1OWyIZkuKI7CdetAcKgajENtQ6RQlkuqFEeaBAm5sJJyEeAWzUAKWSvDTSg0yhU6Gu4O57jZ0RhkhnRc2IK6Td2yTCIlqjBzSQ49FyLsbRXiJEnxoTKKG+AkL1JYUm3K83nEy4wK0e5QSeXiJTE6P72dKs8br63QQ3CwqroISFHh4Ojg7p3dC+evuJ+R6lJK5Q6WqQZVOkLWF3YIDgUDP/gHobAMV2ouqFCEGE3IREOjqCCnBogW5s/Bo6SpFJnC+53jyvhFVY4DxxCdgeaHKq8+4UqyuTq6T3z57mhpv/+3r3Rvbm/fby8xch3EFsMRU0PNIcL/Y84o4hxSXx36unjyXllMcxhxx3RyetxaO9dG0WNTfnD0JhBmTgYSjvfkgou3MQPDUIz04hbNRo4MToz1npNwTiez29d+/ianGrRYx9KMAkEw1XdTlvoDnkBurYx5rQo6wGWTXFb/4/nFzmGAxK1OA8IGNUQmmQsBTFjCn+I3wZ6Hz0OMrVWdPMN3N29M1TvMHYcUct26IbTovDNa+pBWrv7SDCcOSw8/gFFwUPH4Y9RTv4ufj7ucz33nfvNg2Bnk97ykFiVfVLCFFhF+KEQs4VVA6iIbVhhV0ioaIH1DgegsUoN8YGusmKUkD2r4tOA3vvENnor/4uevcXAzTCgFW3VYN+FYTjYyM5Xk1oL1LGhYsWLBi4/ncG4yL6K++uqrw+MW00EOKSCX3QrsSqC9vf766yjG+gu0liS2s6VPOtQ/LlQNcRqVHOazJgjRtYVhZGbwSaMGQ3lEenCHGmilW0PNmIRW4/uylgQaQF6eERdSOVjPOnYu5SUgiIVLNeIcwISt1gf5liWfV2Pb9Ui7hMgHzlm3xDG8x6y7+ER0alJV2REFQ11NPCr5FMC4sY5WQblyH20VahKnC4c6gSxKaohLZ3KnrYkhxADzHXEyOJzRoiDAChwCEDIwi7NUHZEV+OqUmUYoS+8i6XbXJEoHmmO9k5ALRRZUcBXRopaIqhnHMoMpK3/DIWuCHMSJA8fSLVjjw8EBe5w3Nla4t7Gfoyr+v7a5ATH68LAXZKQfjlSD7e7Ug5DQRKrGzafEAUEBJaLWuHSALQmbUQgx4XOZcBuyUl0GB0hlsbCJy+iY005I8FgM/mxRQC6fiIAWIjxqZWuLNsgzGOkZCyVNr2wE6+Re/LRay73Yi40AndepfdDYDJ7cYBIjQvrEEiQNN/inwpB7SjDmDAJAynI0HHJS/N/9u3+X06u/+c1f9HrpnZLol1XmQkWh6EYY1/U8oMMLQHozFCbYs+CcPSFTLwpWVWxDtGMjOz1LlO1Q4LZS0YlLZ5kcX+IkIjpYPnR56cI5fIx9qfKTqqSFlRNmaB3ArHOhKvyBz1zW5DVVITlFKPhCy8pzaW8kgQ4oQ2DvkzmQllvmABAIMcEdPIkiNGO59QFWgCeBRLEbl5ZIIhglDsHKJS2cJgk4cCG5VRDbaUiVOZs0h1noma8/lKwzc/0oiDOahD3HvKixfnWTtD5xQ+xWUBknF18PeKhFcAgBTCYlrRXME5oPmeAF/pTNg0huI48QjmozqlLTNYIvXU7SJE6pMhBS/2CCXyIuci0Ol+OpHmk7sATlYJXc24Bgw4DP8EFMCwABoHUxEZe1objEqvTVgssAY45+ph84Hmr7oQaVaAjmAw6X5oY4pwEWCAkBMwmXMFGtBbLX+kCIshClYHJjEgOFA0BnO5e0s0xuoU4TF8sDZzCxXPGZC+ZjMAzpt1EPBurGc9HIjbTIwSF2wR1H+ciXhi4RB1Pp0gbJQo0RcbIY+HUwhwKpMVGSS8RRgSTqgpjK5RVJqBE/kuJLvFcJx9VIBB8HMEkQy/VzcAqhyg0qmBgtUCrM4OLcglASIDvLphNtnmsVnGCA7NAhpkAhYjYSn4BNWE28IrlfTSPl+R+9dIFbKFPqAqE+q3QymmjnAgoAq7OA+NJZZk6MJzBuEji2mCyC7ucPDng1k1nZ5qVVigDQ+HNyFncs4IMpXhGctj4AVEmEaC/gBWRSR7qMcJJE554SU8za7DOY82xriU4XyAztSZfgE5j30n1xegmzqReff+rFF1+8cuUy00gmY5y1/dDFC7Lk8Jh5IEvvYKrthWGJ4UyLRnMC/kHsYwqwjtKVD8zoH7lJL/EJ4wEEZCoA5lAILY5LSyeXygC/Rihp4CZPMvKPmeSrX/a3FjfP60PJWohcCgtzIwApVgpI8jpLd9aMJjXnOj2D5ksQ3EuUXJGEm5sWB6ChFcVIEFRl6Ya+0OV6UT89aSaTbPdaOS5wI5tn/aBrXvNSv6HhrAiAMJmh4rKG1AiWW9QgYYhxKCDinC5xjVyzBR5yVHKnjRkMk/eStlUyZs3sxDTIiWwRyowOi1CkmDkU6y1EmwFCxUSkAM3B7ZTmz5rGUXytKmYSTc5Z5dHgyurkXqAmnKmneZ0FybIsaL7Ii6lylRVxJeHqK5fziXmGximeGwgnGr+Qo6raYGz4o/ZJa1FYQeUmlz6WjpQ7cVQikfDdukMYkMItCFNka9M7ck1NgMaP4iqYEFAkprIKFlkWMUNrBAPpAQoacEQzpLJ29+wzjwGnenRTFo+LNFXmCA4dk1AP90Xaf4KESneCWBfcmS6pSnNyyLmyZE4nbNVyrD4XahCMY45Ol9ySqOFyDwUxd6BGjZku44I0v8QSGuMaZu82dOQ6710C1HKJ8tI+NXPwagLsqJjJTItiR1DjJGHUmXQBfuREUdQcfCkXiYWtWk+fF6PmoTKGc8fSEg84KdQXPvdZmsrqcuvll1++c+v2v/gX/+LZZ59lbXp3/85rP/wOz+4YegaD4d//+3//q7/263yl8HNf/BKFeu0nP/31X//1/+l/+sNmi8Of+Rbo0VPPPLd57sLw+P6bb1xnw367vcEdEvIQQcz+YBLdZb3GxQY+6Sz/V1A6ozkZ8ZTpgNimUQTni3MCJoDnyVgiqjDKyzjKvZA+ds1+VB2OwWKn7gnlv9xdcx4iLjbkeGn1FJKi5+eopr5Ee4iS8kKHmEuYk0UihOuREir141PbbDbtsz7Y5qScnnYNcrbmWOvR9/k0L4KPW2w87XV8q0+/xTMs81HRWJIm9jq3ZgeRpjb1KzPGuChzzgSVVDoQ62BLdjGIVv/D+I5NU9IlYTgl0RMx2UPcuHRcEgXNiZJL6+SfP1IHXaipWp64IqhRF75ntz+oAlRw/cnawlE0UDYP/uWfmYM08Zb2VKfBzYoBMZDiADEQfAJ9JW4mSLIY1gMstDv3bvC3tbqJh8Mbv9XjpqOji1cuX2pe0YkWe3vUOwvQbCQGvz/WowR3wQMqN6+D95ZazOkPj7TFtcGMncfs1c5WvXgdATWc4PRvenS5op7ay5GAS9fUwbE5n8l9LMHn9QjpL+cKl2dU69gy6oiwHWVvH++LIBZy8QEgh31tnTNzXFzdZ1eagC41YjBjpZzR5rCtj0iALB/rLjHDAFeXrKMD1/N5Yu2ywj7g8MVcmohqi7/oN0CuQpmZRqFyBmUS/9iV1RoNDnd3Nle7/4ff+ztL7W98/euv9jo62KTZU73o7FV4x8YP3iIY8fXEXmJlPcWSns1Gy/w/6i9+7pIsZmADzucBx0I8wGB1D82jovTdBnxpZ3dXlZu8V7Vz9+5tbu16bT4jfdxprb/4wmf/7C/+GLjqIfjAPxK1HAyOCNqBWqK5qacIT5jgTY1BEzApMCfmqnKAU2kLsyqslIw+bcz7AeBHFxSeAYNjWpamU9ZHcfQ0erCuniA8RzFqxAOVUFuic6DFxZ/0hIRNaHS/zkQ2HSevyIhcLi9vVJJYDJWYD2JzgktkkimHLBxybgH8rzhhC+BaVBbFIF6hOo6ODnd2deIQmzplavv87A1Ds8E7H81Ra48+mJ48ugpaPDsfeKvDA6C2VpjzSTYqteCEunPtXJYy6izm/NzGl8LyLLAXV26Qo7d2oHZnFXfm4rhUrm2S1VNnSV8udTjLbjy8+d4bo8N7Tz3z2bfffrt7POQdL45cI2/Y1dquPgIw+bhuiy+eH/S5J9fDUjMkF/6VBkrbTxmGOTtoWRtN28tH+0wtPvPIuYvHu8ujQ63dtNkz4/2+bCXRSfcQFjeOjsVz1olZpDn/s7dTTZro0TXEsiBw1Vk0BGInjMzuZ7pZ7aKBln/6jSYarbggh+bir9FJRYqmTTekwCXVRHFdoTyRh4PTUgomFDNyQeV5hlafwQ3VdOsCvdi0OoxwjOacZTSOA6Lo8gf0omO9C6XFevbXhm0xPAk5EY/t+Y1ywce7lTViavOqdJemoZm19CUxl+ATSDt2FtfQhOVQRbXM2F2oEk4Q5kgitC8XKhsfLcNAlh7Mkyb8eKyEdeGZTGZzWd/kM9Gr56IZH2SzFaO5kFXSL5mW4SZLZcKbiPL0Yuw4YnfqeLQy3scUvA0B/lHDe51lY81tw0rUmncR0mb5wzWwbeYtDSAn0Deo9/dIFKVAkLJVOS5VXAgkiIP1DFiClzQJJmly9dgjbzhlQDafTWZINVNvbX/xhU+89867jEcUdtjs379/b3NznZfYNrcu8FhkdDzw1F1+IsNAKqujQ7C1nVTeePIjf6BEMmL2TPB8qRjn5GFL0HIhbjHaws34wfNvLnIpclkeLPc4lgziS1G8Vid8Wpf2pvT3+vd3xsP7X/niy59/8TlmrasjrTDS6u7cuN4ZHPFC/XKPPUN8iERU8ijZBRS9REHZNfsdNbvqeAgRaQ+d72PoNlMHbVXVWcSMlOqhdzAQq4fXwAzNNEXWtwayH4ZQVZntDAkJYipLzKKyyC0IThMbwSIK8IGJmiHIixqceHwYtmGT1C5n5c+Iq7PJwm2JkEUwptuXi1Yj/zWmdefbXB8OeGVndOfW2rBPrRfbMhUoJkJb6YkqanH61YifbUhWQFKhSLtE3tGc3yYxDnU30tPAGJWDysjRfrmWAwoHnviAZblzBW5B0KYNTIEvklDJV+E5agZSOILSzosYmLO4ch9Iq4G3mKhkk0CXUS4wi9NiWeFZJbopUKNnjlTqbIQLAlKwkFuLqENuzITFElz+rKHjiVSLjPgUxzA+KxtWhgaYWmxiFNqEzhpRdaeB/Whf8E17qFz4JWZBY94OE35M8GMrk2q8ybYgVlN+/MOff/aLL4pMZ+izvVtTBC7RjTJiFviEsnaTlKZ+AKrgGYCeSVVj5xq0cRznnFTp5bJOlErB+lBZgMazokaNXaejXgoA/FqfQm4EsnJD4Dsj1DKmkPvSaFj2Qb+L8YZ0+72bbLf6oHW8s8O7J1pAeG9pAw6HvOcZHORj4ZLuSzVHzwW3ZfAfJWI+FvUjlwfH/kOp4MPsSs4ajkIt4ZRAUZhFJkjRDhzql/mVTyDQvT8jQshlGgq6l+La8jvNsm/d2V1uvsuJCE0/cOyr79158+ccrvv48YBJra5FBxticWsxKoRipIuGkSYqlaz0fEiOHhmYMLOZpVIpwhjzHOYhUTvs8M/uhVY2UKCqlEBitNJXX7JvzPPBVggVcuw3RzeQCQYCJ20qSqF0XOm5hEjcvgWnFiJPcL1WBQI1FTOQRA6OJ9kM1NE6udQ5OKPj3eu7h3f2+TodI3i8uWaKHNcFy7CpXyM8EG2K5kEXpdgLEaW6+5vInhEdl2HUTMzmApZo33rrrfMXNpj23bpxk23OfFqQhTMOJeCBuZ97rK8vf+Yzn2GOwlI1D774MjV7SDllkkc97Cm2FDb4wOHG+3fYEw0VQAYNpoYAkTYeHYTRjwBz3jEx77TSLK3I6YXKykYHFxTgw2GGNviTk9yi5AKhWikRr3gTTEhsTG6WrAYQAgMthO5iwBCTjEnSIuyJZFA6+meAZMHEtyYwBA5zMyQGAbnYxBNlw8U5nHvS6hBXFQrZ5uwKhUo9UEZItMHEERIJkxeXsh1MUuLCrZCS5W5IXVYE+PBL7PLWQg2XJDUt2dA3CSREGu3Haa3MZksCFpNQnlwjCD+bVDpErmUViWCWNMhWPhgIjqnNynoGP5mIgKlx7E5Px5vMB06PISAczF67w8Ea9/p7uD1PCPDnweERixGQw5wjw4kT/7A/l1DprGVyswPPiwACAmqAbD1JGMgvWQRdakFEl8S0B2ICmIY4HTC5iWhySEAzFZ6Mg2qGOwaXWzzSzN2I9e0pOGvAnDgSOFyrIBpNFGRk12ZI1EEyhGg4aXnaeJFb5BsGNycM5xLTOc2tkdLtFi9Y/M7v/A42/+kbt/VAKBqILcn9DyTYlyXd+J1SdUZW1uIBvwi12yzEQ0oNB9mX8yS2LJ3eZz/7Wfq0f//v/z29Hw8weDKHz/zlt78dh4NPuIFDJ7m1ft5PNWCI/ipUqt9a7N9Q2oWdMeOMBXQZhVBlRUBz+yVZeLLJTcWlITM8TyoPPCF0KHxIYKvCCreqs0CG20n85+Hg10CTw/MklT52eJb4sTM+0QhJoh00i8UzG/v7dGjuV3VMwKIGa1vV3u56h6dDbcyFHEADx3EWrpHLfUUNn8cUGr1NaE4u5JlV4jQNnC5hEXa2hFkZlzSBJ4g0Z8Zlev637u0CKWMxaChmdyXmtSUaO0CrVwQap1yWhI2Wy9J88sknn3jiEQOjyJJOH6s5A9e5z4FcGYaHNQpDQTMaQqMVyhpBbQ66RISD0txPxgSM2COOuRmBdshlZIkQ9s6VoNyCnDbaTPshy/jOLXXtS0/5UMuPgesCglA0NBNLSXEygH5AE0IMmqYiFiRQVb4IoAF2ej4ODvPg4BPcsmUSt4IKocwajFkDETzdUyiJ3fSTQylRgYdWyqYWMpauYFtwFprFhDyKq6ik7QyycqtCux4tyxyIE5BUWFLOlmvNMw0QANLx1rKctjjjK86E85hA4E88Qc5tBzgiZjQ3GsBiQulnzLj9MivXi7N4isbNxc/fegNVUZiejXsTNkETeO2y8IczeijOxkN6UY9ECeBARaA+DARCcNpCC/LChJCLmIUYfw1AFCtKnpG98dkzjumYpdAR8VB8aUnf3iDEsNtkooslMezli5f05YC4o5FxqgJiSW7r4u06jeDk2rak8UPFMactJEJQ5cv+RnYWmELO3lIkFIZklWKmREE6ucyQL3Tjkyisw0m5BV6KUyAfYwIdZvhbq1J8yYq5Mb/Fkh+jAgtZIQhPwCVCOLrktpSx5yHGPKNJM5vJbyEkkRd3Jl1bSVHFvrmJ52vMERMHFFZOpWdhmFEmskhFrriSS9oxDl0jFQ4lAX/S0sGPVXSHFK5fk0UazNTne4dsvqcuiIlnVlh8IoDAL9TFmS20EJ6eMG1hUidgyCV2sxedwqcwMQ5tKm5/dRvL7fzoWO9qj4bac8PEZf9gny+EPfLII7CHc6kpS0FoLqJtfqJYhEq9gh1pa+IsxzM4M2VxGU+UMZ0xz9CQWodpiskVmOgLJt3bBIrOWIAiA6T6sVI0ovULF7j7w3rz6lWy5Ke2XgYmz+USOHFlG8m0trV0IImmhkZaWeG95qI4ILAxxNwCRwS8p8t8eOviMhAcAM1BcO8KhDAn4a8dIA2n5c5D/vqUsH3gXxJ1GqCGwEWBnLCeuo4UYjSktRg9Q/VrnrgNCWKeIBAzzyFmzghCWoCmArjI1SAytwQqS0AB+AWm5QZfEDthNRPth6xHqKxlSZhtjtPSFZfuDtTfKNQ6TES2ml6YG/FVtvX1VT4u/fv/z/+Ru4Xtmzvnz11+4snHIN/euXNvd7vdZqPoeHNjk3dI9w4Ol1a4pV2Vj56/RAf7/o2bzQ5bqDgut3v53IXWaHx40Nc50UubPDjvxl6Y3rLM0R63mTiyHVlKsesnjs2lRPZvAR8Uij1tB8czRAWoCojJj56G6Ywcyq4OUWfhEFNrGkj0Di4bO9vDmGwhgODOJfja59X+Qk+rKhHhLqCEFN+mUiIQWffUlke2arT2md61Rht8S7rTGfV5xLS01pNw7QxKox06oJZX/mJVUIwlLJ5TId0aac9UZPs8IHoaqamACqpTbU4h8NCHfQpxsB33sILkAL7GEvmlKNWb2Bd1VwUHdXMKYuYLA0JAAJWRQ5iFJoCPyLqxOyE9SePJKFgIIsZmTkQsdWNxcfJcVJjxT2yELnHHsSPJZSuGErfQWFyy8a0uzA1xQrJC7pWHHv7+97//zOOPiWe4AqfbSWKUTNWsID27HHqE1763f3h7h2M3GC1giK/yzgO5QcTe5sDXDRKmDvgxZytxsPdRo8mnC7WOQLWJZQ5ZTRmKZ/hYSb05lS1lNJBnfHFzEfAapbVVv+zBUVcTyLI0aBzOh1nolVgXjofTIpUstSS5gVkpITY0N9WCv84MrxDGFjS8Pi3Ns5ih1oDyefZHPZDUosV0KJwNhu10/tSVkY3jNEefg4HFuPzkJzf+2//27/+zf/YfX3vtPU5yZDM2nS7qP/PsOfqZ13/y/vXrt8crGFYBKkjEKu7etUX2Q4bakQrDzEMmKSEJwmqlkYYCILgUOzt3Dw72Hn30xd/93d/7oz/6I/rMz33u88zXv/Wt71B9dGzwD1oa94gXh5/66nOXuUke6ND8hx96UscTtbShg16pCJ1OqLxyMek1wbH0gln0PLst7PLUs1nVVVPY0ocVhpTCtgIT6+NyoDH1BFiUIYtL/k84PCgFbU2u5hQOj69yEkxfXZ963JjPSmVJn6qiKQFm5bJMZfz/34UbcDJVjAWt3b3m0krr/Dp1pMrDjpoVUMNRX2mGoIlKs7MkDHa56fAxpjp6EMsDVd9n6fkkXV88Kwo20RjVs8iXZuoeiEerUgMASgWRNtwJDcl5N1DNudAWZPrX6M1mch5waSmx70Da4tLyf7Wt1vYOr3jd5mPLnPDfOuz07h4P2GIZL0EN8XCZSXMVCNiLsH/UH6gfxVY4v/wzHDOKH+3dLQX81MWO6fAb7KpG4oXm/S+/8PAWZ/KyfWSslaAB79VEbwsfhyDUQCYNeWQoCclQZJV0mJFWya8CXVQ5Yxm9gKu10NvHQid1jSyGfTShiUb7ha8aNUpSjBCqAksYIfba8AukAJXAYtEpSGI4GTikA4tsTU1cfHZ/YCEGUC7BoVfkLixeYNIllgzrgx+TwPAf/FRFVhAboRFIZPZpTwjzRxTLQIuWDqGshr2QCGnsz0mqWdt0ET/eVaTRWF2W6OogEYLyo9khGN5H48e3ZIHggCAsC0Lpm6K/SlvI5NMnBHR2HjKS0fJKHznwhE44ZpeYYJ/UDeJCaorylHE3ZokohbQoeLr1ZXcPDHpNHPZ4abwPfrvRB6fDxDTqQMQ4s0qp8vJoBXzJFVylZ1BTAbvxsgsvD4ogeYUxpZctn8cIQcI+sHEaTAKELqaB0zH2U25Snjy+gcGRYcymEKc9XGySjXeeWu3Hn3zqxq2beqjGTGHY5waE5dQbNz+4dPkCPRandfkYwCTayoYi6DAtFCm8DcJrB8Mme9bij7EYT2LxAIuAj0rMnbQPMgcVRH1mMkIB58QZf5O/Tcp7RrozotG8sLYMltxH5Rk3lsfDHe7jjvcubfXOd86tr/Y4zlllbAz6g+Fad2V5ucuXe7Z3tzEjG5K2Vtkvr3MR4AZ5xIwNzVu3b6ytr7RGfJC7G/uasZU0wyi0Ft6iiTRXapXF7BTW6sPHTxpJkCsH0/YRcdaevOAleRHKJQmAZlK7SmFrfGKzLZcnJ6yPBVGnuhNcGKzDwqxTgIWqlGUhsnMLjqxWWcClLjHVEeWVth9XmDcXbQELX4jPD/b2d3kugWXoyiKms0JyrstQgpdc0GreeHAm1HrSDxNgrp/sM0pGkV1MZeW6MH6BgECdmalIYt4S44VQZJrYfGNupqpju00NsXpWMQwbbRv/DL/F8VXsyJ5YHJoYl10AKUOIgcEn8KIVCmhyzPAae23w6WIFo6MDCXNAZ/waZGRZClvH+HoC7l5UJVUu6KG48va4YCIsEgh17EtiQzSzkj2FQCLScnUu+KOzA48eT7lIVNGlmDq9sTcqka/OmU1LvJyEisvLPV7zZUxnUGeEZ9Mzk4r337p5cfNqhyWiqDtxiZUKVILWh6t4d5u1KpqLIEINKarmzKlfMQwHlNFODmIYVjPnypyJppZIuggNkyScbLO4DAvXAl3GTnxsbBhTxq7mzMmfeYGA+dvW0bA12H16qfdYq3lrf5ch9W5nHXGsLGJ6PnIkg8SR+lGNaBx1keUXcaARPANL3fpQTcl8siFUb6Q1a4qy89ILHFh0ItYsPlqQql7VixMoF+HJFOT6ayUxghyOGm+9f+PZpWtsAltqN3f395eOd1Yau/EEHn+Q54qRWKS+XelFAf6hjpUCQ1eGyOtK2rRTRjdoEpuTTEGTt/BJ5oKUW4rnbwuyTwWZv1EwqRVFgQLPxdLdAVYwJrPWmis4Q42hKTfuQWLWr6lltnwQ2KtJSgSdGS/98XfUHB4Mj+7e7/IoUC6dF6DBsxVInNISjFPHIesBUSlYLWUhDZyT9Jh9noSD8Yq2NU4Bsv2HTQQ8FedsDfY1Hx302Z/Ig26QeS+VmNsJGg9vnZO4d+c2+6OhZX/oI09soi2b/oiB4Kznz59nSsFzcpZauvF9aq9WsGQBHw6g4JKJsY/ZhYT+y8yjAtSNglYCCA4qZvQ4viwIJyXMB+QaASBStEcZuXrnJ8mCOUGvhfJh6BGfeOZFCBE6n7Vc3bbxPzaPmIrYrEnoAcVA3wveOz4CZxBL7XQspHEk+mrm55rNx2txae0mbi5xoqJDdmDpOwGGm0nbuN1E3bpEdtlAJkkvoI4gDXfRtUEndjmYCVfmb3JnUgsAM/Mwi6MwQRhnQS9jfHZ6mwkM4QEfgiEgGCf6NIyv0zCdO6WZ1nNDe3Ur6oJzgxWb4CfdTFhiczaCyIOzkU0GAtZ2rRnZtMqdC0YgpqbwZwgJeGa8EAx2ME7dpkoHW2LwqXfyTG5IkVISJdeM4Ia2AAPBlhBP46eRMxzCVQZmSWhsh5A7p7g/xPe4dNmDZ0wHs/nIIsCZEMmoxAQJQHCGOQiUIRATMj+Gm0Nvie9m6gOFskl4Y0E+YwI+FLAuI4+FWfr5R//oH+3t/Q/vvsfYpiPaH3/8kf/i733llVde+fEP3+IgoHssn4ViVuaMsh6Ihq3scbmM2fhVVcIEbW0m8GsFuPza1752eDhkH/Tf+3t/j7oAGbfhlrhGI83gRGdIlr4WPeYbjF22cYG2vXMLEttkobZFQ+fabgVTdtR4lByv9EUFoSSEV4dMYljkTiGIZ4WPn9sCjp1DKay5FXAWhSVBkSrqSdL4vpZZqrsYZxGnFsehD9rjpgZCCSEpCKTr4gihClamAGYuC/x/7YligZniu1zkYlv6Cy5tZ7oIGtrSxc3An/QzZcQRPHyJ+oMEQjOhFgrPSARtNASLLpoUNOACTlWLMxUbH1Emn2REitwCT5i59gt8huSBl+YDuWwSu7+L4zmL71tgHDZX9paXeVmy0bhdqwH/ogkzlj5faQlvtNwZrWy6OosRCSDzAIDPP/88E6pW47YHDtTQ6i+VpSFRuw3NWaA0S5nsVDVPx0UKCUKga+yrcczKsz4apZsVyB6+IEHzonxJAzeTkig8xX96LlHjRFpsJCJCSZMV8x8N+h7XAnkyZxY8QhHNlQySq77oUCeUm7UF7iIQmwpISdRUxpyBnHIJExyZmCA09UbJRDXVvPGFS6iaQZ0WI/8Xl9RU3NbMX6TBIY4sE1IE5MgsxDxbCKFcERKHoEo1a+mKo1JY0RCV2WaJ8BRxAJOE6oesciUhaeazwNPMJPRJHY4Ig7r4Q2GlnGmhJkSN0uenNHfngWwEc7h48eJTTz3FV2pgAlyn5fT7DK9s3T1/dZ2W1Ynv0NQi5nUgF26Qk3BIFgwZQPiFilAalYA1krXJ+CfZsMJanEQHpCzO+/BQlEQT629qp6NA+vAJM9Xlbpcl+9b4CAREg8+tn5G5XOmt0Frp6Djadbw15C6PhacwlFDgRi6mZrcj55uWOUApPgiEokNKhN1kzAilvEaGLbVG1ZsJQF9anDk4SxpEgA2/jhOo+gHuclWwqWY7w61Gm09/KOR58nmISzQPLxAb0JcTY0a1PlAZ8Gs+JV0SNfMCXJhAFg5T8Itom93wUpVwKOUS5glqJCY5t/B0wiPgQmXmgSppKmvl8DHKz9d+TY4saEW+KBQ4iVrbpGEA8U/Ra/xJ0Twz6wAVCeb/Iq8EYsMys7IWdloxDF6Kc7ds0cTOWqT1FMxoxi/xFMYZLmBiueCSoBQECm49UZ5eFwhobrl0LMyjHnv66gxvKwCm3WYm16qWLNAKQpEOxGgli0SNWcNLep5kHlKQP1riFA0liz9612BNAZmBs19zfb3LUIXFsKTJiSMhC1vDuJTNIQ1fSGn7g+HkWGc4s8gdtBltUWEyVcpDEPQA+XWW0/OkHAb97rsjevuNDa1iLUVfzS6VsH8ZG+fp/tohtpVH5FKKv1apSWIlYx7izNqfaxzSCuHPTD0qTtGTONdV7r4lTfbG+AzGNzExa23Yv7SomECY26nPAYoTKHHCcnCtU0kjstAWoBMzfhNuoWmZc+MyUcQzEyaqup9kbQ+eCYd7MnqW4eCzL7/SW+LDeBysdp+s7tLKpSvXlteWWc1/790P2M7C/JC5wZXL10hjDpoTT8uPhoPNC5sH/QNO0eArYXTMF86d2zq3tn9w79UfvcazHL3wiyweoPV6S7EA3YgdozRCpi/jpj7p1sSkdD0dzZD8cCEXeTLjQyhzaJ8UxqM4lnbjQaOaaSmLS2RbZSATLM1sCNqUCJTRgLrDBvrCq4YBnmbwx6kYqMDIIibsZ+VIOlsoHIWCRUuHPtnNCV1rHiyH4MuCR0cHg6aOKWAxiniJ5/n90dIRpuN7sc3ufqPd5R0WVu0PKX2rF50OdZXu67R9FuHeSKad4tR7iGOaDLtYM5egKGZUMeYNhDBTOrIxclVQVnSJ4C6e2YUoKSaj1PG4Xzun0NNjCUtjpgVSn11ooGM92IwJItboBn/tD5GJNZHtyEmgrSlIjw+G/riiS6eYb8Xa8uIm0+oeXKisglNAPYjDV1PtRykkBQTHJJz2JdYBx2jOIuYSggCPeSLyxhtv8LmV/qGbsXQo/ElTLgcYEtANd1McfTRZ8ME1xc1P3dWCzXt81D/YP7jf7qA/tuCruME8tBV6cCBOAuKHSyphBogWZPopeFny5lk725jwfG5EdfsYfGgGYQgOtWnz1KPLe5Tx8Inn06GTniRoN1D4hozr4QpPBhxWTfdVMjOPaflJS896qElh5VaUQsrrsGsFxb1Nzu4EIUqadqxEJnfDZwtWiW/cCD3cnu9UcS77Fz9/bX31v/ja177PcRyb5ze/8pWvfPKlZzc2WqsvPvpXz15961uv0zR4IoTJoDMTJWhWARG3B4VCBWJJ19QACzcjGOKYLAL9XjhrQ7dwavLDP/uzb2xv3/nqV379sUefYBnrm9/4c1770Jkz8Twc0zPViHZ1/L3vfe/zn//8xuoWTHbvHWxtXnzzzTdZkBqNeaMfRWRhy7Un0CZcJgFTjaTFpoBHe4kqNtpJceY5lS/g9BhUocUeCztsdEY86mDvJ++xMKFy/4ObdJZ0thKFMyEtl78pGYsuICmCZNB8SUJ+KkMo1KS+xDoktNVkOphbTQKkvpxG/5u7KsX8axV5emGxg3pshrzDwXB3f119dT6pP9lYNre/qbuUB3bpbBrtgTo7OgZ1d5rqaH8O7HQ+HX/uE4R/YrDn5LqQNYK/Rwd3M96bMM1BXRf1PA1Uu3CdkqBA5bJGs/5zpBMUkcYxRKNYhsbh4MqN07vvvru+3trs0YMfrPdGdKadOEMLidxdiT66Kz68w/eWIXITELcqWL2kZJwpzLiK3YbjYbvFObZ9pkUvPfvQSuugMTriIwEa7BGvrdCMf1KMIEjw1EVcVhJK5wBs0lIoL1T68oD4uDZVUR5ESaFtp8VRgNw00H9Sn5pTJLzMHRwVNpeoJJzPZbRyHIDWpwYIxO0xM7Bu8JA4d3iMnjGSo1s6f8OEFjQvIrGd0UH7x3Fh+Z5GLrw3PHqGHFqL5qWyqVop+k0nQJ7p/QQJr8aeroJCgWXFP65n5IbB09hXp4UvR9R2shLme0f7qkoVJjWmiqrag0XUUsUBBCZazE1FGJ1kCJHiKCaJeJAakCCqCiE1e9yhHh8vj/UKZ6fTB7PT4A08ESEFhwPPc0dvvJAaTCc03LZ5gQ+OnvmQSg5mg4QCQo5QLJOGO5VIGZCEbVWqgmOS+Vgl17hJMfSYh53X5oYPwIcYII8mH3no4V/8/PX9IY+lOdZ4eOfurWu93u2b19fPXe7wPY9Y76FwMkqemFiHItGWx6+14wQfU0KtL9lExmQ444DANEmjv5LJC/1HT8y0vNMY1To/0HQwEo6mcmk2G77BqeoS0TvuY8nB/Vur7f4Wrxe1xxvMHZvHnZE23Oi1CFoZr9Q3mNXzGRDdE/UHh3e3b2Pec6ubaBJfDqAD4FMiLe6HeXx+sXdFtYOFOTFUZ9zjeepGck0lr64gHrvJnywQl3KRiHTq5+FMBxKF0kqNPQ4fUhFjtpl4qXDRJUWiRElEXAfbkqME9Q7PKdDHfRH8owGopSZTzAuZ1809BjP2KHWyBoTJYwM+z+eMkHlxCwm5M+Zme3OwP7h/tzdSS8TGUb/Sx4aLTjlRAyMXm4fOAkYiFT8hVT+eKKp3yrVQkS+gKmiRmOig7suegdehVnCDVRE1IYw7xBoOrhQ2KOYq0AEpOCVhJmAiiz5Rd4GModw0VchThhU3PFUbWSkj5dEJwXH3BhoBQr3ggfIxWAGxCBI0MLhqDcRDQFbCei4wTUaY/4WbWmNkiHNYwNdtdKOnmxqQpDP35fN8gLh22HUU6zPSQnXH1xtooagri2P/7u3bO1ceucCd0TG9hSwl4SoKf5RUVksBU5IVWrkW6DoEwTAwK9bI6pOTkJX6qAFulrKQgWXZIZIC0tzNbkIhLSNMZVF6OsyeHI832hSiX+20tEbPF6hBvnDQ6g77L7RX7h3c//FGVy8Y6u3/xiEzM3pXjcJ8gAw7yWDA8RjgGsMJac0h/CTNEAUmUCIRw03VBKnwWUxRFUVtukQMicJWafhTa8XWAZIw+EauYs1FY63J4hkXd27u9Bq956+u9g6PD0dHjd277fXHNHcI/4ELOlB/KCsmH18opj6FpYofcp0gTlTxW7LgEHZMnFLJT+F7clYlIYuiMWWxvC8WpJZGrF3ueDVqgqN1GJl7lrvJiQk65IDxiS3TQQlZe390dGu3MzhmOISUGon30UC1c0xzo8wuthPGcRpEZ9WJaWpdIb6g1bkTcVEkmM0i5wY2wcwSzVDxosKz4txZarIUwlMaHm6zp8D4PBgni0NgeAzCbEDmGY8595kuhiVLdiKgAJhccgAQVmbZhb2YPC0hwXrft7/9bXZDN7o9siAHjeOgtXeStSQ9AtJdXasnhY4OWZMZ8LIv3IaxwgPQwZpMzBjdE5fwdFZBqy8ztX6BE5CuLyrEWpBp6UypTc9yQKOwwCkgOEajMyDQZolZgAaHflYcIwDUbwwnYBlIjCxiiyCJFB5dhPXSDhQIXZyMk+bWZmjyYDxVVcrlf4irpEw8CuD/j7r//rIsOe48wadVqMzIzEpRWgGoAqqgKAB2ExRDzjR7Zvr02T1ne/c/2H9q/4L+pQXFmSGmSRAgQYCELBRKi6zMykoRkaHj6ff287WvX383XkRmJRS56xnpz6+5ubm5ubm5vH5xmTLhQFdxHFZ0uBC+5nGSSzgJn7SRIQDC6mAj4CQ5nHHIiNrC53gwzi+B1v1GbXSrTogPGz5h4VVKJuQ4j/bpAmGGMS04EME3PkV1ckMyqU8NwI8rMSeHAivJHDj9/kfXGTRvrJ1zYUEAE1/zWpV0UYMkwblmjeYF3EnMUugjAFLhShXVwXBc0vAJjqIU4Jx2JMGBrB8RSJkqu7Oc4aGoUiwSGgu40+IbSCAKLhlaU+ANZJfRqXJ2PDocd24oEubVfRX8eJG6nMpRIg1Nn2iLFy15JIrxjJGzb3z7mUPHCujyRtfFAhAIjVYtbkd9FYPDLobuH6yOMEf9/phVXecLmvKKtGi16GiGcobLaKfjnHwJjpTMpGPLyTM+AXA4GYRxQ2epFEOwhO+88w7XCH3uc58jfPPmTYSJleMz0eAbTUYkbtT6/ve/z03QWEU+/IMhvXHzA45Fr2/0VE2WyRJnRSv2L5GZH4dRIKVdKG+q2TLaKZILgNHK5V3ElUKg2VEiNVhaGS8JxvyQ3HFQwAEvJXpYEORcXMKgZp+MeLRvYFkyCfIw2v8KcY/ClUv0yzH3cPqZchkNIBOdqJEkXuweTYkTvmxsPLymoIOjWqkWGCagGo+FV/suBTmAdrpEhj8o9jT+gyAPp/Dw2CWaIANZ4tbA9957jyhe/GI7nG1SxgkWjkQQzqRApnXL7BRrUqcJZkxHkaQQnQSIfeOQNa2GTfuMCQ4OsjgCS3A/wkVgpVge6R7Nm8PkggM5qkmBsgMHw4lFyg3W1EBW8pIzAwbi+9G+sSyZnIKoJQp+BE73CTI5EqRo0eNrOEUlOLZMxMxHrJQNrAyBdzBTLiEAo+FnDlFShYPiApgzKOAicorhXKIgKFTTD1yNAUxGsQ4VCEDsMgKPhJXLp7nAERr/nep0wkytXCKkARwIjiQwH+LKtZC4zdRAA8fjK5cUCBRwknAEWCYhbIIktOOxzBumg6p0XgWKUjsVAQMdsE+UwdY6gMqxVN5MpwxUvkXRSOJMre0qR8E8U5Lnn3/+tbd+arKYNYYNIJ/f36f9sluKtmeWSEXWOXmuH9I6OQpaLhpApxUrhV6dyW0Z+KuEzd7DKcAJCJm3hyMvxUKfBkgzpC1p9MKH0mUNVOmIBTcejxiisyyt8sa9zwiTMBdzaOo3rXIOGunLRsVID33gfPS5ixcYvLXr+vAPW4k4FBoO9VOwKmA4l5G8CJTZc4lAAy4SphCpnCQjmCYYZUiZ1FI4CC7Blh9NahlaehZDvyaXOX8QPUsGERkB3py7AycfH0RjAf/Uoi1QzwqZW0bgRBKGK0GiWs2JeXPsg6wemDiTN76fHLaeEJuLbMxEMz88IGDK4irlsLBIAHEn0kWPCYaBSluEJXbU+pRmgpmYL+gbApAk+CRxry206IOg4yRLDKTHIscFD9FSFhnlHI0RPgSXy1KK/UWDZi96npQ0EycKJ2kUC9N6LjY8KKzWxvDj9kwCpCdtCEGGmobL5Ih35V988cWSwM5gUFSLklrnM0Q5RixkIe5wwBL8DHK/CCgXNjPg1GV4plcGwo9ZNVflKPCRQomOFI+hDzjRYhQD9yTHbM7nHWwpQ000B5tHlElZnvY9ncv6m6mUMVEcCFo+po8vDoOmViplh5edgCFVMDG4zg5geV2LlCIYVpr1GfWkk9HW1lbl8lMcOZ21Wix3zC4rrXN/eF0vc/AbeHahxHNwXs7hNKQc++hhCfYR3BJaqs2opswaLBEOTOlMPCYROrkhhAngoye8u4odXq3oPljT0eDGFfmpXEFCoyjtb6g5Za3iIIr4i28glomkHAKUw1lp0DGXqpykHA4dLSuewgEsfKkNChcbUjGY4PwpJyhfevlFhhqvv36fJRImWqSq19ofvH+D2774Csr+0bHuip01tS+6fo5ycIb34sU6FgcBcez33r07tDg+H8VOC98wvHf/1g9+/M9b27djIUkf0YRtbgvqdlsEpmM1v3q9pWnJOE5kxHhxwhceOXbAhTo6z5P0Scxrq44WzRvf2ngLCVB52sPjj0Vk41BQmh+xKItPgipfzjoxAK9XeisMvGY0Ht1pCUIsy7IOygxJyoW5azUYoLGZBKAd54k8p/EJgiTGUBXXCz70gRMAkzAt3iuVQKAvPg/7zU6Hg+MM3mrHw+bOXu9Ck+Wn1tW1FsWvDmNJUxQ4CkUpdPqDQjRj9BzdQOoLIt9YsSdDOTYTHSi6sLSfmVkKHqzEwuQRH5GRCzj4OJaSeI7Rt61J0hxxrmHkQnmQrdOKPw6hSF7VXuy8tblqejptcmsmRdYMnduhyCE5zs6Av3HhHDa31tMFDsP6dDwfH+IhJc6Ac1uupsbzcU2NTSduSBAOBFzxFHIOls4CRnmiaKK1SKU6Yj+2t9q9+vjjb7/77ssvfQYeHI9S5YwQAiTQfJLjCOd8Mw7DeIihWoLEIWR+9w72j4/1AStZasmZ5X600mEwQ4aKkOSjN6dKORAnAQYkJB8MFWyLfk3bXZVJaAKGg8+Rz+OMiT9JhML5yBI4Y3SPt2vr6A9f2G3DAbuv8jlsr5YemjmTtGkd+OlGpMixXmmpornpq8K7CNqUp+xqQXGeHeJw6CmHVEIOCiq/yIcGyqI5m4jOnrIrHEIjORCVV2lhP9qshjq8KzDGOA6nR0Kf71w83+Ow5mS4x0U9cHPz/XvX37nFyAe2+UNfoYuqRVF08MbOlNODhH3ClZhBG1OkG5CHCGq53MIWix2kND5sE3bBzTw+wBdeeOHa1ae+9a1vhXjqq6tdVq+279/70Y+PWk0NLy5efIzp2WQ8hG2a5cb6Ok2e19NGs9G9+ztHh2OWqj/zuZdpEbDNLt3e/j2NSHwl6xLruRwipRpcAEoSzsAcUMU9FCFjEghMd0+qoBPO2y1SHHGGNPSpcmzBdIY9Z8OJMrKlRFS5CqRTcuKWZEqcuuFEJ2KXPVOgjXG1nFZHCl5cloeXCLonGDj5uJzTr/W5nG8mDD85/KsHTO10RtRD2YXAJHPaLz6P/GEe0AVOqM1HE460ne90qTYSyp67hjKJYkBMLUOBVgotbcODK+lShboMFwNgG0K6JbFnSgRIKwbUd9MrK4ZsA0EcprKc5D9iH8Fj1MTBu+gkPw3bORoLDSdAznTGgqC6tNNbt25ubKw9felal3eTalwgW22j1lyNWuHkMh1BWPJKbTDk4wBjXoriLQCTWyp7KpHj9GaGLtagf6eFoPwXNhvPP3exXhuy3Tqbq5dBmhp6TdM1uxgW+MMeZuKJUjTkrE6SfFhgYlUSamVOe9GGrvHxqT7gKfl81qg11O+GSkDHzhikFho1pNqmO1d1Q1eGS+FkQ0iigU0iacLqVmwbeQbBUPLV+0wgBxsaXmAqigU+cbVgU/yLcrIMIkDfKgaCIKcUiFX6whkfCTg7fCQhmuEI4KSxepQky2OnjEMAkqWiCD21ozg563YBgsrkv0hsHMueQYsCikVQah1lis4r+TobVXBYIIlRWzcNskq8RBqXjqCntUIWThQrKFjsyJUnsmfjInrD+VTjzJA4yOQrPYCMRhLk16Lb5KhvMI3yxShQtaz9f20VRHPSe39jVhWZKjMWwqFa8MPMEw3ijUUwXUHBEiIyV8G3vKgT/YhnY5pzFF5xJT0B20QcICqVMEb46AxwndqntqtMFvTZCPDncdsmZXviqadv3PooxvPsCVU5q8utxFt3P1nptlbX/IllCMCJXtmw4J07IzTRCU5Upxr+plIEUP2sDpXJbsVJQY8KZMdMkDQOxvMv4C3J6kTKcms6EfHID9aHAl2FakUrbkz0HkZteMQwvTPVzK7bYCLHZ03G2DcOQSMMqYAUTONexpLgoAhSnxYv2I24EprLB9d7K0ymJmNJj3c9t7fvXdp9TFeKTaU5vDUjscXWJs2IHDXpCoXAj1EbP9I3O7GEC0mqr4/q0E+qnajrmB3b3BoBaCR3m6IiMsUF5ZTBp/0kggWaW3fxlH4p6RLkX+DRkskZweejs7FUqEzEgU+JjbLa3HLFLyeKzh8d7+/uteJSSp31kNmMQbLbS2oQS5ksHmH7dI5nluVMTBM6Ez9HYUSjNYp1Ji5C5n/hUCtjSoahaR5EKxUaH1N9IVAubDlDzlO1bf5TWh7kpKwm6/mB7ZvaTAHXO2YiBUy2I/oJtQX4csKzffoudV8SWs4CzHL47IQPg2q+lkZrse4RYzoSmJOQVNhd0DJ75EjYvmk7qtGsNmccHNQ56Br7TpPmYMjGZNU3QY/jnO/27YPHH5v0NlIHmWku8QhxCbzgA4EKEg4gv9gG9TghMcXGH03DdPiR8XiocxGMUg7nRAAXrT5VqSKBp2wK1KW8QChi1EOJKwBAY0vP+sBABsiIuRFLzhXt3a6ONFl9Yq/OVt/FzeoxX3CiM9UoULmNNH9HDWP2HW++egymHh0XjHrM4Lw1K5feim8Pt4SGi54Xcw1Lwid5UPNrzLw+pRRq5MmsJH2I4rvtm38P1MbxHSNOUWHnVyq684BLDvZ37u8f7zOlZbmPbroe38ciWxGODM2IGQ/CwRd0k9g+peKc/Jf2XYqc7xKdkzw8COtEotC6Bc+JQiRFwDi/x8AQPJIJk85PYZeXyY+EbuUu5WgtCxIRh0d9InitpzEs0RR7Uhscjo7v7rfHMYwRTdaT45ZxhS3jMBnK7yyH7gotMAlkVTbrpAASJUyJC5p6JCymTqbVY9App8qJI5DqOYczhYTmCOgU0vjt3/7t3/3aVzklOq/0OcF3/cOPScKqIKfzeIRDhqRhDtoEWKdm2sZBacaU2/s73BbNW/M8uqRCq9c5/sz75qyqcB6Bz/vBKvYEmkAoUWyh8auleWw14iYtCVEdjzu1NEPDLM4EkdDECRDCK+q2XCCFI8pVDf1UPKWqVtn556UyTGer6UYeayrC0tqI6ceTZkQUUxDsgU75KflyTqVnUlElUM4MECA5vgkSxUCZAiLP/UO9vL9+vs5126ubXYB9FheVh7lVKoZ4Ssv/R3BkARYcOuB0ztepS1HL5IokSQnzAlwUODwXPvhB7qQ3jst7dDTisPx0T8dPGKVyDmVyyBWZIxagKTLEkaQXoM9dPM/5+guPX6HUnc1VTHC3pm6sX1GNDDV/ZmKhguhV4ZK0l5jPDC+XpHg2PrVmRUI2UNbq4nzOF+04p7+9vc3xmWl/TAVxy4aqSTeypMEUyFCCCFzx3rSpBj95nqwDIsLxPKVa5VQ1Qx9pbDX0OuocSZ3JqjKNFV5o4iJ3lZf5XlGCE78QIdakYIlcioWGdJYfbOMgUgJzFqBxzB6iIE4IgEtCgBAtSEwt3O3xCH0TORGOUgBX7uEjT2F624yIcAHBYx4cBreA+1ely2hLAdtMGVt9VZnIYvRXxbyw2kxa38yCJTFjyqakGABxpn+mb+QyjmWyhJwQSpSXEPKjMel3v/KVr5zbuPjaa69R9QBRbC4MwVaxG3duY5O2wBYdJvG//dc/d6Vw/cvLL7/84x//+J0P3qHtkwRM2KPSMbkI9u++/U0qtzQaktI6X2eaeciBMlxyRsWLJBnnFwqUCT4koeqgkJXrl4S4DFxKS9QSZOmRhEFAaA4TgPKDCC4l/9THX5pOOeFDSlFGO5OZh6Q9E/8hQEh9anYPSU4UHRDqWltfIXyaVMGqDEI51nBBCwcEfbMCFLCH/Z5GfsSyBFqinJM4gO+IMqsPY+JkocCEK0YvlIJ3uWikPNK6aZWYaGjamSBRDAnUKUSUswbhdHYpVbo1FUQxCfGrV1fwa8O7QMgRN2XexvA9zjjoghswixIpWYSh5jB+WYbOxbEQIYrCOQmUEw/BHAzjNLgKboVZtEgg7vcAONbFgQ4FC4hQnTUAwuYhsjNAKcAEAhoZG18UYi/TZAsKQj7TBQJediewIAKjgBCSI8AjWzL1jd4nsIsHcLRfUvRBBfhTfoMD4yiYa0QFKWokk8ilAxIJE3tGIFYUTjonwacrdaqT8XoyTsDPaO+l2NNJxQa8ELGUdZZDhgsSWsrQnd7q6EgjhPFYSs5xG7cCJpy91RVGzgzqaBpt/jhf7IWeyBxmMsHMDRBUEq0DYm4DTVb9NHJO9aAAqVI9WpahYxBny59PR/BxaYpLE2DERkHmtS6fzuutnI+MJD3naGnnLAAifcpr57DrlyjFxtgMH7dchZlKKQBa6emXDMJMrqZfkkSRDFIE8SmL7QM/hAFiCvhwIAzrTreoIJUxjJIDKCYB7UhgNGJLZjaeHRwcsBiPOvAxeTQEfMYwTGr4IJDHruSlVKUqdnbOgjCxhHGq0GKskuEOmIjR8J0CIEnMG6wZEz8QFmI/Dcl0HHCmjy7hzPASnd/QY5YJ9BWOS6JyXi51fvwXCFB8NMcnoMnO8ne+SdRRF3RLqp2SwcmYRnsUVh8dM1PLAlGFxkYLGqYw2TsAzwU28mSThSQ4cHSIKBzwR+kfSAV6mVpBOMxsjL2l39GOQLbeRiAnEl+2MKQlCkcgGBBjijU8SEsBfhFnOk5hyjlsrkwfYA6cJi+eSpyUERKcRSEt76AX2gzEGgxH+6YJnAUzwoPRMYPM3sY551smguTZky9DHI5sFzKJR3lZOKD58XTaR4fAjysDUg9JVWCdgRKaE+O0QoyCxPIx0uCPx0ge095irAIhcgSO2URKLggTQ0pXn+nwJYmWqts8ZDYlt1BTdFhsnWRfxSm0TGFnZ6R4LCKV1K5Ai6eUxOEUr58CDm9YeLra6lTHpOAcy093QFm4tBanz+R9mnOOn4a1iAf/IRWxwIvQo2MuJfzlHskO9nBOHoHlGnSUq5WiBPoJ/JwWMiZVJpjxbYFZZFtildG8Om86d3yphbiRyYA5nLFZ90K3GJVrty2G44Yn31jBmzXbr/ZnHBij+49H0Qzd9kcslSMURTkcXPIJdAUTdpxlKAiZH/tkZGPtBfpaXJDGhXK/81tf5XJS5LWz+wnTJFSKQUq9osHoG2+8tre3zVILFBi1tTrtv/mbv/nOd77T7HXYFam2mgz73r1+HdXsxvnWZqX95ls/f/sNHctYXV2XfLnmolHrtFgCZiuOO3GwXJylYGOVcW1t2tTUbjSM878VGThORcvA+TVhrXrFWInhbE0fytBpLE59Vutj9Xp8RkMVCIqHTToREq3UzSZkwFBYsto7PHhyY5UbzTgIRouiRuiHkBld7OG8hjF9c+f+8UfvP77x+P7+wdUZl1VrrRC61KPoh2MszC+FEkSdHV+AVUk5AwgpzINsEEWpVHvRu1w8WO3f7c8rk3P1Dh9wQ0qPHcuIHN/+cGv2/vZGS1+afvI8uc/X25ydnKxp0bPPWA9Tzhohy7JSNC6MlE9J8HVOSZoWGpgWTgMSvHlPz6Nmn3OXgNikSQNQDRMZe6pM/MVujjQndEfrWFAOOi1u5uWMeiBwJyblqo+mLDH37+2wxLb/8V26maPdQy0392VGvTvXbnLPJPt9HKytcMUlA2DguNrtnf3p1m79na2NjceeeZzXJ9ef4pXn9k5HunrcUEmHnJ7nqC9tCnnF7hD6lvhS+ZYdZAVS60tR2neKNU3oaOxNqbHddI1Rae1e96UvfJ5rOamFjcHKcIDe8YrheFLFkta5/Zz65kBao97iqhbOwDXZ/WP3sibzuj+ocB/EXtyiyHfDmYxNe5LJ7TvX643JRqPTYyOU+xI5Iq2TAvNG+g646tp15/rS99nptLy+UddRfS28RyHkz9U/uQbRcyC0cJVO3Q9TAtQu9X+AwEyqHvthHBwBQj2QKo716yFoSsnbnXW1KQ142BvQKRh9Ih1dqjWAUhDO6XExYPCmTtTDH1o88sQugcDOg7SDQpJM6ofuULWhRrJGghhurgJLntnwI2GjaRVE6EEsFLrBZaiMuuZcBcQCv07tUVEfffTRa6+/gSjOdbsk5BwUFAbV9vHkeBhnzFdMJpgrt1PRJuv4KRiQsB2mTIo2kkKKwUNr8FXYcGwXw1KegEINGV65fO3Sxcus4j3++NU7d+4xIR8MeKGVFztmtAsu1uj3h51279rVJzCk2BZ0DIPW7a78yZ/8z9NvVjkHjWF7/72PRsMZ56DZEfnggw+qtRUxNj8ia0yuGQhWxRUcwRosRlieS+Edch4lN7eXqHHdvWY5yyIpVYCVcOGCcDRu2VPwVZdyge/uoZQjSIwvo7/TIh2tniw7PbFNvdj3ikORhblNkgSothzFUM6RO+UiT2QOBbQJyz8bT46GB0cHB5zB63Od/mTc49o0NvZ0JiulKeibsnMpYL++36XMlh5zPip7PJRVCWlkhHI4AyOwzLlbvfEhexJ58UQ7ezDOQtokyETEZGFV6L/G/UFzfXVB0XamlKPGIXRuWHLayUCvtNB9q51H0yhKvGgmEM95pZ3/yF0qQ7WFNOhAOLJBpw6mueQ64qjShRyEwX5hep/J/Dt2WRroLTpL04xBkR4sPRdqIX3YKEXoEuvCTJmr199+8979LVaf+TYgYqCkHLOcTSdXz/X27wyx8zSkGmfH6ZGP+UaGGKdtFiVybgvfXDLE0SLdsD8YDxoTvXfSrc4uViqXGucn9waz1gaHS1frj3GRKsMcVtZ2RtsaZsRtgBYjSWSr1XUmpQeiNh6ukD+4yhA4yCAk2xVGjQgfbMEw0xHzx2iGQRBbeqE91IJp4YsOtQ3AcrPA1DsAdm+CqFM1gq+e2gaKLoE0RInbcDqwGBLWkK/CCFL7u+DThMVg6GGwjEVPY1TxHQ41IQyZgODJmWz2o+xAZe5mHPfVgUv6BPrFhGmZCF9jMVgRt0zeVI6EYu2TMI1cUhAdghPDOvXD4ouuKdAgT/ZX+O4xdbhNLnfElclwxO1y9FiUyjrvCZl6TuVL2qzzMEJ4EhOCVKFQRkIqrSslGJtwD7kEJmmQGb4LUIRJlBwC509DHGGqkLzTR6YM8cgA40qJGM8AbzL2rkyvjG4gkxvNz+NX5qvk/ZNv//ef//znlx/b5M0ePuVCqVcbs1qrMZ8NarPxFd5arM+PZ8ezw6P9vTk4Q3Hk4V5l5I+7q5xyUWNigku0YAQ0wTRwAaT9AJqT8i1cIIQliYoCSTGp3pE86YTMKTPJLapKJ7uImLV5nWPz4pXHnzz4+JO3mP80Zq3RYFgZjvfubnXqHd5J4rVwSGmoy3iSFEu7ETEI4GRNfcxdWWJQZCVotUpNtMK4CBKxCBln7bE+6HnhInrx+Ckh1n7LcsjYZlICtEilAc4zoQDPGgtf5sq9vxshl0eC2ok3QH3vc2t4QF7t6TEF6da523eKZeM7jRpOihp5VLvxOYcQeHiyJhPeaWQwzNSJg+Dc4n5wdIjGrHdXuDuaemHSeevmzSeuXau3msyn5q2urCN1hY1BG6WWaRNCAkziSVVvA+IiEYvGqKChvXqMsiuVxoc8LWRLC9WjATH/hUggFDYtkUniimFdCvvHYpd6LruFWhITNJNWBOKJWlhOesYz1M7I4wzEALkVl2NP5xcCEWcZ7QTHAS1zXA5jQXIqqik/WeYYa2qdL/rgPL5fHw1avO98sNs5PqiNxsyYKI1yTrZImEgSKckQogvBVPFuip4St2rC+pPYMaa0qdBnpuEiUTBFbKESlLuACmPRsyurnAuZhm3khCBcjXqifLPbeeutt96bcD9efX+iuSqTOFKtr+vAU2+ugevvb64B2dzd54RLvToQZ1K8lCfvXJKj57+ROZ5alS2zTGwg7uztdaoNrsvHuvLHW8zCsUy1EqUJLDxg6qKpBk21VKkEjsGziUsmyaI4eSo4pOhSwQzmkiIUcXpMFmChCRSArpAFC2FJIoXT2eeUEhaHosm4S6aZFMwT8JIrWRWar5bpYQN84MIRviROkChK15zxKYL5ZrPNciTdBWVBbnw5a1Zr3rtz98KllZXVNraUFNGsRUI40r2YUsWYzUJTRjHC8CNlZ82TWTO1Vu/ovI4SR6GEKVshjnyCioAdXIU89ZQLJUshrqEguZlUjAac6Aw/y4FcfOs9XYgngUC82cYnmUjZwtdIRA1h2LnEXO/g7j0ONAz6srdm8uoTn2NVp9/rcfHdfgdYdZdkdcZ/TGM5pzcaTUYf1ydTViHmOogp28yFpbHqVYuXqo/1ZSlW93SwzK2sGb051YryuTdErKQy55Z2sqUhCL5jkMs5iWhmjsKPAYKm7qynoREhVWFKOXAaoa1OuN5g8PxK7dnN7uY+XyA8vPD0s2//7Od3trcuXb1CEugk/UrL0Jp7WgPVRJG5SOFMU6FEPrgq0pbgpZpyrSlOLsxThFxS00n6aQxn4vIuMiROEkj5BmbZKzg0bCGr0/hAcnETBfJSX2nmFjpWKm404pDDUlpRg0pk7yh8HIxLfzAOrOMczw8/2eVqcLdH0D3CTKPnVGekUU24YQQJnsORrBwgPY/KVNj8niqPYAtnZJ5LdJSsMDFBImJp1DgbShByQqfFtwu4MJNRCEWkPWC4X/js86yqGI3yA8R8E3j77bf5UpbahlZ/Iik2dzh8/nOf+dM//dNb9+5+85vfpDiRRG+UUzLmGx7yhq0rFTkYY4Bbmer8KdUmcrGrBv9kwduo0CFKbGgOkEaGRqOmCDB2l4gLZ4azb0wqNdIKiUSOpRMllskhnxICQjinIkC+OE52H9Y3TCSRKpg0MomgmaKCAGEY1ipJOEcZB4LUnOHBidK67jhUQixTWraS7r6zBbfjXh0j1bl6nqFz77FNuGUpE2rjKK4WwjGIQc1m6IEL0KFUrl+UlRxx4gG7pQ5PzTjriXmzdikc5x3cvDFbOLa4SNXfP2Th7O71m8hntj+gsI0+3oSXmKhN+nLlEe94YKAlgfhoFZNqhaMz5hdqLC5wvvXOa/eZ8NQfv8CR5NUXr7EEX1nVJD/YM0fJh6qIFU7EXJai+iJWCBlu3HIqlS5sCSODV155ZaOzCpNrO106TpYOcQwklCqWWccVnf2Zjo6pEXoFvD594WTCBTRkwWsy+HxIE1a5locFR5RRYorOD6AaTTjzw4ilzAkx4IDsWIZtGTk65jQ3o/8FhyIZX6WO/iy6d6JEBMFAhCGE6EcXovlxMaAxfSUsdDWikpREPIxG5K4wbINsHMvbKo5chUMc0IiIoAQuSHRapia0cMq6VGvmhJgcSHjRKg2kNAJ6Sl/UL1FwxSYHpoZ2ManoGh/Li0oReriC2hm/4jB0p8hFbQG88CPHSBQFScmJKj8CLZcOBkDAgDCkAI2z/E5GE2aLUuOMft9ZoBg8cjgIy0lD1psBkwkJWXHmirQOdxlVKhzggiCxDFwIOF/QoMCjuBQzC7nxSKoAKxA4zj/5wo+gMU/EPfghCD44uhSTs6bJh3WSxYMNRJSjjP6pDFDYjOMwdOSi+Iw7kQOXFIADLKpA8uGxnLDEWgoaoQwHv/z48PDp5MYH/qCED0ryqQkzwUycTH51boOfTFsBBIjlhXIhyQoNSvoX7iE5InanImEIXpIUkdSmUtUnQqd+XK4sHwKk5VCX4aDnqHLSYDJFBUI58oxw4IvPM+IClHMBZ6kW33zzTTo1JqXPPfccuFgVW2/kwysyFB9nsuSiTiHmhM4rkyWQszaQVExC7t38CDp804vH9ZU6m0w//e4/YBDOT/n4cP3Fzc4XvvCFp/WlDM2W0fZcI+akTBYIjziyDpczFBze8AERpYjQIofxIQ4D9p1MhBakRM9pHSDKaGXfWVg/jQBkgaAbRk6kciz54kAjFtGVkwDBKe9wJgUkMhI+EMWE+UYNRaewGIotqrtASUlAy3V0gqGCV+EXETmXIjL9Sg1YhOGdPBucpeizHskXatkpl1MuARn+qTDhRQLDeZZ8SumAA2QYSwAX9FJNERYNXIEPQnzP0jN7iUcZxDuFtVguFy5vH0a/Q+nImUdyZFWR4f2tW7cwuZxm5Rz06qVNtFGjgWgvqD2dlwwygxW63VqDEeDGpWtQcI2AJk4Kp1SpZhMINhyK4sorcMWSwwpE2BBKDjyUIWnCAi3GKoqKT7YwdOSFtus31Gm2mnNaHB0xB2La3VUtQAcn9nOm5QC8ZR4MB1mrz5FQEjyFUE7+awmTl+lkTigd+WZ+yvI1cpHigfmDBg7/VWfhjKqOuyENjP1ESZxM8TUjyw3jJFWUQbHR3KHEwIa9YIYuyoBjGfv7KM9jj19jtIOqQBlEfGIhjJ8D7kJIkovmfHJhwYRjgEDwxVhQWELzY/KLHlNpy66Al2FLYeeyxEzGIescPjNghOV8T6KWYz+V4Mmk//pPZpihKSKi+3gQQ4syhsQWj0UCIDhLu4Cd/WsckM+MLguwjEMY9ogl8PrrrzObq7a7hDM+CowFkxuPGKh3uw3e9iaLsHLSRl7WBZ9SPgqTYC7MbslGZZ4DQU9mID8a4rWLjOwA+RbcSlDF+sYS1qc8khFElLwwHU5geDnxaUg5NochRVFBplkCFOWiUAgcU8BiEwEmAhrnaCkzdTo0fmKZ5nzu5ZdJSkJHiaCImWSU1OHIBUyejENyhePrWa2KFl6VeziiePwXcOQoeUaRnSkskS9AiszpKMLDw2POZe5xP9H+fr/S5dIELkumJ/NJcOSAxN776L1GpVHvrTD2G11a1/dXmisML3/2s59x9ujW5Jg540fsmjK8XOlgV1db63pXflplXFrpdjmo17p4HgbI1wz8imU3kVQPJ2y+Sme3ZPtIAsOe9IGAZPiQiRbKDg9rPW7hKA9dCxL///BbiIKCq8RL4tXjSUGchuRUyCSHl4pOKkt7CW58xzoKIkKNmQhhbC7+fiyAdAKjrAPpfcbAl1KSEh80nz7zpo8hAoYTEfb/9BOg+NVmmZKzUsna9mIPJyJPeCaCTtMSSRHE1dNPud11wnKUc09JZMVCfIGWgA4zBodT73tovKAGU/3uP/59vVXhdAMzJxynQQGTS7PFFhpnfPUpT8RRq2kZiMX59fPr/+v//r/zXvnVrXs/+tGP7m1vzSdjmib86GZYfd1TJsM1yIoZ/Iij+OMSWwbMUKEkGH6WMGVb2LjXgqUPACoPXQwMIe5iU2k5jMgkRDfQIQCY5mQhq6XQEBkrUGz/QUul1SYmVkx/OhvBGItLigPMcqLiEYG2tXBCHzPonIz4SCmrih9s3ecjipVDHYNRXOHICKcyuQqhEJWOFaYzy6hQRgAImMNGrAlyBIdDZ83Y7oMPvmSok9W1WnfaUd0di+YEzgkfzCp3RsP3b2037m6tNBnSdR/fpOV31zssXTV7HGOqMMTD9wK0r8BUQXCx5+OdW54R33gyxJcMiu7fOsMF20ri6Y2G35xSkBjiwAQVJPo8UiIOIO4eHOxt7XL9/N69bcwNlwISxScHhdNoUlmj+Wg2GbY7+ggJsyX1TLUKg4D6vtbU1ussVDVWuMiCBXpOSaN0cfVmLyY4k3d3tm/2P/7Oz4+eeOL8F5/jLoLxExegc1DVKt5BiIbz8eqZkraroCDoxwHEK0nrUUFphloEv+Kzpb1TNlLxrYh7x0ec3+fqqrd//vb5H2mxGLmudjrtjt4r4XUSIJXeueYKR5/10aTjAWcxD7Z2tvZ3jmc1Da+7Hd0X0eggIgp5SFGnK7SRJteGsJcwrTfH8yp3rVL2etyLx6ko8R+396BPwTwDeHovuHV1JcOn/ld7uiqFNCx6f+EHHYoNnI1YZM+7pipj2Aq3sFltAKY3JIATy0P4HjiGbiIVpRIhnScqxCiZ4SJ7vpmn/p1PgYauaOIRExPS0Z7TtiKvSxSOdDCgbRR+YvneMTEBcSMRbTtpY7howoSEYCCZK0Y8BB+EVVxOTdS3t/bAQf9bFb6TwyXhDG5EhyzyhmiUVPhlByflR8Kg4ULq5BJEItsypvhHS6Oobu8uNmClnU2bjcZ0PBkc9zubHQ5O1iodrtbsH6Eo483NxspK9+BwB2kdH+tejsuXH//ggxutZnvrzv2D3aPz69Orlx6/eO4x7tOnjXBQut3u0pdTQIiTBSej2QuhCcboy+JKQlPuUeDAVSmiRKUiqlXyl/BdXkdH2hJmKYgURTg5p7XcFnQciVAYHk7m40q93aAJYY4GsUwBL669KILkV3LIkEgU3RVdijkRDP2RrpJ87dwGozR4PtrbZ7Pq/t07pA0dPZHkzAdXk3lY4uRM/IcDHyI3EjovAqDlvHKSHDidRRG1kHvGKaJE01nkKAdyRn7M+AV8UYPUbKkmqAcdiwTY5OT6Ub860rICxlgaY0ZK2ClT2rRe0aCpqPrIAirAkqItcXbWo97SVL8QXIViqCplHQRPuip7pSYHAYoTcNFyuOwv5eCyG58wdiunDWIFuumr/crwiR3aMSdAP77B2xXT0YxXCzkhBTMwRp87Hw5mx0ddvoFap9fWdIKlPU5W9kfH+ii1dF1GOIRrwdlP2UGfd7zubX085DT/vLISPR1dA5OK0eDw7ujo/GyANbt1Z+9edfT/uvp1Ds9yFBGWarMRBaAgZWeicItzWC0lrCVoQPCJcphHigdr4BhZj1p21EuzhpBAf+EY2OlCwNgijXKpWCLpHsFIkUWmb1h+dFuHgZxjpCdeWZCtuI33uLV4X6pcFQc5IaxA4ElppGdstYZvNqNP8elXKKqkpbKbGVOQ6JALf8pQZD1PTTY8UIHzS7bOSngBN/82YmQoToIdVb36mZC8x0VRC3SSSsroR4KCL05MM/QhvJgbgy76zlG1AZp0uyn2dCaX0vIVCJAQC9FQZOyhnOWUgIMHRQMOjgQXJ3CFNSXgI06IGLqaIet4gSLApgNhxAEL/HEG2MXEb3MbeLVyrPhqYzzknZ2d/tZ7b3x/dLzX0fV3+3du7vY6n++0WvT2sEn/z2V102r/cDCvdjfIhfH3zva97to5dFgjfeiDcPaigMsivotyKZydhZMfVVM6yJABKi1/yAJMI+MHKd0KzZkxKo6v1Dx+7ekPr7+pyzd0InFC/8tSUqPVfOKJK53uOVRBZ5lVETpAsKB+VohSwwbXZCNlfXKCF8J09u2BJ6fOovEbhJ3Nf4wEpBDIz4oR5zExP8iHbwgy0WIahehieEt1ca4f9ZSOoYz4GIcQL+QX5lfkrEsMuMbTuIyON9T4lui4ehQCifNu1Xnj3p3bvY11LUBzPGAyZtRNQnTUbVGnyW17qIUgCFciXXKG0GZdQHwgPILiejduBvJoVtVmT/FcIrxQvDKdMsKvK2zOM7VfKLuyjcoUlgIur4FFXqHMUYNLyEuPxldNn+UkZXVpizi3wXOTIafeW4d7mxXexaDvzwiLkEHJcpYoZNSlgOvUQMJlTSgXsJzqtCSL4qfKHfKdm2Zre6XNXvKPx6P1jfVbHa1RHPPZKqytZoJV3uSFznx3eGuw35w0r7Za3HzJO3btmI36fKiZebisyozxmSNOy2FH6X2BkxDzq0LpPVSsoowOM0IsJLOGED6eRae1Df6in0V4anSy0fFHgNUV2R8WEKKzo4b5iwnWA0VstvEhRYPjoxVZticUhG8qYCWUI8zR/ysbXNnqwpAYcbPCHMKbkeA+WiVlrFenes1ZEzEVmZkzUyLPy8CPFbDK9r2j+3cPzz/GWxFqo3T/QRULqyUJFdFK5TKRKZ0XW78xU8DkgIO1mXDys9MhCzERp7KQauZHrJdcLnIJ9ulByEH/BF5UqFY56NXjgJe+yCU5ykCxJXP79h2WnXkdBN6jP5eAqrU+xxt7GgLwJJPHKjKmbouOpF69UTnq9mfb/dHz1zZaG53KrH/7xkc79+/PVhpcaXS+2sTCDvhQx6zCy0azVm3an4+Pj7kZ4O7161dWdc8FWiEWpCgx9MBuo0fSHuVHDbpzg0M9y6Uf1aASJeVTSDWSYh1WpZWBi0gWrI5alfG19dWN+qjHWlyo5+XzF968+d47Nz78wrPPs8SDvoso3BS5RoaC4bJ9I9NkO9J8boFvzCLb9BtMSqUd+5vwyanIFPIn1eBkfpZzllsOZKyAiNVUXregHF0Wr4BIgr9UNNKqTXgYTNVQsXzhZlwdHo8H94+auupBcSFhBmaSojZAcEk6KbNEDiBCc1SENSA2/iJJwRAIwbri3cId0HM4sVbUAQEaI0NVr9kST+yI1dOiUy8ju3ROmymQBC55NAQf3nhhnH0YtrJZFumtNL72ta89/8Kzf/VXfwUyqu9c8LU8FydbyY47T5m5YXpYhfnd3/3dP//LvwABBwNqFEHWkOw7R4g4VgHmwLJMEg60DceHPnDvBKZBFXMlNSQtcHuhTRmFy/QdIC086xhDEAEFuPyoHIrJVFDjcnURiy6QpsHylobU1SrHQJ566qk8nQBiykXgRHVk+mIv8iKASAk7HQGceSAAV8TiEB2PatDUHetq4Os1c9YJlBY+Wfoc7N2Gz0lHW9AbF89RLi4xwScVy6P6UFtcSQl+WiaLcQTsAjnuH4pmSQ4wKDZCf5A1PHDJNz6Lp9AHDr5MZiTXAtnxsabNR9qdY90cB11Kx81x8LC2usGeHuNOCkJy/HZD2wN7x4ccQjm6ieW8Pt0/UP3OuCaFyY/u+CYABfXKEYAyGxMo3pvHd954443VV57hiOja4+eBj/RWCoep0+xaZSkqwmmzT4B8Xb/WTwp3elIEBe45gQjILD1M7mnRHJnwyC4LvuXTWNd7MvQgnE3ju4WUS1cK9no3PrkFvDiDW1EFzQ/AARhpVSKJiBqMPXnmPYQ9JTUcDglQ3XoU4sL2AbGL1qkoO4CkibB8CqXs4jUrqU1hMZxWbOPQ9EIVlTzkLHgpcDIcqQKhnDtpkX+630rdLK+Ma0GhnBdA2LDcIODsCJi3RLL4ycyUYw3MCY3LI47CjidjTkA7F7bagcT14EzjtQMfNtnCKfI4+UvCQisUAc2T8Z/ypORymt6zzavkVeVLo+A4xblNLtg4ryYZQmAn/OlnL2uhvNVi/XTr7j5wDmGhJ80Gd3QMaM7Q0uZ5o8EjTYYdHc4OsNHNGvTWtiC8zZxdwe2CZ8vN8MJXXZMkHo15oplkao8YKIqcyBaUF6nhwVlTTIyHd7+B4Ejr5A47rdSXqCCg0KkqQKNcLvQLLB6hrKWxep1PHuGO9naRoTmAPhTsG1L2nftpv4zzqWHT/1Q0I2ROyuUqh8t0HgQv43xq+FHYyzjO0QKBsh+RMFrH2fuH50WqGMIk7SJtOBHxRAttI5yJEM4ZZWDOVGkDClGq2z07pAOcFRgM1a/pO21qgoFp+iCUMwr8VBaKFplA1r/yQcY5lRFowkxW33jzDRrys089R1+PsoEAMrrH5wywe0Cw7VgYkNkVoqnGDKpsIBdZlEPktbe3SyI6QVo6Dpuwe7hFqWks9NccPsSRnb+cce3aNQELziHlEpkfUyZsB1eGuERGJioDgYepdoPTgA2n/NS7Cct0jF9ORULHBmXFmCy0nSQeJUanKvwTdWEijiJHyhvUZMndxnPyHCjoSCvCtIAsC0M+pIUpcR5GPCdxHAkpmpITH7WscDglO8udpBDlSmjRi0XJVMKijATgSdlLf4QKM0B4DK5EwXkpTeGMBtwOTCpbqSI5QwBBtMWoZWCQnY5YAiRRNklnk7SdI1Am/hErTCP5EfEiCnPI+FUEtSidnbB4INdAY0yoKJScrodzahxlss4wrsPS0pHxZk/OggC9FfidbhI46sqhY45o8B1gV4HJwqf5AZlUyiMyde74GWi07IOWcVz+HOVAUEo4Zgw4mVqvOKLBTAS2MWuoHE2VVkxvS/t68qkNcRXvqC3RzI8miG/+oWmVr+v+uRhpZdTfcMDyyZnkR3OY4Q8PSFtiAEaAsqB7lh6GiGVodICABG44yybSRWuUMM/KS5IXKdkQhdEchJzZ0NnSoyMenWOZgsNL7RdM08kUHBDnoeFOxSPyd1SiEwMtIOL5AS5TLrMBbqbwgHTLYPAzqeW4X/m5zJvCEur/zzmETKXTpqgFRGFp4KMsZ/K6JLFyGZfwRcTW6mSEgWVhONMyVk6oQMyn4McEmbYzTYNn48M5AdCst6gTlyPwuL19+M4773z58SvAmfM7rdCiXOXcy/k6rEzDESAjaDoQZ34IyoagODlh5p8otN5wcxiQpJbA/Ri/ev+ChDg1gTialtNmyg4UqfQEPj6QpYAx7edYHiEe7ehEXShtsgYpHUnKNQ7z4JAQrWB6BhJhrK6uTeUVcHNeEOcg4/qF58BxKgKihoFF2tHJS5nCEYW1IghXFiw+CwfQBEgsLgdcQAN/Lf6ZlCkVWVLJrIQc7NzHxB0csSJyzPCEfg5ZL6pZYZ4gk2qZEzqqO61ozbpNnRG+vLlCj7lzWRu3R7MZax2ffLKFDLnIkHEmJ9goCJaZvOZzvTs7mOqoH4v69M6jj9q8oudSkxFwwpHXr1R6KLhmHy5PsGhWTGk9lMX4E2Dlp3LzPd6devHaE5Su2RH/YD6c1C/Hrtgs1PtBFIwjHozxCJw4ySIhuUQLKggkSjxSlaIcbJyONUI5FkhUkHCBh1sk5zEnIRSPqk2Pfh3ltoDuMapB5WAgC9bJQctHCEMVYvEOaMnRR7JoqoMIpXIRb+01ogu50OSwCMk0lItN9tlxhIFTCR0uVC7ozqcD9mlYMSeJ99rScmSkISenJZBe6ItsyQs4q5H4nHalT//Sl7505cqVzQurrCnfvnOLsSnthLOSbIOx/F6Nk7Pewnj22We5fIPsvvvd737xK19+9dVXf/7mG7xkGqVzGclUSqn7b+XEA7E0HG38zREfpovLJbBfcepA6Jx9Rjgac2B8YI+jn4iby6hJyygSF9fNSbmAa0fCPZAeFqoPWuSb6p4wfOJINOYz9lNOOI3Z0OLCatoyQERH7Cj2cCBI8gGbbzFtM+OmRti1lQ7TGiridF0hYe3hiSsca7QolOStMutPJ81Zl5ywOM7ZZzKrVQfjkIESMDYUjYokxrYi/KzUtDw90Royd2WJzvSTPehjlojlXJbE0ai3Y3BArA4UQSLOPhAg1WrIxFMRhEqXAQu42PTFh+Z0Et9dHVPF1Wpfc6tZn5cyOdEaZdUXsnvnVjZbmMjO2ipmqN3lcAxXQ6fdF3KB4FF8PY9UkFSvsdLjguX1Zx575d9+4fj2Nq+ZTHf2MFv9nSNiV+Lyq+ZYE34tjqNWsc+7slUZ3du58/52/2/eufTqs1yUUb2MBJo7NaUac4BcnC9qWSV5gEMwmkMgVw0qVL9q3Giwlv84nVEbD2eT0fzKSHuz1JI44aQ/91V6gXWksQUMz6vjo8rOMXe20EGAyjdra5UG95bPjqurfF583ruy8uUvf/nO9D4dCdskdB7UAgnX4sx1Jz6hySE46PP9Uriy/KW2E7U6SKYOWEEfVWdLXfXoV3g4ZK6IqFmL2m2QtwISlB+905527jRZDrIU2lqqSpZTBtYQqohwzNkVobLHvq4e5IIVfoKOAMFPSJO3yZGAVDcQVRd2PGqWJxsoJfafcIKOOS+ehCNe5BRybAlHIMMBQpYLlIVbcmTKE/JE6mhdKUZBUhnBOBSP2sw4kRGeaiWAqbxGICFNVeTDXmkXAsf1mmycUDodcOAivFl/ONo7OHy21UJnVlbWmJ9zkHlv74CmAQSWpA+cy5hXe3p5gcOV3OW1xm3RL71UabU7Fy8+9tHNj3kBH7Widejjwitrn9z+WHobrMIGAy/2mHJBkFhwHuIKM+OajUPgJ4rggpzw3QqwoI/syNfSWUphftBwVJ0wpow/V4GafzgnkYg1lA2GpR4x3ixqFhzHImYQSRdEQq9CNwAiCnCa7VanuzLirMk4+C+VwvmSNnI8UTrzaX+pCJqdUx/W6hK1sk66D1pKeOYjfJbh5UfCMJD9Au0EnwDLSVDTUzyLSNGKE41ykoLsmb8pLyk7U3tpl7jVmspkOjg46p5vIQeq4HTilIUWM+twQJfAaU04wZWQkbyFX4KdFfQ5aHfzUUR1uKr0IMk0A7K2h1lcIJATOObkZL4SWhnix0ITxIFr2bwEy6pvECgIenXj/t3X3njtcO/g4sVLvPLFlMCXR9HLCWc4ooW1qoPLG93r2zGtqnEj02DKjX/hchERnpQwaVQCw8zW9u15Zdxp6uqq8XQoi9HvyySow+MMaWs6Go9m849uffJX3/nOf/pP/6nLp2hm2lp3oWQEdNJXj1Czg7pzx9eZXGI1UcUlFcqxVBSl8LldRp8cAtMLZ9R06GrWv8AnC6DlOlUpeIbulMRqwRzRzYkEJyE45QCcuUKVuHCygFhoJdeIBUeM6wW+6fM5YRR7tGEmwkTYSCAkFN7Fwf4qHDRJS6YUt8hB9FTQ4MdcASLAuEgypC9VWbJ9gAGFlfkJIqksoh1lD5zIJTJmtKW6swwgJ1FGRKogReTcCfuR/XAqglEuXCMFJVH3odZHkEohNwk4CkjBgZOlM4FD0lFuYLQLCm762SprUbzkMJRQ8K0ZISJI6fwd5wp00iKsHJ+pj85MnUGP19s41Tg55qvR777xD/PRnfqMG2Ba4/7u+ZXOrH+vOete2LiMnR/qFuDxiHd8dGcDpxP4YITcYH/7sNtYvfg4OBwvhJeyBKhrHik/tVOGO2zf0i4nBC4XRyhybEZA6ioxWq9q0lIFlclYRHKszJ669sKND+7cvfcRWx6UfTA87gz3P/nkw/VzcUJCQxNEKz7LthTBoifIDh9HrLVUbQvyvGQt6yedSJXv/sJ9h7j59TvKhQyW6Z7K1+2ojMaJGz1iS3lrbKylseb4ALPCQggEuzW15k6d9oIUVKboNtHKUIqC0Jm5h8DBYDrMkaCYLXJ/4miCWYt0M6S9v7M7ODpu9/RhW2ysdo8Rm+SowliqNBznAz8FTQOSD1xtX/eSRl0XkZmrHICgGr6qXoqXA0WKB/4a09EOmzcgEHxgsl88IoinUsAhBHLWOfCIVI3vYqbCljSEWNN/ELVPzS5Ztii9w951Oaevyx83B8dqxfEGc5jNE8p59jCxVFhzBYdl8WaWBIzXGR/E/IPgSSbxJtL9dp2+9nu7Rx9Ma9fPbZLkPq8Y84b0wXTW4iJ0Zd2u1dgyOWB+125tHY/+6cat//j0k3ysvTbkEmfasz4UEFZW2vqgTA2XwKOAnPZnXYQj0HrBWQ0S/SYKbYw3LWBBhiK1ET/wSKcXnXc0wpRT9MxofVGtUNFfqHfZd6tX9iftWCITP1YGioPyBaKmZuRuTqgvZijiFRcdq4qDg69wJkVQ2YsBRuYwkzTZyOAkCtH0qENmAcx9mBTUqU3WdqhV9WH1g4Oj8WCs8dVEJslZxGvnfAxEoxcR5ic6fkmRzLgcFUvDOGnCl7V0KJA7krtdnWyTieYl4tShm9MTI71C2ikqfqQJuBCtBCFdLkmPzMkVyslGRffGggv5Hh7v681vXmWFxXqN3VbSs5mvm0ckvJILaXldpR8Wdbuh+dH9VZ1O27l0kUMGzWeuXe/378bNjfd3jv7uk7t8eokl7sl8etgfT6ttGG0xKsSx7H6sd6S098NggZWZrXuTC5utC3pjnq8iIWFfhsL7yqESC15CniXGIkgq64K0E0GESobCwvcCOfS6ePQJ5bDwa5PDzfXNzV6zV5t22vXpZMAbMxy+YpWcI4bv3bj+0ksvcXkBowJWjSDf0Q6FVa+gtmDjJKSEZd12dImpBT6lwIrkZ40RcW4MGVoKgG86ZqZMX+mKWKcgdinTJXyjKVHBcyaAOEOiC95KXEAWRqF9gryVLaNBSvUe636MaNAAboJggYzbn4dbR/Wx7uJwb8taWdY8rYqaiZK4C+6k8ckpbdSHn10AwqS1nxFykYCUWXRyo2kozFsBceDUNp1UXC4aBYheOclWnOQKgIKRU+6IsWAJuNiIb21xIMILKJwMytkpbTofRFCEiPriF7/IcuRb7733D//wDyvrayxbcwiaU6VRE8QvunNS6PmUy4UFQUWKIsNMrPLwq+EFZpFyeQrhoQ/tEQjGjFhcltLpXIgyDj4cqJi8GTsa8SI/TYVFVHJ01lK+cDySCre6uuHYzLXpJz9DQ4/zE7E4tE3ch+xhP8eKjah0B+AhP2Ycsibs927cesABQg3hq5/EhfxZnZKzMQ1ImmjF8F0xBlKiOO6CpQJI1vgQE9lYEq03pRjxLg7LbFpoW21I8qOY2nG/D45PJ5JqFGXBUjOXHh7ohVoccHgGH8djIwwEpgE659u62pKjnZxPqR9x1fJgfVrnpPPxx/d8FBQeWOR3kaFjBxH2fO6+fsxu9rN/+EV94rJj1VVeODIS80UgP0LnhMkomapIl6oYfB5RAzKyiEyKchAlSbCvqymz0MLH0wkg1iG6K112QUfzIWijI93c9PhXX2arxm8Yxl7JvNXpgbNZbdGBrejdR93xDcNjdfrqlfGZ1okoXzjijPmRbpp2iYoqlWb2+7qZhZ4Iv9WKE9mFGRHbccpNCDQBfsJJApo1JcUmKpENcS0JrUgUGmXP1KLFOZbqyRSQDsDwUlIVoXg2mgtiBtyigS/hFI+Jt8xGDgjBebkFRYsACEF8isi5eJW0Kp0kL1xmEojDppbhBHBF1jkrBQASVQbxaJry4xuk5Kbco9UQpt5JRN2RCmvJIWi0mqWr995777Gf6A5o2gfndof9KiewhgNdGE0uYCIftILNHO49f/3NN65evbq+ssogg6EbJ87ee/8t0Yz6BF/CTKfnzZ1Gby6CtxBs3G1RjOGCUBoVqlQkpyoBloNZApZEfsxklyhQCs1M2MGLfW9kAgSGafUIjVRioOB2ObPSszIq5G+FiciimNEeyQUBEsvkl4xAMOXMJEBnmgnnqAz5DQWWxOLHMvA0BE7KCJmxfwGeERSqLRnGGzbYNDgp57v0aN5AoFqpX8uZpik6oXy0D5JgdLL8z6RgOkQtBUwwS2Ap7ZmPZaDDGUJARTvLmT1MOzispjGp+NmbP+Nql167wwVivN1Cu2OQiI7pLXKs81D2GUp0XpVtbX/SYWXi5RzByo/OGXHRNCCO0JA2sdgHejS6DxB4O5bGLmVGaeMCJZ8/fWozXagKDlGkwpkgfg5DnLKoyiJfRxmdMHA7FnoIgONHsitSBd0gLITCGa14WvyCTZT5MdScGA4kHlNLLxM0AhLAgQYFZJjTGuJHk80QgIaLpVg4IErVpwVB1a9iPXoJ/r2kVc4afKWNpc9faGkDykgNH5e5KgdgQ1GRL+J3RkAiOxIuxlcFD5KwY+0jMIhABbFwLxux6cOGkSOlEyR4KPDVWqFCDRYZLaRtPsF0LjxihRWOEaDOlRTnFo3DWYKMbA0EmbeLeBWAtDjTAQdtZyT/4mfO0RBGMRZiao25GI23GcvRQMABn0PQjdULGGfMCmlxFM6lyI8O2Hcq45DczJQRymgOh7Rg7ESNkIUdXQMIDLjRLs5ncVfm/Z1bhDmO5Vbs0tHbtltaGD3T0fa5Pk/lDwdODoh4uDMT/qaBlNHFf5CgTjMgzgvrkUtBcrdDKh3Dz6PnJktSzdRImMMOGEBCZAt5cRUmKqNh3DCqXIFypdsjOcJXVmkkICzTZGhZAB9Y+wnZrSxGQZCyBJYYy/IhiRGUU+RVfjTwIfCM8CiBMyk74RJ7AUzFPCsq5faQqDP5AR8eyj5ofjyN/4sSX6KAhJnLWM4pl7z6soRaPD5KjvCPvXgUzIJq+k1JNGaUimYf9cYU0MkyCIdb0a9WsVfmHHNIgOEpPv07vTNpsQwM2hl1g1nmJMImXso8mbcFBDRlFG2NQKRKSGVqiwQnQw/HiVg89TXhLxLDrR7sF+008BNOQiiEY6gbXaSDomBOwogI/FOFc6LkgwkKZTQRwkV2SgqQKByNtNXSt7U44RdJhO+deU7Y8HpKzlSxMXoxHXyS4y8c+72TiV681VxP0wrPNajiE5yd6m6WYpceg36qqWBdK07Km48H6ivxQzJFZ+jlpDnsobHbCgKjF+StNUGOomu1hbT0pCYOTREJXzRjZOIrO9iGY6SHvuE3H3uMVZSdfl95DbV6wKdHmCX2dEQJCkoJGqSO0rEejbf59KtJAoc3lnqfP/+YIEWNkdZh+2bDjD26n6k9JAk4jIRpXARcC+TF6h8dK4OHd999l1HBiy8+T+lM5FFoPiS7h0TlImecnNdDiu8oj9lyQgdyqhxYwIuG4XIJoZA8OBk/Am4USsAjLOEynHSE43GRypiGOhKfdmA4Og8q+e7u7rFt1lNj0rTasfhEAdHYOnLSMIW+El+ni8SBCOFA0rJFrBISm5yjmAqSBSNrFDpY8CmhUObAOOWJCx1A5FMbXFzbaDUYWEQrIu9xk7UNb+QLJWYoYiCyjABMWKIC5X5ENGNUrPOLGhtz8eHaaLTPYGLj3BpmBQd3jSaDD7bWYzpRrT7z/HP/5vf/bX84+Mu//EsWWdb+8R//43/8j0899QzrupxUIzsu/EGYmLcoROglMiFbLgNTsdW+GQbRrji3QevGxClzVsERdGrdsY4Wy80shhIHg4ibzTbEX5nKXoTYlJ1KVAhOYedKLRVNwhBWRxQ3Zxun2dBWryoc/HgxjRoUn0NWHxnCdvlWCxPIOHopxfMhFIktxGR60gNCkaPYYMVIU3NVP6yG4jEF5dSuvnQfYokRFf0luxxaq6X6AQdTKB+UQ1J8qVy/kpEWo52FpCF24bIoKRDvBbIBq1g968wkPmurCFu3V/IYt/1y1hpx8W0d+YHPp4PB5JQv54QGSjofssbDRb8dDesrcXprwpvv/T7niYBI80gbY0/z5gVfLm0iFqvMERkuPoEQFx+NpkPu1qRDGvS0zt7sEVwf1xobz59fPxquHR31b259/PHH1Ru7pOJbuuCwB4Isa1XdH9ferxzsb/3w5v/FdRzXfvcFzN9OT3uDRywO5/oVOomUozmJDWfNVYFETRSNVpUiGI4vtnNcaHo0mfdn/KHeqigIqTfmZBrbVWh5rT5ho09M1XRtuK7tRiturzR/94++sXqtR/9x//abW+++u37u3DF3KXAoVlzp/4VK75MP3l+9Kx6GxyoXdzxpkrbZxoJPe7pjejTU8iVK36u3NzYvgOmdXsSrLOtxMUjs73GjIV0R522JSjKvxQHyuH0PII1Jqik90SEoTch1b7mmGJI7uROn0mm6q5vZhUtRNW4QGnJC82LMJlHIaXGEwspHMvqaqM7chwBZBmYhWEULTLVWEoli0dCIk34VJ3mdhfHxcTBLWm9eEqYI7hiiFoyy8MHkASIOmLF4SYKqjqQFrhHwxVy4MiTz4yjlKT4DVzIJnqKNI5+80SWV5rwPpye6HQkEvSHcbtcanX6ft7EYLYwYWHDRM2NZNgm4pOvChUt0xv/9v/9XBrj16oZY5CZw7dNQDxyQZxtjxMUVq3qXoMfWHcM1BsHPPPMUcFUEN1xzmxalMG9mN/kU2FxT9FDsqFtUBmgqd5xiSEZB4ORsH7QPu0S2eLSsCvT0a6CTLLEkux2Oi5sYUdFm0FKAMUahvyBvOLQPtVwnSznEY1gVR5BX4oS6iLLAIPeuNNqteqs54bUFSLrUZ1FaKsXS43IKt4wEte1coHDQguTW0gX0oaEsW5e2JH6SLehntCViWc5LcB6JooFmeFpgKkFy1JkBCiLiqbzipD7TYHfcP6rxIk306S5peneqREXbnJyhrOvSc97kiHav3S/qOBlbSD+0eok2A4mNoIGHvcLgZNUAjSPaelQLTleGKhRqZCIlvlKwTBzQg9BQVfLiljK09L27H/P67d7OLrrKSza0PloxCZvNlpr2vLJ3f2cy7NNHdyrDWqfKnXcHe9vDeQ/jyAlT5YK1oOskjZw4IRWlA0K4Whnv3r/LgAzLwReE2TnhPX7mNJwF5vgor9R02ytkzSSHD7zT9R2MBz98692rv/1FbBQHICgwH4Xjj0qTxOLMqW4BYZjEyR/qAjOABsT7q9qS0dRItt2OJNCJ2hFPhJWHvteO0WDGrDEnkgYZzEAu6ZX1JMQYUhdJSkhLtHMW9oGQvAwhHCSRgwL0RHGtlwZzGMCwgUK39lIjemDPlhFhPRDiVQ5kqz9GfTyGbJ2Ljpgh3kVBg1QwQBlhRZRlcBZuiT1EieIuogttISelDgrKIlwO8ESYPkBaqWGqAPxn/BaIWAkDUAnzp+qJQnI/tDBBcC8CHZ0irMxVHRrFynmAwhm6EBgyMVlNd0kluwFGHLaQyiGY6AqVE6PxGL8FGb48of4TRYAEnSVVxtFd5UOdch9djKslbF2DpvfrmN63q5XN2uze/buV0S7mgDeoGQAy6BsO9iG8vbW71ltjc73CZ2FIyJWilebx4X63zdxDl5jxCubB/l7r3m2+2zFS7MKlyg1ArgXpsuggH5UCH7dI8+CQKBgxuny03b0PbZkoxUrIkG8+9eRz73/wJgcXuOcGtd3b2+GVtoP97f7x2qbORabsmH3AQJFhfDmcG461QcIfvIdmRjTX06GKBea/3C+ScbmcJY+Jp0Jiig177mJwMhBMb2YwgFfyGE70pn0w2zrFPevoBDSawEWjumRV+EkIsTEWb15687soZxZRMc1BpyREjYQwbjHMVr7S68pscHx4f+vuhccuMUQSw4ixELL4iTEqSYC79klYLqMzdcJAEOA0gtFOIZ9QJFIpx1CMMj5h4PhGiEdXbgK66guxLCX1I3I7rQ9Kbsqn0zyIk9OYnwp5OKkHxT6IsXJ2apqF8/FIjzA3Bv297a0ug1LVOV2ojq6eJdci8Vm/D2JAtRD4CLzc6MCnls6svjL5TBbMo17nzY8+/ABjt9Lba68RxZx0MB96JsX3sZjM3D8acW3exUbtYDo+XmtjPb770Ue///TT7cNj1k00HpJbtH0/n/bdKmQuuAudzTnajArCv1i28MhcHYX73NMEBIFDGkT8iZ5HXzySjG460kjNXEbgmtyx/RO+4DF/j6FA6J4SBJ0YQEXHAR3xk5pgyJOeVjno6wVYNaQe2EoraeMi6McoS0hDL44oEy625lyzal+YcBYBjSrctunMmxWOnA11cJNlBipD3TVKc/f2/sXNaZtXMBab0GlIQb3T/6qYBvDGMKs64Fm8VFIsRrMhyv6i8oT1mBf4nP4YG7jgOrN/doC0rZV1PtvEnOvw8GjABz2OdYYMXxZw0afn5CJNKZWFumfB8YJdhcU2GqPY8BXdYj58f0WncO5utJkbDs5vMtHbWu0dr/WOuOeUg2tVXUr2zs7xsL1erx1yZI+BApD6VCemm7wtR4fPrap8GLM64DLHwVRrULX++Oh4f/eTGxz01BKFMueUvd5lRwWtP6DhqBc3K8LBckCDW+j7gYCFyaNGCgEnVRk/JYuf9enBMxvNTo2prNijFM0w8k9eufLJY4998PGNtz5477ErvMW74qwz8TIRwpJVcHgm3MDMPI/GX0KGABDxLHVYsJxKEZVyKsmjA7IFIJdFD+j0NEDLyFUP0NJOl5SGEp9OlfOWWNB0zWwFYwCneozxhoDhKIUGZ+NZe8I1ieOje/ucpFdTLWqwMJVAGGBHBCAHnFNg5GIYljv7xSM5kQoBFhQpWvAVypHwih8j80QSOxq21jFjzQjO2ZfgsaY3DlMNZ7Lgu27wFUiZpJqLXkUIROC0blKvX714Vcu7jHzjMmVFRE1r+RjBTStsfWAjuJ2DNUSyZg+Ee2pW1zeYwo2PYzFTfKQmahG7XTD4hFvSwlWs6SmLwE0esWgXPLgiMbIgUG9KFRyyIgEE04QL5EVqmIzyL0iZOHALjQgVJEbAUgDhL1JQZLahoGn6xOKUpMiBR+iYiLPm0VnwGMhKAk18iiDgqS7NCRMnQdocANfAWtwxXQhq0Uu4R0NMJHGDLDdLOALuRueyJB8qOC8ZM4nlJq9oHmOpW8xbMXN+TSl6gL6Y5WSvBq/cdyKacdXuvBGLkprIxL6GCqaw64yFIWkdK88sPIcCuVwuPnDyZaEVrUBJAOIIN6pNTHO3t8kJ0PH5u0wYxocDeoJSVYhLkJE9l7rcqGxzwcv8Wo95ezFzCZnBEiUPn+fAJ//kFHXSlSHWH5IYSIDRlZl0IjGfekVJj3EXcK49usZe7vnqV3q97337Yy8+mkKmQ0H4IOdbb++oZR3orNOgJX94vgX+pKsWNB2y6DHiazl0TnxoktzZ6QVOAMxOb51Dc2z/ICs2giGIQhCLTuDj4I0lIJCtrppZs/EQAxc65MyPKrro8kEAPywBv0pLRtHKBF/prSFb+g84JJaE4g/HRvSYN6i0PzwYacvHCwE8OhcwpbOkcW8QDSS1d6CaCafWDc/mAd9OFKJAoJlaEZN+YZ4a4QEiIPBIB0AY/IzJIy6uz8owBco4DpchIEBwQaVICo74TMoVRiMGBwiHIne7beoCISCoGBcR1FQWrYBPAjDI6jN3dXEUi4uJVrq6jAWxATw+1k06bGay8JTLRQAgm+QgUBeMsdj3gweAMFgwlX4DKMb5T5Hxi234hJB/wCyIJFhhEzLKwwIWlCgE1hIpYOYE5ilLtzPSXZA1DeDZRAEZ9xDqxJq9TIeAk2R4oSqLOqIh4I73984kbvylTM/EXML51R8zz5YJBHPgTOLl2FSJ0S7ORD4TqBwjokwqY+ZS50COcgBZOV8eUWkqMSMEwUXdlSk4DAKO5HqMfoqqI7mBZ9aCiYNgtHJe0IAUhozYRa5BDfoChg8Oq3WZTo4yJD8aP9PPgTJX2DMOSb322mv4mFe2Nl988UWEgKJjDFEwrPF4GOdfwuBAnHZN8+QeQzbeeMxkXSIgGCYDBXExK3NOjUEQOPYB08FpaAIgy+RWNcYggOS5QF11UatxCHr0xZcAYmyAQAqXM/OjCwIRnMKYwqgL2xDycpQD5iQDcxRJABpuP0XFT44lKocJOAyKAzk5gSUHToZg0NyfkgomcUSZAr4DRnaRXe4CR3Ry9xTcpbQOK2HklYazel445IMceTY/WQfIyBB8GMjhRcqToUBIEsvULEKnzUY6yrPANHLCCTYYCyABOKFQTGUVCFF5TZwpPry5LZgF0BbJixEoEJeF7AhYhgAjIAqoEJqGJIUWPZfxtU1BWv4XFHxKi61T5vZ0UkrIZWzaCohTHawmNBqoMQvQKa+i9mXzm/oGSSy86Kw06t09f8nMwLxzhBkX5LQPDyDjE5VTnUZzlNHsGwc4xBEmPszQvrSiGtpDy+Iyd47iWtmGfEE7HO398WvPkhCnTEPaBMyq2mZ0xI4tc1LOtwz/FwjDDLlbjOaznGmw6rIswBYCwiWQ9Z86ZbNEPhrBOC0GDUsEVUxZr4XNWRCNwQY0BSkUHExlUWrsEEQ1mQNeOzrCkBKrKmBSIKd8+SnjC3rSlWMdY5yM6cBpUhk5R4GZwyczSU+ZZk6b0UgI8zxaRCaVYwkspS1HnRmG4JnwDDSr+ZF8zYAhZWYyzoMCS9z+oqyeJksl0sSgE2xoigjOEsOnU30qRHwWNu1ByMIpTEQOGNls5IQ84jBW1ZVVi4tHTZrYOIx3nsDEMhzeuU9LH3KIJBxAXvIG38TL1Bw+qaHL6kpC6OdUOQAwEzwTwZhK/gAJnE4FMs0Xn7Rl+jnTTw1klsqYIhvzyjLwQfQz3OyZIGHUFceqGkRgUhMlrnaKS41kckJEx4NjWYYnNklFpjmtrbSKBFr40XnNNOeM9yl5ZIIEmlaNo5MCi16MvAicWSiXhaxNChyc1Ziu4O4bbzAI1JcNY1Gd+tMyk+bDGmKWl3FNp/CDuXggCQ9qA+EEc/sOnxUEpIGywQBzOnpJ3n4FgljQz0lrhSjCLJrxqLfrBocQEMG4VR9fwlQZ02QZcTGIgmdvGX744YfMPetrHTBxsEBKksC5hSlIUDNCOQw6j0aAWQLGKbfEpeRGwCcHJqf4QojxKSchoKb1kGvXbtz5hBHs2xc2ub83K7XzyhR+7YFFWYqCOAs4BEDs6RxPA89AOp3sAZDT1B6A+DCwieDbgUqAqqTeaSgsxmKjKA/6moRPDRbKD0bDC3zaJ1cuWlrSIqGsnhVajc0uFolSlbNeB1CY8cOYMJ1yde2FQMlPtKK5WsKmY59FWI610r3zDWtGFDA85LhNs95gTUvngqVqtAEosHSlJKnJiE3mQSauWH2IVUNVImCDDwJ+73vfw1hcffLCZz7zGb4nx1o8iwuoXIO7mmVOxBX3z3a6jffef+evv/l/kCmvlSMmTvM998KLMeXQJgnazhqNFvzlVFKVmacp9gkO9YoBx2SJ4yt4NJ8oOsfO2NOiBiJVLHRSPJpxbaZzCiwCql64HQI3mQ25gwyOfE5GeURJVdpCzizJkVdVH8djPI9M0tJtIJJzZMO8SLUPYUghi96Y5/qTa+fWiIltLysziSS3gCjPnAt3GcUVCoqdsFKpKDzyEicheWUIOGTBGi9/rNeyiVeNDQN0CgaCcnWivc0qRzvAlRimCNHJVJuao4RiQI/zDKIa/PjuIa56wNkf6Qu1leM4N4Sg0RDOf8EnQgQetz9X+KqgwrGYPG5EF9LhG+nsQmpln0MtkqgX0OdwVGuj6HQqxZUdpK1xUQcbd2x6TpjRyE7p7DAucFiVFL42Q/tDmsq0Opxp7q3bT9gJ7NWql871nr/41PSz06Mx5+j3bt3XYtZYo4fKnrYxVifcgjQdvH7nn97+2y/9T1997LnnKj12K6v7nN7RWW93ZiEH1SIwwS0wiVRAIP7jSZeHrHQvNVrdxsdHF/e704EWEOtx9q3RklmPDX50cHKwv0+rRnNUIShS7FrNOLdUb3NYpHHu/O3D/c2nnkBtKJDbv9papbq2Vx1/sPN8fwNqjHzm4/leZVIZVe5Ux/vbu4OmdlvrK7opmB2snZ2D4cd3KcAkPoGoXXMWhibXpY2l/TfuJAWOWoHJsTIVODTk/MVz9AGXLl+lM2uv9GgjR/v7Gnt56F+b09sRBYds89LntQeybpP+kEkC2k/t9PhsJAsfjaNZczRdnTbO1dYubrJ1y4scY76O4Jx0gajuakekXgIeTMYMTI9G2jaI67KnLFVTIrZpwTk63qEJ8wV2wqvuH1vcT8jxG8mHeqMs1tg4sxPVRhOI5ljUWtQdEuDy0lZvZe18vanPgmlgo11IxRJFq6U4Cp90Fg4wKhcnFYgwxIlyFkBCyDLbtgcxh1KrsVV00dFVlqUIszQ/ns27qytM8GEBxcDo9YdTBgSs22NeWFaiaWKZkOrzz7/IEf/RQFbuQBXSn0xHyJxTS8NRv9PlFujmxsYaOo/wcdQRHfx/+A//4R//8R9v3nhXJcW4wUm8ph1Nlic7tVMcpdAgtnBezpCKFGXMQghslcspbfWKdPotYzpchhihICsloPYid2yCvljFGgQnZKl9qgo4LhPneASPIX7lDtlybDkXoRWxCWdBprK2sX54fNRqdxlE8pYF9LkQOufigKmVaZYRTsBjyy2ZiSSVMu4iTEWT8LTEyqV4UHhBJUJlNADWPYny5O51iPdEjZhOTk5gSeNz0cIksEVseStdst6KIKvUd6hFxGaVdoAHo/rKKphuJMAjcWhKkHPZq12Uez4+wiYzGlIDjPf7aYA6Jw5LOEoEZUgRziyJiZILXUgHeYQqOcgCozKm44RQEEcCSgmwheY5ky1nUQaCHGkTNR7dNfMF88Gg/+bND3lz5ejggHW3V1986YVnX6gdDrlUj3v/2/VWR7t94/7hEaaeQQ+2dnWqVyZbvN8zH67RLfHpiLq+GQBT2LFQa5964kF8cuKH3G/d+mDQ35Wpm805NcItH7xMxvRRvW21urq+wujvaDrir1Vv0qs0miuHx5Ph8ayz0eWzwTCc7JJkrQGPSwR9hIDTDEDvoqltaYilJiZMMaCpFMwkE2GIkkDHW5jQMqtg6nJhUJRK3D/ABQOKywnLiKTFAcHH7jlgn3xlE2IqOJ7qMsecEFK5jGWgmCtYCcLJXGQwNG3wlWkonnMHvkSQngM5OhYNIhCkY8pd0yWT2QEvYlWtGR61TMESIEhZsQtQgVqWH2zgdNaMAV2UhaGQXqLRW0ExGqzQDcZB9mgAmCLI6DipBswEE3FzDowA9WfmYc+tRvP1+DNXhKlKDV41QpRiQpQ1eC11RwouegczfLo7tEcn6BnTNOcHh/c/4myslEpvNk3iYDa8Y8tbDP45J9ZZWyf36aSmkwRcWDk44msGK3yxfDhmdFGb9XdvX2/EiQJGCxQgWE2iQRRWv3g27ynKP3Bl/HIq60AUTXoFBUyA/DCbBKxpEKc/pWPlMygMMhkKQOSJx5+78dGdra3r3I3FGUCa86B/cHTY2t/dppOtT/QpxbkWYfmVxOvMnMYH8T1jDSxwdDHQYQRIj6yT6pQoZCjl/ldyUqmUdfGr+hVvWpeSvgneirPP9WnscE+PKGBXs4QK9c76FX2zxuLp6MiCjvoLGTR2yNQLlCpClCFLNigYAQx/jhWuJmIapaNZjGHQvdGwf/eT2ytMDtdbdCQMRwXHxpFDzL+gLh2Ny/1IGGmTwpuyKsVdSYx+UWkeafLUOFHg82ic/JiYVCeiqKD6SB74hbUhHQmj+iOplS1TyWRzghzIOKcDOZWjyo9LyR+Uu4uWKZcpAISIBBu0cpQp288J8+OihDkuArEcBylplGeIVwf3odm5vXW5P+7EUJQXpbAsvCNFQ2ROlGmeoBQZLDGTEYAvpToNKSPn8FIq21XNy1hErje7ve4bg8H1SmWr3UJifd4NpW8bM1mVBaOK2w3OcHOIe9JCJ2kp9do+rxjXm/u16n6nxt1bnMTirtVoGWmwCmWMYM43md2QuWXI9Ec9kVYkwJJe27nXKJ4EK4jwGgtWrIHtndQqQwYTmmCDvnCUS+obOiw6xFUrTNyAYbvoBpRTSYY+DBTSAFXiaMSHigiUNCcsWCkhxeR7dz4w5C5HCxEn242loOUQ+A+5xCyY/LXiLFyF+CGh7LPDMMiZTD4RxauKE15a5/b5ap0BSr3S2N89uHL5Ard0cJklyWsDRZMLZCQ/XKy5MUzAceqJ3VCdWgfMpT2NOsezhuMR76EiBL1FJiejFEMqrhLVCCf6P06kcyJqMog3hhEdl0Tt7e2zkEKm9Pmy+/EnzqPQSI0eWMRUJpVoyZEL/Sq+qwqeocASEdgjhBjH9bByfMILHvYu6zDZXk9XZwzOaZVj1sQGVoYskTS4I3k6mfWb9dXD/fvn2rxUOOT9OH3cS3NYJtjq61nVg4FxzJHroome8NGgGi8gktfwcPf2e28986W13kpvp6/+TouNCDqkoW9qhIvalz5QIfJDNzyXcS/m+k0ltb6FXiEBZODVjGZMBRqVA0j+7otXH2sM1vuHdcrMzc9rDS4/QM7daecLL776zntvswn99nvvPv7UkxcvnYMsxxkpSiu9UBliTbINpUkZ68fzDQPOEn8J9VRQxS81B8KZGmGVvlSjCkdJT5ERIDBJklyRUDJ01yuDJ9kIh9gCgQfeDFBFGB7pSUXWqaTGDV84uWEKkvIVkAd8GpKsyniiz3UMKsf3Dpij0ff5CA4tnclBZJE8v+O+4DvAi8fQ1TL+iXDIKEEYTCj78DMcDlNHWIzdg+diHgJeNP4YmGmlDIWoc02vnOwCDuoePGloSrMvTMYiNpZe6FPEhyxJ7etf/zrHgt754PUf/vCHX/nqlyBrOpGc5UzRYV7BDXHb97WyQizyQRGY2t2+e48GX2X1EbUIM0H+ohxVplVnDv7QNXBBWCPWx12u8CkaoncBSQAn0ISyuaIUOD4/SFo2wyipXeaNJITLj5BCECYIXKTMh0BSXB5xDkCN4+7dqpa5OeT57NPPPr75uORosoEJA0KOhClxEAQoV54aBf0ih5RF0EieU/Bg9iyj/JgxQSNsOuD4Ea4kHJVO6SzCztoK0hvxXZDRKJ105j1PTOFY+8DxGqeGDjgv/PmsYmqusUwcb4fUuBcZHGSFY41BCcJxxRaa0G5oPpngsfjLJJg6pTbxeSsjwhK1Nc0zMK/fkJf4j2YJSzikSUFQEOCM9TloVrn8NKVj/EAtH9yULm29e535wNpKh4Xpv/u7vyN86bdfIK1lYoHAYEAsq+CZfE7aJuMAJID2kguqy+OSgyCQKLG4YpGYcFYDwqzh9vf2aj21NbYxOZctlUZoxeyER3AyY5kUJ5q//Edfplz782MWIudtbYpyszYlYo4ES4O4xGB1bQPKDJvwaQ1Ojj881sFbcOlTjw+PYJ4BEBACnBB/74Pr1A7mwy0In9VQeODkMv5jjz3GPdpwxUnzybZ2XP36kodx7mw6LR1aYbrFcvYTzz/LAV5WXamdxoreFm93e9BvxCdP+yPuyxpQTF1uNe8yFbVG2SoNp/rQ1sHhNouS/b37lI63gC1SfLKIRpSLpUCWlZQhnPBr6kpROiSDCiM3CkV5MSqKdfUbu+SbVCZYikm5KG1oCxZA1RpKYgipHMsjYZhhtiY/HEKAh/lER9jYzAKz19aZXwJIBu3FQU9lPzhgqfp3fud3PvvZz1ZmPVL1j8fU0V9/86+MT0WQNeUiIRAI0maho7wadU5PswAN/FNdFFPFIaGQbQiKZBZCuVCOiTIu2nUGGt+PDkPWwxQJquSCpkREYQFTQNpmI25757lMp5TIjSjVb8YJZhJWkM1mWQFzaR81owqQGLKiqKQxfip7ynfRXspZnxk2/TIDRitDHMYvTMKZlKQDZ0eUoEs4SyItIf5Ggjl38s0SIyfgtGUtqZ7liLWQJatwQOQiYYzGlAyA7HrRlM6iJNhS1g9CE/mo8siHWl5GzBk5UKAJDx5zrBmWZY4GcnR8/Prrr797+yb6w+ozm+vPPvMsOCC4JaJacIg0UGYT4ZGOERx0D3NHwHktM3TymeZAFuCzHgaFRls7fziS4zIue1dUhNlrxOd8CatqYmKfC0UAIvkxEQl9y1EgUEQol+k7CQRxbqcgGIifMU1QjyXeMpNOEqkWnDt5mYKzxmIuqAUzYThTv0jpgs9Euxx2LhliJhPN4KrIS60MtMjF48MYM4f1cwssMFMuPGLBg+GwJ0HNBBAGscU4MLVfQdKoNVHwD/CyeMoPaYRfWADyyg40hF+iYBmKVnbEEg4c8VCEU0BoMTpyT62WEVYXHzngTLzwRQHFgwHw8S1/Rs7gIyk9xpiNqY3osCJYrQ6Ph4yy6Lyy2puNJjfuETsccrXUU89fAEjmURzlC3JspupoyGSkt6N84zmvYQozlJZAdkCghg+EQIZnYIacGQCNvB0FDy6aHzlWQSDiWSHRbjTDEr4pygI0YcYP5o0mzF4v/fJUJyjSgMQJU7uLEyQqZZgLgKap59+ky5VIdpZPObfTEss44DttpkAUTo8xjQJBRY2xPFsReixk6CycI34UupxtCpuyyQIiUPbTckyqU+3zsS8MDsfPeUm8u7qGJYxPnSVqRDlHPcdkW9wFWfFQuIxG7rn55KyNaZwiRfo1vjENKpPNkDLCEgWTPZP4EiaPLkvm50yEJWCZspOXEc4klSVQxiSsUoTQMk1BTjaupSS/xCM0aT6mXCaeIY9I8xfFRxQoBKksk6VcTC3TBIcwW7ygOQmqmGMBqu/mHEd0QEKOWTPaIjsQfXTOJQKilpKHFU25l5QfyUMNyeD4omFCXuLyoY/krlQLrRd25L5sbzJxAqSwkmeGnSqTAQ5CzlkFDLYNUaqYg+OFiM6wCTktCcu5BB3lQyDzYOJAwcQ+O2uCDKXoHcZzDYRIIkizdXB0wFwVO8yj6RDIY0jSYrs1v5vo1XYWo4PDRYVSKuZN9FOkpT8zWXUKKhRfa+eijln/mIs1DvePDhlusb4RzibLy1vpzivS2uXceSzKIsqnHZiSXZTF64rwDv+xvV7nwlKJOu603LM0wvYysFQZ40w09J0FLfp4OORka4L4J7QR+jwxzaGY2weH8M8H/QzEJyEygSD9def27WeffVbEo34JGEGLoaEAEQVMwtdPoVbKzaUo6heEXDThC1s+YBsfCkrpmP2TO8SBQwOfygITIAx/5Stf+cEPfjAYjxhjf+GVz+nariiLMUWxcJF/ZkfQgrXlsGOK2BPaWBBb/oW4S5cjxO2JHHLMry3gEv1y5HJacV4UNfRW3t7eITrPZawQ55HqMFL2KZ2GM/G8aPaeDLCaSrKoKckA1NRW2K3BAaCWI9r7EgwZhaLzR4vzTSE+0cFZGxzG5/y7vyYBjs6YoiKzyoSpTEOmjWYsrmIBBQTwzYMCsKM2G0D9BOex1AgZDMfzn3nxErciVvrf+ta3/uHvv3/zxm1YjqK0xCJjWFR81vj41u1KbUsEYzhLfgjreDAUfTcnQJQwTl6AhmMDEH2FFhTGowE9QGxKQUEdBkcs2CpHwzHOWqxkMd5fUE8XbWuzCdqsAensMydqVEZKoaSQIF87MpKsiuUYgBwFjoOWZKeD1mBj7cABSCyFQQrX1lco++7NPaagFyq1P/7Cyxfv8eVvjvipHZJGLn4luJILCgzpIQY3rJELVyyFzJGuNmW447rC6rnui8JxXTCnjX0KnaVAkH1mmTPE4A3iVNJ0TYtTY7F8YuE4TqGjISo1b2XgPGPd39JnZAYyCNNhIzrXuGZhyJ0siJac8d3thRERXXGohi2Pxek4aN3oSKVjlJ7Gq5YAwqpXp435qFll4Yw+HaeJTbyYPOMNSFoHfRBpdXpU92zI+DoX8ibgI+6Valy6z01HcM7tRxiyeBvgaHgke72iWB0F546OS08+03j2hYMv/vznP9/+wbuVbm/laPr63/z0yu27f/AHf8CxCtxeXGcxituNSSd+PBVHqdEUpKnqDpIFM6TixD1HWKcHw6a+2Kq+BXmq4UXFiYh4EM/Pv/y5t99+e3h8zKyGE770rHwy7n/89TevvXKVycxxfXT+qUsoLMR8p3ZDtzs2Brf21satlZF2MmkpEDuoz568+uTqC09BvFHXEi0HowmzdLta50A1wSp3rILJSX8qPi0Qp35TZJIGRu/n6aigwLn+7OiI/oyud+/wgNpfW9ugm1dr06dgpsTe3rpzd3tv7dzGZ1/5Er0FwybuGCLt6Fi7R8PjI8KNY63wDvb3dvb3jr//o7e+90PdqAInbX12oNNd46bUS49fpWuh/uGfRoEGcGHHhY6OjZMWnxo8UnOpbTZ6zcea++sHvPo6mmjBmrZNj1Wb94Xc4Myj2p1QXV9F7RCL4wnpUAMzjjBUauxzbe0ecfqYP0Ym1CCqBlfSKf1paqoak5NUI7DwFBV1Csix+KrgwnxgfjCgRQL18XyL0wKnjFQ65ep0qKgxp7hJBQTksNg6lsPlMucvrGxeXN25f6CoeY07NwjoNY+ZToXP2hXu7uAkvdom33QaMQLpM3ZZXevp+vtKfXv7Hm8wccZhND6+efMjf6AyiqaCSRzJKfcirF/HIBCF4/M+KtQCnx5LMYG5SIuqZ1fWpUBLiqaaKRxhsgihpdyLcFhURowqhLQdTFmAaBI6YBlSDvagtiBYED7xe7JoINM2Va4omzo59jqanXZ10HeXVU6ci5wDxJ4URfEYNMtpT4fL8jkda8hJbpexHh4LdhbvieoMMkAk3pMkgbjSyoUqh1N8LDTQI5DaEFuSVKJkIRek0Rm2tB60AA0eWXBnrfLG8ujlFh0wobeL2nSdquFHS5LZpK5dtDJvOT9pgvSKTiPKRxOPgFPRIr1N7ndkfVJPDTXKEjiZUgrkXEzHj5kmGo8QON806B/+7K03bt2+xcGYJx9//PPPvMCeHO+RcKiE5s3RfS6SqjUbvBdyvCcTCnXOJlFQVBlqV7rVzfp4Vz04JxQKRYIxzYQYQCzqiq7gaG+Xr8xwLJGjO7KTs/HR3g62FEpMVqlXzgfJ+rXU0/G5G8TVqzQZ+3ADU7WinVGVwgItrJZLC6brlPEMaGQrX81DLctREn4oj4/rYJ1yKovFvvGtUSRU2siLQAltYSZyqpwRgRwmFbnoNRLm+YxeQlUxBC3eimC8iCQLF2yKbCaorLF0dAbxrkeMQVQc2X9iZPz4USF12ok5j8ZYVhPBHuQknqgWZ4T5CEwZMSchQBSnIKgX2CccEMiDQCvSWXUqCxpEkL06Q5UsAKcNUMEHdMrOuUiTo2GCBVcAGWwx4kZiIEdjJYZM+BN7wZoeYawTozhaXpRISTHGyo0DvyHXwFcqkZJhAYHbJMUoeUoB4po1joiRI19BJm90G9KsUzI4v3n9jeHxVrO3yuJtNE1GmzoZAP3ReETlsXPz0fW3udeiUeXjFQ0m+uQyPNof8RLP2iZhFnjgbX/nTq9d7Z6/DMA4iio5swf/lk8pRgIBiA/QaA6UkyzhSwfCkcoJeULb+Dhxbd588tpTr69uMCLSijS7SkeH/UMuCtk+3F9rn3uM5hKiihrlFUS9MCrRhY/aqIBBVc0tHeGKvP61vCSHyN6WXJrJpYvRKeMT7sSMz2ef2z77XBuw+txlNM6d40wF0YSY70RBGWguZO72VRoJIA31+JDNjswJ4wO3HDzDtbWkzqkR3sYg4TED0Z2dns6TaMxMx83yDIljqI32U3Gqb6RvggiZgH1Txvej8tLd4jQTMZMxCwaSzuRU5s26ATJwpzLB/Oh8DUzzX+VYANSIpF2mUA4oXBKTUrj1hmT0dMJJXOrTSi7zAyxzWIr/VYNl+qYFBPepdLNegclRFPxuaNTK/n5jOORKbyDgICX7PrbyILIPydGVeDph6M/yIPM0miHS6QiRUf/8xo2trfeZGfQ6hzEy5+vgqCX2jQWNHkeD5pVBf3i4vUOJlLumITE3rNaO57M+5gB7iepq/o4JXshqqRQ8WsF4BZghriY1OuDfCwuucUHotqwLrPEqNHlhP9XowqQ4LWNa/sgO9ka0YBl8a0hqVrnIJIQzzmsz04QUO22sLeiFDOXDbDuZKfAhAYLlIbsfCkx2nBZUbGDCH81sobwAA1UQyqXk1LHwWZuipEzPg6IgjF4wHmNsIZMdsg1lkHBSplrR0kRGU3p1P6zJ850AXk9nWi0G6O2YJ42r1z+8waVnLi6k4JCVHklVdz4zPVSlsfrMj0IIWx3nnBOV2A7GF+zZszAlRtWs4ZBxwxQjj+MNFxafeLU+nOTJ4MgPD/EhFd39AqWcphCpZIsslFuMPYYxlBi0VAnTVd0sOm7rLZT9mT7hc3+Vg1+zQZsDeRVeOaEvnrY05m22Z3S7zZbueTs4HI2Hxz3evJuPjqL38VybGhfnoxG33vAdEZmmCRR4O0mSJ0S5G3xCezL55P23r55b7aw/Nhiw/xqjF1YD0V5WQljs0cCUJ1Wg9dl1WZoLpPf2Uslj9S9WHzXlor4aU/YStV4PixeGu3yj+3yr3pmO6GsQLjThil9VYm3Cy9AvPPH88f2jd269T13cvHlrdXWdRSKIDzQppG9SPqxjhw9jsiFLTtDCnY4lBk7wc6UUuGfAjakkqrTk9P6fVGcBKWL0y7SkjJwpKCqMIRDU3UkULtGhzqw2ZWBgRm1qLI8yRrNFn4MasTQvwOi+wrIQPCrM+1yalcg6TAZb+61RRV+OCRKKLZITtssL0AvSjlB3XzgKRxNF16k2rVgQF6cSkjRjGmbSwBVQ085VpIAGj2I6ORMWoXCQxbGihWq2MaSs/0axeXndCOCXx+yoI/iZSAT0SO40DwyrlqhmM85Lbt29R7jV1Ae4IEUqKlBpMdm4eOne3KpvCgxKKuMaszholuVAmSCLesFnjEmogRj8cUkBxjGWzCxhssBBCscJDiUKgVBJUMZI2U65CJFLFteiaNBkCw5X1dHqKe/GC7Ol2+7IWNwWKgUcBAAsG1He1fYquZMFcAIL+sGDOVnkR3Q0DGZg5BhPyTMmzGegc4QmUThXZ47NAeDsFvLoO5p5RwP2htJN3dJHLN2S/OgqUBc5LQnq7iH8mBHMeMdHTu+hRl8ETmRAvmJAL4XWGLLjGIzKjzlSLChryyucAkSZWcJMKdGQuLMB0yLC6q8kyFRwIFZ8xJiL4/LHa49IKqQUvJAUHA9k+VAbjhKLE1ZsDw44B034cmud02rD+To7bMCpTT4Fy+26n/v3vwc+/TIULMkgRbXmbBdwQOI1rCdEtDg41cpslEseGRnHuSsc5oCtVHIhCY4oLnV6/utfJ+Hs/JRbnlnMZSHjoLYPBecKDvj3OSIdBEnlykHhWcDlNDL5sjpHp3V4HIuVTRFnIwE4OxfKRRY+LUAzSmG3U98cYNsmLAZDH3MIJkB8sqY4HHDm5DK7IISpE7KLyQCUQK89P3tBFGjG8YVo0Y9NI9+kwlBGIjjQq5Tz4YADLLfefp87T9nkAcxQDmr7B33g87ffBKK3vGJNFp/CSh8igE/L4tUbZHL+ymMsp9JbMgmZ8Nou57v5HiUu5ntjhlWFy00QAPH4sIFDjjzSZ6FIH354k0qvV9dUrkgAGypYIWQeVUDpQ1S6SRQ+sRDFBwGa9otI/ULHOBloZPIA2YXlPDhZqHuPg4r4XIpCeXEs96OiNNi/+9Y/CCckxsr7d77znYP9Mcn52AVA00Q4nHEmCRCu0edjaIxuYIBNbw5mvvve7ffff59H4Wsll9akbltmN3QYIpnJLKsMWQpk5IyZAw/CXIIvPWaCasZFfwQQbhl2AaGiXSmqO3PqLrao1iWCD39M2UVawlQBygZ9rohTwqBfYklVbIIEHlTSco6ncZYgPGaaOaFxljBz7C8XWMolP+YAZF02IJmrHFCsy54E8EAuSIJSSYbFmQu6+zOxy8RBIAlZ4EQhjJonD+W0S0nKUTlMvtaQDDkzoEyKskQ4PTgLQYoiO5yJlB8Jk92Njz/WB8fjk1x8ip3TkVfPXwIfpUVdab9u4HT3vF0SllNWiLc+QTA1ZoBSPO8S55yKgGWCT7mYPTFNIlMcHEMcCtChZ8YnhaziXG8MdFm+LjodtAx8+hpz5UwhyCPh/AjEQJANdlTBSKEDkYrEIJOFczFOpFqIjqggmWS7hOPcnVehfUKBiGnmtMoDUspQpOwDwXjiM9SCiIuv9OEy50VAOMihiE9EPF6K2la+S7EgQZ/RhXIM8860tUQ/jIDbRU4ZAfDhVwRjpE3APJ/EWjypFEw/i9KZK0cTLjO2SBNckcKxpE2MxA8Ei+dyikU4YpWGNRSFeV88Oi8w9BTNECLlrIUNfgwHeYEKeeqRMZXh8aNlaCQW00+/7InC08Mex6pAmRpwHBNnHAMelJP33CWHoA9JdLjaXkXDgyV9UZlDiBd75xhjuBjKKIa+9hdlK0I5uxwoYpLa54QqQUnOxgem0kUrs1kCDgQfruhbafU0atDgDf7poGmbm2sXzHPOC81URuGXOQENamlNI2P/qwZgr9DvxIcgAcN3KYiwZNAcilDlcAstMbQIGToZCDS200UxKeDIRHQL54rgyUkMdpgoqIEvmmHbCaMJ6xc2yb3X7iltVNPp7ICYJmkz8ZyFgXBMFNUPWaoD4Jl0ykCzEalAl3NsVsgyclkmUm8bk8L3oykkOgU1M2yfKAIOl4mfGX4I2kOiTpMCORftdKwhvxDB00SwAJo+R5HxHQANsoUunE50AvIQvCLqDD08QeLkQ5EqQWEJNWOlYn5uFRA1iJ4wMpFCFl0VQF+luhI2BArZUTqsHwhAcj6O5ZEWZZoKl6YnhKHPwAk/IZeSm06WlR9NR20hnBMWzTG1AmKcCl+Bgqb1GZ+5j8pldY5Zm6mBDBtKUricEQEvQNPTRTGFYUwnAcHFLyc3GRAKeuJwQT1mIkupbHjp64UZt7MxZEK8dMhgYqYxQWwDAuGeS3BQLYhzzwYM8L01yoUDqMfCFGOjeAySClDRTnV/awvDvrO9g0RYhkamtFuXPjhesJ35LwdyQXLbd2yWQA4Ah1W5wMAXlyHAVotLF+sDDhxOJgejY+HwYQR9oUrDHjpNfAwWAb5/BZBFGSC1dodZzOj+Fp0R75SjexSKshQ5aOY+mujL9lBT2WPJkqGkCDQ1mOSwG+PVfXaFP/roiZcumEMzHL7GZuAYAj6B7LuY5ccUGw2HWhWfgcTYQL+sVYTjS10rK33F6iLWKgxTuTi45f1mLaQ0W0yBt/o7hLm/Fzk8xbc9ec87JIfnjEQzS18Pj+SCSFJvM/9Iyc5Cgg3A5qocfxpSjn14mGp6OMKDYnOm6BRhnEjF0J4AK2AMsR6UNsO1wssDq/soHnWdIwhC0Y+sTIKmVVucUHUyBEhciqW9JhxTamUfSeJABulLdcXGhLKRWoAmR4A/UGLlET0m3OKdM14T1zJNnBMM6+ydBy9heEtKlLK1iggve7FEqD+uvt7Z39ren3KDHPrVaNHIUX9yivJoxEnWFKbGRcIsp/nEBGtFoeuSphnXQQoNSsC3Yk/ZFdQKlNSRL+rCgsuOyRFLcZsk7EhW+rojwtL8ULsTWmvFkGrhdci5ZPaIos6USidrkZWGJqA4I0JsMhIrc8EWU9SqV3GGwJBOFAWOkTwyEX5M6q5snJsf99ernRbfz4hhejrBHSUHDZr2XVM8ZghwcmehkEOaljYM6wIXZa+ZjgJOwDyAS4PJlD0ibRSlW5vH52TUtoeHOzu723G3GjMAnH0uL9SD5IbMtSfhjwSqBqR58id1LfvqTmfpnEw8a8asCXGTAZKst6SB5k2bi+HgmdSkFQ1G83ECGhsHPvWMfJAMOIFGCcFBVXktC2aYFJE2JmaJB8wL2wtKZuIkJ1AohPShWh/DAzWp/CQDfmRYJ3yV74j2pjBKRcXpVoLJ/NbuXUz2Uy8/9uTml7f//g2O1F4YdG69dmM0+jve+zh3bQX62x3Vo7aNqB01CaSt+uGfHUVQ9fKfLcLJvN0kQe1o/4h1WA5vwwQKrg/LRvMiOYUd1Sq700HtyrkXn7v22vd+sEtfUZ1dffZK/UJ749r6eHDngw/e3Hz80qQ+73Oqt1HxcWfURoK6f7g+ZtskyIXOELv62OaY17EP+s01zqMoszZHhjSSYVMBnhEostCN7OI/qpozLn/zf373S1/9Cq/bsHkvyjDM0jIfLwSFD1bVdAMv+FNEKyIT+kPsS7WFGNK6OZHtdpeFYI4ucmMOEgbfby3oSjeFVC90qPjtzsbVl5584t98+bd4zXZnFyM4OeYbkqPDe7ssQA8O6F+O5v0J0zmuBIEOJUHCKAUKA3vwdnt3W/BOm1omU1bPL126zNLq+uYGOMe1tdF81J/uYqO4B1W1o9uVqT7Rod5Jyy8QjUQYi+gbafOfvf4Wc2Y2niQj/oVDPzkc7bBTge/+k+SGOBaCiNZhR+GHtBnkFc2ZliIkmV4Z1PiuJuaPW41ZPmf9ibPeUPCdaCgZKLy8fOXylXar+8H71/mo8uc//1Kn3fvnf/5nn4zodlf4fkD/WItccA5vbNuw/PTyyy8/9fQTO7t7cPXc08+8+dhlOmxyPXd+nW9OsktB796gjSIMSqvyuJ1KGi5CKojg2aAoCp0PCSxkUuAHZvEgzJBwCXAiaBEBCikpim7JlWug5ElTUdOW3S4cDYyFNuy2+sTAEQEF5JL8C+T0GyangAWiNd8gp3VKrDRKpTvgOFSqsqbJRJHY9ei8VMAMf1AAHOeVBluFbc74ZSKhmFT7L0AfOmUmlsovyUROtD1+edRT0cvrMSwqMEsev9AEapnqMD0QE2F+VKAYOpCIhFYYbpEou6LQIktYu47ctqd3pfXufDgZ7+gZU51pKZVBJ0ef4bWBMjNpoDMjC/3RmcJMrOZRIEiWC12QfMCvmFFfJm71/6RmOta9szsoxjQhBxU5n1xTKiVNznJg9gmfWGw2eHi3gDi6vsvXrr3y4ufYRFRHMJ/rOinGGLze0Gmz2Xa4tzftDzFh9XhNiXKoV4guYaM+Xavy9gN7uYy2lJvqp5B8ZEwxarxnunP73vBoV/f5Tqdt3UrDgRX6MYYotMEZh2Z6zeb9Q30KqddsQZ8zqfC5e9jHNu5xbWG1hpVRKy5UgrBF6uzSZDIEpamlJmdq+8Ti6AHQCz0jzziMxFnpuFw4KUnELHEuGGnLMjSafeeeIVEp0hySGEiNMLLlzxD79Cn8YTw1HNK9dTEclW4IzZpJciMTgGC4nM/ZASntiaIEhRLrIEDc9LGX9B1xJCszTADZ2h4GJ0EN5sndzEBffWvSxyTbJVZjPkiiWJzk4y3ayxEhqh2f6R9/ECRsnwDObUSCUsMqMV1Ck0oQH2m1STzj6nLdkA5QMoy6VVuIpqnhTdAh1vwTQbfL4Jh4WIAjXTIqdiV0cpVWkAVaMudqSs7hM8/Z4XvDYxZhYDGYYncYMQ+4nhKZNLR8WWmMDw/ussM9Gu1X42OzABkJNDoH+ZVqrlTtH+wO9nfX2pfgOCpB0ovmouLjytII9gw+4bss8IrGqFThwMhnlKCsdimNVftheEus2oWYh1u++XJE6IXnX97eOrh77xbzfN6tPDrY3+1sdXvN85efaHY7YyUVQ0iGoSc2UFY1XHBOHDFcaoz1tGUC8q/vEA4ygY989pmwzz63xloO68yOqZpOVfeedTkGj9hkqCktJluabUdBpQOFkyjl9EyrISQhh4sAmJZWqsHAVOWAbz9+UyqmXjvbWxcuXcLA0mGPphMWY+BKiocaRqakIhzBMzzH2ocBlVr92hmYZ4LICrjpK2XpkdYBWWgCxFfyILuEZrKZQuqXz2IYHAhm/Bw25BfyHyWtccxYJi5gibdHoZPT5oDtqs9IdmN//+JgwsZTiz/OlygD6kCGiPUtlTorUyZxKvAgTspCO5VIFXdmQmtpaGJKtMfVzt3uT0dH17uNu7wbx5WD7RovFXFwR1VMtc1mXXpwPsvErZpaqoxZpwbWmnFjp49qjU/4/EKzwyp7P5YQmnHTLhmQNilo6ImaUbQJotCgiT6JR4Ojb28LOaxEzT1CGsVZA9PsWBwX00vUjhVX3ubUAoMuxwlRYtSkqMKn/BSWGTzmWq9Ki7o7+0a8mcOBNHVrqpJoR7wMA3klLNSAADLUoESZpHKUTXHgkz1RpXYVHPqLUNGh6WwmmExq8TnRq3oJBmMtQzNSk3bGzMimLNRAkSl9s9aa1Ducgx7EWWYtVjRno8knH995+pnLWskZa+3HQoyeWiMErA5V50z4kYioNFknCefgsP/6z97glnu4oTrcfGGAR7o4oQafqg05icfhLJbAiQKHoDRrLiTmyratKCpeOqDOVrUiihNubaaMOnZVH67qMsA7VX2f80g7s/VBlxs55kdNjhxVux2dzOps8MNrs3oXfJ0X7mC6w8GGydHO7dqERdt2s8Fx5hqn+ohRQeY67opYqTwAvG9EBZOvtIEOTiPleVNve09Xq+2dT250z59nwtgPduOtRHAhwxoexi2kBnMSS6pit3G3IydiSQreAocA+oBvXZKdbFd0aOzibPJ4s35udEgh+RYc+MNBv81RuG6HG0/YiGn2WKTjcvX65154nrfV1y6c56Tk4QFvF1SZ80LzOHapuzFZzLIFbpeYi7o7HQtO1FFUQCQouF1AAAMkbWAaSfoguWVXQi9Bc/Ry4CxO1Aqcu7GRpx/LwGVC1KSqYOHoenmgm1UWqqXk4jVAmSlWx7iPfrh1hDnhW2MayoUKaD8gKJmccufOb8pMxbhqIZtFkFAjb0wJWTIWx6GHKDFbQqhmDIVlJrFkqcEH7VAe1XQuWA4HJMFzrOiyyFjTZAbtV6uO09A+AU21kLvln2thwWeA0mPkyNk9TuH96Gc/RPm8dw1xyy/QTE0DYkpjOJihvUV9BBQ8OMSGkEqkaGAIIiBwqGUEGbU4C6lXt2jtGmrTCNk5UXE4nRTn6TzcH8atQCRkfccL0JGJPHHlOgmQywIFZ0oRZKgZlIe5YWETLKqUWBRUMgw1gqbyHQuJHpcpYqJcokkULgCFQNODykUUdYovmuFywI9qIFEPolLQgU/0gVczcB/sb3NG4zBGAbW2ZO6JTfQLzPmVNaYYn8VkKLAkbRdwPB25giBvWooTTBk3WXR0JpGj8Pg17v6QmYka1Bg+1qMVoYQwDoRFYTugXMWhuJBVdLZCAIIHEF/4ofMUWKks1aL4Sf4A1X2QRGLR6E/WMU5n8OyEmgmEyUgNUgmw89CnMbL2e2vvFkONL37xi6+99tp4exc14Egs1fS5J38vaES3F/kGFwXZIB4ZqlcjgA5GG1Efwioq9HHk5UBOKw5DGmQdqSqs8/IZLujyWONbunfvsq7x9a/8DpzEiq6JBy9cwB8Xo6eH+JEy61qMCnuDw9qU3UusNpO3Tz75BH5WLl4kfHjc11G7Ht+4YwcpdoO9LBsnelBZ1VFF5/JgzVxRChZ5YZ6NHVyqwQh7WScyl6dzjsyo1BNIAjRL4cd5KH3sIU71Ik+KQ1gvbNbrrJbiaqyOc/j0JXW3R7v7TDtH+33C3FsN8pxvQ4zHrJ0+/vjjECfta+9w8OjN7d0d4FgS3Ntvv8vx3va5NY5pX37+aV7nadZ01+p4OmaBm/d3SMhWE7WAE1dF0yDAYXtkhbThjcVcxwIHEw75ciJhO6LseFTZSg4IRAGQirB9Www/km2OQg0lZ4jDFUso4bAMIgKFqlrreDIEDBCuqFBkgi5Rxm984xtcUYJicG9m5Ma3mjpU5jhGUs+EYwGajU1uD0fZXnjhc8zqKR2UbdOcHasNBBijRqZRnTECc6UXWrpUylKBf+Ug0nh0GvADn1Q97BGAc8vqIRRAcClyoCjUIlGQPfEIBGkj88lAn4ajJyH6U/NakAhrQDWezquMc2bYSaImFvGuDssqs1EmbgQSlOVpTPwcAMFhBo6iHtM8DTdD5Zw8wil/JCy0IlUKp9JFlxbDkZwclqwrriDjp1RREahoaQF6EU+6MhEiyBpkyAXNTDh1f3qmVmJ6DzLhnDyLYkG9CBktpyrA+o2oMiADJYoy/YwEEMf0kAZ172CXs3gYVeT/ysufZxtsze9y+ag+a2k4LisEmS+pctQLJjytCTsP85AiLY52Xbst60HzN6uOxbdjjgIyJ60iVuyAiQ8Dqq+oAAsZfKJoLwSgCA5ZkwV9ARaiHeMiooBDEB/nLAQMCHCcWkAh4QWCExSpgvfUloVdECzTdF6GuPgZTY8aP2dAUtScBPqZlAP4ADG4lJHyggmnNg5EOZZHKMbwQUIDiPGIQNJwo5kZwhDJYT+SRpQLHSCMWRdN+jQGfiE2HnFiRi7w4xEKhhPAmXLgJGYA8giOA6ArfQCpR8Ix+Q+KwFkWLhxJoBaZC6RcImGJlMBGB+iQf/UY/BObsouyuK0DQYtAcKzDyisUILMHnByBq4en7iJ3JCBn0TLrAcj/KCA47Cvz7nM1PulMzs5iwSHPFGrOdYR7jIVoLoO4JIWEdH80HFELhskBy8ALW4xk5u0uCM4CeKZGwPRFdFHVjpdvakYTZpLQAoEQBFW0ApnyAmGJGQivfCu7EBn7TGzb7+1vQ4f2RRLO0MB//cMP6YJ56wFuSWJSyuuUQ4EzP6cif+OAM1kq5wqCcVwE2hSxFB8daOrSC2YLepsQEKWwHiJyJ1HVlGhlOgRs/6BpshGlqiKQXSlKVICn+Hg/jNs7GN2hCdhMuIIH8MWJMUPx0BpRK+CiEg6YZR40BRJaOOBQo1wAjRMpPsVzvuCXk5iI/aJhJJpGK/tkgExPZ0Oh4Os03JBFVOCUc39QEnAWqUpIj5KWhKfRzqRWIvzpQZo8zYRWA+pp+p+e/sEYovZg6Z2ZrlwchyFCJd69czsrP9VNlB8zEUMUpUvx5FwWAtyHx4y7elGHbzKchNZwUzDc1Z3DaLhxgICGqTLNnKkDBpp4OQoOy6UHgTYmvzCnpeSiT16UlLwiO9l2b8yzpqRYW/VIEwTcJE3jl/TJyMyQvhx+EDlLnl5fRQtpYD8ZrvOiKCpEK2MwUB/rvDmTPsrCNWWUgih8ssK3o19QICxJgseaAfNSNzj3G7nXlXUTh4xTYocrLXQVbFociyelFr4kjfakvknA8oac84jhLjjSfxaDG41hXITVj2OTzPEpaXNV259cS4rrdjt0OnywmpJ22qv0j5WeDkSzkqGxkFbCNHpAc5ghMmck86jNRf+YVQI4838ogEMukge1XCxJEUsW0GFqzJi2tbJCR3zaEsESqewio+Ihfg1xqgXeCZT0wLRdEog99ahK3vNTl9rursCGdY8VCQjymatr164d6HrJJpNc2L7UvWDmzyL8K8HMf7mMkFsUM4uyyGQRVUDyb0QtZFDGNPNABPTM0X5OXASEcMpFOnuKy6IQSMwqiU2Bwxky6utTNHFLa2IMNcrMlPNhqUIqBnGgzCwgkYWicOAy4uOXTgxMVghpiQwQAk1KCdBDodhhApM/JidwB00dEiWtm7TLaC6xOfoLEKRo0tDXR7taNO3qiB6ftSrliMDERDo/W0iJ38SnTVg0RYavrHw998KLzNk2Lp577733WCjRHl0gazhNIm8NxDIOxiPKriZEk2GcQ2GUQ6yYetlUYpccyA0ro7JwwzxNrzpUof2tT5aHCcMolaGycE9ig5sZ2whLyTgVqyH1eDCecM0877NjtUK0EjVckdYOZoReFC2EA++qGjksIzzMOfgxHzFUHfPtbFFgw4GE3c7Kzv091s2YGK0ydeL4tzii1jR7pLgQILcgtOxFmTlTixAkZ119ZP3S5gUMkgGHV6IetbkV5W/UuN8ZZu+da2m4NtjGoh3Nxp2VzrzTxKAMObyEaYuRD5c5EYYkPl/ok8zrsVgfdyjoGBtLhKvxooffmYyTzrEWzYAUDkgoTaN+wGS1DwqiUSwVwREX5Vm/YYl1EIuRq40jT/LmMLy6dh5Jh1xASFdMibHChQgoJ5g6Ba585TT8jUogvc7AawZendKljHQHsU6a6AOwoBOvqSlzOAEbx0oAAQAASURBVPmawjDqnPEJ7+EaWwOj6sXm1T965fr/8U+zUa26M/z4Z9ch/o1vfGPEIZDZfKerCb83iUQqGIvmrQ5bO8GcR/fd33Wqt8IVCmIsHJVDUVMfFAcduUqCvx+//zavluys1raH7P32/+HNn16Z3uWFlLfe+fFmu7t5bfN4dsyFyuIfAmjIhI+qVyoHo67WbaPy8JH8Rquy3qtxGVJ1cuft93gHu3t5rb91751vf4/aX7l2mS9/1tpcblH5+JOPWMW48uQT4r9a2+vv8jeqDNfXzrGw+9d//X/9+3//759+4hm05f33P+Rs+NPPPkP3AOsUmcar0jT1so8nXX4TArWlgqNa5of7vf/yX/7Lh+/rXPm//bcv/v7v/3611ueQ82GXqQLnuUnPLe1cmw4v0kDt1JF2NtIO+GZrfr7ZmG3ydcKLE+3TdDh+P5utN3Sd1aw/bHc6689ce+4LL93evocOb+/sMrzjZlVWYY4+/Hj/5p33v/djVsxf/MwLvAvf3bxcW7v6QWcHCcCg8uIyMOqpqcPUcYklWlt7670Pj/pIuDme6YOK3gdGe6XAWlKvYVBIS0JSCSHMLFwRBsijHY+spmj7RVH8qQW5QbPbrD+NQWjoMm1YH7UObZmx76PH1dgYYANANIHUG5yR52uQFI0PNr5Qewp5zivjr3z11SeeeOonP/nJz177OfaZRoO1X1nZRGe++luv0JdDHM6ff+FZOGR8Ri701nAIHQYcjFSYwA/6eifLZQktVpMBh5QqV5ibwqpFu0pFjB9KKSlpXk7VOYankDA0JaigVkhGiySpFZwgUwIumT3wYSNk6xajBSa2NRidQIESwSkBUvHaBa08MHNxoiypNzcPxBfMFKbbnBgeIzdpMOWmY0JEmDA+t+pBT5nn02EoqHSxq1/QXMZyLi6uOzdjkDAxUGKPKAMJqCILlzIq+DdOgYCyUUDoJTkrFGH9xODVyQ2EZARkPbh2P+dQBMq/0vn8TJ1gMuIxCbaIoo6lt3rthmayEDYn6cirwtukjLJ1xXPhzH/xpCJjHWgfdY7eDTn0EUKIrBJm0ZKgh4SgaZ0hNhcqU8sBmIUSTa+c3akkFNCDDpEVecmHdqiCp5opKeje4Bjl3LqvhSfAm2tY3hrWhsExEDQVdeeYCY263eGVSW7GGA8xpnx4UIoaEgyl1gtlQR8i69PD5ze73/5kyECER836OIGkFiZR8pUZissWFdtOo8GxThXMKsw5OP+rux7Z0sVSxUuRHSwbN+vF5AlmMNFrtS5NtN5sMzT58P7Wc/2ji70omMqYWpkFmMQY717wVj2cahIqNCghGSlC9O8IC7hOwcRrdkTpDwcy8PAXcgyrF4tEEREthcYpuyFhaNjgiDN8cyg7qYoRwxFQAgLAcYTpWHlrzVbAVICot00pxByWOaSPdBnriGNjQhvbonfcwuoteLGVi3fUjLnkq7zwjypoURJDrhKF7SeA5kQr0AhbA+XofBbtWvyEhNRfMFaJWg6zy5PrXc2moRF9VDZeMKxeWBCN9SAiaWg8I8NF1hZ+mAK9SEtzCsUXoqw6UlfxSK1qJNTQAQJAhRhj9Q1MouyocpzEk5DUnDh5ztk/nX3lideWoxUodw3Hgjuxyh/ESTdtcH9ltbLS0c17ZA4KpaJKeNOEzjiOCcIrkcP33/8hS7fcIgMdMlHvyMnhAeegV2CjyQS5Mhv393a3bq0/9iRimsbMgiiyMdviIPQpfgS3i2IWD+lXUlT5xcOyM02gkLWOoaZIG8cj4wSiGLE+fu1plkE5qUA907j3D+7rlYRac//S+rnHX4QyQoqLR6WbdoV41buRCwSX8/71PYdaqZpwDmdb/5BMMMDE8poqvs8+s8YDt525RkcrVeYNjRb3tVYmnElDCrwQBxx7UdCUkuFQCVIRwKfU+Ax7w0V7LPr3UJVF0wDfyEZVKrMuGnzMpQlFumoIs/WHRQWKYwzAVA4qcKIk4UNK0xESRGPh0Q4EB0AjAHn+CmCKAp7pZJpOldMCx/HogMOw7yrNydUMwxUIeshhaxrmPaCSIQkpET7Oj4oKuANn+iBnmmciGGgcU85ofnyU5DkJgSUi5agzw9YP7qbEtTSOq5wbjI539+tjLoBme4pZjHphpNdQX6Z3mX9zToUt1MC5SIARCiOpK1Vxh2sr28PhG+jbanevo8Ez3TLHgzlfLLMgayZLfm9ri3knyxjqNSGrWT+zGYo65zTa4GhYuRik6RPcGuMJzzIk3wic0D0WUjG88MmBRTBjXsDcPwSn8Y+anOwHuqvOVFpXtmTBR6Jf5KZfSqeyy3Yy9dBAxH+RBQ2B6VYsnkBYiPiqN1aVlEp6iydrxg/Oh6jI27HuKRyrOWPQFmfi3swovXpmcawYn/xlXJhpRnuHvgYc1hmiitxhKJbJAHFqitObDGxGyFivoxPHTIc5wu7OIXOcCh/yinaEN0sGmFz0iREgJKEQgBUrm1PhPTLYsjRcPPVTyljyxwyRgEwIc/M3fjuuj7BEzB5AnGwTvsRH6bR+RRlBQGdk8Dl0hOT5WEfs5AE/asy5pvmoxl00xwdaKJketHTR06QVdzOepygrG211rnxEhDkgy0mkbc94u47vXOlEM4c7tABt89rr7O/zpvdOQ6sCQxSE9Wr4cT3qVxZPNYbthEcOQanWkB9WdKKLL1pdzbUn40GzSme7vXfro4vPrTI1Z4YMvM5amVbVpBMa40jh43pbFVoVDQ4hqEW3THDZMbEGpFY2r6zMtxnvPLNycXNaWeWNKMYkqDzd8rTBS1Mc8eh1u7w6xTh22uIgd7Xd77zwxIvv335f2wxH/dv37vLqPQezXLphaCmlE/3lbH/JZxeHcddS+mQsAnqyTQtEKqlEHlkgk2UCJ+hFJyfbINmVHRQkT7eDRUTkCAtoJlnJLZU4QCIGUdU2+k0Nc7afAduUj5aPj+7tNUayuRCI7k/E0Sv0b5FNhLTWo8KQQ/hL0X5EkwhgJEBu65s3UvDAlyHyvH06kdGEBeB0iqhIYjo0JhHnJ3IhFXqpYkSscydKit6cYVx4C4UoTafCcBNl2Tm58e2XKQAhX9a/OIlz6cofM9z83ve+x0vlwO2CmobLGtSLEy8ZSzTmB3kKbjMaWcIC+ADtoIPIAyJRNlRQWprC2A1wYB5HS0NKnOAO9kQcKbFrhGNhiySiE6LAF1fhABquqAISUzPey9N7K2qWUUaWO2k4k1jAxTYAhL4qOGwidoTcMRDQMHHRLbmQQ+m5FIxslaoEU9BwJOZA8Vh56623KM60J8PHzJjSsb0Evjo3SuG14vjcHySAo4/oCcfdkVKzo/srCQPprnibOsbN3I2iChIPTLOVykv8sQANyaCtWPjEofsO49MUMlwMBE5Qk6EvnNIGkYTAI3QykKhWt1cgn/iFs8hTyMxKqVmcMoqBIGEphKpXnQpwssZhUiWlwEQt0clzKyscO0V6XPbX+NwV4QcDohvI+MxycSS0T4BeAFlB1uuzhAN94QWR0GHY4zjwcPjsc889/cJzHBS6c+sTXuhmS4aFhpu33sawsuFpOqSPhKIDcRKacx7jfe4KGsUtFexhs/n5xhtv/PEf//EP3/gBH/ZhdZtLgX96/X2KxtvgEPmLv/gL7l/+v/8//xM3BcvwRaGoZVZy//N//s937txjwZ3Hn/3sZ//tv/05uXzt977+Z3/2Z3Sc5MWhWfAZGiYJip2oJhpaHMfj8Z/+6Z84Rd5pbXLzw2uvDaBGa5Uc+JYvtzfyhU/unwrzotdacfPFspSewkU1qenS9VA7LOC8++67t6/fYIH1mZc/y2noC1ceg8mxviwx6e8fsiB78+fvYFh4JN97W3epuGdffoal2/pKHUn2GQbG57ZdO8oETec93+NjPgLJEwnJHYgUJYY+YOqGIDnBxWrJCSfMTgmWgiRQbNFHKb0pixRO1KAticXgUhmFnhCnrzDFfQWUjkYIY26/V6/xLUZtd1MKjo2/+uqr589d4HWkTz65wwCFWubm2bV1HRlDVqR98sknmdPf+OgOJ8uc8MMPP4QNdJM11uFgj7ygFhzH4CY2GMxnDMlUrRFrP4KpFAtjWOAslDPh/Wo/qpyTDgjSpo6seORrHDMQ/gk+T6bWU6Z56K8/xwlWxIWj04XyC88+Bx3oI67hQDfqegHuNKlHhyQmH5wg85/ZAxdg+dGQ7BMg1gkJh+NJxddPEaBy/RgD/QXcaE4GDgMchy1YwoVWGHzCJyrWJE8AeciZkvkS50ZFaaF/ZpSASdNEJ5sREhKl2DC2sWW5qGKo+WyO0ZzLaZ/k0LR/OvY0BEzLgZJaB120MianQmhTjKNohhc3L/C+Ra/RwvyiMKRyEbT6zAuSHa7L12E9BhX0QyZSJgg+V34Bp/lzmq9ev0OsiqYdXNnbmIxVet0V6I8Ghxg2C8RJQOP9DyCy7fHJYSA8osBkSgDngS9wKLNY5tKJ8ikdgxnBY3pAwrCHYCG9xeAq1y9wF9aplNFJVyBYN5ZjM25wmKqVMKlylMPOhbBdZKfenACqgk8qCqVWXOIBgUAHNHxiiYI2/4HjI++cSxZIhjwkIB6KZTXQglR0GZGRY+0jcKwo7HkZLpbaz24aZ2YnIrhi1qJgyblELhYKV4o5EYS9GJ5L+AoX8smUSQqHmTgB4wAkSX6kLJmu0oYzhKCJZ4QlOAS5PpWBtj6ZFDRh3paJutCGaIsvkKVTkBzJ4c3Iq09+WWTnajjcfkHb2big7z2YVZLT+iqdDSCUzjRz0Zz70qOBOco844NWiOSBVUOmqJnHBqZAQoBWG1olExnGbNx2g0MJOXy32j3P+Kq9+YTEGMcdrJxk5lTOVQnCldn7jYaV7wOVZTlnM2mG7VMcNEF7s7JOcr5wgEKUEy89utSUnAATXUilsXcWfdGOQMChFWAQSHhBmkfywOfJUeSOYvBKx5NdXs/WoUgcjAknatZMGZkaW+IqMxw0I6+YHZCKfLPCE5sTBubCpEBBEgiVzjgRSI3IrJKK8hAuF4dHXE7uR6WNbTmGx4bgn6S8SGJmFglzgkcLZLKgl0k9WupfJxa1lgWLQNxHltn7dWV2Wv6Z8pkSMA9oAq8PshsGjiEwbFLxmEYX9MKUA/4zKeEXi1a5jM4x42QGcoAoHJTxPUsiTPJQlYUqgg8C2pETOmAOHc5MBmYoUqnFGRMfhzVeolN+ND9Qs7qWox4UhqYzzQgkZ7E7P35qILHFT4EqgmogMgNw4r4vmK8xncE+M74B3pzr1mMsAxXH2/rkiyMtQxgo6ZcxfSy2so6EYHWUGiBRWj0jrPzCKwICCQCisgsrwYgaiLkjuWKdUom0sE1UASCIEzLTNNBYMYCH3CMA2RqP6B8nXW4Taq+t6nP0G2vyG+vadp10tKo+qk6Y1pGWkrIY6kKRFwjM9Rh9atg80QJuvdfTW7+DAVec8pwZgwGHxU3hSM7Qs3jScgQ8AARnNNQkHRVD/xub1/SuSYyg/FJ/pgZyLP0Xssu0wuIRCxHnCM+mnFGAA4F9pucy3ZOEaT5VO8NhZ3WVtwtpCzxBoRW9D6c96GG3bt/hTNXW1v2vfvWr589vQAEc/Ez/1xgwSxB0WR5COWMqUChBGT8jAKRE+ECscv4tngLuiHL6UlgJwxlmaiZon8ik/id7VeSJkiCuJhtDJQqZNrBcUm09hUt7UIEkvnHUH75i4wtrHJrDdWPpkLE5YVRKgyeduOSrk1psZSYiX+YxjkUruYoqGkWuOiSiL4Zrow/T4RFD8KDkzXq13awP+CQpq3WxgQUxqCSegjFxFfZRflJ4hh8Im+87zW/fvcuW9a07n2C42abSwSi9IAkN6euEvUMVi7lu5AxlDVxITcPTvpnU2ue2eAwBK7vQZvukhQ7bOvh+dU4nUcicrVdcu8tF7ZwJQkA6dQLKlBsaR5gtFhyTroNbtNO0Z0irVMtcaATnAnBj7dbqtK2kzLYXXSjMs+6pj4zO+VY9G1VeYENcEF+ptrCA5zc2wKL1h3GLbheJi7py1Z6F/Lgoq+AceETOJ3W+Vjttsrk2m/dnQw4RjzmhUK3065NBa3bQVInqF1fuTSYf7LwHzfHVuLdebwnXuK8ZSHyRbrq+rtcYqULwdVMxp890MLS2xmvEHKpsaApdb2tFIObCfCxGfSGlDCEHauz3hlUXw6Q1iyFp4rxbK8ZVe2pp8jnuohyjMPER9ULQ0SCLCg05qFbZcKhw96Wul5YL/iMFm4nQQWeh6Z3JOFsDYWmPjjXI6asKaJcMN3sMXC4Ku2RuVpgegKq9TZ1cJavjjuqi9vTqZ574/Z2/+cl8fNzaG//8W//07MXfZ6GB966JRf74OKgzyQyGo0uoVdGfJi9i0gan9ePd4w63+uucmpZ9j8cj1iy6LTohOONwT7U9mZ9vrW09vlF5+uJBt7r+yhPd48++/z9m/+5/+hoLqbf/P9dXzq1zKnrGHkm9Q8lRCPxaZ7XS6u0cHnESaarvd2sXm2a+x2d+KlMOgn3/B//8zBNX186dHx6N79/d/erLr1CPK7tbnHdq91Tj77174969g9GQCuBj1JSAS0pX67Xu3/7dX39w/e7qWvPak09M6o13b9xsn3v6S1/60g9//MMnX7jztd97lQYSm8SV1396G9P/5S99lrPGk4EMmTZnaU318yx/f+cH2/XVL3B6gCOPz7/0/EC3celLCDc/2OOd9Ze++JkhW52T4Up3ZT5e/fa3v/3k1d6LL744HOqWWOog5KmO9pC7R+bjy80LbIVufeetn//gB0fHex/X3rvfH37ta187btS4+0vHBNscmj73pT/+oy9//Q/h8PX//rc//elPN5i6jmsffvjO9eq7vT/7DKe/xxtjhMU4iI58cu8+ZgDdZ3flxvXb97cY8XM3CI0Iu8hKl/QHEMLkVAOOej4+OuqtbaDl2B6ZINU/Q5KwKuiPmrHeVCmGWXyZh+/Cc3Ya+yIrpJYNRtiuJtelU9JwDQ5jM1LAGIM1mXL7A8XHujKUor696A/tt9548+13WxyxfOnlF7mTq1Ebrm90vvDqC888d4XtCl+EjcS0zDcfNRtrAN94/T1WrodTfW6i02rgP//siy+88Dw477799oHKSEHUdtBGzeTFgXxgvpdWrUou1tlTmDIA8X/pcCCc8EQkmoY0NnaAEVrko8Ys1MJQS7KGBL6iwp4rACOQ0aBPT4kOZ6DjM5oaJPF1ALQukkMEoQWNdMZqOOyjCRw7pdRE4YMsCyBTL3NnHcPX+pCYkFW5/t77yJbPEK70Ogf70S/MFhY4+Djp5R6vKMXJaNmHMoT+lEflFryWozI8FAn5F7IqITmhARGO+opnHrMLIxyFKoRDVKbvsInE2AHBhS4jQuo99Lq8xGzLb3z8kJvGCYTxk+2NHkFHIOVko1IoSs/oAcDRoH9ewwxFui8YhxX1yQL16rxg2IgvirQ6vBe2f+duh36TtwmoLrauGMzIzvHPOgkPsY2JdkYxYt+egijWEHVIUiAtsOvCcwmfd10gpgkMppIcgckPp8LAGy1REJlFekTKCw74ANEo1jtYwz3aPyAFBq2+Wrtw7gLzCh0u4CJFdTzaQGLXqquPWeoLKvQI/ft7IWLx5jGJM42hMkARb86rq2sbj5/f/WRwn7M5KEB91oelTu82d9x/9dXnWXT7i29vjY53YiudQQVWozk4PuR0NZkGk/qEjs6a9wez6ggKYFJqjK/GJfQhyJOLsJCHPrbBZxQOo/7GbLEhYgowmcXrnJBgPKThEwtGcS9QVR+HiRu5JMOQVlIw4DgXBzgEHYtP7qzC09YFxwoSCEtoNFocyJoKKklUVSltJghyOey8EC9pGev22uljdNhP3TMue8AYTTv98Y4u6qxrl9hVpn/kdThSzdxjhmTgEYCOYlD1aILUheoQEXy3Bdl7HgGxEAA3StNUQdALRsZ6RYbzUNJeMiIWHWAooqJKIcJy6uTxfBpjJ6kyJdJIT6TCiSpTOGon6yQQmWY4j16D98AYTior+h4JTA766LIqmWFmoJEqRtgaJRhHvgodoxQCFBFGY8ODXsnCBLDS1KU38IsNUEPBSfdlAIVe+IKzX4pFmFc4aM/ZBMYAZVtBNPjECo+xRZe3sWpcFrW9P2utXN4dDdCoIRMGTn2EzrfgZzobxKeJG9U2F7q12vXBQb/XUefIYjS9JAdq4ISTYWMu1ZIxmnW5Tn02uXP9zce/8pXjuTZo9S6fVrcRmXL2H/DCIokfcwUOQaRALL/0vQTUL4RqlPEinDzoKxSvbjFUogJqrTVITPgwVKXCpV+f+czn3n7n53xqlHrm7ck7Wzd7a83WQBdEbB1OsQBUCaWb13fphGhTg+GgUdO1ZrUWH3RHYtKcX5cLHYjeM2oBsq34tCkKRCm91oRtpOBYwJypNYiXuIFwqo5YBiKE23GspFvTQZnOXJ9EjgvfvSJGC5c+o0tLdPxIE8FJUSGlxQtZVdB5wxK4RkaqBek/8lcS3aguS6pkOSGW6mjS6jHU1zlClm5iiSHqcT67+dH1a088xV1ttfU1jmyzOA4ZyiK9XRQuICq9m7aywmVdpQZwyI1fmc34W6RWqkAWlqj4F388kGLwsZoCIq7IpXjUrwtShmCLKKbmoYyPw4mTFApJJgGIDlGh24qmQ0kNHZYC3z9LOUZMiWE/P9hf5H4WThCX3FCfgsuEVy4n4YfTIY1Ng7WlUdUH0tf299cPDi6snMMqYH6Y3KET5IJ6Bb6FKXGcxdrDYBaUkxU2SjTdOkzQ/JSJW55OO4hTpddns7eOj7f5+jrqybnVVns+PGYyiW0xKbTx/tbucDxuNTR0TzVpScX4gTcFtg8OeB+aKVIoC6ZdnNhpdCKnMtIwGH9g3FhO4GkUKyPRXvroptQerY7RGtEIC5LIlG6GznRa0T3RtA9d01WtsZpBjx2nEKMPUpH4U6dCQhy42cJzaon8peQxMhNu2Af9BqZEBOXUKSifcCKFxJQw/gAaXwGbhKhVb/eO9SaQm3xCizfMoCBU7kvFn8R9s93KIFZagsloQQrBBGzEsIR5tExUtTLS8bnqdK3TW+v0ORDb72ORR9iXCccdGAJRRVSTLEwrhoN8ZYShZp8kskUU1/xAXjzEy+qEDQyuYjBgrQiVJNMoWBzW4UZv8RW1iWGlgjSYYEE2qtfjW8ZAkpJuTOYNX92hz7KupNTWcnOjG1csrss/qoxYcNlY67J2PDjYr3brrbUGweF4stpeVU4VBnh1zjTQPdIzyqhWpq3mgHcO25Vxiwux27HArRPeI+5+BmEw1liX2hUyU01ehOUV7ZgBwUOX67R39/nK1yq3bfBmSW3e6uijjpXZyrnz54eDLcJ8RWS0f/DJB+9t9jrd1U36SpYNsEgaysV4hUpxP4uUVEHhpBVUTPRrkI3uRjrCPwTGO5H6qejddJas1te6T/R2q/M9vfGEDjTRUj7roYXF2uBovlupnruAxsSXtPT+F8O51rR+7fzlG3fugr8/6n/3R//86itf4Q1gVgUZfHNnJ3WhO9lV9pBcaGaoQPEcCkUOUDCcwKM4l1E1eMKlFk1sEkEp9jT9kxDSqqlFimUCIaoSrUUwOE/9vCqYwhKJ5Alok0WU3PlG9fMVCr2sRmnnh1t7/NVZXNF99QuKiaswi64qx6UNZ+MWKVRJDuOTJYYMoWtc2Gqt6E0RDhZoeY4YELwUKIWlXmNC6AyIgy0zATKYyM9kC+5dEEk1qCkv9FgZsOgauizlLjknB5ADpcgUZCUFVWZO9a1vfYsNFsJaglHuIbIwB4TlklqnCia9ZMgYpSQ4EwVXpYvC2KwLDQJpeCSJsRjIdhnHFnQksHQiAwZo6tgvL1VEiVIGpLKsbBMtK+eCT47kLvGnClEqOESMMamgq5JmMPdBXtZNUlFTOLNd9lVeP8cvtAKQYEWMsMIJYE5cUs1SQgspzn0+O0ZGTQxWZ9TVxJ7FZaORNfUX1FTjPKouG3M0h/POPHJHh+C6yIU7ElSwKJPEbh7wCeOwLVDmER8kIIQDPxDVlckRC57D8iWkhTMFnh2AASjYERaeVAMmBOfBaPhMX/HhFBTXjrtnHkTNXXW8lUNHBCaO+kI4+OwXOCyec6UqkJhDJp///Od/9KMf8VUcFh16d+5w6pYlcJYhUCNxFSUNUwkllQ72SDUb6GYlQOiSqyPY5hSbvoVHFJpmZpheUkDOOIN886ZuGXvh6WfIBRy0FDRO+zJaJRVMWiwQJC08UApDghEdoNM6SDBAjXOTxmc/+1kuKIRzigDOQX3MMuUxH9WlS2NfsVPhkRrnSg0IEuCYEpjr6ytXr13ixDQdCPz/9m//9h/+4R+urvZ8dI7ceSuIvcfvfve7TKueevIiC3bHfCKg2Rzq/Bn3nQ3/9m//lnYNM5P+hFJ8+ctfhg4DBgrFsWjOCj394v9bgqJvgJmDAZk+ceX3CDMIgU+IuETg4ChLe9Zm3ee9N94gdw490UhhgFJMu/o64vFIh6ewdtBfkwxW/uAP/gAiH37vZ9ABiO9jg6aMssPVvtQmSRVhInnQoM8sMbLVUSY2TxA7BwtxAhYTcmMKWnJgKrlUVxPmsGRYUrVHRm5kTc8JDUoJDkCcrajeYm9qLKKSxv0YIEsnk3Ype9gDBwGzLfHDH/7wC1/4whPXrhgHfWM3GARE4QJSle+8dZ3z4OzuASE7pZ3plj2sLgKkvORFQrHvCVuM0GkScOWGAqtEmiAB4KlVJK4kDyWPKHwzE4CFR/KcagGNEMklgjBW9l0FwGEYhQErZVEe1oSCsXPLUf3VNe2tDrmKqFLhXiMUjzZIQi69IQxHJOeVNnjAwR7+gmbwgEfWlDeJgjZycID+cMSLikBJEFSB+Mv8OsellKlQS9DSY0IoWoFjyqkIZ8qG49tRHAIk8aMDS74JnvbPrEHQluBkDWtmIPxUwwo/qLIjM+iYFJhCDu0yt4ThPNpOIoF+HsQKL2ci7FAPTgqDQ+3gTCEIi0OrkB+XfDBzLkQpfFK2RiijBXE8qY0yq2pbkVEKZgQNAU4jpylxFR1Lw1IhICF5JoIgwyMDDN4KQj9JSBJaWjCZWAMnMyzqURxqjlJDFltnabBCDOSP/uiPfuu3fuvixnlearn/X38qlkJ05AKaWz2roYShAxBjSCuoxRgfCFnCHtLTInUUH8XmMiQxEK+3UkgQwBQEEI9pYT81QMXGtApimaBJkR0QA1PZ4sdA4zscxBMKBB0iUA7DXZmIwy6XidBOnRdFh08KxaPtBkXGYVshSJRjnSlhSoBP6aAJgonkvMxD4uQUC87aSZJf4jNlEbY9aoBORRae/k6cs9LAZMDlTYUOBiJv50iGBNLwxMGCM8Nz1hTB2RXx6dccQoZnZRd9R8aBSEggiAXU+FIXOdWp8nfTKBCcXNEFxDh+zD4IZul0rOH4KKSdOAhdcnI/4vMImjaX0mSkyhsGvLfkkY8OaHHlBaZ+OGx0dDkSEymSUO+clX7qs68QYGaNDw1Kmnl2LkuPZ+oYOGbDSc70wdFGxYK+JOOEsMe7R/d37tIroWKwenAwvn37Nru/vGFW7Z6HMZogQphVNUjiA+fQOeofMfA7M6/fEDCXkYCE/mC3FE8xqRyKyQEmV5T8NLZfbk2ZauQiy6NAoeBITdoYuipgKL99MDE8AEHAAcQ3TmeVKwRHbLgBBE11EaMyHjGADE0Zr8ISjzDpSsHX+lKpoOXsVKCTrozpGCBltPJjRiZgR5LgWTnaOS0+jzmc8zScJI7KOG7FSAFMyiKZRJEJ57Q5IPm4HEV1WghGgGbGXAROFXwRdTJkxjB1Z9MpKi4nSvj5+dMC4NMiGMZ/GuIvH38m50vApcdyZnCI2HktNeMQgGcMEZJHz0AG4oI4oTGBoOH4qTZDS0nlSszUqKzQmUWeROEMdEbYGNAMd+W6AoEEfZkgBzIVo0FEBgfnBdOILihEkki43AwCDa2jGSlYKqNzhJ6JBKKKj3N42S/BwTGTSl5afF9OUjyDDybiWvBYFBuWGATAQ9r0Cjjb906q6d/GOocad3d1e3J5Yw+CkOWwHWJhnhmPy6WXXdLyl6TqngIzJ8r2C/Y0DonlcjPoMy+MTpUq2ku/rwn+hBMTDAFcCH1rrMnSMTMLVpmZi7EAjV9tyaLuzvrQ5oydihWvr/U4KReWkwI14qYB5ocIhFwYE7JkDA9g4jB7lhK+k2MySMXSATN6s2xSdb7uzbWvE+mw4SRBDsq6xYqubHUc75nTKnHsnjLinc11pQk7zR988MGznz8nfK70UKqF9FTwwsESj/gF4IQBke2Xk622g1RYb8b7yZkaFMT2VGef+ULXuYsX97a3AaLXyJADRvDMEgdLCke7O/QCzItZarh67bLwY/ufD3qDz8QCumZpwVOR1y/9C2X3Yktl/1SCSlc4C1+kAFpoC0Eu5AZawikS+jeShQkKK2FqRGV8Byi9o/C9LI24cGxQlJkv0xa8xKdu+iM61tkkSulZLBzEXjQAOR1Y1S3DWojhjACqpptLOUKoXS1OrVJnQzZNWJBQU6HnYvVLuiIN9gotRCSIQrEIW3FZomJLJaLEJLlw8SCfnmSfnC+L6OwcR0tJV1gZHQguaeSiYNJMmigEWH1p3Nm6t7O3zx/7wZQpVt9RL82CmPEoO20xura0hUIGSo9k+It/gNR6Qp2DPTEJBoxTJjKpxvk1pmfAWVui0XIL80pvrdXp6SqxUE2WiUfj6dFgeNjn5NCIsNYjmeZGrSs/SV4NRplH9uHrkcZAXkgYTjlhxHpojdvhokmDD5+ctuXILCXmKEzcKGsi8CZmRNObs9qI06knlSLOoGmjh5GH6kIGm7IYnwrFfpI1kIG2M6qTBs1z0m/JHu3ppcXpjq58rAzWZewmnRkDt1Z82o6NMxJyh6x0o6N31lZWe2AiRZWooYvtfaezjvGo4DqdTIg9w0LSsrDaKdBoT7n49FxUlFRHEMlNArcLmDwf8oAszruFBChFjGyTjQACJtJ3KmHqlAYMhGiCIoecyII6Asen1VTROE7I6SdIxcamznFS72n1Wvgwba4g4O6ZOsOxTEDrYD5OerUHRgxtjfxGz26u1l44+NvXEPv1f3z99559deNKd6XWPOYgQqHt1DKS1OoIdMb6yCQni5uNdofbVo51VFrkwnGcmUxJSFXKSjK+nE32jw4fe+rqYW26NR+sbazdqPQPzrW5hvbu9etb48PVxy9OuD+KsRqsxbXZnD9q9uP+jdFMX7iNz/UM4gDeyvkNJgisx/7pn/wv/+Ob/+dPX3vjD37/306Gb7/5w59g4p/+yme4SozDyGwbDmYoQLe9sgkNgNMqN5Vufnx7//a9IzbJXvjMV+vNzf2j8ay28epXf297f/jsMy9y1UOrvTLi9vJ698Prn4zGzAPbR5z9qHcrDQrdZvOaUv7Vf/v7N978YOPcKj1EpTv5s//bn/YurnOUaaXZm1Zbn3yyf//+cOfOkCtNDg/vzDqNO7c/vHP7BglVc3590sKK5ae1apd94trP7r7/zX+oDXh1oH5rfrByYWX9+Yt3+fpXH3FOuJyZzrJb1Qd/LvY2UfbWpQvf+H/8b7XRlCWbTU6iM0HdG3WP51y7jvDZGueQzfnzFzDBmMtKuzXg+/UjDmapovjP6IA2Hcs80hbNn2CGlQ2dSEZFaCxquXZpFpYfVQxObylh9NFSRD70x3Mt3gbQ4Ck0PqjK7tlJ8VIDkigKevCjVqa9ZEwgH33V4Lf5vX/4p3cubv7e72nVnnUxVqwo9EsvvQQplv5//OMf33h/5969HS6j5D2PvUNNevUOQ71+dHQwGg1QcB2ebHK2kVPEKst8ysF2LKf2zN10BRRELUilFm8FV2JPZzFkCaKxZG5PB4JIkfBkNFFOnssLBHcSy2eXKLpqR/uow9HtW3fub+3o0AdvaI0kn8K5Bp2dylHAxSwOCVAQAhlO86fBYwN4xwAkGufB4R5hcFq8HtPk021aQ8r4jxgoZ+0k0X+dqNkHk1KJiiNxzjrN2IHDmPq+GObSOxoCEIcwHcA3fMkX9sJZbqWiRZbm01pnJtz1Ol2UK41mgMSj7CRs+dFZAy9kvxA1dQfD5Codix4NX91JrD5xvFYuhoxjnYRs1Tvd4/Gky8kVbtLHSPHpCMYx0xmjGkrKdrXyLRwkFzkVwPwrdiKeHhY0BKgVW/YnGSCESMGE84KEekMhRpNkTOAmNjzWZgYzj9WVlctXLrHxxmuDganxBQmwzKRiFtFb0TEA9kHYwOBqIHoa2g9ZRLnFvANAJGfua55xPQEqx7Vm9Ssbjeu1/rCxhTXbOD+kjf/Jv3uq3d6rzbp3tt66/dH1GgaPdaBw4PBaJh0ebKAWrMrKTGkSoVNQRMZxIsqlWSirXxSW26lXz5+7/JzsxuRYk7HGSCu5nfgQRLXep4nwMpg4pCfVJfnJ1pGDJJcqPXKkQen+oEUrQz65FiSZQh8lXnWP9IWLWlOdJGNDcEEEqfBkPBHQSXDZLs6Vh/1V1jHnqrHKjxg450LyYX/AX3O1S15jPinMcY9QE48kqSEETRetjNLoAmGg8Robs2IML/BIWtUJUgrtss3x8EFNLuQsFHYGdGAvBhlpegP/iCI2IGJZHyVFqqFBTLnJBvqp7PDAuWB8jBq0mNBLttqkVHMAzJIrAV4vJALpEo5Mk4f+qGZDYvZzLI9IND9Kt+h84vyXcpP0YYO8YRTasEfh6NqUabAnu4fLxP2ITyrDC9/Sg1HO0KSxmIZj6aiHWMBu0j9wZOuAPiuWbFhfDILiMLoUzS+EWpwvUL6YiPls+8a7F7ocMr4YnHA/0pgZ99HBbq+uocZ8PKXemeJsb91du3CP99IYajFCQzJBbeGZ88XzUkgNgno7Id4lFD9qfCu86O8QovQWoWCmGHsPaSN8ponLoA8P3uI0Nplyy+HwYPTuh2/vHGxfuXiF5QDeMOOo9pivf4aj4Aw5LvYusTxhaZ+Z7y8HTHYsiiXG1eqD0kI1Qg+ohYCEFaQNSBqdmKG243OLTB2A9GY6p9apDrAjLQx0Re+/ampCDUsxIpviiKkFTolwlpJGDvEHBGpqg+o3lMoQBxAI+DKHJ82CcZ556vG33/6AMbvoo2XURa1FNbBGi6W9e/vWxc1z7l9EmnFM7FSx/gRZj+Agflo9yLrswMHOGGI6pOWR+iweRdwsZZ8ACXFgOi9DTAeClklOaHggi5ofcyDBA2qVdpQMNUdbIpdyQVyDuWKNYJrZL+Ob7XJ2GW0pYMywP4smApC0KapIYGDx9LBfxlqKrkqvLs3HB/sH7a2tC8UaNEoh9XoYgUeNE4dZKKVEYvXkY+lpEcRUUsyjXmdQr79x92jW6xw1dVadhcv+YDAZ8WKzZsFAIIjT1hKjS8xCMmjEAIlFiGptUB/u8xF2plO8jkOSyEdVEANgbCYUgEWlSJcivjIYjWVYyYXjuggmbhBWPxhtl2fh0XSJVT48WJ2wujoiChmuAI05mZjkFDAUSMQ5XQbvdKi8CsUzykvGakpyy9rIPF3GJBajsSTR+Mp9eKRRVyJKxPLDf72NhCgY50hGqlJeKibGzkY6PRZAouJFenFAFHf+Qor/ZdVVJgGn96XZSyyadovqsKeyjNsblPSj+3dv9e/uz+e96d5n2pcUzQx3Ol2rDHj7OabpWCv1P+ofo8zmAhHCqnLsKPd9LQ3M+YITc6uDONiXWrc24+lBZRP0AhI1DSmtBNkCAK10zvWoqXmnwji2HWtxFb3JU2fhWHzyEgfJ+IxqfVxt6BgZ6wBwyKlArAX3ObPEPGv22M7ks0yr6ytxY+8xRwF7PS5O1Kc5UAjWp1hDU+GoXIbHVONk1lyR+RrNxxcubHABI5ugutxS4xTXL2+C8hUuiq1H6SfvwzfbesuY3FtaHRqMh0wNj3mJZ3iP0xWsJvYHE3q87nSyc+OjzQuX+aA9NU2m1DMljf5Fx+iy3polSBHAAMsPJ7kgK4bK0hWBOki1WrvWmD27wotImmcXI3DFxrFpNHPCG37T4z4HzDYuXqVEq60ad/+yQoVbb69zV+r2T3+CrAaT8Xd//KOvdb7G2sJUXwcfaTiOxvNKAEX1qlqYH6VcOHHF/8TwAv6wUDE+FY7LheqkQE5nW5cfHxxAGmLilEum6lScpZSyIxdCasFoQCoegOToK0nOCJd5rpZ8WUGoMKOZ74+bA5kJ6gF7ATJEyB+FwLeupNxDMukENHWCmOwI0VTKC9CsMAChtXDCpcMl0PTB7j6DF9GNMyPg4FB6xnmQCuEn6YMIJDhJwgBCz8fZAqdVI4kDSBBnsZLrGYhleZpYDJWKUTiHg34Bil/gEAHOUT6OMX7vn74PAQ1UZZYgo4GdEMPLKcFPBqsg4iioISx8ZwzvwPUogMKaeaXWokEJMxbGgsgH7gUPZMrHIJIlSKaLaG2kNfkkFvMmasEEfg4ApDiOAggm1oAAIjMJCkRQQtLnMQinYRDM4Mp5gV9+JOxcgFNZUOYRR/3iH+7tA0yfg+ONOfa1YrDF+xWQna5oR+t4Va8xNtd0AloXhHCIsq1WO5kOkEN3TVO1bq+DzJE82WGlyYV6gL6bd1JFD9Pj9RnZR1ysPlnaEJFDtaMS7AdNzf/EcYhFfrzBGwAtQItOOHoTfp2kICJdsoslfmp5YTkc9uRQX0EqciGDyEtlpPeTj6lFQ8KPQOho0BXP4TyAQys4p3a8f0wk5SfGZGmLbAP2Y6tg+/CQzfDmk8+gJGF8Q8FAZiWMrkVilOhUF9GFEEbJvU+OgrniGFOOBnSvYo9JS39vB/qsZYDAGjHH37b398BEY7jIAjTOq+7OD4JTeWSED5PgE7DQCFC5cAUyYZJT49/4xjdYYgCfCwqvrJ+H+ORCm4VgKk/ndMLBDNx6C4RUUFDTaDQ4YMs07/6NO+QCb3/7t3/7b377y9ySDD7p4IpyaR44n3OGGh5IQmHZ2OHiix/84AeUYjhSh/SFL37llVdemc/vkiSkrp1VAmylcjM1eUGNdW0zAwUcYRAMIRXH8kny3muvUTur+kRl7crVK1/5yleOrijcben129FMZCkUTfhwtk++rec/TzfJ2g0npBiCQRb5BA9acNk7PPCFU8DX1nj3iK+EdaCjXTadBNc2Hi93E8uABWqKCp+AGXuQb0wN8sLAgqYr/3U6Mmoqzuo6LfWC0EAzpn0X3wjL4bAGNBq4Zf+c4rDQ/Pd///fcjgIdVxxJsKgcJ6eTrk7WqCDKTe4kwad/JxfOCrHsjuTT2llRL/QH0CRr5asWoAYYfkCiXsyYcTTMpEXH0NNwiJtCRnMANCex70eQ7XIUAWRyWs7gwwmxdg7jo5k0O4BaBFu46DYW6IsIh6BfBsEDjwDJZTaWj0PTOEKq5jCOU9jBaDnVmWEzRnJiywwvIRN1GifjO2opydJjpgAymckvDCyYfnQg+0sUyo9LAilHLYXNZNlfQuAxxcZwHC2yeDMa6mFRB5o4pcbB4Sd8CdoL0DFW1I32nOA43zsHnCThVECrGYOwXNicxVKAJOCUgaYSQzZNUHkkFpyUReqvtMLOARRaCiYUm49hAcLyIgb26mOXsaWra9q4dXE0Q1QfpIHNhfMXML4sdQ55h/LoEApAxUNJh0kIvvNV5xEOTiCBQDBc+Kg3ga9//fk/+ZM/6XTuEEt7//M//3PrrxmGSLbkQEgl46CeVLZOo05ZLWVEchAwfeB4fPLZV175LBHHfEu8XzkecV3P7bdvB75emMBBgSV6pY2BjIoQgjI1fJzR8B1roP2MTwAnfhbTM9h5cBMtuIUOCe2TPOdioCkAp0titwg0Bm+ZLAWhLnxIUpktnLlLAkkPxQ9YBL1ukcPi3i5qCgwyjY5CRMkohq6wF6oTOKAThQ/PqDwBtANMAkVWi19lVEhDBGNMbh4gRqpY5RDD4i1yZAjm9DzaFUq0IJtDIJh+/MqDDXPiAEz50UkIk8R+JkJAdM5ylJH1/7NiFjAsKtUkVmO6niOUUapuwZwpFYfjiBq7qo+vXqAXYydbaaPs0JEuMRGKXXwUns6Oo160UOCqjnDlEhnyIB865PygWMODWpJJhEOfY0RKddH/clcXcN5D4rsd2/e57ZqvvqjG/cLE9p1tOtzN81cwaJPOgGY46nNnyLjGagHt8cGyfThXv2hslFSJcuBMCsTaOZZy4dT6GHCrGVpbrMzLcgOTtM6C4hP2uClnamCmn5Gp8RQV7xEaIfskZxCLjeJoEI760k/pgCQKRvO/cCluqChWnzUoDZsDbWfkEj3cj0yj3YWJNjJAF61cQKLMIZw4EL4Y8yMIGX8pU+AIM6OZVMJJmyJ6ClVPzHi4iNrQIhJm/ECE32QxShFkkZ9gKYfZpRR+0ZzLaAuch4Zy2odifXokWdPN0UX+Ejw8nHqZw1z2MvDhyXMsjPFx0QnfvSwpg+byaTMDknKctx3zUqyWukpyjupj19dpGaJTd0w0MnECJCF5EUgxEMF6AKfHx0dT8N0vmJSTUIckLzPm9JFEyc0nQOODbIRIhSfDn7IsfsBU7rF8nCkLMZx/MjwXtoCkhg++l2IRQEH4F/4NNlQKAjI+4aAChKCaNlxyIydvsWtReXL99m3Gip8cHo8mXOug7JgWdHgFLS7MJBl0kD9VyQyWeC3oR0DrEO7CWY9nUUsDDe6zEOeDcWV7l2n2vD3makhimWiETYv5EcnBZJ0Ea99sx+fEWHRj9hi8sbCHWIY1vQCt6R+rLjEXY6sATnhWQeJMIVuYOG9XUyLwWaQAshI3MfYnsQXY69FSMJLM2ig0s3imjtBUSvrfibbl7HiEAjc9wQlhcgENRyxssu4MEXI20LGgcX0rwmn2tMUCEAoePTLNp28djnZIDtdkxJtJLJU013ros8miRTkXkwW5TH8pTE0IIbbtTYHXgGTTtJ7hzkUB08RXzfFJ8n4fnPpqmzWTwfZtEFgSBU6ABQrOQXPoirNxrAPw4S76gieuXUUCDMnBMSkjO5w5JHkUlt+UYzlK0BJLfiz85VKbchH7wN8yWmbJleSojKBALGoX/UmqyjLpjGxSOSrDgSgcrYYAjtVOrXPyCUJq3KU7g7AoWRQkIcz75akRIi8ccdguYxSPOqSLMrGsUYpiOZLhKi1HEw8Oc0XqwqPbsg3CZKTdbLFrskzwqHzO9TIkGE3mw/GM+4gaswqLGaSHKtcINutTdkuwsZSfnSjS0rBV5jCODnhvzst8KbtYKv377+gVfg5WY0lgEQokl6yCJRETKT9BKQyGfkVceA5HSJ70QcUx84Qx2fhxk7SAND8WXVl/7vRWde8bjVTmhHY174/GnH1mtsjmEu0TwuAHNeVh8SLAoKzGUzbcKrDuM5Zg+Ra0bvzT/pa/sYdMIcV5HNah5g3OQXMEm3tdcbUJ1md9dU3li931OJyEIQzy3p5KU0ctpzJ4x2QODweMtOiWDvuH3U7naHDU11eCq/tMiJgvey9uo0173j/HlfTt6uYqloyLobi8rYnl1SaXKpdrVLnGqtKVCRtSe2rGbqUEKI82yFoVLXNL6vAXC8cgS2LUFCEZEcQUVRbWJpDD6MZMDGlooAM0dJUAadEb1xAEREPaoHGtalQLZeLHjjxJijR4NBQDrTqNBkkEcEjhWMkOroSpBSke0mGPiLYMtcUcS8/SCXhX3UapAwdSPNdaK+ubHBfFhPX3tdzAMjfHwY+57vHCeu/ieQYZK0eTj3/01ktffKHTWb/fkSnHIotEIQHoFLZdr05T5sP9g83YYD6eTtc3No4r0+3+4XQg293s0XcOtieHrCGubp7DKDCxPNjbpUhra3y9d37/9naNTz52G+M4UcWoREobewSdanPnoM/ZZ/bT4QNqyJo67J7rVuqTo+Eu35o7t77R7NZvffgRi5LHR/1XX613Ni6NWe1pdm7eusNhYBZmp9X19sqV3ftbw8pqtXf1sy9c+9onx0yWPvfVP74/GLz54b3e5pPcNL17dKyTsjpczyhK91Kw4Xow0MV3bIEMRnxmV2zt72z/5V/+OXv87R6nF8cvvvT8H/zpH3IkbXjcZauWrWqOVPM1++ps7e+//dpnXvitp588T7d648PrVCGLJ65q1UXhoLl2WP/JT35292fvrVVr+93ZU0898cx/+B00Z1w/BFNnrCoVPtM43Wjt3d7t79WGdw/pGn/yg+/TLT375POv/Om/+eF/+SZjgvq8NRlwm31jPJp/8P2fMxX8+quvnOte3JqNVtsrrCu54VuXmPSjt51ml2N0GjjG9lssKy94K3hMdgkVkM7bbkQRuAg2dD9mC7G/HXNSVFgNCHOErTYRqToOKXBKccIdixRKhiDHElArwU01jeeYPUvLaPLR3v5rP/oxS/yvvvx5BiWf3Lj1g3/6Z5aYu63OsHLE2w5oMSq6ygx5hYK3sbdsfnz4/gf05VyBz1ySEReXWYLGdzDJ0ULATimX6N2t3LTNxA2sxPI6HMWai1hLfPJqDRS4GQ9Iuis1YmTilDqXCEHlcI4iR9PJgUgiTkhdBgJXmzdnZJbS2U4I7lj9FnEyNkHHQlWmPj4idFpO8Sxq3BWLhRgd96NHYc1EmvlA5yxycUo5PigJfLpzSRKNJDkcg7MTA3dpy2lHKjJVtfCDXZVNPCHkhEA9hvUADyIn2VtAzH8Y6SKrGDQjSalBYdRIbgqFbiRkKrSQubLQjiupIBeTmYIiY3AsGdsgYls3m5OqEa8cstkTcwxqUi+hRC3AUmt1/fDGxx/v3+VEA62FCOlWYWLdt6YmtMjjgSGNuyWl0MMokTgRuvuyCNEAcPOZdrMO9Qrnxx9/Qo6Namvj3NqVx3TqmTcyVNygxhsR6LLOgcRIg8ExH43hzDydCD215gzMDUBV36askvopq2ADn7O0curlNXVhVFAbXl6pHc0//vJLl//d//xMt3tzMttHkt//zu2b7388G22y1Vnlpr4mV7NyRpbNe5Hijly94knnqwuzPc3hNnltLsJDhfc8Gw2us4eT0XzKrGbY1IJdfZWPo/Mh98oTn3nm/Bcus+W5+/OPDgeDDnt6bFzNV3VbdGxfwR4GCgkCF78p00VDNtB+oU78hhPDKBFaSjwEEgXy4E/veCGXOBaZDsZILP4vHbGDkHWM2hKJOb3kGoUioBLPNfeg59J3MDgBxFBH/bdeZNAbDlJjBbAJ5gD/hNOwAK6U1ro9jfsTwWdsqrGITFZEccgcY6IRhY5ecbpAlhEczrArtXQsyIh8biZwLuaFoMakAZZYDkGArlEKcZI5QWIZhIVkLShsIIelvLpKxjrPDh1lFI5EvPnFKN1EbQcY2eoZM6et+ISp3ENvSevk+Ezl4Q1VN8Q0H+6LzRjQgcbYcMgnUFSA6NpKy2dg8ccLJFu7ByTBCa3EtnMRBKm6g4t+B0wuyaO32t29zy03ToWmoN/zMXORGS9vwTVWQwfV+3vX3/jJ06/8DiMolzNlRGGRqfM45RunDIYN3Gl4xilFSRmS4+Z2cqkzFppvrJ//3Gdf/rtv30YmvBfKjZvcGNQ/HvcPdMHgx+3r2q1n35S+e6oFlMtf+IKMGNbPmp1JLqirvgrwL/B7otTBqtpABCCodlA4n6Zs8IaGzj7rWH07PmPVjU+jd+YD+OxSGt63rXP2iOoLDVfyE4yBJlA4AjKiySU4QJpDAdQvEBwBkCOK4YesAEDG6YoiktmIjMScA86XLpy/c2/bNkMy5CM4MsOYtxGM3739yXPPvaBuQl8K4I+ORjeTou90R2ZP+T3YlVkCH64YoaFmTkHsEhEguBwLJzzmMhpeTkKsH8uB8UT64DkyjZUwwyvSmpQP+kjqnG6caxmUeKJMPJsXPy75RhPxk86pIGVwZuYk1m/2qTnXNtLFvcPR1s66TvNQKqmn/mSzwviFJRF7idFHZSknKctHR/8elYDwuNuXIfpOp7Pdam1V1OOP6nHlLirKS996jVxng8kLPWHsXRmrFP9f3v78WdPjuhP87r7XrR07QJAUd+1Ui93SUD2yezp67PHYbbcj7Aj/4OUfsyMcdtgRYzt62jPuVrc5oma0NCWRFEmJBAhiB6oKtd59v/58z3ne575VBUCQW3aikDffzJMnT548eXI7mY+fnMn70EIkuWpVrxufb+fLLmX3NpEEBckFOVI7T48UNLbvGWxte83YAtmkqfDoCwpmtFi34a0G1an3T+pOsJFlosPPZzyHaCv2xFQ2Y0nIkZriMtToXGEGSpVOTelpNnaEJUW46KNwqyWEr5+lP1ptxU2FRY5OllRdanC2oKpW7plZyJuRjJKY4U4h1R6EXUA+MVN66XzPY/qZFWWvqR3yuCNbxNjlGyFLS1vHRzad38q7aB/thYVnh8uz7JvK1G/G1/quQ122N0ssZhkU2J+Zn2e8U5h5uBDB05CKX3Grv6iO0dBC8B+srJ6t79xnae1J0utrKXcYpPIcorc0soKr1ZB5iFRakroM3qH/en1ZWSmOy7idtrYW9dBGOnhEJRV0vYtZobNqT1dnL0lPP3QleWVjbX3pbOfo9HDPHeDN1QX7Uz4otn5prW4DZnW5vBR0dYXV8x2hQfvq2ZurMajyLH7sXjNWuzTt4rL6hs92V4xHxc5BgEUS4xPP6JsTSkg7huZ79x4AXl5aJOFekWYHv3vvo607V5+9dIUgNjPwUFmxZC4xlqvqlexckKWaQzh/MiuY8Qmo7FUyXT05fm5j5vLJ/mKbZ00m9DJmmkT4jAGnZ/PeVtx+5L73yvra/sGqU6tFtxGyK7TgRuwXXvjcwsnsn/34J4z+H2wf/Mn3fvDlbxzYlfZlizA9pc6sDHOBcMCiKNHpCJNJmiGDi/xf6PlJ1qR8ipuS28zhGnLMO53arOhdiymEsoTGQQUMCCpdVwnC0DlxoZ+DKjPqDJIdceH3Fl6UhVC5WEBnihuFtXAwe3x/f/bwxEsNSh3ktXLrQTZEtePYlKK7IiQ8UxZOGr+GxpQs3L4YgpYuUQ6u7hVJLWs7MifGDYRAllG6QGpRlREmgo2teShJXj5Ukc64tlBrIc4xFIMbHfXwNI8J2P+sLAM/OntoGdAl6Ef/gdN8EUxvuSokaQ1ZEB1+LObjABpjkf3Y6J6YKgq7kObKmA1Bu045vZmyfcZwq0QzXR2MTDsTGNsTBnnhF+AmgYqpVutUFI5EAkvrZK9f2Sl+qFD1VFOynDmUmZIkTqGmpLJgbyNJ06bHxohVv6Je9/d928AwEu5bvJMjnU7evQODE8DEHJ8de0CBksLVvrrS2AqVATIPa8guEoDi/OQ6FXLYonLi81JfMA0ZhRZ5u2BCHW8kq3jTozR75eqjXa3Y2eEZOx5Uiu7SO9ClpLBJm4LJRseFC1K/CqTA6m1KHcoPQ1z5lVijWZdVsiwy/Blcj3VNVSFrnF3OyPaKjCSrTtOvcq4v2Xy0+WRZS2zYwUnSIlc+/JAFHGlplqagrEFtBQwzaWGVkeVs/2w83occq/f3cwyrgfku49ikIHtwelX6xZdfvn3qk3vHPhanoKPtA2fvLR4TPgw8QTZuM81TUFckAZdojo8RpgF+Xs4GtG2Ur770RcA/f/0N9P+ja//U4QRhUmjXFBJ7rx89vI9Oxjssl3//939fEoRyERJGPbp5E9D6JPyZWMsCy745+GoAr07jydraFaemX/nal+Vdu7yufyGHAQs5su8JQC7lfve73/0X/9Pf97MxKKuT4FfBkrrInuzejL5Sw5i1qLdQP6q9htPVjOvkQemaCXvtUrElP1q676jg/GTHZsq1zRs+bPjeM3+V16vrI1Su7cKpIO9Grc+cf+tb34qV8OysnSPYdnb2Ll++5KdwuzEcoHKTlI/5K52IIb4v/jgJDN/qmphRPQyvs3pjnMyTNh1WZWI0X+RtIR/LoiRGgC4pyMulnwxR0RZe5sLePDRZ7Y6ZmCy9W00O9W1UAuLdVv7qV7+KXT/5yU9ABq2BCs7jTK/95JfBUDIKc3KJ7zLj1zGTmNE1/lFzXkBWCNh0TJPxRGSwVgUbUqHjzw4EvrRoQ44Iu2g/x0CFh/SOTN4nqjCkD5OPZhERBokK/1e9M+pxH5v3oriq3HRBjVtMcJUGE5gUmL8XeadjJ+HOOPn18X9HGJgRz+eogPhTrgua9qcSE+ykDlTeSOYTSAJXkIDbYY5Ax/PBi+lmBdipgSyIHtnbFhUzRYPU2Wk/PcISP8wvrdsIJ6WnIqSUu/veeyJf/lxsIa0NGqz9FPJ4ladTL2BqvPCz4ONHmKacGJ2OWnC4yz3ceiTGJ/7oRmqQY/XsNuK653q4CQ0or5upqTO9upbj5Ogm2pVih9Av4PxUaopjY8kVP/5KnyVz2GKb2zt//+l/+p/Sl5iWmeTp6R/+4R+aMJydXVFuWt3zTnt7aM4eSU1y8McXHOgQTQDphJMhQN+EvD9ALdWCbXHxVTAt3vWw0+zVa9fo/5MrLzmw3HrvFj4cHfTWQEiaRthh+EKEYiZN0PWV2gCd2D9Hf4QR6HkBShP5WIMM4IV7wF8w02ELrRKk2ubEihZCECrFSYVFrlS+Zj588UExSepiAlKtM4hubTk1DxsnMJlQCKfj+kIbnNkvKzcATPG8MY9+Y+tS2h+TOpDIxzkwxow0gBRuSqQGoFyQ1700yR0z+g02amY/FRIM5QuMriZWY76/Q6Bk47Fe+URmUwuGVCKVpUE6tQkT7mZqukVqoAYA7GevERQRyitGTxD24b4Go0kIvPH92vY2VA3WSfyUOBQ4xiUArH+PgTG5iHwMZoB/vHVGeIGQl2bJ0GGKYh7CKFtnVBezl8jMYT654ZhevzPXTxHnuaX6xY2NxlMxA8qEp1ywTqidiv7/SVBBXZZCiTvK3T8T1Pnjl/FKEiYUNnCT0mE+aeSPTlZhME/Ej5HiO4nwjrk6IL4z8k2dfvu3f/u1n//CvKUJkJruPB9LT28f4a0ZLK27tBaubl7O4zwCWoeQiG9JE/PpDlrqofxhEQR+uspj+FPwjDBjoV3f6SxgQr9FO/6UPNpPUG7PD82QjQLYKRXL49tpKuEh8IRnGtXHhkcaPjZVQeJH8j4W5jNGPl21z5JR66iOugB+GkPq+1g/+CwoA9OounbjT4H/L5BhIKEywZ6/eWNESNjSRkWcsjg/EzNRUCGiHJjET352ZT+9UcaMhlUjGuRiBr+PNutnx3SdnmjBMYvikMqfAE/oqL8hLKSUXD2WciHqYCD/WIKTd9I80/grtpNSbyFIyHaAJ/CPl/aJv5KR0qiOoQsHQ9VdvPXdhw8e6SDbJ86ZZ/YKx9JmPmnjuzbuj3mjTX+/8/Dhzs7R4uXNmJSVlT0UqI0/M3P18lX5QmtsIbKXtZypaOor727ZnPjpkPuIiU4eO41Ncd8eQFfmWivL4k/tvhSjEGkrRnZoxRPsxNSGpp/wU6VVka5LNnS6jlIpKDZO4WTvy7Ezq6bvQaQhUcIITLxIs027Dcpidw2zEjW0QMotg3H6weOTrow0HgACTafZLGtWPIFWPF4g1dViAEZbdZxdyU4Ffd9lgVR0aGgzrJlZaJevPyeSclWupoWmmxcBiZlq605NQY87hdohEWdT7vr1SzKO6bKPZDcqPwWiKw4PV5eXzVTP7t493PEmie200CCvL0bszc4ZHU7OYv+h+uK/8aVX4XdiVKwOnX8n1+X+nbKMwKlC99+Kmq5Uw3TrAwtkVzmrhBoJqr2A+QksEJP4Ef8YeCKpQOPJkf8bVf3macdDhqsa2k5nyf+IZwy0nGiRDjSdhKsE17HXxZ5gzbB7+VG+DUHZzJOpnbPsFecBXy0U6w5nIJmkAtCWLpPa/LfXHavK7OdFYrIMlHGkowOI1pBHJ3MsZX1Z0r8VB0w5E8qaxIsOp/Ozy+wLvVNLWxhT08EnfBy2/6rhJ4wd0YbpOQ2haKqbhlvp/3gOpk8J8mxduSRmBz9J0059e50xEa4CgKiqyl/U8SmXxXybbGlllcmVQzGEKgRK1o0eadzZ3tvfY8YR65lJh2l8wYa1xZ+BS8K9gCndmJqGNqdb2gXtOcY59YTo/GGOHLCzT9DBHLqDYV9vWRade/7s6Oydd95zSpPzbZ+szUn+yfHWsU1DG2EOiHruPFUvmPzKmRrfg43axZUeyuKhj2ovzx95Hs+e16Ulrypsnzl8m1lhuJUHBcxy5s5X5vNGUUjw1dSls2UfHMzkkvpK8aUctWn4mTPJ7BjwiQycpf9FRRGLLP6HM9XU9QvSrqeGw4O2FSoxCHPqECu0R2QedyUhaWSim8/+koeoQk5b80dochXRrZjJBvSFzsoTm0kKfOcCnrwT9iWttokHnHADrIlmYjLqnB6deu/LWeTq5nObhv/F/SiyvZOj84X5q5974W/ef+vG0dLp1sGjn737+dXrB2tTG9BDoemx1qTaxahE7hxtnh6eLpZFw/HazO2D+3Off2n1c1c02e0PPpg7dgi5u3u2/73XfvSFP1l5fn1zZus+AxmWHhTrO3/xw6P3b9/4rWeP3ZzuXpD62+wIEz2GunX7wZqjU324eOJsaGFtaePa6tH53nPXr1679Gt2gS3MXn32FTLgE+1UswKZwp3PrWztekHqKp787//P/ypz2WUH81f/7Xd/9Ed/9trcpTky+Rtf+yZ/Zv4SDeJakHLrhdy8n9VnAEc1ehGGo+M9Sb605RX3z79y45//5//kyrXPGyDPVjdsYfz4h3/jOzx7+1srq898/tln8dPJLrO9heXVP/6zH7308g3Wu/cfefQmB87d4pG/0oyQc3f+6o3Z27tbJ8dwvvLtXz95ZnPn9PacL7IvRfJD19yCj6KHsvmF44WV+eduXn/2xp2DnzmV+eufv/Zrv/Zrv/R733z73/07nfHB0fHW+w/tNV85W9+eufTgT3++s/Dc5jeet8tj4/6b3/zmf/vffp9+M6g76vWuZQjx4DVlUoJFXIx0ZXxQ6iXiFTcS3D/5g5qK6i19zQ6vPmQY/ey+8lH2Sug5p0pmJlqPTQWLCmJDAxwcH6QPOInU4YoVzRbdMT8JwESk2VyQW73lvbfe/MVrPxu4B29vlOS96brmMctoLP3CB7q/8StfWmVWenljfi6vjhibkRhxpVcPLyvdObOfXoXmJxCJS6+UnU9Noyp6YXTVpd3Yk5r+lZ40dMCRG9MB+fwc/adZV8ARAJomNWpsGRQyVlTmxlf6J7gol9L5k2OnQPbJdvkJV6GpQ50kd+lKmARSWCMtGLXMvFHpyk8Jlb0BBrBiyHTMGN/AoWHQCR+T/emMT8R09oyBH1d0hmxO2qClNUqqKDZj5KTcC5ylf8b4CfKLdCGo5KQytQjV66fKC+vtuCNGLo5I8AtVS2EgxQS+I3pLLquOwInvYjqXjgGaPhF54oglU5nY/GY8nAAzLpJatclVltXNy7tLd7YPDn78o9dsj7704jPO5GbOWRa7+p4e0VYDzZIu62n/YhVYlQEgJiRlDMjWs8q6MmKTyA6vXqCa1NEzNxS1cfXqZU9hbG6sBd4IWtam6mWFEs1wlgn0xuVLcDquhOH+1v0LLpkMFHPCh7Dtwo2cCXdDSSTQsz/snlbP93/phWv/8X//6zeu7BhHkmdu8S+/94Of/eTt/W08w+2cKMuF8mRW+fmZSx5dW14yIyj1E1s50bgffdBnTLOxSECb73Q+3HrAog4NlizQn57E4Pk035OYmf3C+guvfuml0y+bh3z009sO/+Zv7ax4xGsmF3o4siAj9P36s2BiH3cAGG7lclPNnfQ06elOEYoWjSFD6K9eGe1SWz/hTMAqpeYFOriBuuYjEJr2nGgmsmH14ZF267E5ZvQHB7tbeVjJ6Z+xSS3DohLjMKgoryLRUOjT6Yvymm8M1NSfEDBxMdOPQE5PXJILPmzIUBj9w0plwoSycppg6Ck28r2+GZEjLamtTBG9x9wkyxA5tN0EKJyZgONLrhuJqCu9fvnXiRkelFX9t2PCRK5KK70RfS7JvME35tEdutJQqiQ+rEdJfk1YlthJ0f13+Bm2yJMZ4yRdDHu30lqazGODZGN2bvfw6N6jLYOhWlTbRmChT0HRNUPuhGt6D52kXCF0O2D/4c6DW8ubz8qgIALPPkwvPPGhDWZkefTG1U32gaev/eQHvhd9Mhu7uWlXhUz4OJ3wdw13E5d26qzVM0mR1dDZrBl1iD9dWp3/0le+eufuPXfFjLBO2I72M/q63n1yeGyWHI2QudPcxtLi2tXNQ4sF2cOXYE3gKdeMeiIaDz/eNYWPo8Fb0jfCx8ieTNe46dhZeLnMUFZP94XXz/fp5RXikQ3o7CaYyPKRVtQ9jnoUleq/Coq01T+1KSEKofojv1J5iTdT7p/4JsYcZAogKRV/EfAFSiu4K5sbqmGjp4BhdSW3tlHmPVq68/Ofv0aNXbp2Ha0k3HihCqnFZI8GTj/l/SyuaQA5ZhETLjzloOwkvsQR5umMDWBOIono++lrlMaaMMCgM5strVt3bnk4zuSZPL/w4nOwAeN8vVKY6d6ItgmZFBewkbRJ5Bjx/6vAdKGfvYwlX485O7/2cGvv0fZqvlYQRdquEXatp2IuqvZJpYyUVE8M1BjzSVk+JX6r3u39yUd395eXdpeW4XS1T+f1pAa0dixp9Zbog123ZCPsmc/33kXd4rK6bvzyHvk0wMz5HjMPZtRz3od3OSI9oqvJjzzULCszkww5RrF0ulShZqGn2RJl0Z8+6CKnJHLvowhUH7vdoMqQlAJlMYdxT8Si2CSJ3swrwMS4pLeJqt0VOaDJkJFsFaZlgykjsM2L2tLIS8699K+O05tFZj6VqbR3Sg4FnGx1szxo61f+5nmJ2YOM3wWTDpVA/2zL6M5/UjsA4a9uXGY0sfF0pn904sXRR9s7zvXvGPpP2acHyfnizLwv+Pjo2dLSlRefxUOn8TDf3XqY57aXNx892Prze3dpuEsnM888s3ll/YbZ2s39PeRdqyNbDC6aWieUVpzJZ+3PTw/TnGWsOWPnxI3Vq+t5tTXk4O9xbqwxMtYda2jPA8MWollSDG5pNQ9UYq3WV7uqshaPnqsdlXwu5Mi99czYYprtnWinyD4yKH821+bnl+aO1csHkLa2Dn2TY23dfa8lM8yZ49M1rzbPRe8Zud2BhjzbwAbyuZncxKn3CT3P+MEHt6tcnEoF7SHbCyQbpkYrK0u7NgHScm6GoTMaSXy44bK+wSF3+XMx1tL10f0H+LZzlI+FLpt77xzefuvNjS9+cW7tUsQ+aqfZyE/1ERA89UOKmP45+gAssV3FW95/9Opz155bYo17sHIeU0gmD52xkfA7u/muwMH2fYbPDhkuXd5Aof2EFdPG87MlcIeHX335i0vHs3/x47/2Gfp7j3Z+8rOf21nwceBnVijYWUyEYalFtiW8ZjsjVQkUo5CfQmskbTKahoas/YAERXZqx/MvfhaeSa9Ieif1ruYIXwFNM7Dr8fj8IngRymEHomKmeAlnob2IUiJcEbupMVcz0gJ2pua8wXB0frJ1NHegcpmGtdzXhG/ShMXn2pogEZVed2GzV5jyu1E71K1de4JAAURTJFO8Jg4r84Pc1UugJMz6I9O4bIyYv9Uis0CbmyKRO82PSGE5uax3/E+AdaFCkE1tOBdP87ygHWjdhjhzgwobBLHwTSMd6K+C0zRx4jJvSSDVFi4/oEH4cQ6wDtwp4FOj9MO4qnQ2ZJfmY0jl2qzpIEolDfDVS3f39ssKtWyfa447ltVoAEPWWSpvMI8/ETAi7Eipk2a9oBn5tutr8X5cXxsacnnkzin3nTs2wXf3DutIyuac/6Ymi432Y31FV4NmJieM8uJeWlxl1VqkGE2GS5c3U33PqPBX11ctrW1NQ+tThKlClrXhjBYspeRsrQat2vZqkcrVozrdqlJqdLyQfEQP9ZVqndUENz1Ph7ss8bQ8v0eBjpRlzFXEDA1KG6hg1zEDoipnzI0b4cdwoXiSBnkLvOBrlLv4OVQ8EbjXp5fmrRa3e0fZaMY08SgU1mqmhk2bnylrcBlO6C4MJyKARWfAyEgVxz7367/7u9nwnZu7e+fO3oNHbFdvnZ5Soz/84Q9BPtjbUvSLr76CjHf//fdAZrPUKJDLhl62yKYDBzNgVkXrE2HuiqBWoyPM1jPJd3Rv2EASCQdgK/b4mUuwodkQC6FItQJJjEDu7cdaZ+54jiHJyswaxY0wk34Y1AgHlEuuUIgGef3sMAyGDj8Js+8ePNrOSHZ7a+tP//RPv/+XP0GPHWo7vHCDRAAihdHgq7W2lYUVAUoS52fXEUKleCFa6trSEgtEk3I2iQursSxWCwCe3gLvWSD+7taumNWqFyskRwjbD7dY87364os2JnZmTmzHv/bePbbYuAce8u9+97vPHn/Fl3NvvvDMf/af/Wc///mH6r60HuuqBkBwyJ+NBc2pXf/I9t/iQnyBwMDZYktMuouT9tSuFHDG2nbiVTBuLnye8+BKGJWJyMgNAYNKUJQbMpZilCSrIrgB/vGxR2qfZsvKJv36lWcEth7t2GAiMGonY0qfyZaN51UwFm/AICPlpo/2nLA3gLV0UmVEhg0IPmkA01QNtE2NzfCDH1M7zJ92Y64R7O8aUMonZRmTpqh4DPYJ4gs+DfaxenjENo3iYyOnAcZwQxbyMe4TA4CnISc/B0nQBCMPRxSNv/0h0o9iznQ1HwOozqu5eyiu9tTEaVNzicodr8UMzsqbBm3aRgpH+JGYpwMRtMo45mqYJ36OkTYRll54ga54uPeIOdKjhx/RoteurtsUVhxZXV6pSfPTJX1CTFeEBjDmcjC3qRHCYOPjqlsC9MyzN5+h7jY21tAmno+LfF2aj/WUw7UrVzO0O+PZ29Oh6MPDk1wtbFbjEUiuOPYxBCW+eoZA9foFGgAcLfrMs9dEdjwi/+iP/ohZZdqoVJMiopGKmcLiW/N7UTOtMGjUdGGdFYDBmTI34jcxrQAbPx8APwYE6CmRA4YD1371Wdry3l/de+ONN+xEdQUkCfC73P6ZjCUYfrar0TLBzjX6IEXy23W8cC1j8qtSB/DQP8HQAbXb2t4y9t3beoiqpdnMRS+vrstg3hWddv1qNxZ4U9HED7PEFDjgnfoz4h9Tu+0oNqUDbIDOnKSaDY7AI6YJnosiGmaEnPoJNrL0tJsgGcRmGuAxPMXUYJkwB2RaeTI6AdbunQXNoECOGCrfY+3SSXz8HsG69OkixIw/R7Dmkp+PYRy0RHAQNrLX2Pggx6qHkilIqSIyiTcvzVIlj7D7XMTXf+P5zh5BrZ4Ip77ppx7HN5B5Jdo8Z/3KzarvY7Q0zVPFNrKS9gmgWnRvGtImNX0i12M/p5gfsklLPfVg9vXiiy++/+579EnVNrc8ZTSOJntVmG/GZVqSVvv/o3uM/km5oa3apGvhnoS6IMvPmoaQ+My1DPKTHPnbWSpr2jOzhMk8YUwV6MiOAVxQnfdiXmFXZhpbgzXm9isX24w4qNp1Fq1m3tIx1AKBWa2bvrQ6zYDD6tIOEmB8PwcUT/2Z4ByenZXeMU8BDhHFFjAXP59GrkRImrcT+GTocBPfFDo7YtfpzI/RBuExZ75ydbPtBME3JV0WsVe7odT/gD/Nk6a56fk7Ift05nwKqi5La/Z4B1JMR35Kro9NmqahqwNsOvJjc3UksIkCeBIKT4wmVEoOvIs8Mdg+aonkrcxE7lPwTONV2Seq+bF0KghY68wqJcV0QAULQ2ZlIgmJXU/hiQBGvJXCJ+ZF6jBFrCpk69Ok/lP4LKmE4bGePl2FkZIus8vFBz8b7RBfFHUkP520njgA4yfi4KH6/Jy4FALM0fKeS9+Hh4/2Dre3d7ztZI7T1EyWVfZzZpY2sr9hAx61DBog0Y8aLfymLrA93M43gWZ3ezkzozX359b1qasryxk16m1lfE3BQ/s2PQsuF6ycxALdyg96BcHvRhsw738Ii2yyM+mqcZa6xFuVE69enCqojgmlGEu0LqLzhuZSQU4lBNQLfK/FtB366/NDismL9quL6+rlKUjx6+sRRTDVjsPMXKrshXCQEzAq+8EHH25srO7t5UwRDGJg4JoS7JKl5GdBLXy+D5IVr+maA08mhIBF8kNkWV7D0x+1NBc1M7+2vinJViacIycrnO48ltUxgbiQ01RZWRDaNFg4vz3mmsYDSecCZj4mOwSqtrS4uHH9+tWlZVsxAAJWJEg2X7WE9h70w62YD7rorAv//jd/Qy8+3trGt5GEEXkHFFE8bEKa1EGkm4ahoCH94/9AFRI/syvgUiKfIctnQQ5h4WxehZKOQRQBoC+y5tnaSjt2h8183/8XxXcpzZOLWJI/LIZtw2FLJsfozj9zTuLHPtSRSvbv8+qMLi4yphCeqYudkFmzLY2TE6JW4usMLgLZ2yJG4yKnhSaFDjsNRXssJRy+5qjZM9AzSyceD/HVlyU7SWSI9vO6xeK8D3kuLC342rZDrBAQ05/IZVSHYvj9ZkOwT5yXdgRP6rQwlIMe2qIUzmQyHfDJFrBgS8lwcFWL5ADERVx6Q63VgSMu3YzMqTU3t7Aos6NF6PLyzNkpVbe1s7ezd0AfDe1BLSqryCiJvBjj81MBYfvQXPPp1KbIHyBcL6RM7L5nNXqEu87oNHJMRCT0QOUn8raPT4/nz17wVbuFhYPTk1+8+97Wne0lDxB7jBFlqWoKVQw7hVo4hJpWeAgvyQh/7OEFw+YilZAvqq8v7qyy25qfu74463HhtYWTZQ9UxmLdc3Mbtu1WonEWa5tpcWXZawg5ro0dgpdbMkFWFrSOSRSeUurINYHIUg5/CWFzKf5k1ANQrSU1f7sFASRcw4y/ftqV70jxaekqo/4O4WrWzMWxMvghqNNk+LOhH3yi6+TYjxLQaG7wxStm3NmiK0f0kgA4GRLZmflcaqSmiKj4RHWgSAcaU4zCf27HfnV2xpRjYX51I49L5tv0jJZfu3P8Sw9XfY728GR/JSsKso0mz0y5J+h8W+5L6+voPnu4e/pg5+ShVynWPpw7eOmVL628cOWdd9/1Zs3K6soLX/razd/4pb2TfTrB4xVGrKM7j/i3f7QjZvXsdGNh8dLV67u+f7C5qtvubefbcfaBMWBjfuXo0d7KPHP6rszMyeKRJj5hjHzGdv7kTUYUv/iFTec777//ne98xz4Ni+ZXr39Lo5wtbNzfOVzY+Ipx+sqNZz//+c+/8f4vDLfLVzPEOiz03frV5TMbL0f793zj/drVS1evbLjI6NOGekp3QAr9/LwuPzknN07RBjrHic36/YWzlbODvYe3XJS69eKzr7owNXt+sr542Qxhd/9IixweH127fuWlz7344x/9+7O5P9k/ySbyj1/74BvfuHqJ6M4upl4Oik9n33797b3bD30V8fjK6rO//KXv3nt94wsv//TDh/rds1/8HLF84+fvGAsPzxd+//d//2zWO9unc/uH6vu1l15a/8LLBzuvf/jw3vOvvPT5X//GH/3oL2euX7r0/s6j9z+afXR8ZWZt07WOucVb/8333/hg94X/1f/oxcvXvfF664MPcg3q/GyxxNozLFbPMbB2O+HglF5znkPFmQRFMluJDxIdgY8OqniU4IkeSllHAtWQyyvJhJl6IOURvAysdfsJ/jxoyZxBTu48l9/lSNerOUfg44qsDpbfw38NC3nki9D1V6EJpHS5qqBoY7Zx3/+Lv/zSl7/uVMOr2TdU9vYHSkeGwcFXL8nYrNmXJ/mYJbAgygU3PU6XGTpRiq9xOrS1ligaTFT0vzPPlkdpR1N0p6vEj/HCo6pd/NQvPbqzqPKEq+FZ9CfEGR1q6R7M6dztGCon4HVEHA6eJ1xSR2wVHgCUqyoyVeCJXCLFdCnZuxkxpJTP4BrsMcyT2lXuwjxpzml8MvbyoluZTuGmS9fTRMD8hAMmZiCvRlLZRswdHzZOcW8qvfMWJ4fiUmgsO8KjVqqQD/iK/82csYQKsC9ISYHzl0Os0bKby+5mpiElP20js5ebNxmQRZcFZioGwG1q/vxJBsFYea6tL6zNri4tra3f0NPZity+++G9+3OL77xLGdqM9uqxJbrqJ1ctADoc2rk6DneCpvrt7t6+Q6p1N4kZNMhu5dVVTfcpalvb165cpt9MbOBsC0HyzcWk1rPpBkL2KUtLG5cvq4BZ3aOHHsz3bfE4smq5YXAO/YU/I5h8dXNF9yTVEwqBGG6yeXpyfnzt8jUyfLh/uHqwu7G2fH62ndT1I9P/d96a/eM/f/Po/IaMhil9dnVt7uGH9w1V1Q+h9In73ALyUXQN5GqEhrP6mbd6YvWskCyBMl9RR0g8x5SXHzM52UfbmUGDKupX8Ga28YQqD1VYs3p24/c2/nrm0dLrN8OxkjQY6CU+J6bj2x8BxlQx0048SA6vRqECkBHU7zR+eP1EFhUUY5ml4QyRFiEth5b+zoS3dh5JNUeFYfbuHU0Zsyg3PFY3U1wOx5PkuiZjzmDOLC7FQWsRRAXhD428nGe+IhbB5m3uWHvFogJxJOvUdlwyBxXGpVXrsbVMxydDQNLRnzecYx3lBlzmBukatsN5Qy8KnToGDD2PiljlZ+WFCwHai59eGUiOxUrNtVJ63Vw0P4wpa7ya94ORAifkhU1pUSi5TkPq9L/qoCFdjSM4MhRjU89h0tR5U16csrhujnBS0RUpHFUkHGN8M4Ost5lNdbniTbwJGydw9+GjW3fvz8xf7rz8Kjr1K/RB1NpnaGXEqH/u38zNHjHLOHzn5z9xwfZkZlkBFuhKWXJJZ4YpzML6+iUXGMnS7NnBhz//m1d/Jdd69lj6FJIqixdX9FaIvOdVyeJqRUx7XcfpmE8KlyTpueaZrpumIQkpSuglc6379+8aT2eWFw8O91coBLomh1Ya+tRmydzZ/EoeAiWPPgWflmj6pga3x4qV8QnCnvgZ6B4NYUvrDPo8iDEMh+uWUtOcRSNbthoRJi8+78m1NrtvFbniNqv1IuIKUfywHAGJ6XLbj7qDuTpXdYt06o7hcyORAgWWWYJwufQjkkc3eZMOC/qx/CVX2XLNLl3F9Q+4TZqw9mB/Z3np8vWrl1eW5vb2M2pQa+ZHckd5lD3p3s72owf3r914xg6ODu3i1yBFQUbsQzwfJUSrw01nkjpUNONf16MFsuHBjDXq8CSHv/iaIlKjQj4k5ZMeJ+bMfEYMyVUrOArKDVi30Cycvc6ilKxP7W/m+x8L9qDBnx4x9qejc42yW7YLFemfevXM8ClKhpJRMkXe48GqyHTU0wyZTh3D0xwbI58OfBJJIGHwJjyAZ7f27bCu3390Y59xsFH9VKW6Qw4BLVXCmkIfL0PMdERku1pLZGS03RiYgE4SJr8nf49Lp8+b8dI5GduH159PXnj2r773va0rV70wuGdY1caLC8d7mU+kgoztyveNn7N8cOB0PYdP9HLkv8eFngu1WrdXguwPtx696kZ29tLObJaAbNehLKpxiJmL/nk292hnNzdPJ2whuMBs/OBHb8kig7i56RzbxpJf2h55dYx+un8+f5DvSwRn305GQrYwrEDoVQWnGvnb/HVWZiolu5++E5T0tICRYqCzmVw9Ag26U7G9xuVSD8MJtaFO3lau+npdsE5Rh2VJrX7C3s3hjrLsm9s6yQuotx8+NCfZzaxsJtZbnOc9V7WHDrHAgFMuwFasqzWnUq0M1EjIEJdXXOYXMwETwGfs04Psf1zZXNtZcO6+tH1v//be6f5HH24ePvpnX/3V9atXju/e0jGvVYsvuUYT1keR7c2WcVIYhRtW3OtHczn0OlteOvcZNleF8+xD1ibu4GTX2CabDRi352eoMbq8BmqbKvk8ga8WzLpYbU1mq1UR2WlhtK7y+aThwmLu0WVogA2wpb03wlFlpzDx8zG4XppfurR26XRrZ397f+WGJ2SXt3d9OC1beSCjHqNwwk9lKNwkaWF56f4HD7d3Z7aPD+zOe7E8PPe1JuMUQWGNvn/inxFBKTSlAWvuNAYN1ahsu07nVevYKNDCO9NHLPZAyH7mkmdnazOHt974mxvPPJfS1YMoRrHT4aFBW8HcbpCxNNZjbu00hzHPLM++cnl1/lG23fEhLKLuyokZM4CkEMO2EtaDRw82lhbmNy9vbm6wIcHB+XyY6ty9Clm++MLnVmcWV17/iZ2H7ZlDX03887V1OyGXljciXj3XyP0/Yhm5Sg9AsbZTcq2eiHDik/SYSxNyJcOdgJlFZ2i+IDcs6PRp/4InYvGt04JTvauyRDnKIyWkjAJov+nsHPGNmyku876h2MG2eoIn8th4TqJas+VgO+Jw9nTr6HT3gNilcYOoGNpaSwQKgjNUhMJaixWLvBE/obhZ5qf2ILiAm3oxXSeYBbPJ4VS/zKGJnbLSlfNqc6aG7ndE6KsWoWPiurD+JRxGlIOQgBpFM5ou5xBG9sIQOyAn0HB6CRpy03auaycrVPk/fzIbmMafrjchvvmIUdMAnT15i1eKS7BQV0TCmMDnJArbgI2Y1gcbze26vu2Lb9g+b6f1nLYRXzUa0E7kICXSaLX1I9BkdOZhklcACr2gsGJGT9KkuRKHDX7yCz65MBCMNVJc2TzOH9X0dIIiNSrGCYjjJ5BqxgWqY+ovO1CL8MP1c1tLCzezoN0+yWZc7J0qu3bplSfFrSK5S5L46ngRg4SDs1xnabYoy0+opHR8+6lPcUZdhPg9BdSDtH7jab+J7QV8I4EhuaLth+bumJGeyM/Aq+ZYJotdXPeXxoOiLqJSL2S1slIn2YBuldHZG7jxPB2uEobosCO/eedMdPnrp8vU9MxexF7bsatd+OoXxXdljZVyYmwor6mGsUkqu/bGiIEmKUxjrNOY/Z4+3BF/80uvsupdu5Iv4P3mb/6mLY8HH3zISvfOm+/aOzZKwKbVtJ1Hm8ELKNpj39hFeqES1/j5ioNBklwej2YdxgqYzeDy0aIHgi+/8OJrr732bH1JlsSpy8LKJju7z3/pq7/3e7/3s7df+5f/8l/+2q//iiJWl2e8Y3hj09npnuLgVGXW0ORBwLCn12ysxxbbsKRnKdoA3OVaTo19nBXhf/Kf/CezC9f/7b/9t2+9+XozSi4OPJK4n/30+zi5WAPwd77znb/4i7+4ec2XGi9/+ZXPeZpjaX7R6SUaZHn5pS/ZbHr45vfwx/Gmuj/38O4//If/kMD/V//Vf7WwdhkD93yq2CB6eq6C9oNeeeWVuZsPbVVA4rRWkhq1Rfmumz7V61EFuSoIqILara+vIQCw1kxbHxwwq9kxQ/YguMeGwMznuk+z3c8OtC8eOrkwoX0xWJrUGthc0kqWE7jNR9NNumexS0g1Z2vTeTaXqDy0AxJ74QGGvAzPcY91Lr/Nx7oWTZWwmXBiCnxawRM/p81GZW+Ov/jCy0QL8rEKwopTUYGqREaOlFedtJHzxyxNj+wiuZD2ca7wX3Cps4uccp0tMCLLHwJJyB6lETEz4/yMu8AGvIuW8ZNoaJydc/Q/JdfHwsv4SfhHnB0YwQQaVQJPAE1+jsAipsOT9OFvJxW24OTITwfa/5TsnbclpMCewD38hGdMkMXPaXqEG0NFDpBVdLVO5ZwUAeRCGBK+QBw4MdPI/ZSxpaKTRjI6oPssbGzowlc2szP84Xvv648g6RCKiAqyS5zuVL3VBvToLPwDli/P23121DSnX9N7Ov6EVMTPGjfXVjxesdy3RoapQkba4OI3eU2Mn/Csra5seP+uescjT9EdHsLfOGVpSD/HLCrd4dFvzP1zzOKQ2MMisHWMCg6qY2bmv/6v/2ufYpydvSkLAhBJrVURfpnbhcWpp7MxnxT0+OxJ3pLKzk5OyzLx69VuhiFvpC7M2uOYpqEpgWoku2OG4i5d+vKXv/zh6w/8xAF+8Zt6vGjasRadMVQVD/snP/zJ/sDHyIPxegTrwMj8Lq79J2C6RMpPqlmHEmnvQLqpQm+X4e3pSWxe+skX8Mh22hBi7D7WZSap6gSepiuECWaHh1hW+459V0zT2b7sXHNAAM7Kjj8VKMakyhcdJ+R3zHSP6FxJ+1QHTCkNDEnvhGOwGnW+IRCiLhqlkyrvgL2Tq7qRzyeApyE7wxMAA5apPw0wkhEMU2ib4Oog2S7Mz5q6NoJOVR0//Y+qsThJzWcNpCMYs0js5rXnRaeFqml0ZEt8nToLUxcCPFp3eMh01FNadAY5H7F1cf2zC52qwUXwU5IugJ4KQcuNMly0zRln7UH/+Mc/pqM6h1qAAhkRKRZROCPfnsL6HxRRLB30TyEK06f7WMiYuA4XC+2KRE+aj3H20pPUm9oT8cai5tLQaiXhQ2SEfZgPN0wK1YzlJy2pwzgyRmZeBaZ6DR4WTCEqcukrBdlVwD0B87eNjU1f9fjBD39W8hF4GKVmt8aTO/VxkRvPvUgHiiQhtF1rURV07NiVnVT9E//C2pBj4AlQ8dMxgMWMkc0cnGx9Syy5JUYVtPSJwSJXAB2TLC9lgieXmPZlITA98X7mxg3m2zBLzXnRVJMB1n7xpyKn6fn0sIwAPjbv34qz8346/k9JVaiymz+m6GoqjEsfi/azRDYqJU4DT4c/hZhPStI6hMdDt8yA5i9vjtiicB4nVZIZyIjHz5GrFQifx0gx6iumVET4P2IeMSSymmZ7Z0dqekY18bTfODuLeBxIagmgXaemkJ+jHdTW/UuIGsN06SMGBQrD0cwE2cCQItV46H8C2PD8xgZenzLyjsg7l/QJguSpcPAszGXPgQuGmbDi3Q8+Mpfb8ljIiYcXQsDyWranzmzwGqkzGmQDWoyXaeSSJTE1IuTEIqNAU57Kq2yvaJpOP030hBeW8tWfuVOLnS3Wfz669P3vf9+dV7fbQokxpEjqXMJGIXn9lKn76eZmzDU3blxGFWpS6xx6hWDwvvzMb0M3EVJl5HMFkz0oqsh41C0FgxizEgBVXM6n0V/As7SVsK0LPwEoNxM2D3EsL+sv1Ij1+OFxLuqBgqX+DnnBK0LpBNUGQlWC5rQchCxOUreOMLROt2C2WOY7EBUjI8knLMIFLOsgpWIWT2LC2HgMZYhxOYk2ZlAi0vQOVXhSMAFDTNdL+GOdVCrO7sfC7oKiG3nNBZ4EB5lhgKuHiP3cevRoaXZu8+pV9qWmByhLqg//nuV5uueff37juWvf+973Du6+j3JbH6j9lc99wZgbMFwoV7unacSOHP2nY8akDsgtAKwDT6SOPxHT4UnVxpTPFIAcCz8F9InS/Zy4VFDGJkCYxY0wJlhJIRrlI/EFOegHdMZSpfgDYLpoby2nMjp85dTM8LCwkyWdzwGQyrJABlOrJL2IYnLioR8z6a9526LzYtsnS0enDlqNjnOLDDVkql18w39hzsS7ZXcsHuny821bLxyfHh6dePGM8Cs7byHNzjkvcVJ77CoEqxtnOdk4zx6iV4xSvWJi7FDihhlzhbuuJTr1u3fT1XHaoQpKMarOd2wjRqWUrhakzbWIdGg7ItRT9X1JSa3Nd7Z9eqafvFhzRzWfe/LZJfTsPjMWCtLQGd1fE8nCf0GEn4EpNx1eW1rYPc7h2Og6VVl4pbUG6UuLEIj+d76q45+fLh3OLJ6erM+uLh96LDhryOpBERqURN2XAPmZSx4l61VK+NCDwcJy1MfZyvniwfnac2vXn78+cz0vbxxlnjOzeb6u1qw4YVu9tGbnqWczZmp4FWt56i/rLJaDpelT+TjE96RTW0g1uxMZuMkYBkA1k72c1G4vl3+Tv4DblwUb6sDSiDMoI5EZf2hAO4AlzEOWLl9yzvHiALZaJ98V0XQm2DSQ2/yorT35rC8x3d/edPbStwz+BWaKqrR0ObRNgil7Aoi6/IsrzMvrS4KnJP36pfl7jwzH64cz93/+/q/+939leePqe1dylsBoAwyE1PeukWbGg9tLajx3b+fyobOjebahx1dnVp7bON5+9OGbb7x8lk3k/R/89U8Pv89Kwzb05gs3bAdvXNl49ouf/+Vf+ervHBy8/+4bhueZZ64eHrECUosFefCehsD85f2T+e2D5aMsuRdPIhU7K0c3L3nqaMYXtP7yT//cEuiFZ1768z//81evvvAf/fbvzFy/zOT/hEHh8srx+fz6pavuIz3cfnh/98PFjbPnbl5eW579j3/jtyjoufms3GZntjD5ZP/e2eHG0unhr335C8d5sGrmyAdoPYh2YpKwePXKzaizGbbaS7O26A0/ptSxJnPKu3B5c3VjfXXm/PL1zc0fbW97PWP15lUX1B0Y0xM2P92FWloxGM8vL142/Njk3bl/ePveBzMzH/zge6/dfXD6P/iP/8n7729fstw+m730+ed2Vs4/Otn7r//0u3d39tTuzh1X8rdOdu0xLXx07+4P/+Kv//hP/8SUcW1jweb10aWVB3Onmy8/99Zf3fnww9u21OfWlndPj569dE2j3/XC2qG3tUtjsBs+m1mpb8J6G35lfu7qyoq79I6DcdWo72OTDAeiYlsAJ4KCSVCBGQPCHDFgFZl3Kr2oXL0GJ1ukHHIbI2eWVvinJ+kvAPAttoZ1mziQs3qt5q6J0WymX4U11mUJTBaEHRm/pNjfRhW/esGkQweE2KBq89KV5597cWN916smNrCQrVDaQBZy2wFHKQLJ45lxnrY8D0Cswif1rdTUOkBlVincrpMu/PSgIvsiKiHAamreorqpcjFt7HYNUGss0x+p0RfA+oyXTgmK2sko1ENnrcgnvKSH+P8wh9ppBNM/g7y0hPaahumw1Fi7pAJP0vA0VU+Mek9i0+7NYn7eZI/q7H9QB1vzwgZNhXpM7yNMvALQbBj+FHbt6W9hDXs7IKZH4aY4o2JOT6enkuH/mKvD/CpCsSCj80NO2dH4+7GuKYFHajA2ffkxTIYucrnop/OdPRTz0svPvfzK8+zcHEShtAmLTjkprTXVUlv1IcEJ5fVoskGVttrcgMfgZ05/eX01098+qK6ZA3Eu46bQkHFZ+0UAz0ydzB9WlpYtABZ9AM0R8sEBnabjVBFZD1RHVusLwp8OtbSLD7squYwyfPHVKHN2sn84a840s+Dy6PWjFUuQk7PVX/z8F//dn7x7cnbpfHZDJlpZw+3tMv8M8/zPClWrGyVQMnOo16IwUjB7ms27o+OD+QX3ZkwLYixlknh0euJ26r17jxzpxap7MrJLSV3nzIgIbPeyWnKcnz7zzI0PZx50YxVnCAmA6blc6b2eGwyPCl8woq5RXBhrDNUfWVDdP31lkCvtFNaY50QUC02ks5gmXsPlqLUOElgZq3XtayEgfTDF5BgtIr2ynAO/XuBF2+TF8WA+qrt3FmE2P/QmcVlh04dD46V4BkUa1ORWvK+DVPnxs0EXSpA2LO/nPOzJ3qk626Cli1RyRTjKWi0zYN3IVEnDkFaMq0cb4amuFJmPLg2FabraU0BDdfqWGdc8kAJR7tBU/zKvjRF7OV0ZbbkHI2CbN+HIWKd2EyMw9Iu3NCjbBHqhOQyAkXem+VVPTJ+MKkEwlBGE+Zki+D1CJSJH47pHxqZqj8IZncOufmlm9uH2DnMcc7nGzSebKTGoBhLNSBHX7d7aw2vJdh+jlzDXAHWwu7a2YnzHlRHPzMnB+fHS6sqibTsEcDv3b7ODfv6XvmYhv1PjO2YVjYOXQgauXESjIn2n6LmIrZCyBhqLJeIaEmHipXYgxMs+cCoMcmZj/vbu2++YIx2YRUeTZEMhdvFan0pZW+vBPW0Xfv7d3NPjRfBOqtC9CbFimgM6hLD7knwTuPj14rMX5/FtdS7r/9W0KSGccKwqBZIrJvDi0uEmrpPyq9an/k6AO0vygsfdSY6oq7jyMTHwMfnM6+HCB9ULdRsguz5yldFHHcwwmZSkS5rLvfnW+67nJ2OaQFbsZUiOxYsMBe99dPvGtSuyALajwuCq7G20FJ1Q2OR8yhWqof0khsCsetPKXMe04Pk5SuB00oWoFHJkSS0a7BwdzGUZOn/iNb1ynXFhcZ2OazaDJw+kxYurFkd5Le36dXrJ92TcLp6mNwTgtpWwmcAozdXkEwkM+EVS6dVpDE+HlS5yEObHk7uyj8d94q+/FdhXDpT17N6RFtzY3VuPHjiqHYOL0SQVnJK9sbDpGo2R/18HSmGb5la/qVeS9w359il9kWZ9/c/vvbu3uXZvJc8nuqiA5uPDfaKrUUfazq0SGIoaLSMgk14zIaguW+MpvRfNDs89chB7Xha3Z6d7VNmFg7OFb8GdCmLDuEeLlP4P0FBoeghK7G2UT0VGa5cla0qXI0qzmvJg0Xfz5g5rtDop63sk+AKWl1KrqGjjiIz+07Nrt3lqBCT0Bl5IModcWnRkK8biugnkZ58p893ItlsLYo5Tx3yYgMsYBr7W5sjPpCQqF4d0qNNH27tehvxoa8v7YT7gE5qXTMlW59eyDcrA0c4uDmeKk8+CsM+dOVDcUmrnYeazDXlSP9a8ISrDVVzwzJ1cunpNwM9qEVdXPW65s7wybxE3c3l5fmntwcKOcs+2Dz/4mx/9C5+nu7w2sxU1stGbQKVSNbEuZ9hZ9ZXBxRMWjVcur5hHbeNbGGOymO+WiPE1AmMxm2gHd2YdqWg264dNOZZjMK+eeSTKVOX0UA4rTc9cZLcKXPdc3MuDG7hjFahoYwGcHtvE2z7h902gbBNvrDhgddrBYGJlKeOdFZuJkI362jXOPSRswPmw4uzs3Vt3fHmAmT46z5ci6UoKc/JAqOdQMuHBUxjCdteWPUhifDIhUn0q99gW3yr4ozSaBiENZz76p3T8RBuxF/Phm689s7l6+aXPwaCPxM41et5uSRo8wqCp0owf49ZPYyDywvolRtDk9EQ7lxuroB0jNRNHsPQ9k1oz8UVd5PRw6+wjzFzbXHUJbvd+epO+Ew1/fuJZ2YW5pd//1m8vfv8vWOOxJvjwzfe2HuwzU3t+MyZ9edW7JA1T+p+2Ra1JongsiFBlhjI4M+eEpmLyC3lh6t/uwoeyJm7Q/Ez2tDsFAY/+M1Z2ElC65AwiH1MAShI/JAVhZnZFUs0uMhDUtFjXtBdhr+PMtu+D/bOdk6Xky0wPAm0VmSjZ6FJkHMtrDakJAUTjIKU7dt0OyOSMKMAioM/yiwYcTWdwn1IqaZPZBYNsO87l+EgSseuzMnPstHexY7qSsozVFvBTLtjIGXFn8UNqF7P3nSFdqrUZzDaVAPdHhNpHeoS1+AJsZNB0WZjQZRVA6u5/4foZwNSrXAdqWajEPPpDkJTrtWKuuh7xSR07b2lOfSKqU95ww63y4+ODo3xrqG2fW9QmApeaplJTrmkbI/pnU4IHfaQ5pk4H0rrVkt245udRMZS1RwgIXC3e/Gz+yCjSTwE+kegAhSWl40fkDdm642Axn0vyKVSRmkbdj2p+5zl84VVzc9f9rlyyYOtcKBdQmAalM4XtAI6YBZJaEri42JaeQ+pIWwP7WZAX8OiRNA3WYe2VLJGg6sOCxZbG0z5UF24ys64MF9EEYQQWhnwAnCgFoA0z6ryxyMYiu0DorurX33gdT3Ia/4VfmHOd1sMUK0usaGdmHimXM4i6MWSOWNt36bNRwSWrmkNDZK7p7mg9vixeeHk5ux23tu/7CYN2oR9AuqPgBO/uQV4rntvMBfCrzz9jS5qBMMgPl2MmsLocG8DDgy1oEcw3k4FTGCpgnAAKNTpHvDN5zS2hWIKQsQd37yrr2c1N5xDbXlDa2Lh1L8NJIwRvkEa5Ee6tt39min/1SgYP8TYrb1y9Junw4Nhllr29X1HW+dk+Drz//m0b1jEML8bK6+06kCurV2BeuvIc5Eyw9Ti0KR09gFvggQl7KpFlk6S0QtWlq0MwWXBv/eZvowH9gFkuS3K+wnLk/t57seH9/Bdg/m++84fKherf/Jt/s7C0aNXnexUKAo/DmCkJ6/DBnjVszTG+w0CMFADMhYNVC8RA2zHgIS8BoYWGIVySXJIEBmo785QvVRK/2wikyYRcFkl4Mn+eY4Oj+tyfTMBob6mUWlhRl10GZtREpEsZSBwXh1VcclVvEujyg616ynQv01nFYyNKPHKt7a5cvY4QmBu5vJK4Gkyy2VbYolU4eYdTpu5BE5EDM116A/Obkk/yu8QqOcpWQBaBlHIx9g2aRAkjns4IsmIafgCDoVOfLr3jRyTTgaeBp1P/1vBnyT4S9inYBjxV0SeoHbOLbxfxmYT9nUb7SfQUYwM4AnTGzv0EkicQaswx+5j0sVka+ViEXN1SYp6A93ME68A0cJcyDUNugS0e7lId4uk3yk3XjlTWtESH1dHaB9mRHvsTRgOOtcg4OWu1EBVZlhSu9Es14w+pXuWyqMinmDPz51oYkzY3t+Hu4/q66QbyDqjF+lahghTBmRnxJSXXJzt4dPfpdPDmVeLtg2seqj44y9jn2vlV6GD+sz/7M0oymAs52pXVRYtMfNHs1qaw5SfaKElM88CmuthSp+tWYwi4aPojo2oqiILFw5Hcoi0zxifIy89iO1TyAusyy0/urrik6YzCRdjgj2BPwAzxRUTDjwBP4O/4rrWKGMV89A21Fi5p9/ADBZErqg6wfWUYxDUeZE+0XVrcvjCYauheWFzQWRXMuEBECuXAoVSTRJRrUkFmvRmG5E/PzP0Yq5Bd5bgxYgiIgmbaH3hainPMINDzwM7mV8qqOdsEpgtIuhjVYXASOj/BgYkrVe9PSJ/ANj2dT3Qq/qmu4C8gtEgj0yK9xd9pfnKGm4jVMIBd5PpYUkUOpVenFQ49pzO6wPr1Bzo+C9GUVdXkG6CJOgK8F+UnwfBdjYP5FQ+LtR301AAyFF08CMdGJOMoM0ZeUDkJNfDImSasI+XSNmL6pxwiTDm+9rWvvfDc87ahT+rzTcfeWLMbUSs1ZxccsEY/ZpyU9v/l30/HEzqrQH9AIoDMLOUhC9aJJUu1iTyWPWIrnUT4VXHQeJLaiRngJy0ivmNGgAqkRC5JE/XVP4O05ieZAnK1+UVRkZyllTUa27LfUd/Mbnq6EwiMpdtpue4djaRyqkJGBNNp5+sbV25QdBhu0kgSqu7pQQO1j/8Jkkmv7JRCG2obM7TyCg8cnGR/4uck+rG/IwbcllBZIrcQ9pSsAdTXZLVnsyz43F9k3W/dJH4sVwCGho8/GWQ7Mr7/P851xmlqn46RTzU79zSjGvLpfvRx5XymuK64sjQQnhietOwVL1BNOYVOOsdU7GcLdjUHsj9blobqJtYKjrc1BK0y5tYKXPGntXeEwcqiS5lm7Jjl6QBpFAmYs2jXkH42huY8Hw2KVtYI+QQe8FL5XGeHbTKvCFUd39Oh4+zo2pZLt88mJ7QL3cTxl0op9zo69h2pf9GTOUjOmGHOht7jYu+IPthq0Oob9p5D4uiRtGmJp+1pYZu9XKwcY/tvtRvrMsTaEr10aT1PYplU+PKyXakK57jFZKPr1Xe1a5yqBVOqEMbZ5S3dJZSZWo2/khSkN4kU7lrYEDUcsJsSaTPa/G15ZoEmUSY18v3v/+Qb3/jSy6vrxE+WdmFdjfI5201x4RgiUXjvKB1WTepneGWbmKOOUqjDZhYBeUYjNQFPnTROVO3vF//L2BFw4CejD5hur7CCdq1thM7IVwomchbV3MF+niuh0GAgS6Z5YKSGyZNLt84aeoIXTNGHI7IEQMIpED9UhDDyxtwKAROBD4c7n0iYOyNGAdi8cjmGcWWWJLvR9suXrzUlcPZGvDpJqmaU9eOdIsAY0DGQhDUlyJveqC0kAxsbSxNjjJDd3sm9u3evPvvs+qVLzOZDVTlJ7VD7j/7RPzLL/eFf/ViMIcNc+le+8KK70Zurwz1m8UOukq6RP2N8E9Yw0z6AMWkALmZOw/z/LjySNxYhpiNVoQOjTx7sfmo+H/EMh8shfgyD7LqUP2FIDdMtLQAWdN30jkq1KRbQ7vb1FIdRG9o+c3M8KpvXRCMGUPvjPYCzc5OM6ht5vEZDnuS5Ubpslp1CwCJmg6N0SKfTtf6teJ247IzU5eTgyMtVznlO8khNLah01GgS9hjLGWLpMr4TLAQT83pgJ3DFkcfkCRiosRQ8US8GuonpvZCapenPamSCLeAgQS2qIrkPIcbZj5/pUFSS/y/kIHjCNi8lOXDzHeWTfNVkbzd3GTyOLMGmPBhZQ1tl7OxFKpEM1eM0HaRGk2T3XYmLOq27vWK5ROetwP5RvxPUXv4tln2NlaPSrp/MrJ4vfMUz+vMbN2dXlo4YRDs6os4Gyoei5VW2uVkpWS8xdZXjl8X3gevynsjzEDWte3npzCO9a6cL64xQGaQsLa+tUrhr51GOsyurSwuM3bEwXEzMeBqjlWvhSlCkJQkX6oWjGgdUp5RRtUsIy79xDhfwRA6bYt1qkUN5gkmSwW+wDUz8RCcLDrovIdVsxF7L1erFK/nFk6PmBl/HKX+qdxUlDngKR5o/QjNxNe6hL7kaf6UgmBYWk0CediyHnAmWSf78DTZjC5Aj5m9Xr5yYqJPxeqr/gx/84rde+eW3HtzRPofLmKAlDB9nc/m049yar6Gfz+3e21o4Ic8LxybU8xn77996f/70aNUL3sfu2ZzeWFlxHEkRzHpfeP9w5tH21nt3tn/0s/s+ffXSJUPOh+vh6vmXbly6zDY5e9yzR4e6p280BDPr+UkntbJZ3twwPhl0f+WXv+mrhi89c/tbv/YPf/z9H/0//uV/tbU25xXFy9eu6r8Odj/3+Vd+cff13bOjZ6/98vnR1vzx1pUVl1zu/Ov/1x/+1fd/AMP/9n/3PzPO3f7gNTfRfvyD71EXSzMrpsV3P/rQprMDtYeP7u7tH+/7eMHZ6vncpYVl9M+vX3mBfDo/frBz/OZP/9Q07tZH29YDZ+f7W9sfLS2+dHx0sHA+c3TsqaiTteXFb/32b373u9+995FN3vPnP/+SUvYePCQ5Rw+33n7r/YNt76g7d/ZK+fncxtyj40fPPH8N5P/8f/k/tot6/aWXTSNeevWFuw/v3PvgobZfWl+5f+8jTxytri5/9Ghj88bmjoPOy8sPPrq1dbj7zOdeMpTveRfQ9GVp8bmXXvzK8uW/+Zu/8ahzeqsLJOena+tLnpra335oUDxdWkb/7oE3WHE9btASjrJaeiIbteCPqHJ0l6aa/NCoDrxOcxfYQBj7p5gGRN7mlyliJtKsoHxjQ5uy7Il8k7Nk8ZVhU4rzWjI5van5U3oTOCL7lKNyOnWSEiJSx/rdndokRQy7P7e3dvf31i9tuJhGc9bYAFqbpIukB8GVWYTn43JE1kNUzbuG/gtPdfpQkmJoj+SZGjwGOofyw6RhjEgV2skwOjHCMHUgvpNDvb06f7hOC5pwTLp2cSJFq1pjS/d5HEPHV9GBnPz8e/iroCewwJ/I5ndrkZqbj+UOAE9mi1aZaJ3ptNZDiSlzSTqzaqeuWmZq93nM0wXFH/RhUjqyjCqSUGnU+5hpKKWrM2h++AGWq+z5hbcX+CdNAGSaDw0At4D88SeznERcFDqW3oFUafwX9T5uismGVv9qIcSW7fD4YKO2np3UsOBFqNEti53HKfEzWasSUf2Pp3ZPEY345RwJsQ7SIU2mGGjmHlIWDkVtpkxuItS2xfUrl02vZcnM5+DAmtMtkNSrnK5a4VRkwpOLFmyYj/UBaw/s3Z8/3dxcX1lbOTs69Ll35hnuxMjy0Ud7L6zdvP368Z/861t729fEmEWimUuh1IJ5hVlBrxLzeYmyr8yT1EcHe9753/DI7OHZoXkDePeT6DTTAOoIZ5hLv/baO9/85u/Ozu+qSdk8QoTfhG0vfa3mRZYZ0QZ1IVQIQLXKpKKTio3c6IgwokStfZEAnoDRfUu3hI0VGHCNWcbAiDOQ1t5espt1m2XFlVoZGXVrl16O16t3FmSR2JPSzOYA6t7b06YwWCe/+VMbDmtuGKpj2eZJOWfZwDe1jlxFZxvPs37nIhYn3qrVagXq2i3BYdSYmHIHc1kNttNnU7I/Gi2mKXpR5qfmeEqsTkKvteTQspArviQv+dPfzcvC7FhcGTTK/ppuMeXPjCV2YVwwmrZhZVaLmSeoS3pN9dOKNOvHfLExH6oeEUvnmmHJ8pigdgOl5L+70yLED59jPhZCBqcX+d7DR/fupzsV5mGCHEkbupD6gVb/RJTCSQ39JOEl6kbPeaPk+cxbr/31jd/6reVFrYY5c8eHGcq9vGERcGl9Y/4o27uHx6eXL21++ObP5o52XvjS1x255ipTDRAToiZ/M6c1tBjsqKuRnNDZpLXvJzfJ8zF/dUMnWGo+gnVGewk+dFHnZHp2PnJ+6OliNlZsnxcXH+7smhfd+fCWvVSPM8s7vEWbqg9Od/tYN/Cw0krJmaHmRxo+c9oK1wZCr84K0FV0fD5fOs8WmG90KHFlbt+UZG0uUr1I58gYZiR/+xeBwbq5+5nEOHVpAGEBlHRAGKJATGbaxd2BwyOrSzBRmWlw5kAJBGbrVL+eOV+OefiVmy/7CKxmvnTjZP72j+C1cHFj4ctfevX+vVs7ZTPnAd4qMa3sopoFzv0Hd+7dv37p8lUN6cqdfx7CJ2ampkVzMajzTGr6RNv1TyMJSvosPpWZctM/G5jPtTCnlB65ajAqDSCNRHve1tvBkXPsOzlkk5bJYXBnIeky38Hu9qOvff0ruQ15ac1+XHOyS1Zol0unKUJxHiiAalpOmkpJskShl+ufialaPAaTkfYT5Kwz/337l53BzM5ueKv71gdn27ssSs6tjEpcnygKtZ9OW7XmE5nGn9OznUQaOsa0DjR7+t1nxsIi763OW3O9cX7+gw/fufPys1pzr2zP8+GXsyOT85qRlM4v21IqSENjX3fDJ/BPZncw531kbX7HqyOsX3UzS92y8O06xk+Xia3i/MKKmyOea2Ugk6lwtQ5KJDZ+zQcetm62EqaRURVfY/ejs9Pd05mt7O6cHdZo4p3UMveuWVMpfwPBSHOUd0TJRlIKWpzxoE0kJYTzDZmmK/1liIxB57ktRW/XhCAmtRUWn7exqdBYwLmCJS/JD6SnjNMBe3Q4n9sLVPpLrIjVbtEAGbUujoqURB/Q3LVbfmbFbYOnH7ExGhjxZw6ji4STN5OCs8WFFZs8Tt7FZP+q5H/z8sqBC8k1gDK6mt+8tLg0e/fsgT3Vn23NvPvmG//0C5+ngQ9u3wW/fJyq5js9ap4nuDI/tNhbLvPKq75EFBejrhz16oAzeT15dS07zjbiybU+TTt5b8B++/BkROYUEk8cZ7tcy+VOax1stP2Yck081TeTOg4Jqp6dmDyhZgIShlRntx7P/HPHBnq+WLh34EnJg1gkrywdHORZudIVZjCLR8dne97DNQVhROgLQzUvUJAilCS7cDucPN7bpW4BZapnUsViwdZEiEmLM9Qwn+o9FkWYWR4dLq0sL1p6w7DO3nH77q033/BEp9snYmzORThKNvs96KGkp/4sHe6zunhufXbl+NHe+aFJjlYfZBqemhV0xcOVzIqCwqUP1OTmgfU+gfTI6b2HvsWytLF2ZebswcMYcDC71/ixZTYrPl345V/6kg85Wunf3d721suf/c1f3dp79Otf+rLSbYsGKZf2VoD9QFMRk6WBgKREKHTwEuKAXrhUdMJbsU3nRfJF6LG81VLVhdU32S7g8mtCUhrgcYcDFZWxdTolghjaK868uibAYDSiqHy2yqPeh7OHD/ZPdg4XM+EtTZJd38cKx/xgHuZFrRkKsuqYufJFqQCxjLYiTKWhqtVLMURrpwN38RpLwL6YGA0CXh+CanmZbGd6bdKmscEASPFRGCSxOlhh7khZGid4uhqSpYWc9mRwBV1ineWcck0kVL4+DGV7JjTXudaEQxeVmA7JyCGv//Khbdc086kbJerhKbfmEw1O5As+lMuvdA7B7bN2qZrGmNGLG5kI0knFTPDoix+mNcdDlJ8TX7B/BawJVrT+z5/Jg4150isQXdOCGGIaOn2pD83sS4aHq/MzDrdfPsxFgNWdqM6q2uL5XqaG7bos+spPq99UVt+YOJVTCz0s9ZrPGbJDNjRApR1dyKFq1y5thKo6jnTLQnu59TPUBB11DtalVOvhdrd7WtBQyIdP6bIIp7IXjHqsO4XgCWdG/J2x8wYgbuBwF9pR7U/neiJVUqciOMqtaCjaSq6ohYuGu2i+FFb7U8l1AXBRZldHatZLk3p18jSWLlo8brPswdWHeRHFjZVwhjkwO4uZzRaMpidhIc2icYXZ/jQSfgegkmreiAYBvenc1ZUMCRf88ROYnV8Ybm2YiRy++Nyve1J5+1F6FjXPd5ArtWke/WyU1OjipgkMzIS/8Y1vMGcOzTc2hA1gcinaIxXff/0jmM100WAtBKH9Vh+NZdnN2ug73/nOt7/9baklOdnCWJ5d7UdUVc2RMW54AMZ+t7zc555dV/qLL15JNetat6vyTkfvPvi+GCWqVCpbDgbkCXon8a233rpz668B/Of/+X8O5g/+1f/TUPHC2gYATtWMu7E91Pnn5y3nkKc6vnW7e5aPHX/rW98Cdu/qlhFx7yxL0GeefcaxKstrr1NhMJid0/c1xKUrl1BrhsNXKfBXnr2iXqYb3Vg4A1iJCONrHTwZlW7zTTxSGyb8r2ZveJFcIoMh4W5fp3SKm1/OS2QuOUXq6kyJJAXSoiSvg1WXtFthFtYqpVHbLqkGbT9In3Itw6KbDH4HsuQaNfPSsra2a+8QQtNThBuXrr377rsN3PAwNPFjTcXAwK/eF7FBrZ8iLjJSTF3bSmgPhiciR8wCnOrwm7wxn589aeskA4tA93obqVp2GnklDUUnYw1PAJ4oV0xDjqX8rYGnMUxnkfo0wieyfCzAGPkE8DTyginRmYoVmfhMzSNa9Wvo+I3qCYTjzwoMkDJ2fCOZ4Ekx3QpjfKLKgcfVEZu4p2FKMAb4J/50dsifiP/Yn4AbOYRNzzSYGF1p7dJa99ZQNZPzfLj1LJDythvCAwsviJcqqaeVFLSfVj1iWt4c/xLI2pUDFmXlGxK0mXtD8Nu5Uy51Yes5C85MZ4KtKyjAjaX3z4/1n2CFLDDoibNri1E75+eUsxh5VZADsL+19X/6P/3fX3/99fPzLwWnU6xog2hd4/xYqEgZB/xu2dYjicw9NtavmKg4aZdVFXBsOU8eMTsI8YoOzimn0PEXAPiHn/ZzyzBHEV0o2p+oTv/s1DHc2aHiOlw487PLGuM79WmcI8BATOEpJszQ9UFUygQ6GGgUfm20WUNk/LVWSdsd5DsfJkfw50uNNYh3iRCYIiCmbaIzjSva5I22Tn1Duc5XBKfJhkk7iAKKhLSarY/NigvmYmQ2Yy1NJ37iiw2V02o/P6Dzc8KegHABKM/+YAN3pIJsQ4hBDMyAquih1TpTISiax+arqKT2HLIxF80pfuLgBJP6XcAnNMZ0/Cf56DEvKuCJ2Ogp87n+bPDt3lV5g7+YGTqmsYWAKnyMb5LInhWH8N7urqctX3r1S0Vnsgrgv0IFzBN6ggHS5ATk3f2TmJFeuWHC0HOJztKFhh3lwBfZA8IOjzEN3P40pBg/+fWoeNKbbBmLvMFPQvU+LWUaI9Ugh05kA3MLAdlXN3NXoyH/A/3Q8Amomjw+56Et9LDgS7g2togUGgZXnOnaoUdkzzH0DOGOaVaMP0P2pAYVyUu3GsH6Z+dtPxDZOsqcVlb91EM33NHMnPnb5Weew5n9k7wNmuOXkkx+57Xtwwzi9k/fjt2RD1NNeA5JAZyZudHY9CoCND0JIgOyl/bLrLJRCbQrGtJkk4iqdRWKuCIwzZ0OWGWNYAKdWklD9sQUo6TCqRcoVxiMPiZVpfxEUmdX68bchrd2c9SOku8Smyq5uIbveD4nxu7DSDkI4YYUlMpNfl6AjZENPGYv8HiNGVVjDCRPg42pnz3QSAxPHJyY485kyGDvVvxX9Fjupxcafk6NWU/QME1t4+F/CowkPZTYvHXrlgDCwHe7mCcQrc4ObWNG/8D/YfQZ2f5EIcPPziXLgIdIFE6YCUDk/ziaYX/voZ8sdsET7hFX86Szdz3G6oyBUFY87ECLcw9YJZE97EZmJoJ+wRDHeLnFkwNNW/V5T3mgs5QyVRFN68N6sURMbs9lBLI2rfrwzBYeZ+s50l5bw9kXI0uOg8FbaOFnVSh7iOWSXE7tMNyQ69dJjoGTVc9dWly26Bs2msWenV2/fhWvDvbCfIV0j8Yxj6vDgGwYDBeh7eQ8K+K6cb68lLminTrr6OOD6AdzAd+N/slP3gD/YkQvLrnK5Fk9kSSLUgDEknot3cdorxRjLzCLNzFS5RIlwHVMQabLh0PMHWqk1r6B6JG3tIH2T0wNlAqCBzBUppzCZAIHmAqJEWicxtPdhV0r8QOnV7negcYMJeA5AdhIpgp26yepihhTxVAY4vyBYXE1K9OuZtf36CQm4S02qiny+DxzLfmQYdNDi+gmVs0H3p+Zm7NTIftLX/oKbseAqiDblzqOGkXFYx482ksRwDohlBTdso+goTduiJE0iHRtE6NEhONGz+pfPl+0F5EKZh8y7cKD01V1tP30zTd9xtw3nKx8l45PSMLnnr1pqtAFgZWrw0/7wTRp3KdTm3skRlKHIzR/m2uc0/6YI5E1zevUjp8Oixl/dokdQ4jyc1IPMNFcEaS6rlGfthKppnxuGvKC4yMdVZ3uxcM0q6etPcElWUrLm4DRlRZKOanQN2zV2FmuRzvUwks1mVmay9oHpiIUSaB1pOznLs0uuqThnMn0vAhqyhAQUcgWJKrCC8yXnlNy38lxs/PoZPXY1lgdnpvJ5ORAuR6CRkkbBQtl+u4YxLmheYJjGbhavMYKdpPzfRE4RJoZZU4RTUbuhREp4JkdvngxKuh/PxIqN2ILQxFd58NVo0w+TGB0s4PD4YpuWqO2HZv1tWFNHUZ0SkNGYmENqnIpYVJAhbP9vb66BuBsf/twN4MoRxyCoTJplDFOXmJpuzoEl4C+Ojf79SvXXrkfmJV6zH7NGLywcDB3qOr4qJquFHZlldgL49pjDyGlOxKIMjMYLC1q1DpFnPFdUM8DGSSW15Y9IgC/96+5+YW1PFSFuWn9+udFzSKscETVKqj86oQBUWCkoo3ihpVBQVddu7ITv2tNdeQQKZysdw9JTOhsp/hBgUyfGQ2JBaa/DT282qI4WDPeYK9aB10sVoK/woWxh8xI6cRNFRu4YJJaASDpNL23niyTeAWUVOBqwMu1nVQw5Avdp4treVuDxmR1YFNxf+fw8M4Db1qgZq/kx5lvOkwOPIXmXX463clLXjGZsu6w4D8/v/HsjQXvpZwe7+zuHG86s7XJ6vskS15fgnmlZM9NacvWdVNxk9RDz2LNXVm/erB96IVQMPhoaGVNo11LNZwrjztfnV+6snak3LkFr5X9o2/9R49uPZw9XvCqx/qLl6998/N60Lk3k+bmdmfOX3zl5f/1//x/Yv3zzKWT+aPbh7sfzJzcO9h58OG7v/D8k62KO7feW5w//1//b/5XRvblhSycFo7T3c5XfYjj5Hxx7vqNy/tHt0naj3/6+s/fevef/N63vBnS78KvrObzDpvXZ3/18q95F/6/+C/+C/uHzzx7JQdFxmMWbIaHg6PVhaWd013W+UfHO1keLJ4vry7+T/8X//z/8H/Y2n7jvWwKp2Znh4uzR0tzO8eP4Lx549Lpye7pua8lnyyd5MSVPvzql189+/KKgfkv/v33fv/3/7N/94f/+sql1bv3Hsi+t3h26aWb5z9746OjrWevfWFuZf5kj+XxQjY0T8/WFpe9uGo/Sel5sfn8ZOHy6uzGwuyqd2xO71nCHKbz0ueYpiwSinJHihp6EJK6Eh05Kie1BDWwiTg9OdrPlWQ1dj2EWgYZVDWnqVdZ5zxiqe+xsIDQCkyv7e0tvZHg0GbUVWUiWZ/gWptN5LYJQ4hHn5RF7XiQq27en9368O6jh7vf+OWv0Jwf3rrnyRfIQeoPjTrh2GgjXgUxqTX/UH0wpTYwPWOFHh3awsGMvMnK0uDiDm9zDErYIjkFEyUTvT1oo069qFcBd2cn5emJE82T70PiA56kgvkXGho+xEymUB0zwZhWCPzfj6tuFoQjuh4mLn7r5kmbqJoRTuAzkBH9N50lmKIV1S7VUsfHa3eBE5sqY1MSGqq4bFRVQIykgV2FpOGhhHOIf6Lo8ScMXe4YqKSRG1PVH/NMArIoYPLr7/A3Gp7MVA4hlf/ow0de0ZlZiq2HJU1SiyFVQXCJni6g69WUdw9teI/Scv26LsO4/CjXny+2tZHFp0tYWQZkCeBgy9azIPwiU7j/irHNEGiVK9yNMF1bW94XbipBFvBUFvV/+bmbC24QPjjZ3sqFj7PDTMePj47vvr/xr//ln/2//59vnp68MjN3GZ6z+T2HldaqtdEXmUd4U2VgOjnPJ6Ct/+hGAZjtmasLMD8X55bsaHghOprWnCJjmgf664HLIh5+9TA3ohpFZCwvuNB/vnTv7nbVMj+0BmMZLJ2qUKA4WZ4IqCbXke0/HdNzhgEmPSjtMeLJq/T5VH1LctolBDhBLEu6JzD7qXFUOQEDL6YwsDifYQzuoTbLSLffLDkMo6Gq9J5HY+kx7zfw9TXOQCB7xIuycs8sS030DO8DsmaTSoEhknKdn2X0lBHtZNY34iGt7bLsE7MO03PoSXlBZlbIroPq81cprWfPytq6uIpmdwrLQC5GtZSIYibX04y/Di/TOhmDMhX2p5iUikw4H+TpBRWTIrpHDP0iObRpJmmoS+5S8P4GXhSk9G2jFdNOllBbsR2epNTf2r7MtKxYXsRk4d1OWzG99yFgpik1skivvjO1sQ5yiFSRDDisqYuf9XkVSWLy2O3i4trs+b3bHzz73Cuk2mKeDET4czZgkr/v5IFskF61sD3hFeD9rXu/+NGdKy/8igfNLl2OiUZmWxPXNeq6Nw2TlOEv5NjsxwAZJn2amyBMdYSHmQDmZbjMQ3maEwG695HRXjsvzLk17KT8ytr1gF/sNX1aKdJaz6xUVUJftSO/56sLM3lAzDIs8dWyhAJ+Brf8vPhsy3Y+z5j48lTkSOMRzQIlmnGKKF2aiugjalDYqqFKVKY0bYFHnquoEFOwwclVahiSBEnEK/gSUTNj7Zek7Toi8oTa2uX1l1/+LS3rNsMjG9L6gVlu7VqZKKJf/2/FzgThvffv+AxtjKKz6NArSXI2uZRz795Hzz7zYHNjzWYZnnsSL7px4oqWoVkLviOe9Dsp3bbEwE81En4iC6wdIwBF9nuKjUGXbubeA73unELvTFxkwHvu1YObRXxKBuStDz7wFO7lS5e81gKgNbl4LjmrAdp324BcmRpEFU/iG2yKvGiqacEakibYOqNc4sciRP49uZbNxwZlmDfrQPTy/t4zZ6c3NtaYfp8sZBthaSY9tMnAEJDV7JGWz0IbmKF2qpwKDhybrss0TMfLsr0WNbK3ks9C/vXcyfvb937mXe65lTtr9Lnv7ZQ8HzDLp7Vlir7WNB6ytvPFOlSp3azdgxptZGDimvjuswdLM2/d/2j17WzYPfr5myPB+kTbHXcm493KKh1vY4C0037VjjXrKEsVC4sL/lRQvhTZQkXOVJ5Nqauyh5UZfuInj/jS83ZbgqEFoRg8O29dG8nN+nLeLCK3TmqDNSuCc7axlKtnicF495mPMwnHbnbeZXt0Zt2L2mxHZLkfLUKrpD8G1HGK8Gk9vtvv4OMbgqPt1bO2fcpWx5X2rBFs+RimV/1NUn1mYG7O9uL6yqpSVpczw+nDM6ttMQc7MQd+5uZmqKoN/Z09ivZ4yV5x6T9LZ9bC5gPPXrs8557rsWeUZ27vzby4sHCtHl3M6kZBea8kuy7IM/dQSujMfbhUCXcyqirPSi033l1stmRLC6lK+jvm8/2rwbc1pyoUH9Kjj92kitpLTEUi34IyT6VFh1Zj2O2iEKJvElEzExfd68ulwVCjmGu+JNChLxmRJDJEoWFu/uHWNpNodUCg8T7tPnHqkEILkkB1NPIUJ6+IevsoPS7TBJDneV5Fg9M3GlRGs1b7AzdvXrcT7bUP4dXlmfvvvQ6JFzWXNq+YRJmjp5Tc6c0N34911xfOX95cXTne8Y9NtSOXfpa2e4qCggHxVG7EDd/Df/f486Ok156Jj2BauNtAeHA24wHSlY3lq7OXdh48Qr9NlogBmnWBueUbz137zede+OlPf/r+Wz+YXV7aPto7fXh372SX/fuLV2+C75eRCaywElLuZFEZrpYLSRPXdPbvwZ8aFidQF38hHH9oZHVIp4y7wNkAuKz64KeL6yQ+9qYbTzIF7TAEBF4m9Ke5wres0OePPe09d/DRg/Mte0k5msHPFJzvpAxziUb+mNZq/EUgsQAQ7aAA8SlgwogO8GtgLvlLJykVWfU1UGU4j3l7CufnwyW1vUtqLVuqe2dlFdqhLcxdxNgATZ8qYboktcqC5ySKWxbNTFmIt3vqJ60BPmxSR/MX/WfAmRhgja19P4sA0l5bzPX4wxDjnk6sLJGfRzYQA7hc2XFPfrZwhLZyp54aKqdDemRD97BLnv/zSGNahW+fmz8SA6df8QfhC4UV2Q0VyyluzIIerIOKGRIzov5safMN+uTtlqpcyVnNkWrUxY2oqdlZugPlNuqyOCw97NgfpF7Lbx6FVOxKm/g/eNqNLOzqjIQ1K2SB0iQU8MpinvbnFO47duFhDSHdjk3JmF1GPACZx1O0XbEoLTlptYbsnwEtgNHXQpImLrjIevxyEfpJloaTEf0S+QPl1aZd+wbu+LGIABe2YsnIkaGISqlShmFv6A7NJamNR2BEmyavWoAp6kacQTZRfNU8tXEGmFMeEbXktH6Yn7+iOwR6ikvwc2RDN2kOJFeaMkeypNHoIOmXv/FNLfXB/duE4d77t9AAk5jsMtc0F+T+7Jxe4CG8fuEInsI9t3twIDCytAmwe+IpHBuLP/v563le+e3bbEA+fO9DCL9wbcaLhCQB5c5M9A7yhpg+4GGjpBf7KWBfG1raHE6DDXlQEHglixFApwtabSwcC77ZWYuof/Wv/hX8L798md3xyuolWTQVzjC5ZUZ96713wSsdhnBwYu4Hm3IVIcBXdwPOV7/61e/++HV1GeHxijlMHlxbPM8mVM0w8l3HfCA4luk+IuhM2E8Hv7/zO7/zk5/8ZPPyBvvrV198OTjdpKsPKEOCeNjcblMueKg4MH4KFEOyUBFWd/7UqBHR4mBAv2pI/XQXsOJY6JwISWeBR0NANRaNVyYg6X014StFYnKeB8jMf8B/UllkWGoLwzSM0uGvWUyWN/DQgn/6p3/qmREnzw6B8WdpMZI8nWs6DCck00VHUZLzjq/ZSzNlOtcYlrFdhCG9I6gmMRcEi+ksY1kGmcRUvNRmY8OMfufqpIbpUlR5hOnAiP+J+I/9OdLwdKFjWTJOp3aJXXrjnE792FI+KVLGaWo7DLNAef6GM9P4J+EL8ZjERK9OF9R5x5gnfo7xY0D2EZXIgh9KEZ7mNjAx08Ajkk8JPEGA7N2jlBvkE4SJP8+DG/O783SgcyxdxrWBjh99NqxPlDU9HGVloApTXbbVmsMhZOgCSry6eYmehFzM/kE+lWP6kE5ZDzz0SJGfKtuds3gyXXHon6jUEyT1z4ahEyhPCodCo2p6pBiqMzvLhuX/9n/9v/qE7Onp8x0pVzuWv8jQAtEMda0Y8ZhmXxr+fH6wyAiXTmK4vb6RN/p9ap4vo3IdoQp0C8IJv6SPdZ2KdzT8ki2Cx2VvOgvI/tlECnegkfPHn12d9qcxdJaRlGmEkhpPw2guQ8NNr1eZiJaNc1qFuqv5pwdSgE1mL+kFhlH+wWFuuuB5Nzd4c0VMgEpZ9TEnLyJl9mgDmp/7v7XFqRQIOVlA8vNjqO5QzWlq4TTlDUjxJD+TZSqmLtU2wqCaQH5KeCh0MnVpyLEFx59I7zB/mqT6NUYMNE/DjLmmAzKM5U7Hf2y4sT8BL5K84f+YCgBvzT/xViTXMWNGrSNS1QR6UO7iKjKDr85S64LMwCXJqCkUcVqWsrqwGJ/4kgoJ33Uuffnms5dYPOlusnTGaLcQU38+oQs0klCZVc9j6nQouuYDTeG0H6QhLZWVF1XErFdb2sI8ZGEp50/rp4Otrhp1dlnAT6P67OGU6bNXa+nv5hZ8U3B+LyDbTMHNemzxdEN8m2oorBu7RewgPGgW364WcIkvpg0M95ObgEgNgJ8VHcgxrC6VMZonkXX00hk7vrNgBU1189lnWKxvm1ibjNX9A7spDcDHQ7LUPxFvvvcbv/Eb3//+91kJjK67qlTYDBZafHm9jDyOs5Fkrtu8fZrDoe0TuC4JqXB2KdPhjkneKQegK1hxQ2t2iaMmkdQDUFHl9bdkUYTrfUa6EpUjfsc3brqrqw+ysQkA4BpAdjH6dv/8e/EhHyv+94KwkaDThFx1VERbc2o3Jgl0BbtqXa/PUq3O9Ul0jqnNwNFXirXGzOqy+fAb2Yg7nbuUHiS+s4AUOcKjhzs4tKGaDbYQOWE5eGBNQPI+TrRcdJpS5l97V2Dj/GiY6E5R3DFSSTstoV+EjOwFzjhFiV+fcDTUCdNxSjFyNW1icFJMb9wbvZBmk7HWKpTkhMphMMjMf0LtsJBMxWozCypOmJMz0WXXbOMk8dUVfJFZuG2ffW09lGTvOk8ZFHz6WpGZHP4n8Er0bkIhzaLDdlioXcy6TyBhD6xydavSnC3hQbpjJLRWL6TjDPjl00XM6ff0bUcCXF1MqzUewMKH3ueq03dU+Ryx0m3Z4KfstAeDSJ8T0FwMeC1caQzXh6Uu5Xg+Ti6pWJrHNaaaUqz+CjMRKZjwpAEqaaB4rT5fSf1Vr8++SskRQVJIznm7iNFHleyOuzMyVl+GMzSXq2O23AbWaxxfwemdVQCYoLKyoFN2MZzZo5+WkvZrJbFNENmoRiGAlWBIFa8unBizo+6VUGGAXCIBcBAamZtavHr++WdBnhzvIynH5gsL7luLv/7SK25vaEmQ0w6qwnkRZyy2Dj05+UA8JFbuykLGBcQnhxoV2UaVvXys86UseTevXF3xHvTMnM1xMGGLz9j20FNt5HLJ8zcW6ltWO1JpIQvh40eq8/ziUszmutlRK4SeikngMxLW8J/uF/HNjYEniamGEeBIFQwCI54h3PtaxcmpxEAl28T1T7W2kYc/izmhP8MQSwwXE4Q1I9gReQe6jvym5On28gZ0nUfVqZE8QLEHX5TE127NNUnM5TGXOYW9o9wLzKmWJ2d9zjQOPQyInXmQW8/HnJ6usKd0Pu6GorzMP9KrYmdh4oLSbFp31Qo/lZv6qMyDR3nB1IqGJNmzKhqKj04naJkcRsdfnvXSWHbWHKHJyKyuKpyyIJbLqRcB6o0wZoIcBVQVQqeAts86QWfiVw0oCAcbtSSc8URPOl6RlFUcp0/qpFx6aiWlrBSX6Vgw1I4+2xKppkSJiEGPyocqkWIEOE83cXaE4DdWilld2rh25drimYfbD9946w3jwdK+b6DLWJDz2Xw/mT05ODkwuxPOhjKb5KWzk8XTjZNcZHh+5dLzKwvP1Kf/LlHyWTMwMs1TPVwdEMqVNU8+KFuEQN1W5SmDKqkPKp8u5e2UD2e3HAPt7HsR6OiAlerxwqXnNx492l7MjHdpf2HryBDx8AFsswuX+D2c9E2YCT9LmQ4V6LpnYOMC0GTJWZypv495OPPY7+FHIsesao2p/h8hIRduXzwkfE0WmGqFydCVHKWrayo9yT+dOolL23FjKQJcYx7jxeBqAcZrAia/J38rOZAhhxVu6rK3l6Hd2Szjuxgen8ek9cG9+wsfbSpiayUSu39iVFh89OhBJpHn89u7h/msbw0SUeGef/hob3Zz49d+83d+/t7bcB5/5YtXbt78B6t5uHn/1kc0+N7DB95J2C0L0zxBuby0v7i/trG2On+ye86I/3TBoHPksw3zB1s7y16JKtoMqrAdEp21xQcHR+/evb+ztPjh4cHO1aVf+uY3Nn79l773ve+9/gffuXr92eWXcrOPaRxWs8pY9Z3x5ayOZs+2Zk4fza8c/IPf+fobf/IXtox/61u/Rbad8JPT/SOLAm5PKfOH5g2zh9u3vvrilZefOXr06KOtR3lbY+fs2jvvf/STn9/+g+++8Q9+9x+whr56RYsvry7M/N4/+t3Xf/YT+ztz5/vHpw8YkuPVzvnD3ZlHS8vOnewizF65cuOSDZP9B6uzi9946bl/s3q08vzaMkvElZntnQwVrqTNzXovvYbzPWebDKatURaX0lFmt/cfeSvwueevfXjr7d/7vf/o1vvvLV9affHll+d2T1dWFy+t3nByrmm05p37H6mv17pm2KEvzNllSEPYlGGzPusdMR2RNXRmZns+b5XjbPqH9sgI5LYqYIIc/ZOMUQpyedFZvN9Tk7ygxTI+5hiY1VN4da0u9fe9h5W1DPY7KjO7s+uuRr6GrHP7GYu7mgWljCx80x+R1Ajhib6JG/4kvk75Q2R329CS0t1+kZGA2HFXmQ9vf/R//D//P8TkgbWoVDvyy8wu+yBBTA71o+H13fNjXHIwWNPc5cUca817kdoGjZc2LQW9tMVUkO02ifSBkYOKTAdU7kApLH7ExyiVKldEJmZ0oYe0I5kWVFweistEFj3Jj5TSHDEAnDjDYKLHt6erskFTB2zJl5gwUeQk06f9Va8kI6M29eT36+m8Y8wAnzxphamfieJUc4wU7sj2xXdqLxWmkxr/yJkKRD9r/6pRYHv50bm09khScT7RVa4SO2lQvK0zIcGO9kNDwl3XVBZoK+ERZyQifIhcdU0FpqsDkpMr9nfGrbAtTIxfzhaGLPmCuMFOo5K2apHW6pUpDQRmQmFWTb4lLvIQDgpveeZodn7JI7MsWj68ZbZq5kpOlpfyhdUqDYEXbT1sENe+ISnhnK7zWw4ptPogH3Fe9FlCLh0cW05Orf9d/wj3yr6+XxNOflSUDHcYM4ZAqEuwS4mvatVzG6AtNTpsYhVIesZ93sW5+7t75wtLL33+i3MHa/t3drZ3pG0eb0RH/ez9e3/4h3/1R2/uzc5+6XBBR2NtdL/Yrrd6QAx5i9cvX8lq4WQni2cPKs7vn3n+NpLhGwRBokWwXFn7u3vqWGf4vvwR2fAUhxa8t3vvZOFkWQTC5mJXvjj7aOH85LDl/+yS2mSsdGH89Tfn3ru1MPsqnOSg16jg6Ss1KgYMXRs3LJ10YLHaGzNQUtXnZ04pwn/yDtGV1tyzxEodsS9Ko6Sy3kacdx3FhwDO6lM/INglrG9q963dh/BYYARHib5cUGys5Vbq/ml9Wse3ra5u3r770dbDrTy268PZvgg/75X9HC3QzHxIgiBFxuGM7JzlVUj1tYbwLk5d4jIAknCA1X3sD5xm/XwpluNyxKQv733gv5tCsKve3Mwll2ByHJgNF4oSz0/PYvEHTxMAA3OfmFOp7MzskkCWfZnj5Sw3mCWWCE2UvNmxVxQsUVlEBFgo3Nffwo6+P2TuH9HVmTKQZaFotAvKUDrIbZB3KwmV9jCyBo9YZGXdEZD8X3CpfQKQVONXJqtTi4AjpyCSdMfa2WeAdL536AMOPvCJplSxGFwVV6tIaVcfLi7l1oOeZoJ90KJSaDYiuZ4esmfPP/zw5/WB4StWN4f73rJYTo21jipnYDY38NLurNMF5rO0wtrMA619960P7r/z+szcJS+SXX7mBWuWhZJ531BBjCbiNzFGyyAq9dSRUTK2S9zvzCZCwPhF72OeyHYdKzwmI4/T+asBZ+yzMAxb29h87nzJsfqH7/3Cxuvy1efBZ1kzUbDNk5YQHTsYyrV+Wznf8uvSbFmP9uhfzbG+e4BpDdx+hIY7rc13VSWZ9RSrxqmETGtmTXai5GgphgPEE1sisT3WxJZkUh287nBFCMOR/yEBD32krzaaIRTTL1L66GJIiHAAtcY8O1xIL/5g96HtkuPzs5df/fLqCy/c8+nUjBTDGDc7lxUcDvAp/9S/pNGlFDrtxs1rHkreO9qlBiiXdIAFsxQzqBRy5yPG8tcXzUDrc6xsDJot9q/MXibVGXiMUcnzcQ6kWlwwX4eo4ftpWOKmXqQUQDstpf5W1v71FEt8Zq0rs25jZG11duZpJytWJiYP7t0T+PrXv44tCg0bjZtlmaHtR5rDjIm6CjtM5KI4gafooUknxCV+Eh7/9k7A1M+nQcbEnBz0DwQJNN9S3kRKBcS3PwIAO2REyhaq7in2I7y9Wrm5mxPB63ePj2/tzuU0eX7JWH8YYE6NUlyLSUnCMNhWf6wunnSuZwsZayauQCLEnLuoxcNKi+x5Yz1t7T6l+IOyStlaXcOxO2tLVgd/c/vO1vnMA3ckF+cPFnsrKmOdTVycT4f3o9YF2Ub27/x4zqvQNh/yObawyJQFNhdhqtzo8P6W1cFC+kUyz7N0mbc83F9R2bMrLG0pxKqp24qhLbS7L362eGljZ+5862B3fjGHLvtn51YQ0MKDDqANL4sGp9qRtHQa+e8tnpO8QTzjeqOni/bZ6Z7NLhvh52aOVzJyRQmjs0y5jbl0mh2pIF9i0oSrqcv8khtDS0YNiLIUgdk0TB+soSc7WOorj5jitgc3uNZR/UDr/PIwJInPsKtTlIV169iW8J5xq85ZDsWCMHtGxn8/cONkG35KPhusbtK4Irt0tnZzYefo0driWr6gMJ+pChlJFXSyWSfrvuvoQ/drR/VdwbXlpeMDMwG22j5Rm3Nl/c42lE2mfRPV3SUfLNs52PvgcP/Dk+PFjfXVDFUmIBm8FK15ljyovLC4s7u/djZzkIvMM0fZ6WLEmdmpIQHNHnu2SMJ5y8mzA8efUjPI2TCgj0yhCEI+9nNmPcUAPFPh8K3ENI8GlEP/CQtrN6GrG6uRJhApsY/GZfHz4OjQdtnZzPHD7Qcb69e8XaN0XzTb3LRyrZegfVNoYebWR3dZZvdkwWBF+NHDrJ7qSHe2sRFtXbN0tXQ7g5jPnB86VNnb014+omAH4/79rQx8gTrdWPHg7lkG98yl54/2jm5/8L5dgsO9d1A1e7oPP9VztrXz/k/v73x4+aWvfIN+24khtIaprp3dMFKf00J3ulXwVzbOnzm8Nbdmr2zLqhvyByfOXRZXhjthAKP5/RkeeGlJ6/7ee0QRzjjMsWg/3d7bOs3rW+fLC8uX1/a39tR7WY3duvZNo5zjZLb4/NWbz8wvv/fR7bx3VNb897Ye7h0fPv/sc44lfJ4pIhoRSPdXOqcIy9+UVLqwNUx9vUla2qjpbI2Un0+6gql1YqcgOA669CRO3bosvnZL30qsRm+A8juy4IdySyTTQJLk0BYYImTyg8hMJJ3B7J4fPdjzzbAV1x5OXWzoK3xqTicMFWxJa79rV7P1kBIyqvRMfbrshhvDsEgSiT+Brskf0SH+fjb9LDo0OUUaJuaVlWgrWfRY/tFhvwQd0gOQQWsoCwb4wUjiyJMY4yPfq3rUIrWWUuZirkgPymuKaVRJ63EL2aqe9fo9MaitkAnLo3yjtAOTKSbVLJdrhul7vbCpJbf04J9UDQ3ml0VKmJ7J0sSZ9us/fqm7eDTrVJ0XpMATrnHCndTC3wDTZYXKTDhSFuq4q5ev6pzH+zk5URzyioywBUuFOzvf8gAVZsDJFg0chyjVBCYsS8VljZdcUzSOSATawdDkVZYMIWEyNbe7h8MPl9KmyLE435/f0xbLqysaF0D4oAI61WwmoIQJZPbuaiwpf2h08SV2Gi5FdblDB5s0QdPwGfwwxPyKU9nyI4kpYuIm8RFd4QtnTsFdAA4doPpCMqdSU6ligItuxPAINLYxMBYhJt+DmHJI6pwaoXMVtmDow4CeNl3fGyoyZiVmVNjxewtmjR+tJu9BLQPMCKlmDLedARi1nYW0PLh//+qrX//t3/7tr/3Gr5r0XLt5U5J4LbX5uc89+8ILMz6Q6eL0o4cex7h7ewtOosNUWRObvxKwtGMqmw7baEc/7VWd2sPKX9nIInz36IDd8XOXNgwGP/7BDx2Cre2uwSMvVKbLqXudYEEC3rbOv/gX/+Kjr/2aEjmRx/u7QfsYw1Igwlxr/ef//J+r47u3DxkZbT1s27o1tnLf+c53fvazn/3ar77ivNFE4Zd/+ZdvXr+Mcg2l70CHkpv5+IppQY5kxHTfwVKnTGTYdJxhoLkX+h/WA6zmrGAUzSfryL7/0X04X3lmQy4PPoK3ioNNLjz86NE9J7ELp227nfeP1Bof5l9cUDoeqq/sjbNpaA6LkcRmik0VZisRv7WhEk07kdpXgcbOmrxhSZxwB57wEe8TvBAsLNbpfd0RoSxQbiUs3v03FPr+YQS1uokplHhLVahMIboNRl9BHLZ1DBjAHTkWbcGmIq6yheahU0RbDlqxYlquTJ1xRhhYfwhoRCIgO56k3iUH/BAcnIFCMKciU6WP/JhGM+iTZjifa1SNBO8aGnKjqPCwzzWoxYqptYjipCqu6HmsrIppNBf+x0Z2crOlw+gZf3YR4p/OOyaNwJ396T4yQjYAfzpGOO3by60RYiqg6HbFbMEhrcqd/Pg4ketSpsm7yHxRo2EAqiIuSh1ziR/DF8kX2afjnhT7VG2S3kj444ziY9ECL5hJNnypmcbQtWqSR3XQmQQYJOs2pbilgU4NJ9tg9lN4etyprQqjcMTVZ2S4WhHPXrl8aSgOorIRds+EANssKBqC385YajHVmzOfK02uxKZyDIxEi0neSaM8DSAJWjDo57sdcunKlb2H2Uw5PMrWpL0YNn1/+Bc/oX9mll5uVLJYZYK3b05H4QBKeqqjCHm92SeAD5KyqVHKXN9vWwLZdW1Tgupx0TlQgUQDPbm0MVXJ6lmyXzg1Kkjw9GqXwoehYYRRFr+cANr8GmNEN7AYeYTD3WQPEi61q7EbADfOggrPgBYM7RZUlZxJzv6+MSKLc8ekZW4AJls9ONB4llbVWlC8UiwtmEP2IGJM6SSpzcmQYbJdjIJNQFmzy7GwPq0NbswE2a5uLtZBArCiCpSk5jaJhc0KJnnLDtoUvCiK+ZsticzNqtbbO7Gz7iVEMW8QsBQd13Hx6ycsEETUOUU0hHLp0WAuZynfahyM4rW1KT9/ZWEwMBSupXNwQlLI4gXbxa8h+gmYEfhjA4CRoNDpVJHGNbUMwaW9x2qJZO3VwGoRAsqFe9XHpyObEkkcuf3xj3/8rW/99xTXD+MMd/WrFivLueQkbzfxSIyMWtZZqanCydvvG3bXN5cokEvXrube2NGxWZxyO2/lCj1dbiOR1DiVC5tIMZ3UfohrKZ1iLEqAaSBJwqE5742LsVGyaptARzaN1Itfuvq8eCI1tvI08ifCVVS8HEGwBywLxN4TsgXWwNMsFUPoLqozUBgMVQ8Hg6n7OQtLJNSesV6Z5JpqtyV1IdGQEb8n6JEri8a4Sq0QL01ZPcsUJtkrXHp05sNyD84OzSRdnjN/26pX6YMi1F4wP4RNBF6o6CyBqRtjDS+DeN2Ur91gIHi06KXNm/ij4bR+rzEb1ZDriWo89bPAeHGdKICNStGaT4EnQvzIeT+FuY4ckfTPNj1sJGJo4576prCSFhkJg6QUWR1EfC/p+2fnbV/S01SJnHSsQNXPaoip6kwj6XAjT5GVuX01SenlOlCsfkxrTaeOYcDDK8ATcwEyr0XK6v/QkOfDBLRiwz/tP12pJ2AuqBqKzJ8xcoibUI6Z+qBRU7zhAM//euehvr/tlc/JC8KSquo9QpUNbGGRFzFYIBX9ihh6WpdRHWm02ZdK+OEcuNfdrCS5wbuI2moZuJpya7qCQoqa2zs42tv3bSgToFRghBsDwadzxhW1Eb1hgWudB2EOUZMxCejXFxAizFzATy8/iMlzQGYXm7Eg7kMp25IodzYq3uoHPCz8aWd/Abxtvbgsa9ymDKQai3COKgygYCIkNq/7Jx9yzFF+eFgOjWDE8zsjX2r/fCJSXuNxwCYNIAwmrVMNjYH5eX5Ox8a0Ry3q6F0gdDr7tam9HjPzWXZGMzPMbx1MOtOWcYrNoVcWxQk0VQIw989uhWI+bRACupExsYkBhht8TkxYWuQFYQ2XuBW0dXjWLQWyS2xWdI06F5/+BCALR2iNYvQzOaHcqA7zIOGzxYxTTXNj43MQShWAs6tQ0ZkngPetYOjNsZtyMQJKMamC3FtGplt37mVDgwNpKo5XCBCoygEP/zla994Pf2gD4ZmXPo/bcMLmYc+QX7eNIeUkwW801xY21YzF9z98KHuVEE+qHNDmGY1PdalXib9RHODyfN5YW7uxtsvEfW8nbTp7cZuqq/n5jc+bB37w4LZhgh7g7t75yBTx5WefV9NeezbBsoeSeqKtziZSzbCXheDfxTW2p3OM8RWo+WoY3ML1NHilVWrxauhTydAj72ReLScAnPHVQRMMyWrxMeg+NWrIUvm8W5pTtcBP0QbCEM8fhTuElHMwSm1pxwxZ81lRzdj+rt02F+BmFmxCRQaQuOxhHMaOJgf1tGJKHZCQ1BSnPU7qLZcgZI8TnWeiv3Be9zpPzvZT4Prc7IrDNR0uY94cE1C3Npbnlo7PF9Mx8KImQKWwFNHyXd2sbD2iekibvOkkUvk46E0ePsLi1RBrAaX566kaf4dwYEosciIZ4pM9lovFLcU1W6glYCLjeuMx6lvi022D2dRDvXPq4Gp+fnXlkk6uy+n5r/3s+9l8rIXfnBd7a5hxBtZPhTBQsg7EDZ1bifleo/MXbKRO89Vpl2ODOyRUsXaPF/MWYNZOvbXaTyhhQmOu+NQop3Llwh9vDM/O7J56RJaVLn76xz7WRr5n63N/R88Jg6opw+1Wha3yLIXiSum39Tp8JUWJrplnipvIkvATP0c2jjBDQZNuAB5fK18qqaxgDlNHVidQuVStalfti2ajKfwXmPsQvE69uqGGdVhhjxfzt2Bo1xkHDDVdtvrpyPglAxlRH3cIzxycdWqRXUQXxEBvvadTYulsUT2WnSm6NrB/6NN3k01JWH13+2hzfXP5bGn//p6jOCjywNPc3PW5lR/+N3/8tdlZG6VLVzdvbFyeXVr/sz/6o9f/4vu2Sl989SUT8c3nr68/e2P9l1587je//uDReyZqd5b2KMrtuaO9+ZNlR1gMfk7OYmi0feixKvKgRm0FEM54IXTejYSV43WzioWV9aWHbKvXFjZffu53n79pINmCkTXucjQDFpOuLBAycfBsl288H1955srN699Cs/emtdS1tWsk53Tx3CBhpijeuV9aMLcl5r76leurv/7SwXH0yHvvbbnKtHu4/ODB2tvv7t5+98PvvP/gh9fe/dZvf95++I2bmzjrBZqXXnnmW7/7qu2Dm89f9ZE/SIzvxyceGnFKmX8MnZZXl774/Ivf+PwXXW7YvHLJg+tWRbt3ti1R5g/2I9Ury6z+f/gX/50d55duLm1t7x0eedT40Td+/VdNKLcdsi5dWlleXV67cjq3c8R8auXozJn3wdnC6hyLmMtXFz/cXPPdkz9/++e3z3Kyfuz9s5PF/QcHv/jpu7PHNNbyR7d90qIFwcg9dI2s9SPHkYoj2pLA8Om6p2QpEI87n78mO07WWOCdbFyO5j1mh+FQC28dTivDU/XRdbbK06ZY7dnZnrLXwV7j6+6T7px+FI3RUzi9pgCiYysyipp4Zv036cVYR0lkmUIH5h5MLsKw+GB18Wh7yxk6DL3x12XJKqC+UevOyLMP6F8O+z2ee2x7RyEyePUolhAMDVoaO3f5ZXtS58OhVhRDh8wNh7lmKspiIATG4DnaoeQ5BTefS+WFiqDr1P5R4VxdSfxEpefHk665we+EkRt+PhE58C2Dz6BMwqiJG8PTGCaJF0weY0bkYsa8Y2o01mReNVrxABuLlgyDnyoaVAM7VD6u8ITILqUT0+Qj37q6rTNboUkOE4YiOmP5ja1Qlieyirioe6dF26THVnXKKg3iDJ0SQtewnQS4RzGPd6ZzdPdoegpRcjTGqk1TMokQlXp1wyYQ4gN+vrhy3ObDdljKfn/rwUP86UlzH/SGXRkH0zuc6/Kr78aUR6QwPKa8OguH26bC8LdFYe0qZNO56x7QprJHHz8/2XV9w13l6igtbOk/FyMVGJgPaDguFkbrqzevH83N3p+Z3zs4/GD7yLba//v7PzavOJy7NLPy7F5Zxxw6FlWVCffocFSoy9GZZenMxpoLZjq0K8y+U5P28vCY0luQfEoxH2OvpVevPRz0y911337v7u4H969+BTA1djR0dtyYsD61OJg79Z2L9x8t7fl8Xy5Km6HhS0x51bX+NZOe5o3sxcmkNEP8LDcN2x2tZBJQ+BZ8xb9Up6JYM6QXlFLMB8pYODIQmp85pL4A5JnOuiDmgpKqdU23AGHR3PxHd++jFdjDR3elMsd6+PA+/tB8jX++3nbEFjBmTWC0EID9Q9djcAW+E+saxUfOzXrLOixWuZkfGh1yYIbg6hdg8g9q1Jq8wqPDiSK5iMgULGrOwpoK7RNOBnJlwwHHac6bWcjbUnbVCnlshYAvVAHmh7CB0gql1okuzZlOx68yBYWLghoVyIn350y9j/dPTVyLW+bXT/brZOOwuAOfwUeGGa1aaxbgufmQ2e3FehupCHHFYG/yrUuDSAqp+BBZGcUgkmuaRHbGrqlZh+aQ2vFaRrnU2L37DLDedMvKz2CIZyCJaRnzwaX55cMTC1F2vjHKLrNxfLPKwHgWQd7xOnHsf//R0b133sRM86vrzz3viGJ22aw4+/NmSVhOurq7adsUkDFZfY3TNhiIT/ZK0CnyCddUFW/CnAIjAYQncm7uT6woIwxaXpnfuLRy5/bRe+/eWbvyIdm2FzFy5gm00z/BLER1na/OuDN+vlQLN4syBNf3NGNxqaxUOTwvqZjdHtuXChdvSyA463PxFlUpN5BhsWDMlCdtBG46rNZWRrLqWc0BqaDFtGIIIko6aiITJIWZioCEHsxHd++57fcXHz6S65u/8zu+iny8PL/jUG1+xmJmMaolTmp6VVwW0gyMInXhnn/zZycY4AzysolfgAteCcXt3sT3+sqtZ595kfXeiWu4MeCAoGQlRbTmqZI+m4eOpoofSp5q+rZvL7bHuitY1b52mjqLOigVIzyMSH5Mx/gg+ZDT/OwAbJ3YDREjY/uh2f9FeYULMdbUxFRSyqn+Hk49LY+TRky2OMxJRdol82S87hj+EzXteE0pcEFGxarskEoH1WKzARJpZhip8NydjJGl9bPMbC+d7OzvPzg5fri8tHh0uHN4tL04s577BenGTU6j/Fv8HnP1A9La4c7QtqX7LikrtVrc6h2dDzeyuXZvZYUBytvbvC3Wnph/f2V5bm11bymgLSK+qFSoQnO+uFBHtokxZ7aSP5957+13Dh7e1zpWAHIZgVNW5claCgOHs0WdSjx1PjfjMki54kZ0AtyVKxJihp0wa1TrT08aPtryEz91Y1trw6YwtFP/AHQrRgND2mIQaZh1ziY1tqb8amsjmb1WdsNu5BqO8CNq0LhqD3DZDZpMCdQ6Lhvs5uhR5m5torOvFtikVl/fyTBAZWeHq5f6h5WvSg8EuElRNaodT4NfaqcX8zNzSAt3TKKRrTObVUrym+LBFbCl0GvDKTm4qKSolOHSVXhIm2YuQt5NX05zdSa3bKzFfV997crqulyufdmIWdzf2/ZWqrO140zBxC8tzq+vrRwcncydOsLc1L4fnZyxSn/GHi7UzmhT7VjqLOpP52fLx6cr2byKqIdspFR1balENWnHU+bLMfSeWc7nCLIJFtg0TLJkkR5jNmIQ+2pEpO5hIQ0dbgy3NgMcEl1lSJNmHqvpSy8niZMPvHjhvcMDVs8+IWh2sr2ztbS8OLe6vPdw36Gm+xa2fM0JJyzPlC5Mm7jWTvAIeLwCTtXkG3BFgrSJ5DPV1adTPhZp90Fnpn1hm9/ZObh2bcUA2jcEZthBu+PL5tZZ797p9v7Djz581wHAS5/7qi1d+o44a1vzoJmjE4dPly4xR1udX756lEu2Z2uXL394Z4fAr03mnyG2JFmyoO9iTcgf/mZ8qCVnADPYxLz31FtrJk6+9+ux8M01RyO2mL3GoqmW3NnltIfZFxO6a1evXFpnMPfu3du5hTM797Zh6dEj5oOfe+6lzLLccHD/r3btsYX8p0QdhDSyB43M4kVoAxbM5YlM+JMd4KmmeAyOyGUEruRpnQaoI+M/jl9MGjcWD6Ek5GWADA1nNj+OZ47u75xtuYZBWAcKZQmRGUuT49Nc1a6hcsugQadrmHC21KJBWp7EhKbJ5KM0f/o5AaIVpZJK0xTrpOapAa92h10MjM0d2Q591a6NqguV0ATwNSC/P9VC0IFt1cn5+Yx3b3yycxg4ZdTblOu9MyV65xBkrp/VeCC6isu0TEySJg4B8HMCdZZjyIYgKzc9wYJKOIum9A8e6FRZ7gpCeIFqgjKy0jCPVaRWqkrXZCCVyG8AfmLKFgCA6bvJge7knkJJ6SOUpLyiIZqwSldKZye+54t52dYSsFANbQcMEn4TiVIBdor8Cg6ko0YERdZkCA/0DEChVtFoMFmRpPEDX0cKervSg2+KJNw+Ps/curSg4SclqmL87I7qXSmicxHNIZAMg1NchzrQ9EwSL/62yrv4XTTUzwHnNOZGxSdxI/MBVExkQEbhUsr1x8/u5ZMCprClvlxwVd2bwkYy4kygRtSaTXWOIVf/yMJgUu5QlwhC1pkOHixQGowPTPsqDreN5WIswkS6/WMxY/ik1J6pFVSLN3rE/MEf/AFBuvLVV6k5BwXe5D1/tO0A9me/eM1C6NJz1zTf577xJbbDkDoVdDqkiP29bLzmGK5qpxQ7rSMlHUh9izxScXCQxlry3Zb6OoEFlZeAsoO8MG+/xm1tqVCBTGeuIafZBTKz9Rxl5Pv17775rqFiaTPDDPtouQ73M+UKG2NSlFPN/aM5NVIdG+gzCx51Ovnhj97/q7/6q3ff+sAtgVsf/tU3v/nNf/JPfl1eDEPP//B/+D9E//7xtpjjoyyMkQQDnGIgVNNf/dVf9eHak8MT8SIl2TZSxJVLmZ2//d57r732GlKlMmgyhFy+/oxXnJANbOe2K5ln15+57iflRmk0tSLVyAJHc3BOO3/zxV+C7d79D1sXOcv1eLTijFj2eR4bB0v45YKzF1FNbdM2+JMO4ufHuiyBdD/D2LyDolnfHhYGqcr1AH6+6VoShcw8Ciu+u9zTq4quEZ8bl1L1KzkQWfGhIt1m4iCvKOlEO7r0wB4N+LqlgT9VYjqdHMIG14BOYUv2KQcGEhxuZ3FeGIYOOAUYhPm/9FizsdCWVqx2H4ELprTBEP8x/bpJDbrOdvF3iBixTdIv4sdMSB3BRPbPTh2TuoFGsDG+Yx7/eYFN6oiwIcdC+6e66w0dHn2VqvjmkxyDQla5zq64dmOWzxgYUBWBw+S+CpnODvP4cwyPgQmDB5CQMdlIGnMJqELrauzuvImobbtpsBFtR44/JxREfsZIMMJitAXn2itn3KNJxHO6sFRzSpAN1hKbj6BnOZNCpp/gqP2QRHJWkHwwwdBTyVqAKT/JFZ/A39EpF7rpTGL0Lp3FbqB4xNt2oYXfeuutP/vzH7vv8vq7940C2wuZG+hNLQmhv+ZdqKEnVVyMWjZmSZzqw5ohqJ7RpErYmwDwEw0Kkqrjw5kqFhs7u/0OKpThQP/8eP/szLEiMDj1ySq9pVEwlHAyjvH5HZeEQJQbMfslrBadxc8WomZVsk25oKkYdaxQUp2r09Uzc8v7riRXD4Ittctn3TDL17EzbJliyRKGDPOcEJMBlNb1odt6TRVDZJS9bX96JqmIlFV6Hio/zQAjdnVgH4VI9urLH8yJmjBpw95CBodMAyBoH90CBhKlF5PIQHhonax0GYXNY7nmCXDxtmLaNR5hYFzu/dacWYwkMU1Ap8vIiSxUw9wepFLEmy2o9frccrhX37jvIv5D/JG8RuKnsjh1Q9hASRksIwBM0yzQSaHTaqBcYxDfVWjgRsIHWdPzQAFwzGDc95Qqf3kp0wMZmxgB3QENIvk5Wy/XpQRPLcyagJYHYR3h1r37YFY3N0iOiYE5j80F4qHviIcD/m7MAaP1cD23mqSKgmdMGgMjVZAI89HALwJCob2HMmrJ10TefvttG9CSKnUYC0ZU0wEAIzVjQKQRPQwsqWa4psVzeb2WzcqtBdNIZPrgMAKV7bNfQev/+NVDSxN2ubKPBIxtEX5mJyJO6sQPYIcFkFeabygXq2m8t959Z2vr4ODaM//4H//jr/3yLzeHW0hwNV/hLFd5I8+S+K3nM1kJ5Vk8zswvmxN+8OGWmVtT6BFXwAw7mwDFUarkxJm51qTE8KSRj/5I6kStjimPyVXj77ZriJHzFxkqJD6y1zJvt6OaXgxVjAY/JYHpeL5MRNGc05bol7/85S6ocQrL2GF0jiXKNbZCx/MngUmrlopI3rHNG9GFtoEvwhtsjydB1REfW+IENn8bYAQTGPNWVxulIgg7qQcsGsmIpmirniWflPkPcAod++aECVmUie1XiSlK6BlmWgG9VUu2bRf9qwkQ4F5R5wLTiJpOciuGQHZTdioAg5sqwD/dUrI0y6gYOKvTRXQLJi3YmP18GptUDoLeQuox0U9Z+LD1vLrsQBWSuvYaZGikyq73FpbgySTscYceBNM3JM2xKJx2jwnkAuNz+0I5OERkVgrFqmxARz3WvsTMfJ7j8MiGXLZK49d9mhjNhJKMiT3rU7SYYde1iGfhUhUP/3urLtkjcryMen4CaIcGdUsBA0yPm4EEwIe/6ExGZ7nAmgPiAUjCW+3lJzXOX1pOHa15pZ5ubYs59y4TbD7vuLZ2tBWDKhvqKe7kVAd8aXnVGk3ktFOWRoAZC1Pu46yFGQ0QywJPmFnjgvhuR6U1tqJ20P+wyQJSm3RefkwUi3tKqYJSwc7rZ+MH1jX1k+EyOVy9tKFlD3ZNiHYX1y/JYsO0cXbehOuYsKeFHTn6MBcZuUwPUvVh48RD3ngkQW4SmMq2mFV+2ky8vY6trZ2ukehxfwYwZzJmqa5pLq9tGN2ubaxD7lzAIOuacfShM+Fo5hlvN2s1pdj8lnGk8FMCwNAzISlhdQHvtrjw2dE+2lY3N5W+ff+h6nRTqM5ILfHwraz5jdVb3P0HTbCpxUfv3bKf8OyN5yI/tRc6KWUgZyx6En9B8CTmKcInMjyd8InA00ATAZsGbpFoKPFjkni1E586Wm04YLt3z0+1HoGb1jFmTJ3G2cCjD3i43Ta2TedXsFaoiVV21nKYki4dsc5ElzMjjMlD+qqnMEQwtZWHqSSt41lFfcbDK9Yhhwcsel2Bj7Qa38BTc8Xokv6cOeWa4VgNopbOFkNBj9sA9yLPmTfJVjfyJjoweVn8URS6v/MzSyO0FXlRQCZjKM9ImoYJy9BmltRcENYZ4K8bAEQ0c8H8xNya0PeMaG42Vnt9CpeiyjWFT/sSu/ugQSlLisW5slGieMOZWhI400JoqUenazkNW1tdu3L5yubmFUL81ls/9uzA4dHuyVEdp0Rx0K2GnCWV8XU2ZDhch4DypcWX7PCpoFcCKf0YPs9uLq8v17McSuzZxTL2qFTvD0cLTQ4LS2STfeI6bPKRm/IZZvKAz5JN/4XTS5ubC1fz2IKHgSbgihluNsmocmF+jRpKa1R8zqQuVlaTSHVHGyTtdwBYo5387VFhEGtJqhCAghqBm9WJD0oO4YMIifNbQ1R8jdYTZR2aakTpXcAeTz9Wgcob5HERIVSU/7d74wRuAK1aOd/Nt+5rDxrmPg3uZ7mNtgSjerHXlQKdI5jz03reas4GLTw2F6QsLbBtW194OHu07UPfS7Y1MEFq3uonJYczJ7tb773/F3dXfnK2sXz9cHZ/M68fLK+tGCkf3bl7593333vjjXdefnn2xWVf+r63mcuD9w63YcA5h7qEjW5xNUsvy2kXuor4mxuXZvb23YtanDnpZ0CXF7ID62WHs6Nj5W6ezB0uZCrvjTAkrdZygzg4k7x+5eoLzz63tHu0kheycon14OHeRx98uH9v7xc/emNvx8d2T772zX9kD3rt2ix77aO9h/VFHfvUvmoz60WaPIlt3JrZNqT+5m8+8yu/8o//9I//6k/+5E/eeePwX9/+92enW//sn/2z+UU3g9hL+16QbaTUa4O19vLMxubC+iUi6LDfWl5HmfncK8+zyN6bPVpcX9z8wotZs/zojW995Tc8af6n/+2f/tFrP7BD/bv/4/+xDe7/8r/5jlHwxnMv/vbiby8+2rp//8HDu7dzBnO28N4b737h+o3lxaV6zJMt1BINSK7UTu+9NHN29aWXf/Pbv/f/+pf/xZlLCedLh9tHh7v5toOPCFN2BLNlowQ28hmjNzpxaknQAHB24FP86F/2j24ElTTMnx6tXTY0mqumF6ws5PUru87q4nR12bxwLw9y6R9w9ipPACQyEqgT9i6Opg7KHLOnU3PdwwTEcFLNsqCR7qcdG3KYU3dU10CB5bSqCzKRpcpcbyDpS9UFYrtXVPAiboSOmjaFoo3TiGTDubPOctEXmzLg9dxThqMoOAZ96QjoKbWvIplxwpjfrQba9iFP2YmqsaBqka8YqHXKHlzX7kKVNK5J6mf5CwOwkej+2TFjuPE0zAg5nasBnvYBP4FkhBlW0PW7J+WdhAkCnYufQNuVVKWnS28tN2iwKdGTBZjlNzzaMQgnqYWvZGmQ1UlClS2XAwlZIt5PFRdKirCR+WAm0+vKPyF7+DHF1cSQGQNWBpcxvQLTJzzTKSXuGNFxRU9Oxb3e6CmBo4MIq5sMWdCcHqvsCVNgjoAVA20QylgR1VMaS0l1CIl+Dp/bGjrSV04pvTHtN5j84tDgxwSmIUf/yREk8MFs0pBghRumL7GyejYV2N0/opPXbzzzb/7iZz/465+a7L7+/gO9/nj56uLSzYOFKCgDBXr2SwVZNapPzLO8hXxAIeTsWGMZ7/JimZsq+x703wegdJ8oyMuTHgw9yruE1ol40pOxTJ5qaVQxJRqYl8dys4T0AHb6VlV6+EZC2RjNPDzaeffe3MGCf82Z8KPcGPjYnx05Jo3A1ZotY8HTPwUAlEVEFJGw1TEzhoHxXn/OvCXKcnF2+Zwty9yxXQPVVSOriWNXWHw/wGzbVYyT3K41S6SWbDfsHeR1QiNymsPslJnM0fHK4pK7Q8XR2PusnK6WEstGtgkenzJDUr94mD6YHaJYLNKZOLw8z8bCajSbXLpMXD7LfYrm2OFEelXMbDzv0ta0MihOmZhHQLI+IF4a2DPAzNHyjKVlcRkzqCW6nDOrOwNvLTrPsjVSGJH2Tm7ZCWSmAZKfN6MpWJ0jnTdFmZ/7R5kSRJsvbqag3+dA7j3cuvP+rX9486tEMxPAv4sb2+6JTJM+UvWqFjSaYJJzn87CN6Lv7B+wgx2RNMOgwvmsDCbiJD5z+3IiCS1/FI8+TDJPFYkxsdeaOd7avv/MzQ0woUBkCILOeicPW9nO0+RJqWQWOALsfBOhA83O+EiKrHkt0T30MoA4MfA/3Nq9d58sbVy+4lL24lK+xDHcQ6qZvDEIDZyN44hKbSWklOZAiXTGenqgptoCGeBKJyQ2YATZGF1bIfWEjs2Q3d3ZR7feuffelZtf+Aa0ITAldY7wKjWWV4Py2+7kaM+Ma2X2rAQmOxpZuZyfHp1GhkHy530uxBI69dQIqftkTRc8Pa8W4IDwzQCg75bNg/GBSblcA6TmgaHUa8A265+4hqHr0sQF7yBCotkpFfPBrY9ef/31n//iLRZFZLr6yNnLn391czUf5fb2qr6kp9akOt1KlVOMggod8gsr4iK/3t2BeWk12vLmM9cfPLxHLzYwH+fiV40++OC9Z565sbK2qVF8tsesFdOoCNWQF1j7igPfYZGPO5gHK1S1Tr8sSSO+j4NlviQGEg0XdSEcKoKWr1ylcH6238DCAk4gyJKJq7VATehpPyl8OGjpIGnIrNaC2T9JgilSdUuoC7jieLrjJDi29QXNlRXWYboIkiYa1QOCxZSXGmXZ3Q2f0ga5RA8AxIWSjq8RVqP56XMCqXw9RuTtUc33TBn23nj4YHFne33+eOl83zNxGOWZXyX3KkbGz+rSgZSdGvUM1c0RBW6tL8O5xeIX229ctlr/kHnj8fEvDn0jaX5vvQx6aoQ9t3ghS8X/GE+S/6Fm1WzpNKd2Rbo7SC3Gz+w8ukd1Z8Kax39LP+ubkb+mpKXXti0GhANDLysrV+Pt0ex5rIFmz7YXcw6Bdn6UuA2UTHPOD3U5Bw91egZEvLctVPSA8jOAdIsXw6u9slegKwLjCnx4duNkKQvGQ18iM0gszcytziyu2ZHPp75SY7MpnWCNnWz0k+lcmUoPkyiMygFpb86y6fZwtr2i7CAgNiqYywQ9YkdkBo3R2xbZAEmt6D+zs9rUQNjkTD0VPjlnhchP61VFeqILVVRj7RfjQ5UUD7b2O4AM/AEgUFqPzLVlegZXXwkypHZeDPPdoNMzT197AXFnlkUgFRljXvU/m1+bPz44nl1dto+7tUilnNw0SKxvLG5vGUFXtKmKKggT9w5dLyPt0Ko1v9ndVEWbhT2O/LNschlLq6fH1w4XUHWie1FMEvzTavy8HG18PI6tQLd+Y2tpidhQhZEBpEYPD/dmurOFDA/VZgntMyDkcmljbff4cOXQTtXmo92j/UM3ttLStumhSkNlglAjiGKSvbR09tOzRx9ukwK3FepzzX72REi5UT8cwcrZQFoAssDzT88e3n+0tODZZBNI7KrkkuelWV9vMdlJFz2lO8nhg60723funcc47+b1q6+88Ozh3LxPsCwuxRrjdMWHWVaXrj7z4Oj23b0DM+RlQzImVusPY1Bp+MxXJ666VsRjdEgyCzS5Ygd9fDCzZ6Vw9XxpbeXSwlVf+z3ZHvbQw4LCXII88+Ll6zdWNq6u36crbm1vmTq+c3D/7Qf3bly95ZbVy8+8EOg80efNgRbFHOueneyk3G6wQREnojEn9GmuaxGODS1eIzst2aKFwxA9gSBML+aP8QGrGP2rJpfRBCIXT+c8rb1ywCBva//BVmzuUCihjvJLidQaYlL6EwjHn2OANMYCWt26yC6V37XtviGs1QssnXOEETA557Pq4zPG5vJFKq6WbZpbvEkbsTs5LLVYlY+QTVQbAE5B+VN14Qmm5lRndZLz+rTLzPwlMSB75dNhfo36YWGKLSfgZ9hSs0+oJg9rCNbDNNYDCCrXYOAzHgdJJqPUQ5MUVN10gXjMjaWIFe4ayTXEd+TgN0+S1MUpg3mmPsMmwjE7I4WPPrptinA2+dZKlwQewp40N1o+Z87hLKnKrdGyoHFb64BXRGwgtEhtGZRaJBGiS2U06uIPVJNf+Qtg9AWqrU9NiH2HjbMfHaDHnSw1VGZTgzOQdHrzga9h4ZkUFJq7lMfRPP2r5KGis8VUhGnNabigDraOHPg/Iu/Ulu2sv6bkLcQMbR18TRvyp2GmC6rwIFENDxL+J+jRVJ+CoS0Ew6LkDcqc2ESeI+oT/iTeT+d7iycnBLt/9mDqHM9P2TOvPh8mnWJIa/mzzvx9KVNzbc/Ewvd3vv0Pvvvd715//ibzh7ff/oVcnmuwMbF6flnq+9tbCj1by3rMFd1qoyy8CeHYv6DlCCqaLEkyotT5Pxo4kFLdy8IHIysMdANkromJxx/hr371q7aVoUWVSIBOBMU7HgTw/rvvWc75kqGOcP3FtW9/+9udUX7U2r0FKQsfBgHmKcLAiPr/5b1/bSP1pz/96T/9p//UIJ2+U28pwoA2Say8FdpME9nxjSpj/8wMU2gb0CYQSNq6nBe3ZVEuJ+B9Dx1z9zCtsL298+d//ucnB7vOWh99dAny67/9LQ93yAKPimgyf6HqokfZQJUYMMqFtutCTJQ/JY9JbQdYoEntjJ13DPv5Sc5mkyRFYBJJyBL3Qs6jtH2rVV0Wl1axZd97LtV8SO3DeY2nFBvQ4XPxkN5Tr6F7Dd1kaFkAkEs1mRGgojpLmilqe+jyUD1BuV0M8PSBckNeuYZJuaWuITGt5xf+x5RvCJzkklU4rgKEFk5YG2f7VZcGSnwmO1x13tGKrSHbl7+rX1iDdzr17xQeMDQnK+enxDTmJ3g1+fkYB0YkY5b0usGlddoBm2RPhJ+jGyDqzxMw0zI5DTYdxqL+CeF0/NNhyDkjaf1NrwRT/tAj/ByRBDJzcrtFF/VVVud6GvkTMSOeLmL65xOQTyDsn0a3sweP0osXIvPZig21RUktFxvhQM/YJdBf2qkBszuTmUNxu6bLik4Wmr54BadAuyeo+iw/ZQTGD87ifZ/6PtrZYY735p17bmy89u77fM9rqMXp/CX12rV9WU5E9g2pCDPKMDajD0WK5/qaMMwoNIEgUQ6TxMhCa/n+lpiehFhMRG9kHy9zZT4w2RtD7mZOxEP8p7hH9WXz5aMl2GzafAqkpK4yf5S4brXOJfy4u0CGmMo18K1RNRa15lgyMsQW70krut1SJTBzebLQubtJVvZslVqfuTbKYJfnWRSHbFpUwBDATsf+zsryPA3sI4RdCt8yDwF0o1wNfGiPzGb9UQYCr8dh79HRXsK1aLQZHf3s9mu5sDj7WbUKI4RYX71DtNmsomUXQ8SUNc0Bv8R3xTGgw2BGJ6n3aLsnW2qmjnULKgMA2agtgIYPcIkEP6vVCEmQ+wmzqpm7vvXWyuc///kR/99LQI1GAhTHdQxfGBvNHDrcfOhUSSGsBEoMCqU2mCQSy+fEN/IxV0tyRHp+2Zcnrlx+zhhqoAcMMmC1YNYyljNOeytjFVOIgDUqvi7D772EybovQPSDeBuCeqv+q2+ubuSG1vrapdA0Ee6eVARDYR69ABRPRsjkmcRUZDimCqlyLYuIMWLu3NpyoL5y85UWwshDbVqNmMeAjN2RYQNGyGez25yxGD1HB7H/Iho4TNV1LjE2IPjILRoSPbRcQUgZITsw7Y+pIoOj2CiQe8+VsVtKuE4wI6OBLLAPPvzAbPYXb73jO96a0zC45K76zMz+2ZkF/5XNK1ghI6dPj4VCiP4mtVO76XMoY1ZdnFFledvasZektvyCbdKzYFCEplw7yY2QxXqZEOZCXtJSmr/ZiNswjyWOlHTk6GN+EyYGTMOPwAIdP8BQLuXEp3y66PRUZ2ywQjBkRSRJ5mQ8OkxPb8xDILs/w4A+ZJgU5GdeC0BJN+AEKXZRXSPwsE8y1eajJHQRKe6C/UO9ui7YBc8I35Ept+eNtcUsEme4MLc2nlxLoWlRpXvax5X0wb37etM/3FgUf6O+9RIFzfjG1+BqdtRVHmnuwNMVn47vQ6lMVU10a746d+US/NQOm9Y77/5Cp9iuIfJ0eVimdSn8Zhhs3UT8CZdSNfXrDlXhgf+BqVp3ZKMSOdJcqDLzQLYmABbeTTkAco2Vagyd3vFG8fTu+uCbJolgV6s1pC4BuEvs5uKrfGPoLekJNjT4N4wLOCyMJEsHqDpHBeQteryQMOlxaf6JM88Itb1lUQP1WFsgHRZIoe1Vr4dRid1nn+CPSCd3jb65MSF4UmRhGFnUgZRVrIOWzAjDk8Yo3S0VmCQy35o55JTT6YR1uuCRDRIr1Hq/mNZVLbm0NZwnXrlssMoLsgkjujLKnrw21stN0xykBQyGo5dQBYpPxVdSQwwIGziU1145qRPTefkwK5EM1NX0Cx0oXhYATYABzkrWqpACNCFp/d9jh/jG00Tyq/hBuTWejlSQdWbKqh7kI4dNfKo8cYoThLDnPI/2soDlGgYBZkPT8J06+l1ZP6s3zZj33r97JxeXL+97QfNXv/TcK6+8crKQSRqzaG23/2G+idLZFd2cUfER4ScFuqBwtebJBgl6YP88eyyrm5fsb+wt79oryMS65hjgOwuEeGifwX7Cwp3bmWoe5q42BWI/4b2rN91H9zRp2H6ebUzMyNBT0v40MSPOJ5K6Fz8R+cTPT8oLLEkfV6L4JE2aWO1xALdwm7CYOXcqAG3UQ0jHqM1Y+ggzxjTC6Z/DrSJRnXM6jwLJhxgcSmq2JMRlJpcvQtvFY2tRdtCmtIf6hX2oU/Z88w4vZNQBNfn6hpegl22Nmdz2VnW0i38lBE3K1IzXLNdnXNUou8AeLjLSOrfTuGf3ztfWTtbWWYW6QBS9UHpwxp5ZE98R5KCdPqo5dX/CRwskPLn2gmWZ5NRgoHZOiqruqSPiuz2MizrwFDMHrF0Wf1hqdi2iPlQqDVHsyhFRVbJUwETj+MG6iloyXuoV9LtV4xtv/43mNKpqW7VOMeX5Tvr53LLrYGyTXPA3+a+UfAqg6h7hYKylLeaOcg6xOru8eGoCHTsdq0OpSzXfsCwWnuBMHYvyiot1Vu1+Btlw4GlrXvjo/NC5DNEC7y2k/aP9s+y2jW7gclL7OupwKWaQooKTqJl4hCE0DANesWhAVBzr8LTgoWUAmALW3NVkYWm1oL91wjZhb2MA04H2q9zWepFkPwGwPRnDoa/ix8DH0fMYhkYSzI2wMnS48/bCoMPtRxqazjyhm9pVq9RXlQ+zcyGG5PVeeWq3f+B0kVlz2teJqXf9ltcI2OnO0exB2EAGmmDfhDL6kyXfhaewZ08PdxaP/uFv/Nr1r3/pq7Mn17/wAvX3wezBr37ta1eXlv/dv/t3d+fuMfqzXye7/5XlXMtJ7PLp7OHWAfNTnTFDF4Jra/vqo7m9n3xw+q7XjBcPzzKUeuIDCUtZVq9YupsBz65lGN7KYzxnrI/RfAj1yezW8tz1y+s+hLx/b39764GBeWX5ip54OLd09YVXrn/+iz/60Y+W39892T7Zf3jiC94Lq/MGHr0INo3EP5w9oe5n5lcN7ThR6u/Rt7719dd/9t4f/uEfbj3yDeq8ykas3WlgSrB4frrA/mz+7PnnLr/0ok0EwqxvRomYnWh7tOGRS1PXv/C5079cO3p3/0//4I9/53/2z/4Hv/37O9cW/viP//jff/dP//E//se/8w9/2/b0/+vffmdtZenr3/z1Gy8/99/+0Xcs5JZYR9px3t7fX3h0/8M7VIztBBT2sHF8cLJ0bXnn5PjaC897gH3fNxIYZ59kPqzWWYp0q5dMtDigpy9hEY/RRTyIRPXK6gtDqPKNXqv98Wdm5H5QbLrF5dlcSqK84REQjycafWHhaHVlfu14hVbUHGn9jdzBF8BbujxG0FTjhBQRwVmn0MYaYVOISE45ZBbgcIISmstBKF037Z+jn9mTpV7l981HTKvtAtjz3PMZ60BPIXiAJZszveQI+tKtcLROiGbj4O8AJajd/Qwjq/xqb8pGD8k1wKKwCSOUohNhLTlkrz9NeeKfcjhYcY/Bi5kU/1SGJyOexDlh0pNw07+nKOlyLkqXNPI5Z85TR/eNQaqhc8RG+MP04V+arZO6TQ1TVVbX8YLUKqQjA14lDnoyPzNgT4CHsi641MTHfFLT1dk4SZiqUWOL/qmFGZuKWtkzd0j3mKBtKvnGiIrThVVCRIQ0XVq4C418pjqT0WSaRRdopvjWkU1SPi+/MHcyz3zIixpkh+bJBXndQHE0bdW9/FpKNfsyvE+5oCJacQPu/tOaGv2QcDSqhmgJnsYwVRU9Ktsok2a9aMcj3waQmVWX0TlfZZi9s3v03nvv/fnf/JSmun+Uwd67DjOzV71PNuct9bkFWprJCvgDY7m3javgfDWdLvBIUuq4sO1aRuwNa9vOBLqHlROHfc643TBnXuC7KC6wZNbXhNkAVVetBwG1rNxSy9bVWbwdudDmK+15apGm4A8c6Xr5fOvJwfH2L26tbJ+e7M76+vtCHYLCjHdtC9Zhfuo7OHwbmntk8ZiqYTVTVvkRAG2IaUPGEcauKkwIFlMAM++8dddW4+72NrW5vmHb8S6aIb9kYjY/z1LVvZzLG3k7NbaBWdF5Q+lwZ/uA9dPKWm6tMQp1ym+b3zP3c0sxiF44rq2BUnsHh7s1TzmAE2288Nsob6kuGJ3s5mBi6DqpuSfCkMpSjUkdkAxZUcNLyxlPkd1OJCGwLU1YSTzFLR4SkO3OPO+JWhNZwmbiYE7e0lf2YD4KZ46xFPs1jVRNM2+AgCFzKs+pRSayDVc3quEVYIR1Pk9o2ERhYQzpdIza3TuZW/zg7oN9ua89/8UMMn/fLvxJv4oBMoaEh2g+f+ilrZw4RwLJXnOA3xzGD6xIciRh6HqdlJ9OWWrak0qW0R3zL9d3++x0b+f+w/sn9oYvfeUrhBmGWPEnW3qlQdxgSiQyhznMhnLJOaYkVbH8mqgPi7qWZ0hAVqoJXBrLEev97Uc9hiwtnrOJvvLMi1b+3YLsu8Fkj2jitHgwdENPIv3NaoWjZBVYSkY1gyQF5iXoS5uru1srjx7e/+DN152pH5femEJwEQTPcM7jZfNnhz454xA//d3VsZP9naM8lbN4EKOHYsO82wGmGeiRi8VpSCjaBMLnokTSiF19UqXa0BxWq3UYNgIAJvNFtTnaIPnEjNOEeobrdvlRNzneu33ngw8++OFf/7WrCHnqLbca8gQovYeMo/PTd2998PKLL2Z2eurtdV/ayR2pboUgqR7nZ88x+manZ+7kpexD9my/WKpX6kf6vqismRxPcvnFQuJo/41fvP6FL3499hnzc71To+7tABRDeNE24eSUS10nfTb2raoXqR5WN8lTjB1z9N2X0mA6XoqPqlNEjd2QK90D83IpXQsAqkGGyM3ZMGXtQWiREQlJQxRfhyYpFldJWRZGA4bPXceRAAFP4eanSyJTu88FoEQuqd1eIaAoQV0IEZ5UFtrJvC6bsBHb6qc9PdanooKc05QvEgyfEynMqYXHneU6yn4gM52Iym882GdReP2Lz53fe+Clew/szq9tosE3AYnj7OljC9ei9tO83Vy0XthZijrbW4298+2DI/v4P/3wbTEPVrOHtbvsWd6FfC3Bw3o+tWIELH2o7mL8j0KecAIEJ8HwRwOZ5pLJjO/+lf5PdVTWwicDQTi5VOIKraT2k38iIeStXrFIjN4EYHdp5nRp5lE92KiH4rdi5e1jZiZiymWuC3Ll0FbM0RKWnHtYOHm7Iy5lCjJMOnTsZA167Zq/Fvf8dLG8eJuoA+JIPZg04dMKm3hSF0jdxcjUEhBzNHHslkoMpAbA6ol6kTRvgUiB5S1rBJJdfTnlTtR4gMuFSiT5HUFKLQTViLQkrvqOKJJgXhMT8mJ+l9nTmXl3c/ONeUh0dB9sCDTlNudZhuhLeOaWlhd0avvMq0ur2RnOfynI2AvWHsjO3vbewa7+rla10vDc9cLlDR8uPl/IIcWpb26lly3M7By4SbFkGbO7HGJuHR8+s3R53YeUJtuvlqyWLAsuXDzamZ+/piJdqdQxrnRdzRNdvopVPfoI2JHecWamQUFmFyj8QDqrRfCEyW4+xFk7haFhZrZ0vLdKqlAW5RDb62ykkvA0kna18KqmDuXFea+ozC8tmt4Y5sBGt8zMKwETbKT1WrUojPQ0ufxGMgb8THPU/JB/dJKxUv8t+Itc6A9Rkczc5FAo+WhWuCLAoIEO3zvOfpQaJG/NB/rhO+9wiKjGj06rFkkl/vqjsx/ffu07P/hrG8S/9fUv/+Zv/uZXX1g7Wrt+tr59urZ+eD5og6HWs2crZ5mhBfknOx01fd2S1BeJ0GeU2d7bPsjU2Fsc7hyT/7Mtn6XYr9ENldEDyeWIYmbu5uaVzfWNFy9d/sWt2+ac2xptbv7dhw/e+d6/v3n9ph3qF6/epBzIogozvvtkQoaU1rRd92ngZpLUFNwLXeFJ7ZIrvL7IAaxqHmoFsa9z5Ud0NY8UQRFDBpWd3z115X3n3oPDrZ0lDZe+ThYtsrt1guXCZq2wJOpxN1W+hF7tPA6RaMWitcSoE4lRBpNyiWEHT55qYyL7FqpWqsEWM5BTVx6BGL3nI0M67dHhvI3/wUAu+dW86h6epKCKG7zqDGGKWKUcqVTEN0PR/kE+f9lvkMkHsvOOGDqgE2IfgTaoyeVHxrAJMMy9yKwSQoCYqlmN3UVFGqPPruvn6DVww3dkx3S5Y6qfHR5TBboH6hh2n3XsO7duvfPOO1s7Pmxzsry6ikKjK9YjJZjzN1OKxtBlJbp4hbECBL1hEi4HQAzXgYYPMTUg+TntgmrCfPUlffEXc2mUg4SmEoGNeHi+doFWprE4CJNQrgpJU6qpCGzvpZMYZdH+06ULt71kRya93Bjon0Y6qITbH2EarMalxEkVw6UK5RoyPsmtP+WHknTRSXGl1AZZHCM7AE1nmfiiLwR1DE8CF2Lc5V0UNyV4ItE8XRdcwsxJETlEhRDP8fCwXlc8XXSF0O5hXsvxQr9UruHhcRw8YiNUzQcmDwxtnL8JQGUtLcmOsT64dDNHFHoo/IuPL0hGtB1o31zfd8Xv5PErByKxhjNrED5cyBtnlmqIP1teINX7zkCWly9fWmcRtvqcfe+rhzO5lmiJ75rkzqN9MTc//zmEmc85paSsdYTnX/i6s8F7xx95TG1x84K9I/fCsWrflkk/US6jSr366nU/kdHA7ascFkv9rd/6LcpHZPEngoHLgEs60rJOIH/yoz+S6kDy29/+9v2jPcPVf/cDS5u/fuGrX3nzzTdxj5XN87/0RY+W3L/3dXX/9ld/WV9YW8yrUpiJDIdJtoG2Hj5QO9VXNUWEBrq4WrYto9EsnmsiR7/7EZK64RqgwyPMZwzIG6uQ6Dm7uPtaBG1obuQawhjfynDhKAcJwibxXRZSUXhch3aukzeLRPZW5pNER38Oild2TEVhy3B1VnGZioFCUn6UmxZy8OLGegHjmgZkcH6OqR0Y4Rtb+UnRL2BWTS5LyCkHhu5I9XuAzOrtSbRT4ENwLAiJT6f+XWMusD0lqNOopgmbDk/DCEsaEVYg1RGYjk/2IrwhawARnKxOAU+QBnL8McEzSfyYv42wEqayPdWUAGAmOYW/IIUmnG8kU6giS3FPDg6Jh2okV5aI2gRP0zf+nEbYBDRAwhUCEOBJm44ZJZIfvYP4sbxr9ZtcVVDnFa7SM6Ih6AlJLvQf4z1B0sdAfIaoJptPvIHr2467/ubNd1me/s07H7K82C/Zn1vKAw79IENx09l9aM8dztQlJQ216HHZFc4yllSjVK3Ywu/qg0yg7ruc1ta2mR54kfA4UdNDxfjZXU9GMfzG1mVBwqXgx50bJ8gGb2ACAK2MHwspn6R2HR4xiexwyigHdkxt4BHGTyCoFSPAxyKniajV7hYAvtgGhsE3qnxRRKqxhpGj6SYVurxUx5+LS27q7M+eGRDRbHzZ39pRBaMAYB9vpF05ODO9qJtDyvJTKo4Fpgys9k4ygvfixNyjGQihG/xNoRgbFEPGug7VLBLf9SKwAu26RpNf+SsjX3xQV+e3UBGw6oefEMGmGUNrHyFYhmaMsLw8yO3akrSipEexml2InWK4MMRwqpERZ+fdHfeBvvhLn5sm4+8x3EVPE2AK0RbQYylS2+Et10wY+dBgIiWRNvUVI6wK7fup0dVFLcmjQd/4+NIrvyS187aPReZUdooF0s61iQmm3WSeOZ0jYaXkTx/UVYkoQa0UGZkam/+8d+eBEjevXDV3Wtu8HHVk3VPtCKYDLd84H2zlOmnUaV1QcNY4qJqotVzCLgZiMF959kXlKnIgaYKn/6oO+bdhm15w6mpC9sGQal2tvivHKyjMffDq5uIh4eQKzgmj/IStajdgH8MttUN1iv/TkDJ2Xj6YuGoUu5LA5hg12Qr86CNN89qbbzk2sutD/eTwGaousck4Pzcf++iVV722ad8FGmpd3kY+BpKrHFEvMlKXmI5XWfj24e17SR+gkuDXiARwJH9nxxRREo7xGxUwpbQDJmfwTPgDDJJ2YNILJ9wTkNpuOqxyIsc5jLCWLREKrELNco9ylNcVKeBCpVCDxTSFyfC3uSZ1muDKcUHbpyNQNVqRZqDxsIXPoZYfuWpTsNpczqlCOZEdD0xEGmwiA2JQwolRLt/znuHYXGCW62BPxXN/xed319e9BsHZMMCi45N6FO6zEj5UCzYYdndD0q07ro0e3K9Z8cxq1jsKHZumA2nicp/OljF1BE4j1rNCpt4ESUsF+aTvBG2f8VRzUx5SmwCiIG8jnCZmfS1GJyvbW5LUggMGz9xyDlQIoiZY2p+hOeeYAwZXpn90eVCV5DdOfjhcEtv4WzD5fkKEEsiVNV8PJHq2AXrEVcZwvjEEekqkR+TTgQIxAtUoM7VOFz8SYAds/Dmdt8NjEWgQg7bWfuLF8Ns1VXDS/mKIIx98++K7J2p6udQOmklq8OC4JF3efMDQJ0kWImeRJQy5MCNBMCeHB/rj8V4iTYuAWdhSvU0G4HZdronQ6uznpmOmwrJywTmSGqpCV6rJx6SGBwcmlNcJoV8dP/pBVCNRUNZesI3mqmkbdUWq/UzS6Yk6PtrdIy0als7f2c+4QMPp157WgAfYiHkMdOSYJDuEiJcrU6KJJAzcK4HrSDByESgBPQJC8Z0x7D3OLFHcWNCnBEDKKLsAOfcpLO5rL75kbf65jQWr+JtXc5z/KRi69CpxgBK2jEx8VVy9hF0Oija7d29tf3/j+lVbHjNrmyu+Q739CAHV9Pb2BbOmQA/HVnr1+g2HzW/c/tAc2MfxRAowP7199SZ77WevP4Pbdn+GgosP05SEhgkbR5jpgNSGr8CQkg5RxyzTkE+EnyjFzykXCXQbhk+CYCPPUjOj1Ew18I3YxOMVv2NGekaAJwIAquc/EV0/RyydWBKfiQsLiQofO045Oc534NsOOo2UqxlEICbFzpyXMvhmiDUw7O+Zf0e+tV/ZCNTCkgKc3grsknLKQTxTB71PXbzIp0Tm/ATV604ez/BF6hAS/I85+EG2n4LiEtOR7EuCs84YtaRwrRQiWOFUpvWhRrjj8xvYUEj1+5oyJjIuBVWAF5rrLE5UhSGRXJZfZrzBz/Z5dubSpkfTr1FNt2+9/+47P7dddXxmD+jsWCeVNRPHAeelZ5+DU0Nr9twNDnHpOc761XyfHaeXqvMB0DkP7MI/v7xp38g8y7+V6sY7eT7aiWs2pJpVqWTUdNMP2aBK0gplmYvtBwvOcnaPHHD4t77qH2N3S69MgJDWUzqBiHWmhmfzUTSeSwpbSuEHL8DSaDnWLHaFGaUdOtAw7YupQPjGTX4KlpId+NEKN5RD2E6YmPHbpbGnnEhc4ZeuhjatjxUFjOShmfSpxNR5Uf4WMQ02UjJR6J23wAusl5o4U5CRms4YvkxQlZikDk2aQLfF/vG+Whxtzx8xqgr4sFDxHKRJ9f7Bbki87AiVCXAk2Xe3fexh+94jnz/wHAbxhpQSXJ53Jm+lULLqIO7s/MaXX2Xe+/OZrQ/2H+1sfeSDKsvn124dbv/k33+Prdxvv/xLm+du/ZoxLLAiCieX6Eif4537xXuvXz6aXduPtKB2tQ6W5k/zYY0X63pRPQKpoEzFyGRq/UClGdv4GMuDjVWk7x7WSRz9AAEAAElEQVQs3bsz997pcga2vUvLBrP1X/nyl5//wq3lXbYY555Onj1bvX519nzleGf/8sqNG5e9S3nl5O6Bd6j7NvbsUe0/MqTLUBGeO+b1MV5TTjMDXxmmpn/v21/+8pc2n3v+2sycFb156Hx8R0aeajIK48LB6asvXpF312k0Mz5UztiWjQzgqwcNd2dmP/f1X7r/07dyJvnf/fnlk/lXv/0P1PQ/+ge/q17vbD043js63dlfn1v0rQRK4dUr1xdefOUfvPiqtljf2f8v/8v/cuHw4EsvPJ+efXr84TsfvPnaL55Z2Nza2/vBD35k4LQV68q1Q/XdvW0GW1u7R6fesfal1hai6oLFZ8ehe7PzduNXvJIyv7K2sbK8d5LeLbP6UqsaOqwuyee3i8aaOPLXakPUYb5ZbwHg0tHRwc4BSlwRTSvnE9rQxshtY9H7XL4yvHC85pXTjNmlLRcsO40yWChG87HdEYjrS/1dRhOjJuav9eZdt5GOpZA+YxLAZO2RmCgFAh5q5cnVtAwUMfLLv3ShmFPpJprH3rc1Cmq6WDlSxdiNlobKD+yDnP5nOQEnFZd5dm0uGZ5i6dODSnPLga8ZT+DCwdyiPcnV0tNS8SW6xdikViAlfDZ3oQsm8N2LJ78+5m9x4+n4xhT+DPV9GuTxGKSmTsXkThkxmwOrcHR11V+8hve/5uBjQ3mRFigexzr+GuJTSqA/xRXleZF1AAsPNVSWNoahXDBMDDfhbSPkt2y0zVuGrNKYgFtjTxcp0U/wEKW/JA2FdZIfay5iFjt/QGD+VjfQMwXn26iwejfON0ZOGQpjS1Gr/7b4orZdtxBWCnSNbWclsuTTgCyYYcSCJJKWhHjl/FS0gTuUl04Lp4Kn1rR11G2oFBOD8EldjudWNdzMApOc0+3DGZvOr731IR3+waO8PnlodFu8elK2WoZv47wbUEphBs0ZtUcXOz62GGFyWAUbzbq2sHS4v+e6ac3XUkV9M902swXi4l/OnA9nt6MAOI9xLGUPGgjlwEFXP6tLZ2pE7854bHLr7qOZmUspvWYI6i3aFfnE7J3tvX/v7OBk59HW3qMVlmvemlUiGGUlx0SWOjxGjvEICZ6JG3+OASmlG6sdLBU8pIF21k7Zg00pq4eLvgqrlq+88rlf+eqX7Sd6eeWNn/98Z9vryQuPXBxjHDy7QJPvHG7NPtheX90w4ly5cd1kSlu88MoX7Z/AeWXjBiYcHu7TP5yYw72dlK4+bM8NqTXZwNLcYoK0DjnW5je0XT+CRBFlX5oEplKZDyy4X1QnhcGYll1cZagW4UktqiHwqmx+a8YSllUqv+/lV/+DTbHRfHCyVBNe9LIzU1DPTbPhXTCSzmei4TvYtSDfZ/wawTQ3xzyDEZUbxN30KWPien5rFPG+1c9/9qPbZ1u352ZuvfXjo815X1B4ZidD7VWy6RnivtEsFFxBlxfbyUSC7Q89pLt1tytBlmzeyI8jz4yDGIzvZQmxsJi7VvvHR492d7z+deRVyuUculcR6WJhfSuJ2hdIUcExrAsEuhRym3D9Up6A2uv7lgsGFMPjh7feXl+7bOPA1AiG+Zk9RDIIO9w/2bx6xT0xlrGlk6LrDCdwTHRTmebVwY+MTZiAgxw+J6YsCps0xa4m1uJkf+/O9s7td95RezBf/41fI5m+baxCsZJFQ+lwy9bAl9Oy1UBRWWK1dykzRu+RzxoZF9evbG5lDvLg1vtvX735HFHUtibrxnk4Ykdvm8CYz/aZajDks7GbWVyZW13Iq4quJO3df+CewLbP7aDn8tx1ULa15Y3mYA/LwDKDSdYaRCd+hiYuspdNjmEEto6tli1dlDYqoKySahuxbC2jfFSlLpjDnzrlyXmWp3sHtpVf+/kvzNbyru4yBRvbOcq/OYFBhdOvs4P93cWYIApnYrNeEm67oCDj5bacCZCiGZ/Wx6mKNjOJbNXNL6w6c/rKV772xhtvPny0q4/UcipCwqU5snZgI7K/s/Xw8CrbxsswmmWN+AVAtq8+0/HCeU7dIJLhpSyOK11/BFgKw+9qzkk2Q0QFo74z9rRL384YsugDBru79syw2wYZ7bG+Ggt9E++//Mu/VBF3BA/3sh2sIqH/iGqw3Zb5U9Q9lOF6igjN9S+/0xAprAPCdB31ELVfSXUGQ6jzyEA7otiuf4JUaHKVE0jpdWYTpIWEr4jiZ2qlQlxHdul+jgHy4ufpTFXE6okUHIXI5883vvXSKwu33758QrYWbDxBacXakyRY5TLz5PdIbUahRJ4GOM5lnZm7ZeZ+fz197aeP7tkyuzuXL9Y88pLx2vpxbertlX0x2UNG3yXqETYdb0IkAlPHFJS6CLd0OtdrGDjNtyPYuiBJ81Ay5zWPRw/Oj4/0Hxo5EdU3UVl1zyRZXxavf4rRKTOh6+6T/KywfZ9nZv7F6+zYlnLFGuS85VLNqrSz0s8Xzj0xwVL3dM/ov50BfdEwUvsJ4BkiJ1e8oRXaqrExlCCkg3HeqVKtwxVf7p2fXffClOWgGENGcjdV3VItq8wjuWq6BMKZaLP8w6GsXLRpDf9V2fzyvwg/G0NRhc1ZBRjAFdT71WmJmq6A51qK+GQan2OgHXkiBcWpUBA7WTYGhediVzfSAco8y4FWVtVpBDDg1UtAx7H/hJ6tve1rM9d98yep3nc+Pl1dW3RXeXneenzW4gVxNzeuHB8y292bPTw/PMmzlo82F94+27m6vKF3LO3tKgmBuOBK6v7Wwebx+cr80vbxTu+AK6VP9miJ8Cbs6fvK+TyJezd23Axe2Qysm1kIIZ76VViAY9lviQRGUMSeu5uDi8FC+6q+urhnKq0eQ6YAoqV7GwBO4ZxL0xFW0u6nnRx7H4MBPNbfunf3vu8+zS1RzpJg7P0frPKjH2IKDdVa/rbC6V4vrqNDVMSz64Xe7Adakil3dWN+dXnxsAzjUG5qZN+D8rh246oD1AwFQZKe0z3OOJWowQmDSEvv10NqRzNrUozZ3B/f2p+5tb9ysrv2w7e/9rlnv/a1r7103RNrl1fd8rcDcxLzPs+vhyetjvIA3exyCa5JEQ5DI/UoCiMDLF8lQscBEdjz5Dz7u7mrVxdvXLm2ESO/k+08DcQcnZ35XuYJxiq9cHZjYWbt+o1nr9149MIrr737jhFtm6jOnL1x/9abD+88f+MZh6YvXrtpnA1XFeriWbic4yIfZRDT29ML1VKpG5jiTPOHFCem+pT4Jpu4tXSgupJKVLISCWyBh02lLFrhBAOXVtbdavd1geI7OV8/W3p0797RwaHJAJnwLg3WJW/RIEuortKDe0LJE2E16pgGSK3aJf/jDugYKeBnyqvllkIDWzfg9ASptSGmWhGC40p1iVVX8ZPQ2wfRJKblMNQDX8EGQfcN4bEgkZUkAkEB81cmAQOb3kmmZ/McWdok2mZQxUOtGr6SM3tGb4dhEAjNhT/6ZhL2N0mFQPa4wgmwYaZ9iePPqfBFpFTx02WJ8RMTCBbDTTxhUhrb50cPUaiQ4JnivUUe4LGUJwJThQ4pgE07wtiSBgyhbYA1YxpooCdTHCpqkLPQOXD7opBEuoh66RJIR3mm4NvnMaE9PMkFf63PaxHuv7XzPNvnaWY0VW78MdCo4eTgbL8jH/cvOs8kPsA9Kemy/OwkyIdAQBIZyHIdnvipabhbqfxiTjp9OF+uO3DtHAx4JnlLKhQcrg7SAkPnGmBqmBzhpwOVq8qtHJ2Rz/Wwqt0Ro+Go4BHnWAXZhRtJhpGzM6c48uoCHZ+KDJvCVcCEIVAxyzWY3bxyE/LDS/kQtqUmg6+7770nl+xdXLGiO8UQkwPeSb8YSVKo5beJSzJW3qaq5ZaUBbKM43yqhiPe4A/mThF84CmXknzyM3u+aU687RLD/v6VzZsymcKioTpp9kRQawkvQMkqi+00vwFAQqsVmhgagGSy7GgAviljEwxA4KjenFpfz8arGI6u5Hc9m35sV+Lv/M7v/MEf/MHB4d73v//9h5cWf/3Xfx3rDHgI/va3v214cXaqdDXCVf23y3rjr/7q9u3ba9fWbak/PDlxJKi/qJci2NOtLq6+8cYbINGpvZx82mdx1Ll3tvDRR3fRh8fInBKlQQPIkiG32lqhFgkQdr3Gigh0zCf5ssCg0RV9sLuHM2uXNrQFNSyv3FI5IdVXr73dAwqEg3DpbCkzhtqSFtAWHGCW0QJOLYQbv+FKIF3rcZcCylUZbQUwxEgy36kRIXpb7khgplnhheK0CDeW+zjix36NpYjVXnCin4+gEpgwDUwwlxOJHjo2kX3nsBOm/IYPSaN7snJjwscHpnJeAEyTehE7CTWFI51jQPrjGR8jRdKEzU+AXfwsmHBDhTrcZU4XMaEif8FIij8d+9nC0ziFW0hKPoIsaKfwKKJ/JZf/4xIx/JlAojtN9pndiPYz5xgAOyMp0lOKGGIZxds94u+K7ZPgYe6CUs1pdlQGxalvwwRAc6ThbCQtsI94+/2fuY3xzq0HbHJ3a9V7lGVfHBiHiHxZ+NYGIvHyk8gQ3/jVV1h3GyFlLB2bnmiYoRx0KqkgWwmIN3HwE2cAd1dVaJcoUhVVxPqfueWwAT1inwQOGIncv79SV3xYJMjLNf1d/Qngxd8uApiowFTK+FNM6SRVHgCkN8KgLqnuKovvn1Q3i7Pnn3/5m9/85sqldVaCb77+Op2/tryqds9dX6UVPV2hLqqq7rvMLc/P/z/E/VmTpkl2J/bFvmZE5FpZe3V39d7oxjbYiBnjLJSMGumCohkvKNOV+BX0dUZ3NBNlotFkMtrMiBRnAAwIoAE0Gr1Wde1rZuWese+h3/+c933izcis6gZmxuQZ6a8/7sePHz9+fD/uriA0qiduI5n1smu2BHoq0jdfS5FZWb6CSzYOMccwCwYmLWeuf3O+JNMJYCiREAMt/wypa4wR4KloDGm6K5exFARbvgFDxW4kzQdBPgtASMwF6RLa+wo5t62drCXCnoja9SiqY01p4ouT4AtNMDHIk6gtwyGVgf+IxDTFvfD8dRi8Sv/888+/sHa9Y7IB4GSG+I0ruQjOsXTyfqJlkEZCqwHp0oRWuqhuf0GYo+uPcKbND4YS2sRiio2JKhYjlv/tz85nTUY4BpMo1dQk/pi3iljGR+OTSXIrHcIj0QGYY8gStMFS+YI5nK0s8BlSnHQPnk87fv7zn3/ta1+7cv1aMlhLt7IVwoJ+lBfpwq+AOnpnuVMsdzhgmES58sHRptbDpMNt3QCCIYExPqHF0hk76oY6Z9PGOUsneVfDQq3McqDBnHNIi4/cWZSp+BFpLQMkZCccHp2W4DFehq95XDONit1Af3ISU2VUI4H+bmY1neqS1u/N9z9UAQ9Gd7wmWdzIT5nOuAKP4zSKipTKjXmWLm0UVRHpsLEyC4ZyFAu5PB99lNbMzhqYrHYbddUytKU8Y7lHP/qZWAAGm6NLloTQH4pSRQle22FFAQNrc+ETGH92O1L9q5sD1nHZATjP3GhBUJMiymjuUKi7q1b6kAyVFp4mD7ARaSvs85TTLjUZ9NmtN5iUIqWIooHttkpVjBgAZiBngLFFyYVyBoS1lOwrcb3aWYb/kBfwTGezKL1oyWGn2AGDu3PU0dnoHGJOugfPhjRE55BZ/u7fMMtQtok+4nNGmDDBwGRyHfE3E55yo/O9vX0R71ZL+PBSlgu3FrKR4NEbME0Y8RSRZyEYEj93BFuZwXEe1sJTjSpsABoGqhbv9sFzPTo3mEGEOHC14Qm7T6srfCBHpxf34jFmETACTxN877OHAGSUaeSjIV21FaQ7/c70eLpUkti5S6xRnRplZzIXRcZ5PaKpSbRmVkJDtDmK+Eo3XOLJZkbEV707L+UOK7uT4Ky48eooOJqPSQom6vuAtmMNUGLIda+7wzSJoNzBN6KtFqYuAKgjJmx466woPCS9MYvCoF+NSAHl1Wfz3tjAws/DXNpmtiNUAVkziWKh2pFbN7KerKM8W1/lmExRXGlpYG2WSLqDZI2DzXTq8KCaZ0cnI/zFbYDEKsAQkHJPiToF23JScRtNbEHgGY4OEovh0aE8zf70BS2NxFLuLHtKTvMblYPSIjjHWDg77qQnt1QmfXxCK11vMbMFSas2HUdtXarA+I7mjg4Mw7nTdlWspjyZfAp/+8gLirifafSDP/nJT5xvXq9rr7712gvOQF9bvwS/cdhA8CiVZ6EoysO9ASaN5GlWLfiqgGbUSv9oZs9Y8ax0nD27JFShiZXlyFBusfrKb16/RiP75x9+YDGQFgPm6KDJySdz76nFN6/fsAQxv5zG/IlGf8TwlpCQ2MRcIJaneDy7lFt+fCaPVfodEVhHLLBqFnmVbKAZgqReG4iW/VC493APkYLwuQDTxjbffDaqwZ70ueDuKA05GkgN0SYdk9Ga43zc1FnFDLB6SqtCLl/NXGWOZEWUjUXwJ+oOadHs7Botni6frhxZ3VA/PbWXfQzCBycYOLuJHyVXtxJ1U9g+nbMqvrx0iil2t9gqeDMieHp3YqKGo6UQJwFcRO64RqQmd9OiASn/qiqwlBFaFVJOk8EnjSyOON7Z79DGxl0I0JsMSd0nz+gveHVkbZVIcd/+9JP333+r9mnNcnlQNAiEBRikBsns0jNf/Ou02ibJyDzSFlI6dVXj/AKdZdk5WiQeJy5KLyrTGmbIgpxxZFS5ZCupYJpWtAKGUP6Pp3YPl8+mLi1hMYopbGuWF8zToukgWvLaOx7FvMJDHKu5R0zDKBuppnRHCURfMlQophpGB0zYeJw1Kuh8pxHvUM1soEfL3YW5+FMRxQjVIvghdNAbPvJJEQRzUKv3AVMOhbH9E2QZqlbWeNeO/ijFEc4nqaqooyF+ucvK3vYIX3tG5MtM0D8ipv3Z8hMmFDUmhKr05eNljWtrqA1g2NiclBUkiWWO4JJJL5DuPqTXPs5mdTZ2SqPbVVluDH2Y69H2HsWNmSW34OlIT17/9e9865Uv/4t/8S/Iyf6cl5GT5VzCFTFAEx2xuePtxEgh5S9lJ3R+evn0cHqpjlkdlVS7vkpcN5sJzSYfc2KR2sZwhiO5ic++YW5anl5bWpndn3301q3DtReuv/4aReTZO1sL970Fn+WPx3cP3KPhAZV799595+efXll99+blFRjWDjNruraR1d7N7MeeHrtjet/+9ZTLdyhzOMa3fXi0cjy9fDS1SKX3bGppfmGnBqC6OZ26tmd+ZvFkPw3l8ckeSc66o8Y04mDSX+Np8VyW/tWNb1z6h2/8D/9G03/73/7ZX/3kra//4z949caNu2fb6wtLc5eKe6e2Rl1R7cKz1ZkHOx///M0/+1//2pD+yktfPl1af/Tg4eH+0e/+xq/Tqrv/wWcvv/LiyyvXtw72bn387tb+7unZ4bXrG/+n//q/VAoP9k8dCPq3/+5PdXh7R6d7e3Z+S0JKPOmd+cvxEdcqGuJMzT/a26x2VTXtxiRSLUKXcqTpgqm6yS81wqL84d5R7nk+WNnPEVo9paF5Ro3VWgYKR9waSzPOLZ9RwDmbz+kW+o0ZnVh+wUltSexUF3vSaXUzQCsTdhYtoWrc1bVXKUOnESUZ7kcVKg9klDKTpteePJGmi2ALJbso5DCnMc/PaUqFZxsZ6RxXYxK/fEbzyW+S1/MYvCnogouP/Mld6k1qOouelv5Sj5NQyEPwuLby+WLTeZyESav2LHNO9ETfIK1RBp4VhV9zb+wIhcwXRBFUoVk8KkfH0Cafp8+/QsEEFdNA47RSIk8bYEUta5TD/h1FH+HoeE+kVfDF1WJvOvDgCJK2ORpJkoiGXFoZpqujwAEgvmUmZGCgtvbkSW2wRX8LYPAYKZlw5jOjolH8X/lHGwHWq+12y2mxaCpMFJJSVa8mWyqde1xNxfm7mElo5EHYEtUcOPdxeKAmFXsZ/E8/eHT06aef/PSdW55Vube5w+cwGVw5WUrj78wRuxrqkKJ9b5yqE4Tj7itBkyZ8Z06ODBqW5xY8RNqntKJddaYd18DN5IjZrgYzbWQqWDYCXRR45npYJZLH1b1HUefPzEw0AxYfoNRMkTg1Efzs8dTOg01qTPJpBMAH86TsrTtg2492vfFx78H9R5tbR4eedh/psUBevC0KS2xE9MFu0wGdOT7D5yjCUz9g8IQg1sHY6HyKmxMoZ2ePP7vH86Xnbywt0WLa+/jWR5rrpfVLLzz/gq7zzbsPtc8bV666a8GSj3N8K1dzzRQGrFx7bmt/jw7m/Uf35WVjYVkqx1NH93buex1Bb76ytm4StbB4aeHSwsJsJvn4o+XUivrfizs017nNWnCvj2DPzkfrWaKMlrENOrul0m5zY4kmWOnKeTKGo275JgYZh/DMwsR8VaddGt9QdLOTzRTddGbdvegMWhGl5YwmklGNZYwsSDl7pPTTo4+aDr8hREtuVyPDizIocSUkczjnrNPe2w8/+2R/c3Z67XjmcPdo/+TDN6595df1MrMZyZ45y3XkHtZapewTAVAxCp29u56FHprUIBmMcnSfg3IXAlfO8oCYWxHBICyyV1d+RXuaFvel5dnVpSnn7euZo/B3zC7wctiyFLy1xB9PWR0JajLDXw4rvPT24xMkdbtqdklhuHf/08tXVlwHDX9dqx09KXH3tg+yZFB10L3YOquUx7jl6VIbM2wkwDxDwYQZ4Iloe4MZMT0+rtPa+/DNXyzOf1ta+KYfr1qWOjmgKWlQlBndVKmPkoMHcDcETvkvLHvt8MSM450337i2sX7l2k3DDJNleGjIJtG63f40RyL2T/e3DEytYejeXWt5MDP36te+ZcJM4xFbkEFuRSGWTMrFgSVjAy83ZKYcNYUlR/NnzWBq46RWg9BHNks7Zco4DpWhree34Ts03RU0K7QbqQZ2qkx3P/joQxtaxrJgqFlG/MPvcMjHcNIkzMkK+cylxekb168tL837c7ZOo7Q8l+WkK1ezGB2JLhywWXTWiP2i2qi0XoYmcqDFW8rTrEuLyyura7IM3C0G4VL1At238VSCm5uP/GGuDBFQzamuj/SkjDgkl74pAplERalfmeeMwFWjV+HOwHqGwF3sVCXSonZ7XpHy5X/1F5Vx+EZ8qvCcgp2y8IHm/d3tE6ffCv72p7ce3Lu/vnHJDeDIIMM7O1vKbowz7TmmqnfOnQm1ECZHPdLzKZTNx7g6sjouIO42Ax6OC8ItbtreWi5nA2h7iIKZ4efYDDDFmfgqE7aY52BjeCvepHuhRjvLNNlnZl57+aVFF+zORd52SnKc7hBRu8Fn7jSbDZm4VGu8t73nBqjaOcOQnU+nc3XS2Vdf1mrdWgqdj+cX2fuzWSzbr5bqpN7LaUpKAkeni9o9urdFSk8ZUYh1usBxUxOeG3OXZmHnWtDJ4QH6XQwfPqU3d9lqRDn5R3edQ9V/kN544nY1JmQgf5WoCZ3jCgfLc3uLM7OXqt7BzmRZzPA3Tukeo/VsZn9xanppVrm7yFp6hSEdNZgek7Tsjc5jVfFJJHnIMqg7bVPai/OenJq2HZv0Dc0lVf56CvfCaqJtWqIWqbVvVTywdCMxeFzjY4iVzOX4BIA+hWxKxZ1uLjiFRzM66VbRG6UUtlGdl2LJnTYz4C2D3LLruuc0EkkqASn8YEvrGlTUR0u0ylI+GamIJfc6YYuGigglmhMiX7FCvGvxRTOndXB5bSm70I6NQEs0bNS759WMLbWluiEduj4QnsNl/DnbrTWrDw93taJLLmy2ruKoHV1yOqSnJ/uPdq6tXjakwj+PeWFRiYcCGfWM4KGt0s9Y1dDUn0D+BLw4GsFI1tJSFWQtnQesllM6CBL1PZPJJ/pHn9E0CDa5VevPnF52zij7VYeepDCHXVzyvuuBa8b03UkfkTWXav3+iXkHDBdMJ83uLhUN5BsMR9xQaSrkPXVT7uz76LYipfFXfdyLtb3j7qeTvSgBdL9gdCoo0SdM+2iypksmS4bGwVWLd+aWcOlsbp29f3r88ac7f/XpGwt//e5r1zfoRP/Gl56jQ3bpeFcLuaDCZJSS6J2GLHN0X6Ph7rQG++R42pUBi8ebBzuHR2u5oWvx2oYFtP3HWxpVNxKlkGpsvFDamgSckC+fnK1tXH3x168+fPVLP/8oy9B7WjMj//29ex89eOPjd7VIbjZzZcf1y+soWS5aVA8S3RkjtqGwakrT2Wuqxkg+jVebw+gMWNgVqJRc+9R3+YdBYWiMX/NxaB3B1iBEzPleml7Yfrz9yQcf7O7s1ISeUEo+RckAaLvTGnwmHZMADd/AWT8S9ksNsAE7sdBRVpTK5ETk6HOkqEzonIIKZi2biOTP/sDJcZYXT0/qTczU+fOkG3/7gBc4JNfoyZZKKNv5rCKIPI1zrsdMrAki9VI+mdBQDXfjabRdhkPy8fR/XDMb8pfaiVVmNC5o97huFL4AkCrCZADEffvWLfv5m1uPheIJZqCN7VOjZqGQbTZiGtOD9bG0VUIDueMiFwtOdUY2bbngcJGQWl1Zz3CwfTiaFYPdPuywsZoeUjcJLIiySaOiU3A2k3Gnho+FVxUrZIsCJk1hefIHw+bPp5Mz0RqTNJGHJFa1vDHkkxn5jD6CaCTlSeUpI5x/1cHzatBQ4UCzrX58DTQkqAxIhwg5OSbskVwNeNrRmW13TfzGzpaxiVxMoOJ7nuXu4Edo66hRT+PTPo2NVHrU2/yECgu4tWsEo7VyxrD5xYFOjpuD+aQ0nWm7wLOxkTdzdrb3KGvcdFNGIQcziQFbIDEiMSCjOz0ZFMiRBJW63FyGZWOZys5kv2U1PZeXuA18Zcdj5FI5OOlDCpEEKgZ/+Zd/+bsv3bT6aRbkMxHpGmxtPffccybm3HwQvP94GQ3bH9zSGVxZuwTn0fqKWEvPXZN9l/1nCpFZRHKNG+QL8RyNgS1I6gw6Ixg1E2MbibG1HrKJ8RqT/aMAz5/N2Bb63d/93b/927/d++A+Gm7/8RGtluu/8S20qUVi9b2HpkNU5/Y//swmKk9L5K5t0kdTbQ7OqSn6Mvv3cpXbpee+9N3vfveHf/Yn6EFDUwVAI8C2QZpcmL7vbfnsspCdPkCNevA+lZq4gSwjxYYs/o+LpMOeZQ/AuO24N7GBCrVsrM4qCY2nMgN+SSCHHUWmMiC7zuUSU+OCWri37CS/UOGhSarogpDK0TZHoYo9JMENW5WebaKMgbrsDFjhaS7JOHd/wtmEtQ168pMbgKaSP7STqcR/QilsiMW/MU6Gjv0GqP8ojl8llQFmyOngqMyOCOPZ/nLxebQCEFp2gNs0fu4hoc+L/nf1b4TsLv18Pll2EI6pqEIc0wC+zdNtaUd5JiVQjbuphP/7ZwcGOElRMBdCPm18tmfbX8DzkPJ3NJIQI7mJY5Q0hrzxxhuUSd/74J77AQ+m06E7XoQAqQ9UdVyRRg5kV+rD5xfT0lVGLYZWdavty1EMn5XZSspoVehcKQ+axg6t2XTaeS0JSJ5MRQmGbq+Gz0kyeHa7YVR99XFevNg5yoPglzYyJZiEbDd4piWZD/eF3BWJI6sBcI+jowS+ELWnTy324sm8C0xkyrDeIwSy5+yLtlF76JyKTkdf8+LSsqORjrCgjSdercw72ew5Bc/rrmzu7cKvJWdvP9yMezlXVOnXdBDdD1q3Sm91KW04AIaINfFaXffoIungOBqFnnMM+3LadGdEvFtV3AZdjXORfz52bQ4Aq/VkY4JRk1uYw4dIQ0xJRUkXDEFb7BW9EbYduJL2FPG4weToYh1ggMFgACW60MGfQxcgy1SUDh49QvDKyrLbsd+bXqe361Oo7GfZtLYYxA4BhYTgiC7fRpVuh0CAjh5LzTMlYWlTovawpWtoFp+Z3OMHTFk0JfDzqctae9YvRrIzyuM4pzzFapoFpfsp8UiOypxHKVaA6eyLxezuPJCjr3zt6/yrj8rTUW3kzmA7JbgfFS0AzNM45XGAl/IocqU1uBEzMHbwhG1+Yd70lR707/3e73W1gn8A+GJHkYPJIamKxlJ9jiZIS46ee/5l4rxgrYqptsXAKPS732Rx8Xj7oXKh+kwmt/Z3lMvzL79sEXxmObdnwtDIYcYiZBgVZHt7L3vd+zu5D90WV4eCxLCQPRY20UXhw9EwccdjhA0roN3a2TKsQqql5939jCRP6hqESCKctSjMQWzW1jYyMNy4alh1aX2NvX4tD4feuGxg6TaqnI6aW4gqg/oSu2YTvc1jTQAxPBmcRVKtSGcAjAwZkXpzPkvmY6EKcFHLYd3dmPDq9efCyOpzxe08Sl3p81RdB9PzkeGTAyp4Otci4nZz9cmSDgwDmCF7dgkbvygHjoDXmygGeu+98w4AVQNP7n52B/3We4hQa51vbT1Gp2JqAkCalok7UnKrEumySDLjMkqBTcpuR37SbphJvyfpnwyJG/JJrwufQ5AsD8AhqGPZ5VAbI0ExGm0KgwpZ0cuaW+dEEZGPZRVRlCPlzUcP71t0Jqi41Nu3p6fL5OfyWuYXm6bbKa9Q5Q6aIVGOf0+TYhtLDmIQzL7giaSIWSl5CGWANQx/otn5RYmemqhwgBl2L3wC5iMoua5+OVjgKdmzxwhPWbPLx6kRO3fvpp4+aZJi+YzpfTJ4/DWGSh2RJG+bFhU3sasejBo6nqG/952KSBUukBMJIFLu2kd8GR+n84SQdK7ZMjoCDpLgDwHVEDUMhNl6HifBMeHvKxQkibbGYDxExBnXPuIMmeA2v0mEMpV01oYzaV1b5yfpoZuzXZdc1LEY3ZdQk0rFSt9cbaX6wkfzqxVVN4cKKFFoH29tCU3S1VkU/en7emFREMMTMJgGk1ZvfwJrDjT9nVNuUbiz+TWO20GET9xs7FbGS8yERGy4BSV1C48omYucWK9D7enUgc5ORRv3/1L45QYSGCQUGRgnJ4lkrQwU3FKXt9BZn6b3Wm8tnFDAbNXWfHzB63e1P92oPi/54Om+piC6wWgfBc6hOITQqZCKdXRlraMx6//ppRnHlH//m18xMpzyuuiEYDydFjyd0KQNFYJrQO0d1DOtyuLV9KfOiMEgp2zPpsQuIDjgQaGG6zeuXXEy6cPPPtWb7PVBzFzxdJDHse7fv7K2auvixfXLsOWlFzJfLISqMMRS00NMeWj2BAGLf3EbIJ9JI2j4bJiqEPHroAGAA6qtB1tZn3n4sGOl4ApDc+DpWA022JPYeIo1RCF5o0FSy+gQ51mO1PZO0mubmApvxcLc8dDMZlZtjAQpjT7bHYeG1NrFHDtcWaXppmKfTO0duYAFD+o+ODi73VGuiRZ2tUticUgo5Mg2lYgsvadGq2MjYhL2lFHXOtvBUNgaxOfAjr4ZqvdVIDPUDUbAdVFND1A0ZhdwB4BpyAobQVbhyWiI7YXaWtBZWzNCWrNWZkT14ftvb21uGsipnCjMhiHqxDFYsnM3G32ESBjT+0sTw9ZKKrftJOUoks0c1lGy+3BMT/+Ptz80p/pHK9atpub2o1y5YW+8ntMNR3HQxKVpy6Yfn1q4tHGnTZjI4u7c1L1Hmx+faD3nVx6sH2jG1utShcO8H9oL0BJHpBvzQmaR08M7ZDSR7WiJCrET4g5tYCI4yGkuJpZ8g2RaosoVRtbWEYAkw07UsehzA04UrjIVlCXpRt6iKF787ap1Ba3onK0v1ldk6UQhGOhs+BHS8U/J4Ogjsjs2yAIvPra0X+9N9cdAW3KkEIpFbNNQsea3pmad1i1sKVLxfSiZ+jN+BuMtGPCLLnubmd/d3BIirSArSdBaqlB0V/nM1a7FK+vXZvdPrp3Of+nFL+0QqoPTlblLP/jRL376/R9fc/WzTtFFWJVf7ITbiAuR5ssHD7ejjx/V0docr/EytAza9FNRbLJiWLJNcuksubRSS2sT0+VNR3OOJFBqO8n8fH5VEdx84Rp7ZnvX8vfDv31j9vr1nb99x8HdO5/taUwP337/+PH8yddWr06t/f6XvW8zf3nu0sf3P56/Oz//iBbA/tbR1rbL/s7OHlFmMJRfylT/0rXLukPKWmx3QMv74kKa+xVzqfnV05l0MHtzWfU+OMkRp5PlOv7soiZHvsc7w2DcYc+4BdVg/fRrN7/y8j+694N3LE/c+/TjO3dun/3N93UMa1euGmQAM3J98PFt2GYXFq0uXP3qV775zW/Orl2C8+2f/NTRYw9SbT16ZBfs3v1H+68eOcOgTrmzQq0/OTp89+2fye9L3/rOt3/tK794+2053TvIWfrsIZYOEXryjrl9O2ltb1557jnpXrt62aildwDQAIaNGxwXzHhiE1nLKKmEiG3sGvDjqa3HrvPKshGlIBZiGA6tEANtqjPkJbKNhWDU/dNpJRCDz6duITudnT+cOp6bOl7MgMlGYvzTDGRqyidNdA8A/RS1jdkJV9NG++q09fvot2FO1besaovIZD3b3gCxhi9howE0PPGo7Buu+KoUdLHGZEGvdyH7gYjc6n81qwbH1cGXxFZnb1harCuNoUkmNkvbDpJfZiZby665vyzGs8N1NkPAOI/oksGhMx2Fj0NHn+rCuD2MD57lp9oWkG3ApDzLJLTMOI9hsFTGnwnjBhv/CJz++JxD46AqliqFAgfXBZXobdLudes6ltIRzqpr6ekYw2LUV+vHFnGMf4ylfp/pOYaQ/aiBDsaoOZOzz6kdDQZhHGNZGn2GbSTHK/YOU8wfe9iamjFt4LSIn0sbmObFuL2HIP04c7Hk4nfRtMx4y4FRlRhPmrgc4fB04U/+3Z/8v3/0kbYr50fmL51OZcuwLnVMvUCzYUIidOHUMKjrbKduwU4gxGIF7JnGwzIryy6epxpp5JNTK4Ut2oIuOIvi3tyJ9T8t+UkGcZZH1THarLoKSlrqO96TLu0G/wDUjpTGpCTK0/ZqIf+mM7zVIuhn6K1qHq585xtanpPfmX/l+PjjP37HccjXCW8SLfhnEFzDi3EPX6Umc8kdN5klQWhWtl0ine1AdC1y/X8WYGcvX7t65erV0082H350S6wXX3l5aWVxf48+x5Y1K23FpcsbnhRcWV2Zn577+Xt/movFz85uLCxTV3Z3xv3dh0s7WQi79eAuoX3luRde/dKXH8x8bDPypedufuMb37hx4/G/+Tf/5uGeq6L2TmYzdlqrQ8pra6v6F9yrhjHD1tFJhZRmuOT9JQphyU5diM4Bsh6NNyzLtl9VZDzNaJYR6nJevpVFLQU8ic1f0QSgSz8szdS/lM2yVEBBKyNNLkHGPph2nE49UTLhxcOMwBRDFVgxsron5bswKGZrbYzAvbAwN/Xp5oNN52GWprbmzg53Hu0tTM9uH/3Rg48+fbB0fP35Fd2kHnhm/vAgDYvuUEbcx81QFWX+7eM7lsY+uPuYFK1tzJv4vXJ1g/8rV24gaSk3Vs+uz0QxfLX0x7fWlmsyfDZ3aht1eYGa6rZxwdS2QU+xIEhLMNSFZKoM1rSn1Nvdn81nSfAHyDb4ZgeFMiqNUSg+u/2xuZ8VroNssszRuBbRMIwe4+7BrgUsn0w0f/O6QJg2SigDK+PjkcKXQSw5LtjQBlfTHL6XfnfHAiCo7QbYv/fokzff+dKvfRupI73BEZpG9oTdcSt6GsPUq1zhsqTDn6bWN3O28/DBJx8cf/fb333+5ZccHJaEpmYnTy/ORqd7Pmp4j04PHm0fHG7eE7q4vHr55gtL125sOh8tP1DWchJ1eaG9RHI2PzO37Brks5UMArK+sLebWztcp0pyTg9y9Mpkmr/WK3O3Wju0fR0MZSqI3EYUtx7nqpCPb33Ktg5OpMFVjkoeVtYUx+Ub19yHdmXjMnvj6rrSWRw94Jmpsowzs3XDs3dKwuHidjUhOF/YijPAYJ6dL43R3LCtTeM2nMh9hr946y3zNa1dBkjjnloU2eiIqFXCDx7e2dl6vLRg7LtfZU9FI1IUWaqEnbqQSptRGzWShLRsabkYuazSwmdfMMs37h0ch4cucuND+ON5kCsyLHYk9aLE0FRrs7G+vL+3/cmnH/HXhEjd7oWY9x+entwLKj4ishVHE8PGjMCPZt/xHmVtQsYM/fkXlQFgehjV7u59JsCfaIcrVot6QBp5RxxYqobwL7KfAADWnpM2T/fqMrLBnTuNV1bemj757GBn11bKysLJ6gr4o83HW57nffQIczYf3B/hN+5cWZ5aWfa2WuTEEHZu7uHavNnK/dWcxtheSnc2utmZKq4WNR6ZDymoLsXOb3yf5EP7TNqTmeWv7IIwa2qRjRHk2cnm48e7O1tmCYQGVYgPTK1xJJuZngW2NEDSHIMRHSpjFH/gJUTl3mGRVA3Pv5+siNjjASiFsklIn0iY3qfauXPw4SPnQq0+JHoVUk2MRqXcup+UNYUabku9YUwLuZePMyed96bCodRDW2enxfzQ1EAXg2ZdXg2PMgIY5COdlZqethESxrCJ3aNNC7VQjQb9STXRpJAJg0zosXzKu8hBqkRGrMHOLGk0MRlpzNpUYZJ1WPjXci4HJiNXEArZQRgbnux7meZgXSqXe8bnnOUKfumqygULQZr9EebqSbUAS8tznjzQvBmBZoHKwOrsbGXubJ2aubeJ5xyNmLXV82j+dOf+p2vXXzmYnlrazYh9KUyfPnm848z5jDXRMCN/aIQhpI5NeJK+aTwYKP8SA5AyWTkf5UX0anwSPf5EBST2hBnwdk4qTtwSGu86ox3wlJfdqOIeEZn504UlHYRLM/x5GtdIWe9fDXfNOEbyMqbyyd9OaPDzibHh7XgsJ0iPBkdykGKizqCcTnIsZWo6N3V0Bs6m79y+89yLNzVcgDJaqZnLOXeGNMrR5xLGLOmwqm5p5ZyBC08MSQ7on0FhUKr0FxffPzr67MP7b9+681/9V//V8/OuiDtbdxF2WBoMPcKMCz8nEgaG9rbjDXrn4NjF9K5kWdp3p7VzdUury7tb1vw2gUmRbcyWNWRdiQam5surxzOrq5euvvbl3Rs3bz2OotuDnW0CqSE4ODy5c2/n0eajd48OqVDc3LC1cfXKUp4bjYRYyy55VubhYtCnfqWpqdNaeNWJJvUyKM5vNR018hv7p6YHg0DNKunGMCRoSmy63Hv3AxcWLVbZmbmnYhD4ynuQjZFzT5qe6wmtJCdCCr4I1oiNI0usvSYALzoJK69mOvhRZoqCtAJBFUb0xMwBJD45D4ENC9nkMffAGtfA87cwo19MpmWjsj1QMqQq6JlukG0agN2OAbgdFzx9dqxJMDlSBdMh1TXh6rlQOSiYSXsyUtxq+YRX6BxobS5JC1rLZFeubMi00Qzd5+3NDLjpD/EZk5ciHzM23Gi0Y0d/TtojicH/HjBDJQopIbhbK+mEuhFsnB3aODMIjDS0LYVR5elQdieKQit6htfrcF2+bAGa27Y7gAyoMzEqRPW220FN2qUlFpuBhLtxSgn8YHNog0LSxLBsnPokP2uolsFe5ktl4hNJmTCdluRMotnjz+o2fIOuU3kciTSRIrQ8uilBXUJHXI/zaTPCUAHtfpKQBDTOdrT8F/ho4MItolgDqt7D3Np6OFlAwORi4KSPcMALQjoDI3udwVF6hhG2yheEAMyI2GFQXdl8tLdnbi/oxndfp338j/7wD37/93//f/npu4b1qmFIfSq/2nfIM3CAp9jf9I/zBVniqLMmE2QPP8m2QZxYLrwQFAfNkcMTwnPluctkZu7GBs+TGcp8D6n1Wd6d2UmZIoO4av5++MMfHj/K69Vf++brcn3yuG4hrKug0AmbiW7xp04/FJMJOUqo3ODD2jtZoJmeyqjdpJjP6sZi5PbmGuU1IU08PJ1rfEIPhExUqWu6knH5WbTVfuu3fsuDje/87A2bort729S7Hm3vQAJYKIVhbvvA7ol++VvfEtczKJ55MdDvmVL0TUq82UrKaKbSCSvNkcQ1GsY3pgc66MZQnz0lSJ0oIy7m2ExaqOOxd+4+lLoQeP5+puN2Rjr3RUVY2rLEDi9woy5ayUR31AwiMIWOVKEYwI2TohsfsLXmbH2dnHKQDUZyhjJNKjfiJe3yDXZ1vaawI/EK5GmyX7Mqs62asnYjNc6vWI2KA1VKg4NBD7Kbk00tsM7pkLRPPQva0opk+pthU1yFfNLuKP+RbNQ+G/OzivRzgasBCf3jomn6g7nQhyllADDlHLU2v2JOgYl1jvZZRI8BniBdlGJyBjUiTSZXZETSyyQIsVUvYo/TGvGnPs95FeCnWDeO8gRxI+zl9zRA09qong5tgoUSJzaAhkFes3EAeCLJf4+Pxt/FNrhVq/c/uKU93N3PJvTImFWFt6wMK0NJjZSGxBNQDDMYjOeYefF+inUdq9tVixgyCEYeM5SsFhIABz64qE/9UqH5qNfxL+XAHBDPMYVs5vGEKo1GKXTwH6XogEOJKIBJgyQwizXy6Tbka9/+tsb8bCuZkMfJWI2K3Y7GA2xAmDgxI2EL2DiwIsVqCNk0htEdyIjkNMJX1q5ZU7QyZyuUpyZ9+dIqMA8+ys6DB3co3bhnRFqyqR1+7voNDu28HqGJ1GEF5/KqjdUHn93Ru61fWbcM/Tc/fwdCJotEO93KRYps++mV6Pha7FhcSWOe+QGjVIvnKZT5SGA3a6Kg3zgLVYAhpKqo5xKDj2elO4/iWta/wBNCIEoLQiHJAjyfBuuIXSHbp9MCIukBW8EHTy2bSDP48oOqFFYMeoakOSjMyqOnJnRe85ev0+h5yYO6ly5p4SUhfVEojcGJafrK73/4C3x7dJDN40vr62K9v/lQ6WxfuSeV9Zk506259atYjUP4w8Hfdgj7cN8bu9GHwBkXVHLwHIiRRBE46mWk3kH8mYYE0J6dff4NxsFfaPsod4o/thDcGjzA2zMY4BsYzphR9QslvhpVx/JZTBiVgqALAA022AD0VuRBgWPp6cqS81U1HB5L+QD6lKPSCg2QyIjTShzYi+1bWzlt9vbbbzu85f1g+A3JKPtLhc/Xv/w6SFNlvapaTcJvvvCS2tEyCSFsCgL+WjNIEqnIVbIldEbYyVcVOj2ZLJJOHeXNj14K1HDAQI5DWM0gfDadbBGdPIgGwL17xC1jyCtX4Hc9jbNoV69abb6+tn4Z8pWNNZQvlI6eTTMRx6PxyGcSLRP/0pG07hT8NZGIX2pcINod17i8tA+k+ur159XfN998E3nSSejQsBbwkIQv/BTL8n2FjKxksLoPjslUejm5K/L+flaywpCq1CSZmw+Ra3+fXrFmG23xaYEfz4aSUIs17lYdySUtWMe/Bs7ELUrcCh2Gca7zftKIxM/5kZCQJ4RszM/zGBPBE85ROJ9GEjwVFwEdhqTBzcdn/DO+TsY5SV37IzgZqAaZ+DHcPJmpBaKxvF4AV6cOCYkomCOzhMVqEmwc2qKFjQ2cWV1cEL3jssn2YW12Wjk0NL27HC3I/cVofW7p9pRCph0jOgf6+aAvqf9qJpDFmo6SHFZBF0J+yTNMZjuZkdW2Dc0MPiP4Og6JJBmRdNLsXqBsXGkKYWOCzgB7bs7KVCrJpdeAm57JS0mWhIIBGmDqi7oDmLPiURIM+sGUZ746ic7wKNv1EyRVspKWiyavUcW/lxODICb+bfVnlSGK6+vcajwQtlcn0W7424zqb2+AZT026uBg1I+kUlyqXyGV03P0cTVyYSYnPv0fkutQiYuOOeQBPWTDMhUettwJE9dSAR/TPaF8yBVOgidUyQISU0BZ4hCk/3K+B8CJW7JjUkMNLbSrM/t5Cy1elKVsAW5tTV2+XJ8QpEDzo9mqtMEwzSI+EBKMBusg9jjKKO7g3xGDeczb9jnHXCxHLf9uvXO9RnWsEup82cLXKAWgJJHjVzGSAIZOlLftM7meUDWoLI9aGzDyZaDDoEd0PSCbP56rJoZwDjIMSfMf3JMOqfnsNr9BxoBhTkN23P5qN7lVrLd2H/zZn/3Z//73f03GJ3F+gVv0Zn6TDVIuDnYzq505XFjyaORsxn4ExgCSUhzgAV7ujDjlTh2Bh+yRmaWrG3l7YG/XaPPWvbs6bk2PUDwxxPpo0+mFu9c868R4INL28pVIToEg5ZxSURjflWAs7vZhTwCOPJvVHR8AqtzEIRYtbB3c0l7KouQoIyWh+SjTONs9aXcqnxcKsoPOF6DbqwmdRDThE+GopmnUGeRu5fhk5obzBIw7rZ+98OTdS4lz02cHfudLvyA3ydJXXMWuBet/FH1EQoe6G3IrS+MutttFfnyTVSpCbYo2CcVULCmJHka356TNMzDZb8jqXwcFz6hgFCu8QZ2zBzG1ujcTewQ86ujH3+PfyZ2csV819KMPKVqhW7l8ecNKu1L8+P13dr1ekg4gI0mliFFoS8UfG+wt//H3s34jO5UPcQ9P0x55GBXdD68u3z/dubx79qXn1q9cXSKss7sRmtJVGG1WZK5pRaa2O6t9gChNz8iERVMH7hBcXVy9sWTYvXH98qW1tdxk5AqI2iXWxkoL2rITz3ZPRU+JaFv4M+VzsUQQ3Cax9JDnJvC+ZF9ZcfTnJJLG2XYpDJ0jb/gCDlUda8AQia3lp/bvpbEGalhNhSCztEQZNYbF4XNMwvLRRTMkMTga82BP0BO/8eA4GexbhBpyoQ58HT3cWrIHV+JsJC8oO3i5wrs0mOpojDsYNTSWBS3ULc5QaHEOI/RUf5cL2BUJJWQ+rrbEvId//eZP7uyePfhkb3/h6o0X9n/w9uLXv3vzypWvblw7vnXXKVaU2DxnsNwIZ0ECTkp55HWvfBPSpiRzMeMjdz6qKe6MPjg5PHtu48rGxsLV9XSodx9pahfXp0xUplYvG8E92H545drU6gtLxtH7Z9v7tArPdl7/xivrpyvG9CtHmX6f7R4tn84dPaaDM3Xv3a0XXr3ylaNL77757ubHm3MPN90oibeokWJuX6Zht+wg+LR1Yv4rtf9pb0R7bYvr6PBozl6yYcH23u7uo0+cTjw6okiisFYjvGtXrmVJXUvFXljNYeppCl16uLkMTE/cuGrX9Tgjj62FuYUXXvidK8/L/L1PP7IMvT2fmZvNIl3IlcUNWlorL97QSdgfN/m+/fP33v7bn5uBHW3vOqJ0/fpztw/ufnbv7uOtTXSuOe5x4JJEUwiPWqR0trYfmpD1eMWJkMX5KQcVmDCkhD8d8Knh6Mndu6Y0jhbeuHnz+v5+lF9aS+UJkRsv4wbFpKnXci2e8MNFmO3Ss3ulQht4hEXHU/sH20ajeKJr1IZw4IBCZjuGxubDdIqEUQa6gTRL0JZ7QZuU9ts3hlyhPONdyWW5uTbJZSnj7Dn32zHZ4E5bLLv0pJJfd9E7QFovbnnvyVYvxR5YBE3mpt1Fhh4hGhko0V3Y/0V1Ed5ERqqjRYWdaaSqL+KV9k6HlFYgjV+4naxUKiN7nFz3OOOvgAazyUJ5TbST9V0YRptY4z7rPG4RfP557prYBut+rSqu8KQFz4WIncrgqURIgs8h9XQZMlSGP1OlVmwqz4YcQTz1Q4WU39BGccp2+RSvCjOc7ck/bVSGS4Wols/gDw1ZhuCMqbBzS3TU8NXtQQS4YAr1GIpPpTL+nvi9iG4c1P5KvJtu3nKisk32p2PYZ/92oq23aEnPCa2z+TwJSycfsRfLuygP/7/QdGl23rp/aTxNbQ2LwwM4GrJPgJ3OLG7vn/zlzz+4tXVytFJLiqXBd0DQAx1JUQfFCgefNJr8cLhKbeQumKcAR3HnZpe1+ruHB3VkFBQB10C5/jDLpuajWgPtribzkNqqsZGHn6140ream8ulqqCP5pQ8RgmtCoig4OGj3QiFJDKLUfzRTMJS+wjB4tKK2xQIAHGZv7Q85fzMteXTzVTZqBPVIOXJzKU+S5LQay+44RxpNxQcTOfw0lE1qh7xRFtN8yXllgxXENAhPHp0597G8uqV566nh93bdXvjrkZwYT4zAOuYM7Pb+/vTC0v+HIa09KxxfO/jjz/69NZ3vvMd1wF88MEH9x8/Smt2OrO8eukr119YXFw9dqfzsTWNxevXnj+b+tgywvTs6uzc6fzJniaR5pr57PHh9N720f07NkofL626IPLS2vpqltJyR6Q5bW40OtypF77OiGH21TTLGk3ytrZmBouDqfLNDTj5tyBGPsYmTYN6VFXJjNLczVZfmt6StSqWXLhAz3W+iiPDtZScOT1ALYEmKCp4eBy+luCenNEmy3yDtwGLuuv4lFLFtwcH3lio4UoRsHXmdZfjt08PqHd9vPvZ+vZHv//ilzzy8/LlDXlxHy8oGtPWnH94N1vRf7O5S9988zLpPb23eDR7/GjuMQE8+N0Xv4Tm1egInb69+Xhme8tiNAwHdY7qdC9b1EbUb3sb0iFaElpFHapJR7VFFqmLovMJfId2y6M7hn80hqw7MZ2Ek6+O0jDtLhGaNpA2+735wmuNhAQarUmtm93VZeOHk8O6eBD8CKYoGRA2HmS2z6StGFv3cFKSwVf55p5l03V3UNx65/3r7he/dkX5psSeZcRq75DhHx2mcMf5+yyOuETLsMTlnsf7O7c/+/T+g7svuWHs8Pjj9z749IOPPKp5evOF1eUVey1zZ6/h9vL8aSbDC4tu1tg/3CaQszlYTMsvmtDnbSzNy9OjOgtpkcX1BS2lWYrp+0o8VXZpfWNh44oj6Lv3H9CiOj7dPs9sZdSdGmj2ZPK+tfK5+RsvvnQ1N2pcdlfPCy+8gBVWEedXModXcBH8NARpWWz3j9qZKr7igHEOPiA0MpCKDbKHPnWwCkOA9SnSZlfbBNx478233iOBSyvvWYw4PLD6vGjxRa41PsAK/5TRU1q5at1kxMgHMx8+ujYAcPBnWhh26uQEMKZPjHEIchc8yCAvoR3gpZI8lnGEjgy4o7ki9zyrQ2I3UIS5XqvW/CIelyw7apb5p9kdCUydksz9quKNCONKt8GrxwBj+Qnq8u/81nS+WZ4QZrLNKfyjnqVDNRvy0m79EQc6O1MEc8ZlurVyJ48cjG4FzW3cC9NNX7eBbQNgZKevi6F4Iu5SdZ8LpzsIuFelvHeYVb++r3mlLk9Y0CBiJn2+pJNekqTptJpxqHIb4Hub9/K6msZucc5lvZZ59qj8kZDSkJ2tE6VLOSypj0yDODmDa7deBw1P98uQgE9Qmcny5dFBe5sPj/e28UQoPB0lpU/VlETm2MvpUppll+nnp2Qqwy3R28wcaeHd4D+9eDazNj1/ZX55aqcu9MiBh9GhGkIabkOq7Vqcpix/30hf7SiNV2sEUHWZql3cVYP0GcFc/bLmJKmlTjsfVr0AzjjC5ciiXBZvo1wqNKd65qaPvQdkMEUu9C4jOU32cIktI0HnM72VdKK9rBJo5pVg9zvNooYK02UmcQzm03q37Hv8kPFoKiqEBltAuCWU5ct8pB/DuCjO1Yp12JxeskYsAR8b0cOlvElgInNGe0oFUgrCQzNeIDvPwZ7uHe5u2CFN7s6iw6vLn5872s+CECjnxxzkPDuYWZ6fowTtoP+JB5csdlnxOp15b+/xl5/37tGyGn1pN3uoC4enu3ceLL+0UblMRdOrGsfO62a7OMqWU58qAjrZlJJTISpIQYS3xSANYqglx1RLZ0O8iKIQmuSuTabDo7lN2vJUfzvfzlNnD8wHzNXiYIb6uGIn8dHOlpvaahySi22UGcy9asHzmUZS/JP02ISw2sjh36FsgQ3Wq2HopLHmdiVBTGMGc7Tn9JSp5Lz2EAHB0GETdop/7DuiKmQOJoRaWmA38pbqHuhbqOQ/v3Hzz994f3V53vPUK/VQZ3U1VTsMgAtb7ls/Tydu5EHYGekM41GUxh3+WTg8u7KGXA+0uezHwo0mWouuamO4IZX1T9KcjGqWyA2lrloL9T7Jcy+99M2XXrIf/PGnn9i32KrFaw9k2z7aOdx/9NmWI4oxc3P0265cv2E82cvuPbMgltht3IZAKVWWVYVR66+GCLENzCcQTDoLhYAUOuiIOrp7+47d99OdSOm19Wv2MqeM/iaa92R5tH91UQjwLdh+BfPEAvSvAB9mdWVo4Mh3NXDjuGntEemzJ8Zz1iqMeulAW0Srm1tXiK59K6rqidw3p4gSMyrFchfL4s9RlSdMrJCRNf58RkbHQYGMeyKiT6brYu8l9pf2o1LBekUSmFEy4wZ0/Dn6TXtz0Sjo1ILY07kiwPYsCbH6bMC9v70NfxVtYR83GY2jk+v3yvW7moCc6osZS3ZnJB4DYRU+9pcQJNbLor85t6GRUpcxuFQPEyVJFKuQwX8UuahtDkaa3YpYu3PaRwDGND6Pa4C4V6cQtW09GoCk2mRNc8e+WDqip3hrdMVuMyLDz3ghBlX4z4MZupb+ZIs1aTfZF4gfALpb8pmcTsattPkwo2P4neuS5FESxZp2NyS78Qyfv6JjMvUhSmNuytttmCi/ymuAaYfoZfLFgc8GAYxPcyFxGct9LSQQJjzy1kOWlKw9VerGx5cXXWz/zn//39Mz+uu//ms3HClTdRBOpazZEwvOtjm0jOIKZQ+eI2DBU1N0mn7x3jtGin/4z/7ZguuM15bJx/adh7Jw+SZtsNWTlWzb/PydN7JEW/oIovNxQ4Vdu4dbOTloKVWuM7CpPb0WMx5oprI9t0u4clIbnQb98ri2tqDha6oICHjnyeAUJbkoffyMAJR99a/QWmI2tNdRaT2leHL2WHQJgnFDQ1JfypDXFI3JvoqrJ6cXqA4YP1h0uLR6Fbhrp5hdZ070+lWdF+q0/HZJu6SpEf38L/4GbaqaTkKUKNkdTmUa8+ablBFkoQuX/+nioc9LS8tosCaO1cln9M2ysdncLranymD/3va2FuOFF16y8P3qq8tvvfWWiG0kxDEU0Nj77/DbGDoCIhATrrqNxFO8pjI1ykFV6EsdL0f5i9Jxw2obi4zxY8kMHw7lNaqDVRZdx7s7zNYXU2o+8CSXdjXKpMEpBzKACG27KRzsJCrvCU8hSissbIWsAagcMBeGUFjOEKnIGi32TeKHc/LzSUxPQF4I+oLP5tIXADwdJEpMBcipXzSzx35pAjrW2CdfDdNgiV5Tu/pt2BH9X5DBEdz452lI2MaBF38buLmt3Hy2T9ugi5Inoo+BUy6T6D4vlQHVJPS5Z1yTIZMof4m7kcQeE0ioWsa0PB0KBcfn0fYFCXSsRgLZUHaTUTp0gNRuaAB9NosknNTHPWNTEnsSRbvVicKFzvoNTLufSbkqo7HthhcCyZla8TSh0lpO1YVss/OllyF+dUBgqrGtNei8lNVae1kcib5dNRRFSzBUpDSbYjWBIe/sjAKIpo9DkPkVbu88dj/e5sr0cw12we6kSR/HgKfdhS/WECXucTnyHBjeSKKfeHqqNzSOp13y/GtfAlPEF/V6nBo73bl3X7/55tapVl1+xdIkGsVZhpOXb3/723K3sLLMcWP9sl7G210S0nzxB8BorlFCKZc9c9b9aYjxycgyQ/MRWotWWrDF1XmpOCHXyp5I2j/MKhKETDBNTT16FK0ZSNiZSyssDxJrpbtBqIXpxs8GP5iAjlnHs93lFycMSiF2Nf7yG9OHcBOo+BrrCF8jl8csVJcKTDNwIoXIEhwYsnNyoLh/eOcRqf6Dr/8a3XBR9IbvfPqhazc+fHzPSGZmKRdYkQNBygX+5d3w0GMMONOnABbrtYaFuhHSsVbwtH5QPz8bGa5pM02XUWsJQxOgTQADLdMi2EGpHoEpXZ4xm/jo5ETkkeC248psECWKzOjl8tXn9eBgQMI9AvY4zyXPTS7cufeg8YWzJYiFqUprXHZWWTp6QwIAnGSeamH4VEZYoYpQEQabH68v547ss6VcF3DBJN0q/CJAugkHzF6Yqetxdx6TsZ37W3LE0Rq70FJ8rnnKNVsFcmrktrr0CgI2VqNDurO1Hfpqn3hpIacEmjZpdULwN5HjT7891Ao5yUBtNmsiTJKpkYt+d/tu05biKeN4lFhe8wthue/k2s2bN1CIsSIiGJQVknESMBfrJhaFVQ44G6Av80ldyVJYVgvaPZqO1sx0QCUUST6pmxllGaVK1NtRfBbmV4R27rpMfU74JCIJQZsVTBf3z3ttuQwYQQwHPO7HTrQyLY3tPjjIlT7cxaVRZWufBkhQlWMvEPdSyBDEUQWe/Spc8qndYNotXQ7kDGSo5oob2HgcEdqG5LgnMXM/7fM0gKSZFJNXs8d408bV8Cw/C6k+KGkfwNyD4dkcaxhuj7uxJd2pD+SNHFXT6Y5WLtK/iDh0ZDz5KEyOhjfYhIeYFM6RzMQHX8nEdPaWtDwwHJYO8kIWapqpIwwXsvzMzyHK06GC2pATqXBP5sunphJtQvn7hKEBQl41SoLEib89neJwS0u7E2WMsyOC1CKxNXKK2yu/YKDiM8JzPnh2235S0f4JzdKUVHrho2RhREn7F2G1qJTuGw1DctwCxR2MoJFJbtIuxS4zOHx1fgdbEHd/Dqg4+DMcneUOap/2VJ7D5xCaOGOWwtnpN3J44qhxSPvIQrNoSKUzCLJKIUvesDWMoC7KznQBROOebGsngeW+CgVaqmOaVltZTi+XlKaURadP+sLSBvdArQbk+eNXCn96fFSFsKK/k25InmI1H2xb9meCSnI6X42VG0mGD5IDluxYphmDdRLsRtWfMEsrMFUDOmLDYJhx2tTU+phk3udGxMbMqzPVn4Nng3bQ0+72GYCBMTp6/EQeMga0muilS8vdiHWsp214GtUo4ngR/5mQtQkRNgrtbDuhInWrJbkQ7Ebq0a9u4FHKop9ztU5mbD04ssJgN4LdPZr7LDXUTkOLMgKucZe4EafqoTpdPjZir1y7auz0YHNbKXghPByoOqVopWj06YzU+x99DHjVUbL1da1u1jTmcrlQ5GA8Rx7ltPI75Ks9xcUxmHe3djWJXmmV4kGdl7q+mnsQDh5lymCDiGx33I444Pl7Oz53ARpNT6fEU7fb5OIdjqHDq4lZayDrtVZr/UjXX3G9y+3Xprmr162d64uMnj3q4hX2ZSsSll80eWZDBRYlFTLssrqKKxEEpEpXQvWVgPg8yUMNaCCFNdAItHZpCtXIMnEKyvGkrtEOADrNAUMEMn/VNPdGyQBXDpkO+WM6TBQligUI4YkxdFwMoeyWO+B2++MPCJz7wwt/0QmSDNW+skOJKftqILIwmI1WswJNRoqmc1Ubi2KcdxUtuBokAD002ZrLRO6dheN3Hiuird/8zd989fShqdRL8xnodCbmXO+EAz7V7Rq01Qsxo7xtrcCfO8JU/sO9w9n12dzodLy/eDge8soj/UfTtdKv9ARschTNd6YpTXkzXQQDh0cl0mEjOxwbe5y7s2NapjPICazZnLY0hVJvdGLyBP87imw3fH+e2xW/d+PNkHDDKIVVO0MQNRmxu6caCJNY863kxkfyywwATw8KhQZzcYPmWH/yaWrpYnKP/F02Nje1ee+BjTUCIL8Kh52bqWe5M7EiASq8m4I8Vr56NPPoMb3V3eO9g/m51aW1xR8++EAz8eh41xLnqy++evv2vWsrVyVu9ixdzwA+vL/zwvNfWzq59JM/+bOf/rs/X11wopNg2Ro2NMuECH0KwbRTuZ48fuBvevplcVdLo8e7ddxTl6KB++Jvf++Fb37z3b07xON0deoHP/3+/vSpfcKlr1yeP1mjV/yDv/qrjx7edYvF2kuXbaQuOqKBv7m67HTuYP/Tt36xtr+wGvFKjzh7uqTtnNk5XJbE9uz+G7du39l5cXdl5sixpzyEPEsXVlnvbB8cnZn9zx9nCEtFxkBA5dJQHmweWALw2CnlmfmT3GY4j9tHx6asBHR5anrPrVXTKwdnB5szkc++Q+2k7i872Z3ZTQkpC0GpHdL64DSLvN8/O3v+1ZtmZa9cv2q5ZO5yHn4Eq1xoXkj39nuPHBT99J3P1KyVJaeC1+49yoTqweNHd+7ccS3hrEvYz44/u3OL2pRuYGf3sZME7pidX1g8PtpdnTnZ3sR1m3Jn6xvPL59kE/OgjlrvH88cntBCj5QTiZ27j9zv4abpjY1LX//6y7/4xcdSGYljSWBL5iCKCS3jrr/81rAS85VvxLhGCZPA5XZNY1Y3lFeinDgvm91OPjotWa7OK0MrSx7477PthlFhonuf0+TqE63oOgChkiih0cwimk2WORiNhJzSec4DwMf1gOFR3Wa4f0BP3J6w8XloTt1JFYpdlBT14X/ds8lPjkbL4vQxo2/YW9j0LQJqcCkxlYedPV1lHIZOGJO64Zuj+SO8W9ck0NxIlKQrwTif6k0mwCrlAJ0bXDj/eMI16r/5pQcIAd0vVCqlk1iFMUq2oyKDZ0ipNpPEIqoICIU8B4NmKOtvlCrgkevCj6OLF3zGn4lS+S1ET0JNrM42BzLDqzQ6oSG5JqlRjjyLkgFgnFp+JzzTPoyDqn+pLI99CnjUbpU7jdjZEnXSjcsPziNOgl90w58iHTEwYs84rsF2CYP6nmvk6Rx5SkENiBKSe9Yyr5WjKrFA9oStuF3TNlmoxa6GACCVvjNRFVG/VAgY6vynCVPypUpAeGj5a2Z2/2zpX/2bP/94z57bpb3aTgPA1Gx6LAkDVwb/gmlsDRlW+Stjg02M0MzuuKe5zH1qxnhrR0tbkpOym1+aX12beXxrk4C5bteR0PnjDGHdCRFM8xnmqrssVKdNyAGdDFHksdRVtAqp8M2Nw9nFtedexLx0pmcr+DrrcPPx6bJx39SCZxWRtGRqvLuz99a7V3c13SF6VOjValmnGpGd1j5NzBggNWM6SlZYUGwvtY3Kux4WhzMZDUwgaL4tmf6tbFybmlnTzG9/uLU4v/HKl76tvXrrjXe3b98zQnv1m6/P7Kjn6UEe7m2+d+vD9x7Sb1548bnVr335S1/50ovweDBwb/8xxcrVpcVf+8f/GGec2NYnnh5szZ5uatn2t93+tOg9NH/RndxNjzN9aJyspcpCgD6cj/+lnBuVAD2CKYiGb2/r4NH9B2aqX3719YX5JUcqd7yF7u5pmjJH2bZcWb5moeR0/9iSq/t68Xm2lnHce6tlplqsPBCvjHr4cnZ8SI9wyegxJ0xs5c557tB+3fWNa8BmttPj3D85hPPF12+Q7cP5HBqlVnSack05UsLWGDoXFOnJFqOCn6aMebQfLp0u5D3kH/785z9yR8HCpUMd7R4d24XDVc8bne4vB8P2mY59dvNkemF/880f/c23jw9uXruuQ3zvk0/vP9qkPLewcePO/OFYZtTgXOv02fTRwvLCfXeX7J/OHkc+vSOBAtpDoXzRpejzi3UD7AsnVw8Wn9s/eOCiuNy9nEochUFjG39V+nav87AYoQxn0niW6KTHqPZ2NOrjm44pP2ldIzlsS2EtgXxppLmP6969Ox5X3N3dVyKW8TCWVB7qvI6Pa4tizoL7/Mo6VN0qJp7yiLZgFiQKf9hJSHFG+wFSptp/wo6clEGIqDlilMo3NX3v4b3V27NGJmdHqZWH0XAf9TGpHzJfcxAJSsYG/5iMqWsb125cvzF9dHbrk/sPLm3aPvm1b3/bTscf//AH6Pnmd7797X/wm9HJckLAf+qo8zdQiIk7x2d7O3sPHxzNLOcs9slUFsuOdralzkMzMLNP7W87ynOp/mnxSuWUwmnlNPVfQyGzoU9jMb98aePa9MO776MNfFGfrefTIyPNWa9ceRh0bWMdP+eXcioLeVI0omO0EoWVn56JUr9CCgd8Cm27l5iR4VOzyt9sJZHrirZysCIIGTXAU9swM1NL21vbb7/74e07D+htHmaMMrdoz0m7FTWxLqMUVp8c5bBuOEoxlVrQ9Ge3bnsKpZMI6jJdPmsblznM6tNiFASZEa6x9TXG31FjK72OCGas23Me2q6O1dg0OLjnRuw3d97QJhCGiP3c/MHefrg2inDitMcQt6M34yL8taXUNHdQ27Q7tTAA1He7EYwhN5swMBaI0kd4maaGhWjGT0ik0nYnN9ijhdFa5uAJPg+C1MAGvDbZQNF5alzu6DJFonscW8U12ti4VCuzGdxNuwx++Vh7VzkzH2hKRH9YpabZ4C5F2OxfyNTyYgax+7ub/OePTz74zH39i/sUckyINNh1LFQGQKYCMDU2O6g76SpzuXa4yQNDjBmSyB0ZH2e8AfJdbYtQPASpeUp/FnDlIgGqtlZFcnoMI6ILDHf1XaGhWnO15uDU1bdqdNbjqitMuwBXdG8jzTm/wXXzxo2XXrqqYejhQtRuPWwxm3QTHdM84JC5syNKZ5durC7uLNjsFLRUtwI4Rsp0pp1RSpSiRF2WM9MudqamU1NXDs7W1hw/PbUktkXoc19iJpBhBJchzOmUJlIjRZOaP1RB3czpyjTyKE+swMnyNzrKAKl4XjFi9YJFYUlp8gmPRrwqDmDpuCEdOyCBkQ28kwxMoGUt7UrYHJ8agoMK+LR3gw50pwvLaRCIesYhxnBHWSVcWMorOFolscz1nnvh+chn8OTsLJVlk5FMhGzPHpoV+cuJrUtL8zPXLrsI5+jAMkribs9MbR5tX5tbtFC4q9OlW7odidr9+KFFz+0FLzqlGsydOnASCUhLN3RVqK0mESVIKlZEqrOoE0KkXnKDn1O76cBzRaqnQ2QkwG1E1EeEciM7i+Ytxme7OsZIpUW4xDmd9wjDmSFf5oCX8zzt9O3dfc/HSy/lVU2Pk1OQcDaLufOdn1AlI4SvPcYB8pLoRWiFNP/ls2SvqUrcoEy9UM1bJnnmzqLpM9oA9H87lRHyEaZEgiFq9gw6yykv3ftqTQrwnBUtHw7jgaRBEDtbzFN3ppb/xb/686/+N/8l9/WDR5LGbrk6qHam89hUFcK2jEoxMDSc57paeGNqpY/Xx/NHeysZPM4te4Rl8cxKjo6gVloy7M7yVgzRkVbhbz6k3rlz6MbS2sm15/aOT4w0Hm1aI3744OBw13nNHntr4GZmNo8PHt37zJGp6mJCbnoyy98LaRupl8U9b5aT/LAIkkEljYgMRPcPM3bdz/ZJ8pA5y9wLr7zw+uWXjGAfP9hzFM7BSM10HajmSuGm4lTZ5adMSu1JA5W02O096ebTK8XnMbDARwpv7DgPS7GO0ANAKMGpvKRNFJTQqubyP8SiaS57Rzn7Yj86lVaUMKJinJ7kaYWDuqdMUGJVCu1uYDYzCpigYUiCA0BTMun5tBsY9AGu5oOjjdZklLGn4zzlk2yWEZebXYIfN4MMfa32RbG9/54TnB87YK6qp5g6gxMIASswluUXTOPEKyjxB6oJwM91Nk724AAK1bvvvvvSfAZzLsxrVGWHww3ZfHgaLwJ4ni26I3GB4AAWsWWoI17AoI9pJHIhj0PoANyhT9tDBtNgDuZZuT5HNV4GEhfg4N+ORlhB5wUEcYfKCXf4zZSgkVXOvocxPZ1ct/9AjK53ou0ekpsIv+iEsL1wbEDGs+vH6MBCwQDAaqPSRstO5SkeDrVJOTYYeeDQWKgsqVzLq6ZzP3/z5+5C3Jzad734/Le9MrH0O7/zOzrO7//Zn8OzPRV95//NP//nFkb/4n/5/9o/NG7WDCFvIHKSesgzRnw06Rc3CjVMkNDqhbYl023mCxuX2Id1dyTVFfplU6VZfOfBffR845XXyZ5ZrhTnHYJw4OjSVXk0XoQQAVD5VEeOZuqY4XHuRzZ2URMBA9jZzy3PBzWF2z7ZlztnqMKZ0lawoVhc5eEsxYGcIiwT9VLCOqpNMklDRT4ruWzxcQzZa7aXdGgOfI3EA2OZn55mYnm2msneKoUrfJufx4fN+6qnJziQkzOhdrOn1oNSUWLg8kom1V//+texevODj9j8QRIuGbViwx4ImHRIvclDSRMJz53PPkMD1XU7kK+/vkg7L63nuA1px/A5YNMYcXdGhcL2NMwALJQZPolgR2k6EY8MJULhmY0JjFy3Z9Ca4TF5/jHjY8ack13n4JERzhv8Klm872KNXco4uflZv1A3jxfAeSMQzGXOKRvXYkFoUNxMCENe7+1b/xv3cBLlttbDNrEI8hpuTuYU+v684DkgaQLOP8ctT/u3PcSdYOEo/DziZISxOywaE9CZbZ9xePrTwV1B+RzY0kEdhedg+Chwn0PcgcLB5+/qgOFphE2eIEb/xZ5E2/BN1aS/WCDHWRv1GgCejj7pw90IJz07FlT4wp9IyHsDjPGfp9wNcbOrCb0AM0RsmsmVyA3PUZ4XmX+OvVzBUFxvVCJxYMwAJsWiMNSOOobSuyGcknNmgobj2dzlAf5pxyTN3U+1iDQ2dbCjwI/gsuMRKvITiz8asth4XhChCgGNHAA326e6ias+AXDo2ZGq8sKD2e2vlWjIoTJW6JwFC0jaIKAbEzBNA39kGUDLMp/PM2MECR/lYgzqc/ABhs+xz5k9hqssI57u3+nDrbo777KxkS1OqRt920R8JaUUHTFZc9Xsf/Kf/Cf3/+IdLbkeTcYZWieu4GBvrF6RBena9J2/lO6YWywwMsghFjBcsmSaNqdKPG3kWDK5mjJB/MOHOmR2fLKvZ9nfyXKziNwYDLlGVWMrZ2y6gVKBKi1tLdzo7ADTtwIJgCEDMuLN4mT5OF1UE2atyOdPf/pTkA5UotMuqQyenV1HA2IYUnPOtZHwj5ZNxUoyVdYSdBeeKZDxgB7/cL1mbFVAnZ0kIXfVJsyfzZKi7cMTSs2ri0uI3z9NVbUUomecWRxNPQI/PdKdR79z3SDrFuKsFcRd3YmZJxqW5nI413oz+o/uZO5QxFcplgSAlxEwghDDTOaL/zBebf8GmARrDEMseKTrQJKRlUuxAllMy1J2FZ88GjiBydStlBmHuJNoUzT1rSy6ZwSGHjWs4UcZKYL5Kw/yYB0DYvWXbSrB59rNr4KHqeEbMuDFq3HSo8aQv3UNsuGu3N/7vd/zqKB7LWhS/emf/un7H35oRPfCyy9RX1D6qIJQ3vWuIfUk8qzKKOvLSznOpQXgYx+K+/gsfbcfmB2gAK/ZkLSVEK5Wbem5QJM6qpx1+7PGgcYf/x4zkIfrN5/D29W13NirqJO1sTCif1gsTh5LMv20+4JNhAcf23Ixo8IfoxO/ZCm/QRV/GdEQUSIrvzBWVNyWI24Fyp+DaYfEG/KCTaTbBw1twpTpaVkzGeyWQYt9XmoqyYQB6QvJrMG7Ex0+LzhEkQQK+2SJ0ml4niF3TDBHtv7HcsVRrMmQl0MJLq/kYjqxlIXCZfPkWKh7/9utsPiDKQqTi93dLW4L0DwZ2GyuDIleILU/O0W2T1GoNsS/Cau8d9AAXJ8jbiduRZREuUfVnLt9OIqQUDLCUDNusfj3AnTtBWcaLnMP7n6a+YKbVqolF6UjkgrwjWHSHtBe8OzUO+OTQdyDp8pV7hDGUWb0og+fATP/SQz8UZLGN8Oc3jAO3yYxd1wRu1waW+MRxHPA6bPd4UaVpkoPgCnIpjYE+GRGlBRFIg4+WCe6DlPEhoFQqK/C/0QWRkg+/0fEipXkuOW0sXUMPswwjioynoFLFBGZABcruJHHfgZ0SZo2plJMuChMO9idL9VKTWgM7cPWvsXqEyH0igq401U7ehFTfRGXwaWjk7Thqqf25HAnW78ohIHg6Z1FIRh84NHSOiicJOA/CRPsywlq5A0DjOEWSirQ3KE8uRumHc2QmgoU8DgUZKKMG5imJwh7DlUtm09IVHQNo/WKSm5e+zC/q9boncMfRQ1PEyPxjuJz0kDiU9Ck56S7SQ3Ys2Aa/yQ8HnpTAt8sQH8B2skov6I7lBQohvhtefuX//Jf/mf/2X8mUcX0K+J5Jhjk8tItgLsqIXRmRC50CulzDzzXtUcPLUwtg5SU0XjGKrormNjESa4RQ21i/fKG89kezWY+uH2b7bZxePZrOduaO0yisBWiVI72M3S0RE0wZkuBI2VchAWsTtFZnujsB3Jm1mD4xrXnEnqQa8oZ2LpQ4lmG43NLtyHG9mRpTrqFlxbrGG74Pcc7Tkzmh9os4aQdSR2NswsqtSUYohOVWmQEVQjpQc/sTC8eHtspyp1aiy6Enp29tLxkj4Sx/ze1s4CJvdOTPfzCMgiZEskM6lliWpBPWB2raEvDepFD1Y1hsUpXoaysnxi61FZbKhN0Hc1+S6E+Z8YTKXVYw49pS74oP7s3ef/AIf3PPvnQuUGqRGkMs1V+oULKabVoxIWYHBtPl+AY2Dnz3u1EtSLerB+Sbu70Z1MmBz73rd45sFnD4jenjqZ3755eOjC4/PW7uXXRKp/Q3sMciU9Q0O1KrM5oa8SpChpFOl+0Lwy98SftTZe3FjlHgnArbT1dpNhdbDyzkp66GhczkUw+y0AzduZ3kMX419YFiZoE4G8wmtCSvo4uVjliR9aeNF0pEjQulNxZWCYkTwgEegtPQsdD3nNIno1hwg6veme48I36qna3XnO72YU5X71T1P4910jtcJeWq5Hd032Q6yayXTozte+er8WZ49nj3ZP9w7MFo0iboN0eaX2iH7u5I6Zx4uH82Vvvv398Y+nq+nMUdCyY/tk7f/u7v/u7p7/16s7U1Ktf/c81Hwc1Ct2y93Vz9Q+/+l8b9P+v/+3/03kNeQ3jJRnekpzcY0VhRCMVBaiIgm4nOd3LTUBTBzeWZmdW7h08evPTd442shhxMnu8vLH04qs36YVt3LxuuXnzwd3nr1+5/p2vX7q28aM//8Ev3njj8Psf6LROtnbVhW/+/u+8eHppbWpRu/Zofo+e7e5cNqVWV8jP3HJ1YLluS8OXvXDLhaGNln0erMm+YuZg1KKP5hbFmltd1kAq+GPHBlwC6E3hPAjB7Rq3aXM17a26fHxI29n9M84UuOiCMKiaU842Ex56RbFLQMe6TmLE2C09bIWy08PtzZO9e5uWA9yvLS/Xli/J+4rLhBDNPtKZ7F1bXb83/SjIZmavP3fzQC6sACzMXb/+4vsbV7a3P3HF05kT03lWFvHzP3/zDdnE1czQ/alQLWYyebZHruGpKpAB5dzR8ce/eEeWv/vd716+vPDNb74aPWjJ17yKLMMDE49yjKWxupOuL/wrMN1Ng3E08ODTn+wERWM4Hg3MTrOVdiE1nT8m6jtDW6tI12HwdI2GgGRHMA32KGDVbW7BlC7QnsFJnlb2JvS+00fei7fxokyzMOTZiXQCo2ke+PH4KBLYRo1Ljmt66QJTShfL7kF3tXR1yZFfKxKnuZFMJlNfiHcUavJDKSF6CRGokRljfcZvyvEpI1r8JtqNCZAuigmPsXPM5vH3+LfZ2JvyXYCYKZD/KKFUznMyKjCfKYuKELBRgcezDSRMuZPSgCof49K84NlBI3vct49ggn9EA5zwNViHhryqocQlbX01nQPygh/JzxCrwdPVh7amfoQzMJ16tf8FMAQ1DWmlB6/AT2SQtNDgoranzucCgTFnGuyX2gNi0oKwQ+qiJyfLtTHphnNMkK6QtJJVg+RdiSSPRdCIvpLMbk/UkySKQxkvxS3vbLwairXzAqGugW7q48c7f/JXP989W9mfzXhpDFatRHFmnKn2AfIMU4SFUc1/EJPu/qRTGJWsA5fWNwlZJjeVJDkGH2aR2p9MT0qQROEJCR04Huo7H8wZe9pwyjxWLQbDli7O+KNtsThHKwo8mbY7aGErTdP03CWNy3y3Tgdnd9/75NGDfUtO0fFBl5p0NrVQnPRZ2EqezYWyPR8zavCKwZ0j9oTJqH0sqsWrWll+7tqabmHr8d0ZV9neuOy9APTOu8nSkRM6IHvbLlA9XXTdtSupTxfWV7/95ddeuLz+D37ne2aSWvjr167Yf7Te+sbb71l6/usf/ZD/b/zatzxXqzd/tLdzYkh7amP1lGLU3Qd30zyanR8dRuMGm6NdGHFApwWcorbOIdXovyawWJN+72Rrz/sG2k0rrqg/PrF1enZ4sIMVB2e5tYMxVNDEItsqngU7bJ+fyrVOHHcfPVaxlMgc5WevlKcbmX50/7EicwW27eezgyyQrS0tLK0sH7tT1NhiLhflRm+rCyViWyQXldEyU6b09FVMmsS52TJtNbVQ45a7mw/vbu4+nIlqhe603pVNHkXS1mbfT2dR/L97dkRBeuU0/YL8Gj/pRbT3hqvKNJMlHQyW0TtdWKQO4E5lZiYzpunlWXcsL28srkhFL3y6cHrlbFnej2/v3ts+WXDt5vTslj4/oyzY8ofjEZXkJXjUCzbfBIRAGcykOq4y6RsifSiLzAxmiJULPfUhJwfvvfOmBToSqy7Aa0hvqoIr1NLp7ZoW3r5zT+m4KDQJ1zSyBtal85V6nUTJsJ8gbKpSYbNQkiidgRG1oNLn5s5cLLKS4Qa2rZP33rm1uGLquj47ncte2synekWFxRCUA6TUEckYfVHLJ7GK3Y3KglDokhf2zRs3LXlc37g670bEM703bclI6vxqVnMWp84e7jx++Nmn5Gf+2przCtiv7552Hcrh4ckOtf37s0en8Geu4h6GpSxEGs8gwCk2Cbmbkt0GQg6vZZjIuD19ZjmiePnaNUxjFKjPtIYNLddkrjLCJgtpDlJ8GfKEJ2FkTHm3nVxrL2JXxBFQgw4Rksq4FQkw2JlP7tx96/0P9vdOouRY+lgEFcG9eo3DcDaf2yFZjidN6s2o3CrAB9ogYXumlJ6oGtqE8QyjAvBEnPMoxYYAE6+R5AZpJzCkFD6UJ4cp5/buDsU3ilwLRsO5mTetMRu1knNncqRhPttXDHhiHKlw9X+WlcMIwG24GU1NBXWTNSLAhr40gYliFAw5OebjL8yvXBcDnmE18rRRi+nmwoSQF25GPGqjDkzH7Mxyo6F9BNWwN3xgJBZpoCaZ9cDus5KLzJsLSWDqAhbHSoKnnkmfOoqai4xrSx8/eJgjHXXllLaiBUZcLSj4SiQW94iq6si7FjdJUpdU6KqMN/A46PxX7pjApJUMwpjT48cP7yOmv77AlqmA55RkZbC2AYLNwzWhM62fNi0nO+ocjx/D7bCGPx41N+Sh/DXk025CXV04W6qNIqsMowajAPE1JkQCZI8KpPjgVC5ilhfm/elTJanCLE/NOpAbeMsFT26/VfQUksTDnOBLFcezckkRQzSLPiHO4M3MTjvTDE+syDBYGRHtPG7cYX2CuuBSxclEt7ER/tHq86hQAh/Y+kssplJJThvmCdsp29OTBVf4FOVVfJ2YY0J2rqcebW9trNQ9xcGTxoo86ynBL88unJpouuAK4pncnLxxeeHgcOaxAjw8soqLuk8d5j3Zu768Zv96pR4anX5wcHp3d/7yiteeD05TpvKQnjQN0Viki/z+pM8zCE9lJDliihg/qbZuS22ZkTUmeVEAOuXiBcwZtrmTQKNdPgJdUaArAAx7amgGhGkJNRpExmzaspBQu4fwp+RSCNXhVjk1JQUQYphOt92TdsOMIVOyZaDqcnliKCsoxNNauP/QdT83r163mjG65XkUcfQThCnSczPJnHPfCZdxhy8rXvnfcbmnpv7i/XsLb37yX3zvtZ2jo8UTyypMqkqbbovGX5/32/ApttRX4wjNjb1AGiD7x2crNVpbWV1YWVUQWiTNUWpQtY0GRbKSmpAMdAsQLs1lbhLOG65cXbp0urBy9fI1sRx/MzL3fLaR3r1HuRY1MlRlxMarWUdgrITwyQgs5aJkCRoLPqWoxNfW16+sZSSzOluyfZKlZ4K893jLXQ6kJ1q6uYOtR1Co6goH39/fjFrbCwhSkFXqg81nKNnwZCxbnRO1osPl5xxVHY7rChPyjaqnzHPcZR6ucmSJqsTXWwui6z47riiDo9IaUj7H/bSrqeLfDnYcoXRk8llNXYV06Mg5BvmVfhvPIOj5rNGGHOnXDadshn/4/gcW5mh76si1JI13FHEiEbl2wwJjPcZqL9mDiiQRoEur6+B17AGfyIWvZmm8K0cBGBsIB08qKoYaYfiz+rkBbBz1/LcKUXiGUHzRxY2qaqoKfy9Az6eBNlAB06MD8IlWZT0UYuN9QjDOk4prBDlegHgyMJWN0eDKWpskkPrTIpq61KYjkiyOydS7iQySZOe8Mj/RVo31UCAH2fbgeMIz6+wj+QQwmPYcsjlgGOQE5Ci0Fjgk4lweykU8Bx43Dbgdsaizfmdn2TEzLNZIgVQKYrHNH4ylFubWYfjszU+iVeSmiL09+gzSAhmfOux548oVkvDRK69Q5BkI5oBn+BRx8nPw/yf/5J9IZdttDVZ2Pf42b8XksRbTLMi2rdZKrAef3VeXgSHZNcq074+3j01Tpw/0tPvHP/kJe2P5slzMH6YFcLYdK3Tw8HRCqI3PXHSmzOG4J4kRHebD+XTG4Nk+0bOUrZ30/eBhYPj71MfDbHDFDZ7nkXeC4Oy9+kTK8gl6LGaCad2NTnSpjiQ7tyxR6h1gDh9vy3WhHdVDkDAYjrMl6pOiEFZkAXtqilq00lHxsdT6tTsNxUXD97//NzSPpqY2Osttiw6JzEqi0Sp5Mih3zQG1mL/dBbdU27agB63YxJpEAsPwWYSl2g4MBNzw7TP4D1EuOABMwnTctlGCMG45klBfzdHMcQ8gI+NsS9AcDZOe1VqzNwYdKvegEZXnAyeHIk54UrePjJrHp8noRPkXZ5JryIkZg72+K0pagyEuSAZydR3+Pt9QGYolaIB82jGJp0KD9inPJ+J1K/SE18THQP/gN4mtmsnzpqZhJimsYg3BA57OQhc3Twzw32f7gJzE3wgDVGZwPA3zzIgd6wIw8sqHNWLOE13sBKkDTpDmbhXxCaF9Jv72/FXspqTJiMhVNQxZ57IwQsOjQ333DGto+QfoipiYIIlJx5REd78yjcNgBjy4XjCjmlh4iiHtWzQ08mGo2p/BMCKxeDJzprWk2yitTvTvbYfa8TAMEsXeBA8I1Ucwqh4b/ey58cqy8QblXtE7g02qiGqQnl9dFsRun/JUwfllnQJC/mwjAUnUgGfUSlu7U+ulqsKCgVxvCLln3XIyrMxiJpnngsHtq+yRE51DmfISnYHt80zilwGmk0KPL12V1OKtpV1ZcZXBx4/e0sJ7n+P1735XYyYVBfGTn/zkynPf+d73vufQSbaHq/uAR78JA2VSU2RNOg7AJMsNYxlOdOM9QXhyOp3eKq8fjoujW0JR+Gg7UUhxAz3JSTVKaTbrkLVPcT2ky3bSxCf68RAAByOhbOmdnXnhAJ72FKqfspjLH7AOmk4FsOS3no4wKF1cXdETvfZizg7vHu+gRO+IYGIXaut49dPawc3MApByaLbjQJ2zmJCibCOVEYx1CNWkFqDJGXdrS4GHamV5NBxttELFghP9JKTrZg8teyB9ffEyf08rJ+nd3HexuXdEb315J7cV35m6E2GeSyoIuGAPdbzTAoD3T4PxSVVp4WSP8UhxhLAWcz2eKXUygM8YKKjZm7at2IKdygKerscABpMkCi1KePbnYIs+uIfQijsa0pTMFCcda9vepq7rGo3F5UWjCxGTuzFKsWAr+CSk0GvMkLdDzGcxGTziKUHT9KcHKS/Y2AUnSEQ2MCKkQbDpgtUyRZykYqkioSc5AXC6uQvnykyehDo6OYRzuVuPTHZGC9DGeU2JWE1YzcAjz4aIFNjVGkEdmlyMake6M1xsH7aFNUEjgJoIkZP+TOQyRLkd/DsoC6Jl+CsidueuZxNj4MSiJ4ST+IkP9gpEQi3/LpehdNqzIz7TFnHw76TbxkONiejh8zgjPqshHJdcxewkejFIuriKcSRcFTC25FB/6bNR1m7MV69cEaRmKWjwSjM3tFBk2NmlgPKlL72qW2QUKFSJUl2RgjNWB8xDQ5Nc0zWqe1e5GVGUUbvRJbpQjrb5dzZ9hsKyuZkBpt0NNtgFYrUw9aXd4gpFcH0Gf5dRe7Lbh91IagU4zshVydsYJgj4xX+MpJnZn4MbhFqMbAtYCl3WzAsayWDjcyfBZoJ6lLX2iM/IVdIYtpbh3wmNQ0e/8vu0v67HUtFKtIyekIELcX02T4DBw3Rr2f4DJUI77xewAejok2j5KF/meC4dBG6IZcoVu3NamR5aFTDtD7JlA98MuNjPSm5gznmaDVb443kh1gA3Sv2cvUPIeSzMarAnwsaiYhkCiwRVWhcp4ckIhQFfQPqNbT17PHZqTywi59ydCpEw2gmj6rUz0bSBZzeuh+fjpHHDVSnggamVCgpyamOQeM5QW+dwbOpO1jlTKKph1oZ0GVWfBFEjW/z6a00eOsGIy8HmbkrY7T+EDvDnWSOH2XpIKuxhfA7AVyFs5FkQtarAjHZfim9jzMl7c0A7IXeac/ma3atcjGUWhrFzIDAZnPSedJ8Dfb4LPKLPY03IA9rUmhvzdQ7p8zH8/UKk2In2yIHbbP316U2X2Z7t7HbQ3w9zxwp2O0Vl1GIax2G957/rSIE2fGZ5haTpbcFhODYSTUVARwuGRC7DH0uwQnUcxni81E36psxXa/XAC6vMAcUvGGr+VVWcPI2kXRR9a2q0u9fg8rIGYzWavB1lcHt2VG9xbR+QVcmXMNQkqMgY6Gmq/t72xUZkEtFkGsg9rwQCSizqt2V6mPyc11tD9KgzljmZXaAFuG37/XRxxhu1FqGm82jVJRU4qzZZgz/bSfdQGuKjKpeqJOtNU7UnPWlvj+xbMrVn0qv7tuGIf9blx1Lb3bnSEmU8cBmPTcDYFkxQfAAUssYdu9PqYfG574TrPJbtMnOAlTVidLi/Y0xz7/YtNwsseN7MYLz37kZJdG4qM4Vq52Bfrj1oUy2+pgGcW2MOplyZKwHv1WZKU81Buu9U7gkSApIWNN4J3XZe0eF9uiTGLgtL7z/+9JvPf4OYrj24K1QZsKdO6iW6XHVpoFelOsIfxFR6ou4yuzRDU71SSruJe1bKqRXWdh+eoLNe4LViWPO65h7N1MpmcXU0UhG3ZLdwjTtUExbfqQKxk6PkY8SiEXMKSUKZbLEHQbVNOJWj/rULN26jAzTBmYFH5RmeVhIQJL+1Ne437h6SNiWTZT0iphC1W7wRvLZ9vEjBp00ffIgYqxwVq+lR3QFwM7bAGeLJvXS4tH/nMW27OfOrEtsjr2B4rwBR3lUPZ84W8959ECwtrRw/Pn58//FGjhAc7x5tu4rtsU2v/eMd2jDri0frS4/nTh8un+5Pnx3Mu7ho00XKO0c7192T6x30vX0jzsvP39h55xdNrZyes4h7empvR1SXXER+QgNJWNn/wz/8w90pL9Aerz13zbRzbnnJ0OOzTz6+dGllxg1E9x/84q/+RjNnWv74zp13Pv5UY/3CledPd0+unq7p25amLpGazQ+3FMDW4vb+1sGDxVnqxPfzXOGyVy1JpuEy9q5kv3hxZep4d/d43p0azkPkElF7kXpsdzu7GfXUORJDZy+Y06R1LSWxxFE+YQnYJVydPzpcNLZz354J9bZtu7OjXQ+hGG2vLrnH7tL150zJfEa0LJ9mjpRZmV3BKp9Y10+iH7H74DNLvfduf7I4vbi+XE3zoUYp4udPQUpcRDS4riuK2jMz127c2Mo1VVP6SFOsfSR6tDfX6sw/3sqLiH/8R387Pb12VnfNnyC2hCLD1+nUoMVLzkMuHVE1It1p04IfgWd7hw8/uf0Xf/EX1qBvXF2e+drLP3vzQ1KRC7larKt+ydHoszZIJmscPG0GeNnkwx58fMY9yERFmECS2hqmNVi0IwNhVspTZ8nWmHNQheKwSoy3PpntzUe4RJkdV48dDLEMTYUK+1J8TUhQMX1vI7LiTlXEo1GbUElna0FvbRhEq4VYVyR9TD1JTz6ImrpBqxTubDhny6HrYyXUPWhHYnfbNny248n8Xwgcf15YUhx7/51/k8UnzQTDcVvYeUdWnJrMgqCUODNZLk/iq69q2wf/4HkqXbPjAeAZju4duh2rtrf5WW1pmrKKci4bSOLT/myFX1PHp1Idw4xSHOE5Tx/eRsVr4PkQDCeMYAw4ZucW6msIvOhAxiRVLRUDUIjMKZ+p/UwIZ/aPs4+1blGj7vyJ9Ho9uwwkjYfmo+ijjBYiU3y/5Y2qkq5ahtPe8+8eiEsRH9Rr16vrUWV88ODor3/09sM5m3ELdOSEdk5p0wTrufV0oSX8gkn0khv+nWV2YKpNUFvde2NrBjNkV4EZhGUcVtcWqZQql4mGKAYkPDunit1XLzdbBY1u7kkvWxyp55ITCzyHWjdLyXd63/mTyqjjVa46PqafTllqaiaaaNOHrlY9vff2/e2Pd3bvHK699pz12FD4pIHN6ks6WW1STInTSEICH6+xmWBVbeCPBm5T7uy7cePqzMnpwePNTz/8wEGYxSvLJ9SDF46vvHRl86PVo/v7x/t7XhqYWr1iGLpzd3fxZGl754G/lZ3Inm1Dvd7sydnNK5c3t3e+8bXX6TXxuXFpY/747M4nn23ff7y2eHVtZe3h1vaHn97quYTl4xzfz3E/rX8t/eBqhi6YM0OHGfHeNTCAOnOOJnkzBJ7Zn6MqtXZmO18LublnmdnxXeJiWrtUD7KBpK4kCQKpJZyeX9GQ4rOe3eyYZ2kKTDn6s7W/oyu3Hby8OE3X0QhCrNW52Rsb3pijSXC4tHEJ8x44ieQUsJKuqh3JrsEWwchfHQGeyYCRJCjKE1NPfyY0Dx4/eHS4N7vmtND0qXKq8QMhQRWNNpi7RCubpyu6bDJvMMzk/mo3c2ST8iSrotQhV+eX3TObhbZ+7PGFS1c8FLmwOyNfm9u6VhpDmUQtHRw/fry9tHWg1ryyvH4ws7g19+7m7v5BzgxFFMYS0VqZNbbjO64OgegZmNFVDWiHmlJBT1iSC/EpshiQBBmv9t1PPZ2ZYx1XBTJqVeTdOjmCMxZKKhnksSMEYwNPOyFv058Z34wNGDj7iyOzoTK8K3aIsU5h8+bLr68uz6pmGT8PbaNQRPJJYxi5mvF2CE99bnpCo6wVh5iNM8Jb+8KoLRFKEKN8+M+ebh3sPrz/wXv3PnjvbH/bAbOdzz5WsgTCWOvEkw20ovaP4J2fXjjePDp0fFRLcnacMvWUjb32ucj8QfYE9RxSK2myC1XXZF3ZWEcGOg/p+I9NKJCN2FlHVFMSuTlW3slmhl/h58ifq4xPLGP3PKhD7awlcMS18270vDw6sq2pe49k5WzG1k6Naco/aU0UBL/hk+sCAQlNgYfF/rBgjDu/IsJ2/frV3mU08835ANSeN2BkNTzsJNQQ7hzLNYoKKxTZem4EqsPXJMf4RxXAQEYUhfvCSy+J4kyGJ0Osd7/z1tvGn6+//jpP8GpQppcZbvGIZgMfyJVUTaq0sojJPcQR2jzBxMMUSXhKAfFiXciydD1uyQYDALUNw80kmadN4RpkHfFaChjaCGRE6ujIJr208pkBU3cFI7CpWgir+g0DmF4iRE4DtCe34sKAlEsIc1hqae/evQe37mb0gGynT1LTY0qO1KaQgYALGQE2gbljVCz+4mawypG4DdlcSCtAY4MA6mCKztqhO93ffrw468hmlpAybkVLoj7bNCWhs9olQF0059DKLTl0/tOdvzXjTOdiVJOWPe2DlSwjJbYaSb70CZdmz5Yyf5lx8SHKajpTUKOyLjYkX1LRQSmvxWkjhKll9unhhvXVs+nbHt05ODoofVg8q7+Lpd89wojUqss94+ZT1aXt6FUiPoSOW+4AoDxsR96I4VaHSKm9XaF0M3GmZHjcBpbCXjrUcWHhehJo4zfuahl63bf8q50fgWQVpsROHYmKVAQsykxVXbJ/z0eKW5mmHasegokltiqRiLRR06mdvyWFrmTPltwnNbW8f3o0P23GfHx4tpVO4WRvaW5v6uTDo4PrG2vXT3d0H0tHc7ufbS5t7a/ab1YBHSKSVfeyyknJ6CB7KWl1TosbtdcR2SXnLZ/JrD1ThMkXqpRsCXjkv9iZtgpMzg5yWW9DeV2liNWNDqRq172kHDFQoMXkbdoozqdEyHHJ/IiCsDbGJ9jB0aGJPZhJ9+AJXxNH4CAhCezC1pdtGkjFG+9OTra2dvRUh3Wn+SBLE5h+VWdPGSoRqrHVzoR8AhZ7Z2ZZV/Svf/KLhS9/4/WV+WSq6unSeS8RsF9qOu94GMjM9oM9ny681wt45MqonCZXTWwVxLUrVzXRrmTWsCtrzZlRk1hW5ZheO0pTcnZ6UPLPM61BVdc04Lobz8dNzy+4PnR1vXvMRBybTJKLhrKLnpqPpBcgvapgJs+66unjHPI+Pnq0ebp3YJgoffiTHPmyks71H8KkNo64M4GOCA5fTe7wOcjWpAMMJBHxgb9DhLMcz/RfaA806UFzz2WzfKQ10I3I2Wnup7Mc11Eb/zmasVgPPgMYPCNgP3HF4nkBcvIzEMwEjC8AbacKj81E1Rl71e8AL8uaHoOqpZUVQvPJxx9TgXym7nNFuYhPjYrO4xh3MTAw6Rvka1xdxWX4ZNN5TGo7ACdWFYFGx+eeK+/rFmmiTHnHcAR5eDvAj1N7xm+w10BWWl2aTQLPC9AFqC6kvLgBt+Huxquj8JyM2EHt0yiTzYrOZsZB58kBYAYknQp54+A/2BwtyQ3AHqKcY66uC5cmQwewSccIoHBcAI6oV1Uf4AeAdmibmxIAmi2ebYwNEqVo0D/RZ1mrmX3jGfIuroIzRpQ7nj4JmF7Q1PfqbJ5n6QUOOI1ET5ej6nKyZbJ9o/EoenI4t5S5Bx/roavLGfSD11Q1zNN2zZ3OvSXq1R34f/rRuxR/rr38Qu58nJ3haW8O/i4RdNILOzo5tezr6SOKS54HhEVaMKjlihvBwFQKno9X0qnfns8O2yVbHHkhuBY+6sZD7R3iPZUJMk8gmICVjp7WOcvKuSz6cH3xSiUdeaP9EZhcxuDocTUotXctCXk3m0LPSy8/R+Vn44Xn8Oe4Gp2MnXLeoi7gz20QB/UGz0haIIfTejENoIwwClVS7C7Edxn4ebLTx9fFoGxM5mmKyF+uUYVgOXXH6/e//32e49jnSLjSfxQe2dx1X9NeGMhAX3YGQLdv3YLhd3/7H9CC+cY3liFMl11NYtsgOdhDGp3cECpo0jTwM0NF7FB5UYiTsbg7qD0Vcgqplp/kVEZmZqOsZz9V3HJEyU4UC9CBOywN6Dq2P24vRnkcEhpSlwQ303zgwE8sYiehoUGonlASzzQ1dApbGj+Yplw+xo6LvxMwo6ALPuc4Lka9+H0homC5uAg0/h44wKNydz5tbjyTAOHL2AxoC2yUL4GN+Gka+D/tOYm8I57b3WpVrI44aQ/56RSHdDsVkAH+fJYltExPdc4T/dVcHZ1UAG/3gPAcQbGC/0DbM2DOoYOHMNvo0haRNJxxFQPhHVgET+UJ5akd3TPZ+eDfqyiZ6J27g7rjkkYIr17e0DQtUn+anf2f//Tf3b59e2phfYAJ9N/FdF6QMpm7wT1gUhnRoDVmSFfH6nqkaY3nSKkzrRZ+anOG/HLIPQxGH2y5Ac8TDEjYRGlgXILTJ/x8HOriWF52sNQKbLUqZ2f3797VleSYyF4tSaN1LKtNbXnEr/whYBLSYP3N7hRHYRU6uNuRaypoWW6n6wGMVIqByIPKvqWG1GINfZMP3n335tVf17da5bFIJDs5y1LNuxzSN9x6vC3iH7z8asZU07NZHj061neLjgO0Dvh/tvOYnsjcfC6/lnogj/bDgdKdlxdom35X4IeALoI67O9TFINAJK1evexzat/Zp0QHxqGHYvDc6qEemXBqVIXq3ZQJh51WkEJFXTzJfoDHZIJmP4/mLS7MuvN6du1StM6PLumv6VHCZgMLpNEJDKYeIWncKsYjx9sJRVWrmvuFsNL7pv+liL/61a8+3LqHkvZnD/zvnIrOtCBx8JQLYOIOdhcHdtE6/9Krr3my+4WlFW4L0D/60Y/293PPof1nYAiW1o2VtV//9V//1uVXjGrefu+Tn/3sZ1A1zzmY0DA0SRMy06Hs5GzCJG59NvGZk0faqoKM8YT4WjfCLuk+f/PlBm4bbRwMVg/caB+IOTo1XVa4WkO79pQ0o2OEf4KikRMh/FNMJYpsAeHDzGl0YGcXa4Uxd5fzD0wBNCp7r5GYMoKq5rKyeyFpoo4MzeGQqFhaJKGAHz98DP/DTz4BdrbnWjBXZyUXK5eWktaeDZFpYsdfdCnsHoVjpnOWp81joDqtk5EHaQLJfS/3VAarp9aGSKjLHXBgupjolFS3nmiDpywXc9on+ZwImnQn3XGG4p/GfvSNpiGng6OLYPhsPo8oKd9JgHarJ89ElVhqCuZIseq1pEdR/FQ75spOzQWWBncVKJqqXOpajNqSSQnVmrJ63cvQPquOp0ZrVZURh5u7sb2lTkLGnKqP7X161mpWbrefmTHi1UpwVGphBcONnCAfD58A8GlUbOXCh6NNRwnBTxlBYNjgEVkaXGPuPwXcHg3c7oEwn0MqDdCfAEocMsxrSWvI89C8LZKWvCQ5NUVei6ROIWjjVVmWR8PwZKrUp/Q+Jjt2RRtbRyj3qKqOUIx/BCX0ifxNfFQkSYzBz3PEp8ljSxxMkdSLaFvck7GG6BccYKQuOn+lDxXD7SEAjqZD4rCFyDJFLStEl93eIxsqRUZmbHDqIIYonVA+KysQBn/hbGUSM08C1ozgT1r0BmCGiJW7USr9I2joUxrVE8ETpd/+sHVlnQQOjrGRWnOSB9+CB5uMsxsy32WShdHmQuJ3KDt0jnmVgLGQcGgHmj8k5OQkh10A68L4N5gU26EOdkcmKdhAMp0ER6pk3bpT3tPGIZfolR4ZR7KyIAZSDVWg8DNS5ta56J1tV5RP50iC2RZKKmMjFEySq/GnDjzBE0zgTmhtEYncNIvdMLILg2VWn8gI/q5ArT9ezCYh/ANWBm03bjz36qsv3H93s5tKEZkxRU/8jlOJ5+fBPBHhyY+no0CIAQhpzIZe2sAnI/1H+ZLi3bv3/vIv//L1//Q3JdDs+vulJFOiQzhE740ZPhEtd6LVLRzqlEYe8xfqtuvdo2hrYbPSzBNSYfhI1IOtV5nGm38D3zgY0NKa6ORHKXeZ9sdkAXYs0QSBkeLCyoIxLZnsoGZ+cHa0CYEcof57/ZCz8yX9EerxKA0dA06JNgWElaOb9vbBkhFxJbIIzCsh8JZc2yjPK9pkPRQv6Kt37JzPLk7njW97L5nmJcMBoY9z8ngzhxdKiQY58Rxo4Biv6HdBosJqf/bMUzJlAmzHoCCHuJ2v/vTGN/IrN6IlL0XYZCJ/BzfMxGVlZd3wa39v24nOjz98/+hgn9olxJoVuDrdtidZ2sm4kYmuqBsCsgmZtkDDqj/x3zdOxqfanzQqYcxIpnpuUHwon0VqLZcuvfjKCwT6Fz/6Kx305lwmz3++f+/bC899b2ZtZ3pm5ahO7dUUCOcQ0GeMkxpaa8HLFrFM0cQ5UTbFe1MUxAvMKDxNX3x5WfhLE5UmOqiGbBZHA5NY3S+OlzB4tn8vwp5/jqMrvAuMGmV33JcjLfOkkJSF3cYgm9yMuGyfjURo0Rby2vROas85OiMIFUVo7BG+ETCf9uhf6cKMEMGtAddwY6gJPCME+YloxuTHoCrO4vzxo+2Z3cNFO4o1LudtR1XpHteJN5fzRH6ilZBMaYYuTefmQZMxc8bDWcsBM1eumI2uLx1FTeb6V6+vLV0CS8fl9OqaJ0JoBdoCN8rafvxo89aj6fuPtz+5PfV4O/ys2VQoqVTSRZ3N7G3tueOLWgJmgTk6Ob72+gsfbX32cOvRpctre3s71v0c6CCQ11996e1PP8IKE+alK+vIQ8H84tzj7f0rLzx/98GHi5SLrXKQlWOKCiy3hc9tLs9pzj49PDL+e+dkT62n4sC4HxId12ZnYLs6va8eLZ3sWP1ZmjvWdHixSO5h2dnb3D+a29vZunR12Vg81aPOyu3t7x9pKa2zn+XRwrOX168uXH/+xmXjcrRp0PcWsyhsP/mBSb6Co77npVlbqTOzx4tLJ3P0xBxgTMnaHkPJrZ+9+8Ybb9z7+IO0SyVX1jujtFJGjSD+yPNFi7k9YZNXGj1aJw/cHtzb+vM//18XZ1ZPzxbuP5g++Nlnf/X9jx49WDxbzCUqp4ol3UMEou6xUqtqklbynLuND/bpfMEcrFbDEaeSHZ48+vSzvzz9gXcmr19fm57+8vg2FWgC2JQMdol9vkpiB+9RJR2+xRzck46uPk3AM/15DnUhyyzFHomKGOM2V1YaZiwMAvp02qUjF43XmlThDM0JBFk5VS8GahM9dWdU+30qjkySl1Zm5z23FfX4QoKNGWVGjcRLTe4Fj3JaOnULgtVOJIPQdk6To4khaWO4YA8NwuA/Smn4HjsucG+yDKTaWRjDjn4vROHbOeRPr6EVPao1PW/BEiVDl+Jj0CToAvJnoB1KNu0HqU30C2ATOAUif0gC9RPuccSOHrsuERq3bJ3TiyR1cgoCPIn3OWlANx6UtX/AxmXaPhc+J6Nzd990TMQIw8LcgUIeLQVfABx94nO37xKSVfZo9lbhaq7Qw1ou8freLnWPrU36nIvr1gIW1FxyayVG4wtcXMCIhcNns9E4MJkMq0d/UdgzP4+VWXov8XA4dqfdm7l01YUPf/zGuw/mVtzIi1FjelqOSm4nJG9UE4raLhvwIeRJBiJGcgV1buGkFDU+6K8oiai1TCPsyPauyblex0iX3q0MZq2WMnQq0Ti/Nf9Kp8OIp8lPBYvSbSvTJZOKWmFfvzr73HUN8e7U6dHOvkub3F6wEmZFgD1pcnbng08/e+/Ro0/3rq9cPdlfdDSuGJdsoJMJGDepT4EhkPyUtNSM3ed5xp50FWjY35K5fOPK8dLcwu39h5/c1wdd2bh8tOA5+7mzFfyfWXj52szOvdmPNh8/2jr58dvWhvZ0bfPrl7/8SnQQT02taaWdvf76l3/6Y6ucp/T97t++vTyzsHY2vTwzf+vDT2+/88H84elrr3915+Dw7XffOzg63rgcrfbTBzuWaO0vpK+uZ3WpfzuxkymDLDiqEyNniolaNhUtPD87sO85P3O0nL7gRFdrqfDkFLYrZ7nz6vGtO46N39m5e//uA9vnekn3nRj55vQshi1kAyCJceaI0dLmbnqQpYVl11bNHpxcXlg53dyXx7vv3Xrw0u6XvvMyzJ6SS5Ps7t9BzLrWF4OjZaOAc1Uz3RjVA5RbR4/3Tnbd5P38l4Ph7Q+mtj78UC9WBwgSrUun+6OuFzNWKOkAGsvYWl6adaP4cqrEqTud3dxwdeUqsBcv3/jnv/PPHE76n29tX7/2/CsLz/2//vW/tmD3z37t99Saj996Ez/Xj46+c/Wlf/T61y6vXj7efLA6dfzq8szdqYPHc5dgcC2h5Fv9QrVlDnvlcywtRUwCum4Q1yqFkqsiW/1DfEVNPoSSSLbKZOSDAcZg21sPXYTscjzVgwglGZ0WCAU7lR6K2XPIILO/c24UmOFK5jXA9nf3rBJa+tEUgEcRMel2EqqgWM77xoU2bVocyoXE0E9yQ8Lighn4Rx+8deOaq6dvGlBGO2ycL5d3Ss4Yx56EhHa36cIXlfQWL8nHHFJtTlgoh1YLQP5QZQjvmm0lvrW1eevtN+98/PHBw4cH24+PNx9RsJ9eyH7S1Mmy3aQpoy3r1C4uk+LMkYq0d7Qn9dmTJbdQz+aO5/yGL6X7f1y6zD3at9TJf2HhSvhcS3KSZowWY9P28r8aAXa7ObpRGP3W7CDiGqEP62EWl1su89OmZnftrNAqi/PgCqlYXPOL68qCfGshfZacjGx0tuGfsXfZGbwnboBHXM8t5Fkk4G+JTrEifkQVPYo0osc2ji5fvQoFnlsC1myueR9+bBRwcFX2naR0j7OQFglUra6timV4ibYMtaaNdg53txxfSDNtbct9tdXL5k1vC2f4CYMroYvCsiZ6f0vbEpIKZQECDnOlfM75gRJ571w0nnaPSdYkI220Bj0Jdp7oU64Ga+ZwKxVJdHI2MMo9qn9JhaLnGD+Y+NSYIesuE6ZhGgC53QK0vLVnTsIZsKsUNlmnTjZ3dh4/fghxeJk+d3wCYSwPpEtCydiESdHUinhRneSxugtI5QfIZ2CX2PmsMWd6gHGP3GDgD/Z2AJATQRxM8A9tT3uVDeeQWzCihObC3NiaRrDZVzB8TnZQ4wr9LB+4B0p2CAwAVioO9WEVbYVUzZ3mQkTHUNNaJ/Fx3lobLuMNTVym0lMrU6eLs3Pr3nmZPl6nC6mFMzKaml09pCR8suUoA4bWpLLq5qhuJFGRhwxUjgZroLx9ZKov20krHlM1YnLQ07lIGetF0+0RoACVyVfEVWJCUS1MmzkWh1KJBljsPOd5pn0BFDeghQRKEzJnj2koZUyeYL12sVMRpNALZGt71wZ31SHsU0xnXm81/8jQeGba2Xcn2iw1G52ezBkFnVxdXTjZnaH7vHNysJ++afqemxv3Hz2vdbVideTxoKnDjx4vrb88XQ0DEJlxdAVVEmUzorU4RS9LYZweSJmJ/IxMcsHwMSNSJnnhqUYYwn2aK9klVH6ZB/nCTqes6ohbLUuM0Sh8m53BLEU915mXmy5vrM5NPXQOvl4gGLFxiIAMbkm3o/3784LnEOWLHfCIWGVa7X4wj9ZwtXIpmn9PUxJ1Wq9o5Gge3p6lYLJsUXyW9sP5q3/x3r2Xn//wO9/5zuWjR7JXxfL3SThsqRLsyNjeDoXrqnDXheeWU8fK9/e06iu6eTcvLS0RrQNduVWOTJxJYUbEhhfiqsf4DS2TslSDCiWpTmjVnQzd8C2CEz/wBkoKOsrcqSv5q/p/ZiEGP8ymImyGx/qx01n3fe0+fmx4AAFbqye9biEni7/H253G39X+ois4mkeDcMsn7GlSRpKRbCV7I/98igK+fWTNJ8YFrBolzbpQj1KJtZR78DJn7C4keExV5uc3t0YdZyF5AnmjhW0sBFUM47oHA1OhadCbkv4c7AIIWGcqLlHGsYK3sjbA/1KHLsFSl/mA2Z3VZ4qT9qKHTlH04B/bT2NDJGljn7iZvCZ7kQn/q11A5ND0pDmYoLNRZdU7e5jTX/nKV/7Tf/y/o6dz9caVqBd9kPsNwcCAAGL06uoNy9OuBZ7g4dPkxCeFkmFijrrvZSW/mip2M21c6xsz5BrCRCszzmw+uJvJk0HtXz6jkurQSbsplPH2HFeefHVQ253WpCd/Bv3s9gdTePLZpo9dcAtqA5ijPfh33BF0/YwBEjbkSIxmVKPqiPA08CSScT5HvxUUesyvuMmPMcoQnUNBS8VcgZsBw4cD5MBzCSlN9yCaFFGegMGWoGmJIuOW5SZAROUI5n/6n/4o85qHjwlMYR1ZDdYfwJr+/oRHZTTJPPrkfYMb2zywHe7uSBcqEc2Hha6srppNuaffOHh//9GDO3cg6SJQ8VuioepMyfJjO1D1MjjCdvZzpbUgAAw9tb2TbRQ+l8cJdZV5LcQZcWnJewZ8Jf925Oh6rC65dbALbsr0HM0bN9Zdiv3SN15D1c7cGfnfOs7twwfWPMjDjDVq49BwEnJ2k0TYJZ0eeGw+ef99CJvVwL7YNCowzT2sR/O1a1G1+/Gf/VzqVLpge7h1iJk2FYRa5gDfEYuQpOAzQ6GiCrcPp3YhLCkehapLsFGWtB/727/7OxbrrSYogo4LAfIH4UycMtBe8AQ20DwwcAweMsKrsRkgxx75nQSY9OeGfORTS29dc5VwEqpb/s8BLsT8wk9kkBZ3jLeyD2Hgw4jEhpyBmdgzHP1Z3vEHxv2FKfw9Azv1Z0Zu8p4Z9LRnk4fU9PHJ1EVqOxcd8UKB8pwM9fU0/ooSnE9CPs2WyXQn3SPIpjP2BD+fmVMw0mI4GEm3/TRtg88z8QyhTzsg7CgqMiOtGmA9DZikfynySfJgg0UUdfB0KwfP5xdKa68WYhpybKeT6i7R4J47x9MqLrvuEJpeW1nlZpoGdDod5dMBuz/5kz+hlEq2K3w0aIE5JmhqLDCO2DBfbCfe0KZMgKoygtSOAYCDVMipcUu7HcbsGNpDPqgSCltdupBy7Ohd+xI0VsjSLkEGHkJxMxNutXHD6YODSV2VCMTOrruSHS6BTXLiDmQOgs0h3WJAJFbSLW7lGMkSfxFHAeUYUV/o+EOAksDUNRqaXxeksvkEuf2A9XVnYj77JDq2O599Rp/0+qUVbfUnxwc2Br785Vf0dK4OsSqqa3MdE4UQKtLf/t43IHn04SfOoJgFOfauSVeIxn6wyjjgU9d84OGoxQslvQTQBLttgg/myaYJRdPL1mGhxNwTh60R4c/uvsO5x/vp9zIwZo4PowXcb6W4ykBycgrGNjP+C0XSxoZ96LPth3kV3UnWhE6n37myvCzFnZ0948N7e7fpLy/dvCzU7FtatoibPGmJPrCXu/3ZghhBGPu1r34ZwSONTmFlUlg1LhKfh71TFNrS4N/LRlb1jQE2VqKF/fqXv2pSh10//vGPZ/ZPcj6pBIbyptEsUi09U0WXCrZ/6UtfunT/nhGOpGHDIqE6QakgqVP/PBt8B8kLM3qzpGL57KCS7XM8lVFrByWH5vC5fCOVAoUkRHGjUES569pf0QMmO3v3U7uZAXl/DnTKgo5MaBsYOBqm7WHEmyg95XbTSTVKEqIbLyFi8NZbb33vu1dIxUj8x+Mu+bVdjlSm6maXYHSijZ2KG1nm7kQ7p7Aql81Hj4nx49sfgnTPRg5y7e2At1mAZjDIMwaVOgf/47rnlD9UEkKJpStu63zQAmd7QjQJVf5QxUfSCheSwTa14O5pHIBJVnC3D7scsXvpvz4TfgH+mZ8BngDsvDekvBaq5pKQmEkkQvkgkVSzOyg+T5IqS8pI7tRErJCfBuib2cUyLsU0ckv4bajsbm+fY6gEw+rCj5mCGD7k3zozzErTJ74Z/cKGAFzkqZkiTttHmyG7buTwySgHGHgy/YJOU462wh2e67IqxUyWG1JQgw30t0/bTV4DZBmjjKD2H+K2P5sPtBxtt8+kDUDW2EQjYAXZeNhNA9oEtdQRtjGB/Eay0Y6OxZ20RiUYnJC06HJjV84bNWG9NFE1a4grCMMTK4s8MdxsAPsHGZP3+ArN+gWcBzxJc0OKwojSXG13+cUCo9lsSADAGP6dzYGT7Rn/qnfNig4dgBsbmyF17WAXynBPHgbPdjRaMkBCDhf2wKCEacxNw2QU/syCI/1mpK5mHMv/KJVa/5qE/wK3VIZMfR4YmCEowOOv8h9FH5C0o9GO7VFsQW0GbBxyh/62hXZaHA3DX7G2JzCObtkEgxlECLAg0qgQdVUtaZ2WWOUIAFRKpPnZaJemc11kbo5gaokLGInyPH1HhFlb5BW52fX04F11mxJcic8EqdyJViOuJom7MjKS+QZI0ia9OT806gSBRbFxKPFusUvGKCHAIKLQdshQM4EtYkZQP/kMzbVHM+JbA1fSsfrzgmdjG8OM8I8/f8nvJD2DdGDhwqy9219uUALDL4criGcCy+9f/dVfaXWv3LwEKlI50PEr4v1lYC0nbKTqVyMVc4fODqfS6e3mc7rI29GRnFxdeWKdQVOm44VYGYWq0UZ4ttl8DxlBbbJfDCAy/MloQqvmalm5rTaGwBYQ94bqmmtOc+/OPWPIlqsiLNt4n2eCpsz49/MAL/p79nLkNaDId5GUX+SW7I7lm1dTWssKnZknCxgfAcNGDzpZNx5yS6t4OaVUVXSeRsg0Fc3pxSX+9H9n5nJw+1JGN7Ovf/U1R/7vP3wYDKO04Jto+cQp+jpZ49sKlmJzOzWTcxgQKEU+CrL35GtwtTeqrMm8/b8MvqmqBHE2sOQ64aOB4FMctWpetKWo6D4bc+zsPswzNbc/O8oqntbHBl2nAKf9yKwtjTv7UUOQtKp3BEoNZcFS2wzFx1xAQi8cTrvcyTOhnFqgl+rdNqqTeKslSMyz9De7e9Zi1lwW+H/+v/xff+t3fsuC1Ecfvfeztz7Y2kXG7P5MdG0+Wq+u6PbHpoJ/cC0nszboJVkE3M5oZu4sw+X9zMumDhbpPTycvpzcrV+5bMs9T7gT8PlMX+lK4yr2gMxe//zcSU40eTpnLCuIChp2fvkiNttvvjqg8jt2ZziLjERoM4bx1dsV7T3pHvmU1GEFg7DBdGiiPyWWfGREEGEbEhWRD6WNjghbxT1vQ1EHpu3JIG6yUZW4o55HydAlaM/zNZ58JhUJYMdyHVQ82tpfmXEOwBvkEcAEUTmnSOu2QYxNs0ONNDKZC31mF2f2jhZdG3Scgf7l6YWTg5ONHarA61uujFI6D/NA/Prx4oMHO05xPLrzaO361Z17D+deXKfMfPJ4a39mZ7aGViaEaYlGU99QtXI845qho80dz/eStqMjKoBeVDjZO9g3Vl7evOwyC0JMY5jqz/yNdfcc7y5O358+/tp3vn5qdnt5xRPTGr7T+/e2jvdu5vDIwuJplDhO91NAtNyk8tPHdzanDm6vLp6tLKn7roN5YEc9Xf40BUabUsyeznLm2Cvq4q56A/D4eH0+J0NtuR7MuQN635hP/TVZ2l/aB9NLukdrU5m1/vrXTWLPVpbp9uxOze56DPBxek2livSWSYrYKQXZj4leD8rO3N5xeDC7uHTw3kd6mvmtR6vaKxfzU8gtCcnSDNWftCJpvWNV8dr4xvrUgbOZ3Ehl59R0a2l+5/Rk40uv3NhafONP/uTthw90FYfL3vs4c85G6rjPLgISN+5qH+xKRqtQIPxmm+lY7GjiUGiWiKquvzDu+dlP/tqE/MWbN2jBffTJHeIuPNweT2DAt5EKf/bYA9aMwEYZGHyLnv6aDJqMOCBsByQTsZ9yKhnt2ijOSEEn6RYlXd8mB8fRm0hzMcJZNKd9iAKHlqZOqbpPZdkbVhYB5ajqeMDw00jR6ZGcDcg+gl6ZLd+dOLtYHWyE/tx31LMkdIA8d5zDhfXn/hOMat9JwAtgk5ycDLrgHghQNOkkJFEQ5Qx5Ycs4mcCkqTg3AIoN8RlQnQeXS90c99qjkAnICWxjngToSa5MwHejHYKax01nPsec5GCQzU7zP6B6gpPjLCWx87jc53wbxRziF+jYSvNL5ufnLt98jhZPJZd25jy5MSQ4lHSOmuZ8ljywtQbsHJ8GVn3EQ5e/rdx8eDtvzT1vBdANFfs5ROnCcz2aeRij6obO0i/QWgdbFZJazB0lAX1NDe92D/I2LK0X7XbU11xEUP37X//8vR+9/bEb6o/mNba6V5SnoYmQRmspowt9VZMqVm+ddmmNZK+uUJCItFihpwxWSNGXuDy1IAavxgY+4beYAWppcUH9XKD5Inxe8nqFXgtOtTX7PjzIa1TJqp2zuo4DWoZb18QbMjF1WnJ8Nn1oVXRx2fHVs8tX564/58Gd3e0HdxaoCOWYVKYKzrJ48unNH3/44OOts5PVRdeunqxMH482RAfiOciMP0wIwRhd+YPBR9vI6HJsXS2ekZqoEU/tJymBM8pr4fDUu+1HD/amt08Wnrt86doNZ+6QaCylzPfP9tZevn7y+MD2/NLu0eMPb33lN3/j5Zeuvlrvbx1MH/unPzw63Hph4+rtqQ+ubVz+zV//jcWT6Xuf3P7ZD3/kZXBvKrxsHfbo+KNPb2093lRehm1GWd5reLT1aGfL3nAuzCJFeJ0u1eBQOZ/tozNS57TvOGtyfTK19OEn95dfftmlyFY7TxbO5tYWt45Pt7Y8mn46PbdxMH2wtOKSj6m9z25/dmfr053MDXKx9eLijeXV46NDA8n19Us0Z3SRK4tLrhFcpFV2cEbh7mTOVclbWfo8dWHnzMHO9Nbtncs3rtCl3I78YBjhO16cVqC4n1WV2ald0qIHn5le1nATC3pbOtD79/ZuvPLazuXVv/3bt4nMyd7x3sKps0pRj8tE/Vi+3BEWwaGXc3w6L9NpoqYPd/c21rKU/Gtf/3UL96vuE9dPH01974Xv7B0d3nr/3k9+8eOlxUtfWn315vzzv/P69/7oj/7o9tvvZ0l9+/Hs1b2FTz/75nPPvTqzuLvjFcgzC3l/+PXvvP+XP/z41mcGSvfq3J7xAD57W1OfSw7wLaIxNn0x1PRMLRCXrpMQQqVWjkFGlShCqCmo/sLe6ebWI0qlJD73ks/OPLj/mbcwXnr5q/DPLy6/8MILcwuLxiS2u+H56pe/YoV6y+XUB7uqQychlSAsA7kJT+YjVTfNKdu/xbvg40OhPu02SUmtlfgJHaS5+RmjMhGN6T777O7bKz/7+te/vjCbhbBZSneGaLm529NSepKT+w/ummOROotNq2uXBNHGlUq32MhBkpNDmRDt5j6ZzYcP1O4ljdzZ0Z07t3Yf3puddjxsfuPqdRhWFuaNvqCz9j2954VnS+NuNTUkPdL4OuF0bL8kcx9MMnSS2awr09aVlxnNjKVnw0Ib8Ht75FDb0hxAA3Yk1rhfAykKH21glWD4lta+GDiuMcHqP8l9psGyxhOA/nsWHG6MRsKuOG11iTGY6EOJdNnBOA4kGlZCMxpk1i9dJgOGqbZ2aK+TQJ4yiNNK0KkZupRWj69ff253Vy9ghdoa9MZbb73ZOYIzBI6R04Xjk9le+Ri9HrjZ5ODQ1ExRLq+u2JWBoWQjJdh7A4Clu69sdndfffVl0qKCIINaHUjFwt3VAZ1DWuO+O31EZw3Y4ADWGU8ZlemgEUB6QM7A16yUiCYEIfkpM2Dr6GzebSe85D9VyvI6fZOY9Fx8wNDvTv8S5IbM+eS202EcnbhlBlSDo3reUTHJb7CZXixaUkmdenjnvlMMFB39WZSveUBkLM1TTKrkQLMSFEWoysVGZJb+nYA4ONCZXr96bXd7q8o6pOC0uMUumC0DHC45zLg4u7xgSxE9KVMNL/vh9qOD3R0PuLh7p9luRAF/8115BU/GJJULDXPdxutQX+MXhkrKwhnzyFxOJNNQnvqNr7+Y4QmtvhpzZdYcjImVmbs109r+4ZcM6nwXRZs53Z862uLTxYjMUIiYKvHEJ2fzs/OrXhGYPnH7rXCdlintynHq9etXrxDLe54bmunRQ7ORnb9GBqxN8aGcPc7ElvpTFDNnYTXCksck+0SVJaVpz7o1Lh3QNF0BSaOaozmGAxmQpBmHIXUzBxrUvtBAfgIVkyQyJpVOtQ/tr/GCohfdjGXqcICQKfVu3hEBawZK/fhkf+9wyUp8SjOp7B8d3n1w/+a1qwEtH6XJfUKWUzJqsC4/Y8O+U/KShnxpadvl0bSIzfdPTzddoz5nXvzAwu7Cg0Pprj+aWbpzMPP8mhtjti7bODk9qDZQblKaVV0Xk8s0s8lD7fRHeFKEF82cV7IUvUd6nc5RQBnFSTGK91XeaTFcCJz2oSaPlpZKIDN0JG+NDnIJs+dOjq5dWlmbn97Z1+sdz814o67WhcazhuJtrAt0NMcGz5Rj8fBpyAFm0uEcT5qsUsgj+z2RQZ1GNTX0YmpZ0BmZSoW7QerK5apT43C/KDmbrjXWEp5ILfmvqkcSARgskr2P947+xz//8Vf+j/8UvFYW5QtDKsFWglex8vWkGVMR396G7+yrtgMH+KgFAFT8sNOLXMcWYGiqZylm7tAbmNZEcnqGSoR2xDRXb2XkqZ3Jk0cErk7np5ck39XOEPlKMg270MAYY6Z0kjBG+CvJzfjdmIDCWwigNmD0Wbe+bX/02c6nd9ZrpGSG3WQHZxmfTT/7qTIPxLN9O/KEHRma+Bw527PT6IRLEFGcmjxJygBzwR+iRtK2wQ/IvvJCIXO7HQD7rKvGv7o/0m/aAJ5RCpn41e76aHz4ZLrwi8hWNiIyxFSnm72C8XVmqOUPrCFrGBfkCk+1o8uQ5x1jwkow7PocUz5Ooj0nbcDSzajMGf+9vXffezdKPYfJY7VCNXhF3qT0TcafcPe4R0TU0u0TYsGFe7kv5Jqpw7npI9MKCOUY6ORuH/o4L7/2sn31v/mbv/n5z38+FBMHHuLk5tam6LsLa4aA/EQ0AnwaT2MTKnfiNv4Ge8KuXsogkadC7Fjgx9FHEiL6eOgSOju0HZ9nd4pKagJgFLd9mozCHPJ8ytdgtz+fDuoo3IMDQdyDj4iEMVZqY4KGuI1hEs+FiOFPycuTUZ4oIFEumCFp45jJoOJ24g4Sq7VBGJ9Lq5ck0dvmwHiSGcV6uJUB0L4H72VwNiotwKDNmz7jHPk0JdbDibJDPb8uQOicgmHE1erBlpWFKlA+7W++YXJugFsyM0fhaPHqFTv/VgV/7/d+z/ReXfNYEgWl9WVCNbO+uK7+Ht2ytpzerumvKhya0S+/wAiMMSe3eipUWYUPY07CaTDMXqPwZem27slyrikgZURRbYm66C6Rlrtvfuv1L3/5ywcLaZT3treluz8djWkOsYzXgr9kYFS9RwKAzMpq8Yqq8sMfv4nIlf3ce2jE2kxI3GqvirsjRaR4ThiZkjRWO/aLhzs7m+IiD5HWlJXREyU9EXFwJq1ijlRgQxc8ur8icUR/3KVqTU1JxC9/6WuW3Xf3T2gUKqAK7LHjgDUOmJ/4rlRaYju0IzaMBmsyiqCnozckIiFp9xfbjR+emKqq0uBUCF8QsZNmS0g5Dq16FkLGktDRcR4lrJKltpOUas0/rqc4MKT7BUF/J5gBeNIBeed90vNpd7OxC13OzqOE8CfKDswQvYPYxahfpSDOUT2J9txf6gP+px1PxCrCzkmdgAbGDJz/vDIGMxHpCaegZ2J+AmhUhVPdVDTNvdDPiziZ1pDq4GlsFsxjQiXdqes0OVxLlVZxekZd1i0TxZ6r9gJ0BjQKpbw6Q8aBoaTatNyplQ6ltBKqo3QfPAxbu3taMHtd5rFzG1dQ0tPLc5JwAN4avVT7mfkGhcPQWaZozMzz8wz+95JCyKtGY0AuqD35G7qw+3PaA7gWEkyDql5LUc0SNDefga/mFKQgePotePGQ0WCC0Gntw4joD/7gD3w+fvRIXEUTAN/M2ZkFGtfLSlREncvS9FKn1eGVqTg7d1WeQ3HFv7MwRMnnmAM8DdzRYLbHnzd9w0Xv09a5ND6Sc/MlYrTGuClr2kz2K6+9xvP2zz+mXetwCb3g2eet7ywQLDg1KuLq+PR6z7/wAhoc03E38X0XWM/NOXDDh1apsR+auaUCHhP0ia5hDlWR5QRJiwMnrdT5ZEDKQf6XzCwuhx6oXBfrPD8AwOyDqdzlenR8gL2PduyrTm0+eij10/l0RvoX/Dy4/xCwE7IyJVFI3Lyhdzjc3DLKtSDE/+wo54GQUsUdHS6fMGTzZWyKGB8ltyY1aMPTHOvJKFREaakX8H/y+BbpFZ3pMZ5o6LcqKZuRIaIy0ZohT4l8/bWvo8paJ3WNx6Vf8/zNa8YSv/8P/xBaRH7rW99yxfaj+/dVN2zX033729+evZ2eXaEgIGjDsYiiY0b/8B/+wx/8T/+qJUq6pvzsA5oTWQo/ry/gxeXJMWkaVaPtkaE4PlmTYB1xsCkY4+rG5W0FzUFdxo6nkc/axkYUOKrTHI1b6koEPgRDFpoGn1JBf0QxeblIVSeNsaIcuI8r7WEtIGY+kZlqynE2jyuoTZJ95aWvFUzwSEJEv/hpQII2c3V07uzlwcko+DDVuBwe5vHzo326znv7ezuAXUMKg4KmJerTctvG2rJSi9Cmh81gjEsSEdm0aylnrxLyGbUPNWApdbrQgioA7COrK1HO3TNaayJlhBGRac77FMQMDm6hLI5U6SfNkOUnvX/5l4iABnsyufZsFCFsPP5pmM5Lw3QoSE2cdoDQKg6crEYxM7UCSwbJLc63eBvGQAUMk1MOlWV2j/Mqs2P6K/VOBduxt/FrkQzIIRSLUbIEAIAUAXeha3ykzmfIJgfgJ/BXOjz7d4DkI24Dgx+i8GEKeMS6SZ/2f9oeorRj+GwegudDYGPXHERy7eaIoEZzM3PVNg0vzshR0RsVgCH1Afng047mITZqNMEs1mvZ8PPPCzJGEac5yNjltVgVpbPfnOw1Ci+/OR+j5cF2rAJQbJZCuD0idCYXbRssmE0QBCmqdyAtB8qRgwURjzG9oigrcTstI4FRcsWKASeCE6tUaysKZ4qJJ/Paa1dt7J2e3uYzGs5wjU0j6VadX6WVnkh7yziGNAbwm8JtSjq2lQg052nhlFT7BYzx0XKoreBon1StJw2wwWNwN6LJz+SnDODJUH4dvYGHKAPOdgAT1MAD5ACMRRWa3DVMp8LNn/FZISMkxBF/yMYADAASn1XiVbPsM9TGvCCenTZemZxDqPPkCYkoPMV1zQIZM+VVSR2GxbRGrl3lY12GQ3tsgLG4m6dijdpgbuQ2h5kcbEkpjtZtkqh97CwIjOjvjPAJWLKW4XHtO2QnrEyYyVNKHIwohSHL0MGd0ERv06HY4FMWUK+hu337xyGmtmRwYLL7CpVPltcY09/ttzMiTiNEW5bGilS2tJseoQPk5yWAwjYqI2CC2vxBPybAsLicRbwbN651cgOeRs7mA9Is+4//+I//6T/9p/QSCib+fz/TOEfzjidRoIpHlUiKpsvsaCprCHoWrZMFccXXW7YrpRmNDgg9xIBIV6QmSlaUiUxJS8m2IRq0PV9QmFIB0IZ/EFqx2Q98YhdrtFqGxEm39FIGPpfji/IOLTy/uhltV35xNKk2ANpwp+S9Oobss2W/sls97ibUjCpMrJISUSx3yBx71wddKs3ZktD94wVsw2SildvsCEfBe3n+yrVrG1eu4IWuuqQkJRG+lBnyBgnz/M0XW6piUxWthRuAx0cuNDz0vlh1z6PL8uFZW80OMGO86JEao2H70j5RybZhxjayYmfUI0ejtiXJ5saQVFqap16EcG3aPN1nxzMhQWc016uNDqhWopgSPGWQ2o5zu/aORgA2uexuZMaRBVFPUFLEyaPn9T7asvG3+3wjIVNbizm+93g+uXDFEJ/5w4W3fvzO3e1dQ/l/8p//H648//L//f/28c72totccGPreGd75+j4skbN9bSPn9t47teWr2j+7CEhMftySS6o3Rvlz8vmzKJd2dpcJd7V+0yvXlmXhWyvVrMl7wqfz8KoLw2G5h5Hm5KZHopF6HmWTwIHR0MO9lh6RxtMwPTyDdw2PBKNXXIeT/TnbfbzFSiebQa0cqStTsZqUa+rnGFBKKny4S+KT18cozJraobPiWnJZGlqsivuiE6UNJIgD/2ZTDbySFCeCYqG18Hu8SJRor9vAYLKkA1b1FFqIwZkmLbLomlwzv3RZgJ/vLVr221ZbSvZdAvx9CbXkTtc4Ly8esmNf6t7M2sHlF5mX1m/OUM5Z2N2ZX75K698+bf/m9fUgh/8d//DD3/4wxkblzLb7FPulAgpWFi2PnA61SgrysJeAZz1pt9ibo24d7x9ZW2e9vRb77+9cu3Gjedv7s1PLz1/7ZPv//i9998n9urU5a++ZqK492Br9/7m4Y8/WT+YWlrO+aMDO72etVmeNji/cymyajZM0B1P4JZpbNk7SUe7l4dXz25cv/TKl750ZS5zp70ofOQdYtVaErNr80sH1El2PL+D+VvbeyrjN7/zpW984xtHG0vbc4ePTvPqII2rzb0jezgnUfVDQhgeM9J4SlmM3KV5FB0Cygjv3/n4T//m8N7j5Sj9EYOMHiIsVXkz6VOO1f8ldqQirV8Jix3DufmzRYpgi9MLh8pmavbDxztL73zwF3/1zsdHh0crC7Ln4aBErJakpbeiayDinw05VhKkqp0xJQGQ5ZyGCA1VIavStVieHi7c+ujhwd6PLZe88urz9kk/+fiOoeSkTEI7aaAeS+C5N0/m/Dv5eeJzCCKh5Z5swVRDfiOfcz4PcZ50TKaeYV3XLPWhOENKYBjXusTsTUpVo4+DKFkNDmqr0uNb6poVubINnrI6Zyc4B5TKM/RUqTXlnHB23HEe8tW5HYhJwk+bjvaU/+d4n8OF2nE2z32fcoGpUiAJfkf8T8TqVI0r8Fi2u3ALIDDFiZDwK6TQST5BbxE2qhpPJDomr5uH8Vd+CejkZ9yddp3S6CBomRRlmQIpItMijrLWkJ9vdyq/HLjUNWapD58tL1HKpWZfAyzJncctn1FSg3SFwglydPAgaBvgQzr7LAeklXC8hNt17FZ8KMLwSRLHDohUOdXKYgZ1tXwgtMftk3xTbvxp7LPTqGoq553ld2Bp4wc//OH3bQeurbk9kT/m4mXa/5ZSyg6agDICjEbg6NLqHOlxXDZqYA3+C0xyNL6/iLvLhYMsqVDs450tn0K4Z6czWer2vCB7nJoaZ2Sl9nVyjUTj1MNA929Hg0mBnzpJcvjK8y9+77svzHhwYOehdZDc7Jn72qvVPTj79N3b9+8eTJ+t5c4O8WcW+zp+FFCryhD7TGPHziF+nCRHarMMcqMNd6Tea7r4IKDbAXod6rcrD83e3TkB1nwlr/ntn9JrvvPpJx5OevG1l6YWaCedGmJpjY3gMWbGHvz6yXPfeI1AWFSd2zx458c/Ov3JzHe/+9319TUUni1mlOWx+X/87d/aPTj88Me/+OlPf6pvolj6jW98be2Fm9jy4I3cH5078+yTOdLn4v/VxavXr93bfmjNeEWzjsyaOtKJc4f90WmeE+wLOoyXpMJkoG/YuXtw/73PXtl4YWFleWlmXk4Ux7VrNy31vvX+z9mEj8/6snvwrV3Kp+sbg0Gfenhw+vj+zvH+1PXnry5f2pjZ37NqvHJ51crVzP7p8tzs2eHs3ubukqv9SM/s8eX1ehoBJRG61OXIQb0HE4TNahq+Ndai3GzxxJz6wf37D1fmrYq+c2/TEs70ysGul9L30DK7PLtEz1tEFM7YMSZj0QGbozJIEl558SumeSeHB0bLxx5VR8WxzZ1jz0W47vCz925Z9Nx5uPcvf/Sv/9up/4+4l/dl/NrazuFra+uzd+9btXlhbmX1cOb4/l7N5jOioLf74tpzX924/NHmzqOVSClCDEda4y8CU6LOZnzKU5oxWuExkSumAjMHa/WU9iRl7UCJ+jeM3AFLxYYnGWj9la3dHXGtaN25c9tqIICTo5Svq07woX0q6cYXMS5X7LG7C2AEMPFjGJCzb9KKfIf0BJpWHe3uU0W3yIVOcnt54yo3xkMY7SeCMU05YPHKyRpKcmjuIMSgLdFrYMM+2NkWqkSYnFVNO5bQvfsP9x88ml+atwC9upyj+tNu0cVX7werJ06vSsnL2tM0n41vDjnMWxyZIIxakcy7anfu+CgFdVQ0azmLG5dmpq+InkpcRnLMqAzGJQLgVzGdnS+AxA0wZTerU6bN88FuSgb/SWyKNdJbpvFkHBfuZTxG2ShxZ2n5E8X9hw8fq0cU4cmGHEs4TReDq7UGbSarrTBIE2t7b3fmYL/nIwWiYPKncJJQFuTjaBuqhHRgTTSaeBG14aUN3bcnJ2vmoSRhZdW7MFneahMyysA5mJHPqFeBHQUDvBzEZ2xCzNid30bS7caFoEmwwd0wHYunZMaxKM4GeUxL5llUr/qkAuYXBmwkLe2uUVCprAmqSMHGDGkFfypvWrAaXWtiq7WZmc5jMWdT65pob9m4FbD6IDOF1cXlaKgfZqdHFTDvqUUq/Icjguq5v1BYCzcf37qlvliARqeabr3H7d5JfTyXD1XTxwDc2nX16rez2nF2du/uAykaodqCfXj3jv5IzYt0aQPHSj86PEYPyBYaN+GptSF2+3RQ++uVIiH1KN8//gffvEwxs3QYQ4z8p8cMVelHa3ghFngFm17VPF4212ZnDqaPnIGnEl1VMme29CqRX+aUxJrFKQeKsjhTOtRT+2kAppYz2ppeP57a3T8+2z3x/PyBrfknCyJwEwbG5hKKSqTSMfZ4RgeJMHnh1RkP9ha6EJLZx0jeCmFQlVGbIEm+2kBRNPBxPEPeMyErwx+oI01WMzKk6iiZkUWQElop6hu4RaN/76CNhk93SV+Kunw67ePiea0VnE3N7e1n8htiqrJn1Gb9ZDZXuDQ6pSed+SmLN4gkh30T9PTuod07h4t0qSfbGxvvu8GShq8nETbthxwe3t1cWVw3qzQZNAaQRNg07SBA5LzHPDKe0jfqVKB5gymjbj769s5v2XzwgHcMPIe5vcHec+Sq4RuYO5kOBwTFDzzPRI6yNuTesNgzKP3u6y+999Mfv7upNdNS6XSeMJA88V0fXVjPDHoa+GkfY/Kjk9PZg8PMcF2XjQPHGanOOTt4dHTjuaxlUU4fInZCQ3IyIqKmsnaMRsIwAAdb+UHCjdRwqmTjpNr5HAw7m9qbWlUv/ue/fXvm5lf+2be+hDOzejY0RB//GVmexP93daPHgF8xM4o93bnHaY7cEh0fw7U2RlPayRDcTQSN+/kcMBroz833ud07S9IH5sfGdrCSEwuJhKQWcdNO8MkRJgLgSeqT8NrjmccnWw8fnR3sOyYxaZpXGadP+pZ74MOTMZ6Ce8rj2RrQDQapHA6oeYbcFv3y73agwZ7CfO7RGFIBcrGp2l1Nu1OeTqk50pEBcTX09I2MxUvjhidT4yp4JivVOVou5CmJJpI4euiDraeB+dHDTfaBo6FRoky6I/i5HI+y46Hz5sm9sjIDTnUD0GDjJrvpfILb0tJzGJYpfjtXxtN9sRo8GsqWXTAwdXLsX8VUlBGgdoCrfRAvg6SGee65m/BvPdwcQgfMDuJ95be/Q7vku997VQH9d1U6GQ4a7mxvA1P9SOf+5r5x88GyHBhFRb3frsqApB3SxflOnS1dBSd3Miv1Xrjk4GkBGsB4UT75deCEDzOgUonGLB14e+64kHR/DvCj0Cq4SU9J+2yejNyZiiWh8WekVHR2l0hQjYiKs7ElC0xXxXTYTxT0hU+A42yNiqZ8JpCOE+gUm56w4hlo8/q8ksXVTgWwshCxmceTm49JFTDFIS38Jwn01rkbXo8FHhhsNIbE4gBTRzXmD2eiiqVbA+xYLoQcsgyGJECSpMdZst81ZKeKLwRI95vf+97VpS0Rl08j87cebaaWzWVSRNnHlRda9lSy2iuifQ/P+q0DNIgCf5MHFSGcWU5t4imobw/0uhcfU1B59KIS4MuX12THfNUOsNuyYNu781ioVNgulnHHuooqliR4hkVVU2R/am09hEgAH/o+6xpIdU0ExgiKPaqboUddM/X98G/+Rr40CGzjOhk3bk1ojQ7BodnnpGlU8TEKKHUkPqLjkhO4Fgve+2BL+VrBn4x1wS3L53gqI8FXZoAcPrER8xKlLjwx8+TztW/9Gu0814hQFuvuB6n8h+jtkEoijkXxAoAoQpP0WIYno0/GbbAKbcmPDQDCp1kUhBOmwYgfByPeBLaMeEbdW2Ov4T7GyhS9IfZQ1hU3w7Ley41ARo0lCx+8kME0/oZko7Ldobbwn/+OPs8Bxh7Db/II5/A96bjAycmgpDuuX5P+F9yfBzMQLInu8JuXKaWYkuTPT2KIDvRCEpNBQTSWimcVflL6AjNgbiTsZv7w2Zx/plx9AdpfPQhzNAXZSv3VzEDYOXiz8/x75OqsRZgUfbUDEom7khrXsKqPFcNM068ukd1xs4cQn1RGEsqTrGoTHu7uOqWksSLekeKmaSyRUpGpNgmqIgY2LnfIgl9cTWI+vtDAI1xalY/sbFkLUpXanyf8rm7wqWcpO0sAkusgoWYfbGQzqhhsNR+M2CCDDdtpbSJC/ru/+7uUHLX2CgU8n4YR6/GdOxQ2cx1Furk8fZEt7nGj5GYDMD4Hm6ONJLp4myRMHXuUI4HV3VcNbVA0BNXenlaRz8svv3z5yhV9hpzRHTCfC4buoYwe9/df+spX9GXv/eRtKzhS+cEPfuByDwug82srbEonNNY/+PgTDTuei/qNb3zToaKPtx7ogyiZ8kFqqzd2FhBw5fGVDL2I/jhTuMp0O2YuSBiMlwZGKShB8NOBWr96TSe4vb1D7+buR7fhOTnIaAGAtMyyFUSUmbOJmylTT7MFdfQsSxUzdUbROd3NmZ7tus5PRPAiSpfNP1jKiN58RiSP0SHcWisRSy5kEn+mN0KDDtfgnGda4RqrNGdEhB9apAolANZi0NDjBMfX+S+sZExixVIvT3HP8v07b3+gx9za2YV2eyHTRTduS3RjcYnM0CtGrUUDvcDpwRHInXpIzcYQY9CrB5cQ+BrPhQACb5YdzpRBld8ISskzd/uMQkuigPAEEdpqGchXMrJ43m82PPI4ECPFhoEZJIMYPqi9Or9cyEJziClu8GFCValBNBnSHfG9g8e2ULEMiySnxjUMT2d9SNreUZ7osCRiPO+YGq35y5eviypCOkRDvrqMuIpSgtkhUkAIbjo5bPnA2Ys7+AGzrIMhz+CvXMkzzqfHUQWwXgbSSW5D0GzwMtXj8xSao5pjWfLuPPxbh1mCebQXqbMADRtdCgW06kKeKpFuFtoOE6o4gvZXNolV7fAXx2imoanAnhB1tHVoZXzUlAXt2HAzTTC/cKAe/VtcXJHrXRd+xWQLUONAJl2OoUTUTDyfjNjM4alcFGiXDmwKSNxKJAfLmU45iw5jue1QMxQ+4NuGyiAe91QfmwRgYBNktKzpcBmIIKl07hpn4wHTn51u7Jb5kW8I6FjsNnLYsXx2rIHOwTGJdsAwQvnkTyM59yupa9qk3XXEZ9+20f4Xo4w5NqQ+ENZofaInvXS6p1FSisB4Xtv1/jvv2MpymY4Ak0WQ6hfbALERstPgFx+wuePzxFKLxT4xRClrlpNQje0/u3vXKYFaJxzBr15aBGPmpUS8Faksuse00yYtwoOebjqGhOSduxPqVDpfaBgtMNUcUOqDPzlBVZ3qmaWXKm5l2W+ynR/SW3rlHHzkqfziQgYaelztykc+A68CU9FlAVUmqBdC4QmKMvAAs8A19rj4G2QpiHFJVHiSqJrla7RPUwzvyJ3BAdGFzwGm/eHhGKS08zgE4Y+aMokqDKzuCeXtz5HV1TJCLUWJzsiX5o0PLgfGkGXMfIk2vKJMAZGi0uLHRG6mIYEhgEFg++Ono6oqMjUE/jwlBFXahJmcUqISwK0F7sNVghpV9y8FG451EuxmY29yFNVYEflv8tjcteAez8ikXYtaJJmEaSQyIi3uzlFEpzLCp7GJCEDz8tprL7z741sJHQcNyQ0OsYaIgydHETnp8UvcSbSyrb50A8gHG312E6cmeo9hceFQPzjgAsPdBLAnPweYAaCbQLlmuq0eYDpifzae73//+99cn9fbjqKPdi+GGP+BHQhA1ViqU5rKVxoc+mLSjSrFxiZrPLMfMTa9LtfTlRY2q84p4jw36GRfFMAN3pLtnJ853a1nq92mxu2EZhTGS7YV8iQf/gPnULM/YJxMRi7anyd324MPxwX/AWBwgDFiDZYanGEbBp24EcwqrWxTE6iGzAAoUWowlCjqsHX/qslstw0SOyNdlSe7eWOqQA5ma+sOWbQTnwGW27vLYCK0bbh7At97CDqQA9e63flA+/v8y88Z6E+7YzbTuazN2WFoiYdf9KRSBUCRJ87SfV5ZsfJFB/PBG2+8Qe6BOWWlTInABIW1M5b4gzkfAA1eg0N3EhZVgnZd4Llk4kpfe+p0+8GDl84WaZi+vn5T5q5unulE318PPQ+WswdCF/vLX7q5tr7645/87Q9//Nd//Md/bGtufmHFVhEV8IenU+YAh2uLZqsfLa1+qifb3DR9ogWD8ms7OWnSZOy7sG12Ku/Wz88eabdo36b0ajCuJtQFI6d1qx0lXWzyLp2I7kpkd8aJ9cABjjBwvCfZSbRdMOfcGPF5EmKMEGRP6VO7qjn2Ez2dyEyuDZPHlJPirhLPVqRE81dddWBGeKNjXPKTLmFcmiGyNh4aCNYnqZD/pNsRnaIpd1X10Q5Sg4OpzJbMkDe+namy083ohXjSrUPZi7szprKrB84864KCwXTXX6TQ2HE2j6LMGax4MT70zFxaWs5EYGd/ac4yXDArIpAzp9Ewsv+cjdS9HU9hXt+aXdic+uCNHwr1gKSB0Td+/Xt3HZh95x2j1bPPtjcOPM93OL+8antPuuQVU2jWH27tzB8r9mSW2bPldnj0znvvfffSJYHwHNzdfOnqjUWXgbjSdGFVafQoqm61m1/cn9qYWlp9dLx4PHN5Icu4tt7U2Udz2R/6bGrv1unB3YVMqHZzSmBuytMLc3PLy9ZBFp6fpVE1e7S9bQngxpVLNBGcY9i4ur7sFov95b1Ly5RHcMlJNsvpV0+vbD3MzTzr63lS6Z13b21uHX/9P/0tHNutJ4/CyAieVoWchLlns5lpt54jFRbcY/gs1+Ds4M2P3v/BD2a39lfpxbksrOZLQpHZcLClHYr4KVb2KHqlkrbxyE1/uHh47On5s+U5xH2yuz2zv3u04nJ222sRBsMLJoJWEjgIM1wwJAze/ikbZBoEGgFlGp6zF7OycS53Jyu3P308Pf3Tb37zm6++9sLS8uxHH94GGULH04YJlCOBnPQZ3BWpZLukvQV4HIp+pIVIlYmNjAoaCfwYLM374G5HQ2Zwj3/F89YEHIOljISNP89/RTTem3eDqr3r1RUzcA7rZNgRJQkv+Ubda8q2MFL3sn+rpVTkNsPSs+J3moCq+QpUhC5xCcQxZvh5el/sKt2WauqeCReefK75ldIazfEgeZJOmaN6mC3wYoh8aF1BNefD7fCjGTumAOTYOfp9ps+FWA1qQTCOyu8ocv9MtIFP+NfSKh/YGNSyfSKKVyD7O67PMxdl5vPgnvY38I/MLS/mwfdqPxum+/oeeLW7a19XsFSrJznWLXzah+i6WmzxL1QtXnne6HZt8WyBbu9U1g3r9RbgVWerka8UoaspQfV9WBCGV81tGggmMNuB2HG6cAmiN9764Gcffna8tu4M/OFpLligbVFUpc3psu7a1CyFM330WAuss8nWzA7upx1QGeHw1wJbwYQKTrlYoD1tOez0TLtqo15ylJssFFuh0mu4ZDaL03Wn0KhAz+ZyNN8pHXEN/VW9amHUuchaxZqtayVW5w6++spVb0zvPdy+9PxVY7epead9prPK7iqMdx4++HR//uxq+DN3MD2/FA2yzKco2rQwl3Ab+2mEI0ERobAtA8mRdMUzx0JSQtzhKeZM93nDjBrdX4fOm1cum3rufnqXBvTZ4uyN1188Xc7U0vpc2oe5jHlmF+3r0yqaNdc8vTS3szRz9R//zum9ezs/+yjbmXuHdz57cPLp3fdO3j2uK8t2KC3SBlhZNNu/+qWXd93SvLqsCbt15x71piPXL1nNqDUqa9zOa1y9eePDW59YqcJMOhURPxv5ufrzIDom+poMY9KepV+YOuNFb5G+/bu/ePPSxjV97r3HW1myr7xHQVdnadNfUVV7ABV3dPOxS4OnoOddVDy7d2SkZoB9svfg8cliVDjd0o3BZE1DSg38wEER6kuLFKOjMWG7VtyuHcXysDseRZYLnI2Q9ryRMDt753Tqk53dvdee/+Dx3fvmzCUGCnNeAe4fzbsi3aik9G4WHViaXbmynEVMc2mCapxgdG1H3RhZ9jXXK/MrVzeuTh2d3f30jhVkazWKxtrQ/JH7HrX+hjzOU92fnT566cAoYpokHT/ePJuZW5pf2D9V6VWVrJL/wXe++4u/+oHrxc1ktrvyUJgy2qvHsSNsZSInNdA1v8DJ9m9weeWQdzAt882HMEEi6UFqdG78VstAZNL09y9/+APnM6H3R6ZwmGAwMiuWWhZsGfLkZnCoKongDzkakUqj7fg8ZarHEemcVBMcafXy3OKyMRi5JFMnn9355O69W7/2nd+wFT07p7lSYrk6VgpqBI1kuLPQo2FP00xLJ1c6XJqjtgwurZORfilOH+/vbruSw1iRij3lKVPQois7UhbVsrJ9nBZDM4kVlO4d+VqxGUAfv3pemUOks7to29/NFTS03EmdVT535pqGwIwb4nYRBNXfyxTfmrZfIb5sTpguAhgKSQK6aNrRdvg+0SipupbjVhaj9ECPeXf6bH3V6wDHjzZz4dvaxjpRNAO9dCmKof2QhzLW4sDm9nCQwPA8vU8e88ikmwbf56U4MGdwoKcx6KU0NShXszxvm+XTs9Otzce26dUg7dnNmzeEgmcmM+izhJegCpVyV/LknXTkuzyGWM2lZoLQSQOGfwZin2NGQi60Rg5qG2cXtbhMfToak67GJzrzE38e7Rxh77F3BaUyBmUS9lfk+i4z5LSr15gAiylTK4szrim6++HHD2/durSxPL/0nEsQMnk52ms5DILq03WPElJrNRWVROMOZzLlrNkEGTZNydSi+RV6Iod10YKpajjpXcOH9x57itYVOO+8/Z7exGK0dgBu+FsGDFZrMD/qwdUvPVoyX9lHP5mRBAN7e3bjI4inVKyPUn2/NH36lRdvvnzdhcEH0YRPm1P8DIi5SU9TQ2FlLTdBS/eIhq9D56uz/s4Wk5uZ3NRPozPtkkunHedJU+zAf1ed2nbKyRIR5XXEoOnV42Nv+a4f3V7ZO9hcop5ao+uUj2axG5zRBnyXYAtBy9+InhprIUAMa2I1HZAzeIKq+zWfgHuNKH0TKsxea7gVxud/cltcGrl9MiLxFJpzhCVQ3G06lHtwcIPvvljNbJ6nMa+9Q0MkWIiTFX8NQwrIsPPU6fI9dxRpcUNhJTe/sJSD+7NROLDXrZi1opYcFuZmDj2DMX+2eml+Ze/47OjMA0n6kQdT2Qy4PXW8PDP/wtJchgEP9o4f7q9szFOnXHS+tdav7H/UIwtZH2QIA7vSTB/TnU5lp/KZ3MfwMcEPi6Ip6wGdXN9EgATJIAyZVeFPVRsOcmhJU1DFDkxBlr/5lGNqJ3vf+tLLf/WLW8D04hTnHZ9hUNJ20TD6TMC4dNq/7CcobJhfamMRIl96+ctauYePH+mIlKsmcZ92+en0zeevGVh9+P4deFytw+6ykHU5UrBsKpjPTCXDzYKHTRLw403mm1WvDdGEetAEwt3Z2Uf39//dTz/45699Z2b2Pv9lSsWge8YUwP/ApkhzOgDbqg/L8d6USC86O7XJnUJMra8RRbWdaWEi8ylZmWeAoT/a7FUcfKj2i6hIWdNH4mYqGVQWaA+Pdh88Pts7XExTMmpnJzPXLX+k6j+EebYGNBJbpIYkJn0UJ2mujI3yBmwSYDJWu4WWcIcpoxttjLzJfT2LkdFLsQlw7ysmKEWbLkE1YEyNmG6FG2fJVrimsW5WwphYE0MH0RMlDVxLWugEw1NEO8biblzbIN9KgAjWexHZ3A6d1caBDitqBLC8nOu3bGbbpPrwo/ftqUJFWI+OSsehGrkA/32NJEVvDO1GnqOUX3vla1RF5ncyVbBDa+ctKzCVEfbB4cGf/umf7v/wB1QhPvz4AxvyV+sKqyMzzKMjy/cGK4Q2aGujll4p9dVXl1OCPIsVKRdjR/jlHR840lTNRDyMTcPqKgehgdG9kN4SwcnryTOKHfOfI6YrZ7ubk6MmKXg6p+PAJ36l6zt2pYIP/VlFE2lBcWjwsHoVVgsDf2YUsaILHUUcL6MQD5mWdFZCwVSfwxH3U6MqHQ9/wAzAiltVYzRMClMqaqwhLe6BjGdKA/XVl3o8OI7cSTQ2cRVB+wjvCUxroGsSGEGdROU6u2FnlpFPT//tv/23Su3tj94GcLI8b7vy8tUrf/Znf/b43XdJ+P6dW9AqZZCNYSA+ulrj4hAExsYM9Qp3i//t/V/89m//9jt/8zNp7cxMebn+hW98DTafDFoUjW16VNWmXByNv+gMc7qaQMsHLIf7NhiCZ0KiahnD3N/elqIlacCQWzqhZQ0PCjNjmZomxiaxYm09pEaX1RBodCrqAmEWVxDP8dQuaQEDUwU4LpqJVlOo3O3+7AMDxHUns6O9wi8tW1iR3Y2K7mNCqqENzipfDp8qffug9t69hy4STfSCSdQvNCMMYxjYIJk0QlpMGmGXV7t1Syh0pg/Md3/zH2gWbIFZMnMREAPJULj5HpsLKY69n/idjAjPENZxBwzlGALPHfzPPyZc8fe/mTbhz5mg/IxqDadSIAbddpn1IWmoplWyafPJkv9aqZjqSruxAMBAMv4Nbmbw7K+yn7AmM9sBk6x4AvRZH52LTuVZ4c/2G5K4EBExCco4PEbktgdHw/O8ELGTGYCHVAcf8BVrCPkiR8UaFWhjGJKbdKDW58inmP9FSH9Z2AhPgQ1kX4gERhAJuQDQcccDp0SaJKc6k1Fr0AgNCy5gaH/M1yo+3t9NzapCUDkFyWnga2zQkOSWp827SrqKo5Op4CEvEIqpwv7RH/1RIyG91RElCjOuH9CH4WCYBHQXOEosP/zU/YoyKpqJwJFTY6hNU49UH0oiUGkq9Q4ZsDs3Vo28hT3QWa+q5CqtzGeMs8RFXpFwTokLsYUaqTYHkqPiiaEZH422BlkDLq5EJQ1viD87M15yyT6timvLLzZJ0hK9ksjAronujLcdwsay3XzotPgWnR2jWGEUoLJkOzVB9H8RoAtr9WdLBlRq9dwi6ErwDTBIttQNo6S+5WXXn/3M+pC43scT6sIrJ9vs9IO3yC6uIL3Vi195TTYfnGQ5VbkbQEpIjnRs8MAvaQv8yLAayGx//EBov3G5srpqgdIdBeJu7W7qLo17MQpySB7sbSkdSPnvHZ5K/SDTXdrl2Zi2zISqmv2OdgioqjQf+Dcv5Ai1hsxazsX5Wd3irQd3UXt9JUt+OlkACgOd8IXmmp50XHawldyau/QnO1GMDCv1h/s5s4g8tCG1OankoZTr/e1c7U3LL1y6nPOFXg4SPfscpvFW5tA/M4selwrxWZ6J7JEKkNZ0yMZHn3yMMy7VwZPGL668bCwavjoEG70nBKAnDFGguYdm/vrGJdeOv/PGjwA7pdU045vkkqFRW5f6IqiNoDY+AXS/4xe1Y5BzSCCAmzMNjwfb215o1EMZ/IRXSoA4WEaXl1aC7vhBXqYxD59Sbf9fxR6yILM40Eh6/mgTBR88C8j/vffe0xheWlhGLf405nCplhd9NtO6QHEDHqZlRxQAPluYiTH+86Q4yhN/uPlIxYYoPPaFwfvkDwBOCnPcjFDMF1piNruykjZhaWVVK2o8yV8uAAAD7JNJEsXD+vq7WU8X2YBwQIRGbvQMwFLsRAe7HQ3WjEJecS9oOMikKwyrts5UQ3cZtpX7D7HFa+FdFzr7gNvAAyZVuBTS2XLceYfTY4QDB8Dz6U/2YJJ2GeThG1s9AowMI14VrQLjgwzNHRj+ejRpjWKO0RbDUwfbdOjTnUfzQXQ4k9xTgiq6IDapGMzTYB3EH2RjG4D5oDNMrgVoMLgXfJGlCaTngpEUIWnIxlbiE2CeA/6CSXJDWhwSeuutt3bv3GtizIX1iNxTS6OlD+6oLsSMuqGEPm1GUhoJz0Zs1cTwIWoB8WQPYwk0aOXoxb///ofijVaoCuuNG9mOYoDLUjtk0KoCd/OWTU66knZ2+DBSAROC9Z6ea6+Njd/5nd8RxJ8JDcVJPiLmYywA4lVggRUgbOkQo8J8QiWogSuVlE6/QNxjA0H8VaOCCR7YgqgIhuQCzztosBvz5GdnhL+MtH872CUMyQQ3I7TtTrH8cgAIhgEtx4hLye7I8BzMmNhxWOHsUF7y1mjb5o8G+IciaEhJNCU+GxEwiy1a3azyhuQYsc6TGRco/0YoDLts8OnIZr1XmJWroN3a3oJ/bn4NTtGNKxZeeaHwBZkgbiMXSAD7VBwc/SnhTrHBuIW2D5imC20IcyEaH9uPUoEQGMeTwOEkQ9b4c1zAZq1JZ+2t483Nbbu/FXdU3SDsRP/D2k0eSnr42vVOEpLT6HFo96Znnp/UgG4eFhlhVNYOa0XracIg6dF1cwNAsTQZsRItZl7cZKqiYR0aHF78zd/8zY3LGb38/8UgQ61g2UwI/VVHetGZM/41H4kWB/e4+oDsi3Z5ln+EfLRqaj+qDH9+BngGYzzGyP+jFOvAuidqy+DLgRoUPNPBE32KB8EkQaldgBwAOCzXYxIgCFlV37KYaFKX0DQ0XMaLRAqIr6pjtcBq8F0wZe2dO9tVtT4VGyWW62zmmCBBEJnSQFfLFt82URgpV7c2FhMtqZ7MbN3fdI9eb85De1Cv3HppWvEEU944znymb5fmaZjrujdLvRxqugd2VYNooiQj4Un3NhHsNHs1sOgd+NqvGOXhqWXZdIbFCLEWQv7ZzN6uwfdz+yduuf2HN7+yd297bjdLcv9gZv3B9PrpviNue7+wA+Rd3cWp/+X7/2p7dkWstSWD0GWKQNbccWCRmsP64h4NrukcgqP0gsJPNuZvHR6sTO8bES67khEYRc2Tk21qq3NTBxRmKFbXvLHViUHIeHx0f1Uh/eTSi2rlKOSE8jJWgfzmspkL5sn8TgQ+BTkR1pOunjfYZZKK7LN11XGoPjWMbs+JeMGpwWfXdKubbzLaRNamcA2qe2SOSyW93WSPGu7EHZVX6qH9Cz65nlIpZ7eTxMatCec/GLxFzPDJEYGrRiDyTkcpN49NHX36aHn7dEmvNV5U8yAnlfMTL8vafsnrvSfLNiuTQKU7Z5oxa98+xy/pG1XuUD1rOsC49ULfMrdsaLX9s48l8OJxnsY+25/Z/emnf/WT/4eW++WV3LqyubOomZ4+OM7G7Ahz5mk+j+qCaQpW9nXTaq3M/W//i39+Z37ftHxh6vjw1qOphzuXlpcf7W5ObefCdCNg40d/7vugakQXZuFg5sbyFROY073j+Wn3V6p3cycryyZMnzr3sDS3WWLthgvTZNPjHBqYpgC4v3Yyj7ij+Zn1ebres7szJytLy5D+/6j7z2ZLjyPPEzxa33N15k0toUEAVGCxWKzu2urqqiZnX6zZ9ti+2281b+YDjK3Nbtla9/Za11TVEN3VJEESIAitMhMJpLhaHK3Pmd/f/XniPFckAIJos91A4rlxIjw8PDw8PJRHRKo4LUzLnUGq0esd7bcYWC8tZDeunC8XCzxh1D7sMdkr5zX0397aX1vdoJHSOXFDtLU6mEQ1aa1h2tduytSeakRqVF6zQNn77Ts7H36Ym2QWckXIlXTBU8mIdTmqZ3EC57xCbVgcNa44i1Dt0wkDxU0/D7e3/ssHb35699NiZYUhgE94vYYNjVu6SSjdubSAnHw8BGI5hqEL6cwRSFToOp0MvpEHgwPOj4wrm49Ysn8HO+j1c0uIyd72vkptDgzSSOYi8Xel60HJ8ERI0htEmkwhxr8OYGQLndMT5g5x+WI0Vjpiabo0IDU/myGFUgtDpKxVYG0WSlfrQqtyGZsjdC+iEmFDcqlUzr/yzzYLOY5KA6eiSEkNsh4Q5Z/E7/6YEV4bjlB+5wwMizOJ8uIPSiaUax5qWj0wNoQncwyBX+lJshQaArwMLUw1OXvte5K1DuwAIeGZHu3DMCRxlUeJVSnRIgXw/BSSSEvPw4XK8gxZmF5VkGEIHjpTqwADto+SfqmLauNLYeaRjjNZQbx3RjssV6us/JnxO8MRwZ+uxUjyDZkkJSKetiF8HPzQlxKCT+VgtVC0cYaosLh0cNDsHTbzlXKhUpVFM7UeSxgsVIqYe1gnEEkPCB7SSxqNXNcV3LkIvW+99ynXO9wf9tOLC/lyYSTjU7pldU0uPAgAHmqeCSx4zIhIvR658A0ihx9NS+CZzsFQjGytGbYMwxuSsArMyLIwG6O9qzwwoLxFIs3GcpeGw17SVjy1mYcDCRoOGEhAJ9ELi1wuFFbxuL+a+2jVKbLCytn/G5eWrl9cHHc3WWwa5XhdgjavHmp02H3wycOdzX6/Uxjlq5ks5pu6XmnG/MsMMMEPGE6s0ECZny53rG2aGjbt4S3RZQAYaEZtaxTAWJFeNqP34UdYXC4uZ6e5TqPZ3dqvTbNlrGMWK5OCrtHYv7OtMc/qChnpdtp0tmtLCf/tl29oC5/bqmezqzcvM6V/5Znrl7M3bqNnMC4wIwkuzYS21rBz0Go10jwzwJx9+vBgF0oQKnofuMrJMOxzh2b9WilXn3rm6Xut98lXK/rM9jGOQquVtQheqxRYxl2q62K08WTI2iXvnsDw3lhWEU36WTFFS3WZItymisQfjJPEGuvWqGXhtNdTWP+mfrnajj3UTIHXEc8V0xzm03lAXsY9mqivXFitIQ+9gWpqms+2MT+vYJmtawzJCkaGLz2n+RU+sLza9vDdw2ZrkMs/OjzoYAlVzteWF+qcZMKmoTUqZfMLpQUW3DeWzrM8l+6McpPcYknP+zwetJBgDlTBokKBh0iWMJEfpHkqOVcp1FeWq8BQ87NRfrHchTPV4vLf/e3fXazneeyxdri9nC8s0CBZ/hgMWc/D0IkeAAbDjAwvR3AuYLr80rPPr3/6Xjs1Y0UfXmHix8DMVoYlJCbDFCVyuqhaBdQ/YglV36HVZx9pCsx4rfk5fqTMGrTCcbQCmxcQrusrvOOhTmQwkJltbj1ioZ8SQbGQurAKiZxj4Euw/7TvPDwAyGMGE3GcI9KyiD8uhI72GofUXFYLakeNvUePP79mi3ocoIMFNEDgZ3nuDmC0bDykpXJxsc2YeB6HAhZ09EELOmTY62PP1+WpVSSJahWRafaQojIAw/QbgSzR/LO5wXDYZuKD1RhHKYGRcYbYxsoZy81g26gtkhG3FLNOymIry9DcpAUAgTg8KqO5SD/PA+II++utHvk+FuozqzjImRn/Ovsv7PKIE8D8DCGBPKcwFoBZuZBZsJNYAFC5jLQZkzBR57ZlzPppm2w86UQoe34qX2Q7osuDaaQp3bbBJSTIIclxPlLFnBo/JHkuTpuHyG+bQLLcdEcrwyCdLsWO0p9fW2eEL8k1QaJSuHOc7VKe9DQrt4iVAVsyiwihS2AkXs4DdTSBS4CRPGAIqeTRUqRik3wzAGkqwpWdxv9Utj7uHANR0mn8zxaRnVFgThMaSkIoBB7wu99p42vkgonmeVIqLEqlIlNioaW9s9fdY8ewL7bL0A05ML7ZvcPCjJgbH3wZmiQE4sDgxfGf3g6Io2BeCi0yyxkl6qvJMeKJ0vL4TavFZb8Eh2N++GlE9BS6jiOVwY4MSCuICqse1rR6CJTusiJRagJxiJcrtOJEpj/XN/LfvbHGLA4/1zEBy+CFDNVvQqgWV5meWHlttBMN8WxgmeUgN4/eYOs5RMUpH87ToDc4HsMZlBxXlDAUsRKm8yhczkZLrpgk4rg5mm+OTmY22yhWxr3xjvPcYKxeyFT5gtGR648xkT+ghT/EygPNNvBimq3JgUZwHPL0+buXXtUjfYqzApCAfimyLbIKEGsYOYDBlLbgbTLOEg/rEYRZhcAnxoqkjk4kA4ajaFIyJgP8BI8Fa69Ii54cgMuTENnW7CyXKkAZtcA1koB1OGyOclV6ysnV0lQ124I8NciggPmOLFNpkohsNsX8FGXJflW6zOsJ5UyP4Om4xeZsKrefL3SzueVJlysJKqwTtPqZ9oA2znEllVn4qdzIIAAKxQmjk29Uy4hItCgXl8SKASSsYN2NkQKTdcaVIy6TNid4O/SsflVir6plBEhYRqbvhpuC0aFSBF3jNCuPOUuff/72lTfe+LA30vZwz2TJOXb66xmdDv+jQlQwyJvNeIFDCK1+eD6EdrRQ1yG/g/0mDzstLmkxE1gtVlkdqQDGK2syksZTznAlQikROSANDGMQAI/JFEuE50wbP2o3/9Mv/5D63lO3bt3KdrYAKM0HDglEp7xQkgyTpLvwJ0PP8vu8BkWgyJihYCMcFAxHCKZG8avO+I8al+G8RJo6J5qpiTUyJaaq8bPRj5+b82AXEx7xxw5BcXquddTgwjcGADpgpdPGlq8RdqwI82CLe8JH+L/UPXEBmlSQ6Fme9hALakm21RY/A3EB2PM1AKGa/zRvoJ+Eqgq5RKMynU3L8wgvRlAuHpjo3dQIUQpeVd6dxH5XoUqBKHpC4ZFa1MI/X2YIDPpvLj2lJQ+/hcCmPZGkz7SqiBqig8e6h1kcljH4QUKOzE/UlVoJkl8yCpzBx09nlxOQ/MaFksIFg7gXzBymU54Fv3jxYutA+2OoQHJEHrhTr9bU6+eehLaBANG/EmumLdyjIeVIOAHDgmZHPCHi5IEHB/zj/ceEWx2KRZCBHsWDU1dt9NOriSQLVHcWO0flv8AWB/NXfvWAsUtCJv0h3j1BPOJwcQ/nyPEw9YVmC+ODl5PNPhBR2GkaHI+WDMwB4x5oIC3YjRip5phaA4jBAFavGNeIp/06X/AdJybiBfLpNPDFCE4VFbUdbzhRXlBFOBRSd+ABihD3M951AhwmYGM07AMXam3W1VX9o5yWCdAeoKqWZLM/HsnqmSRMA1R8c+Bxxy/EWJMDc0Ci2Vkh/ujDPzCbYsZH1qhjloB3JzqK62UkUxLi5ys5se10lhXqZr7k67dEOTdAzPyEXBiSkqTM07OVCivSCrGdTM1azaIZqwGN17GRyWlQNaNIwyGGvbS47BVWD5ZoDuD8rL+tktrFzYeaZg8q60sUbcCNGDjjOhkRAgzYnAxEBpIYF7IQs3/vHgCsdqh18JYSVW9S4WkBw4HJ+ew/z/w6ACzCnporqikX/BUNT0ibDIcACPMQ8EAtP92jvAw1HwJhOwFCmmiGlIsZMI4bXYh95oWXWGliGQIdRaAwnHKG8lSoBYA5xIZc4IoTCUgSwFEEMP2cN3r9CqicfsVDuWmwECK42Al53FoJI1PEHkftEIUj0HFSNBiFbOCMYyKbtISrCq0e9X2ySzBHXIq7hQi/5/Xk1FEMSCDyK8EAOEHMafyEAONUOfBpmK+T0ZNgHBtfl22ywJukygGelDwU4XSqJBJP7iFPCk9kEWm8RMgf7YUeVjcCVVHWjua4NH4Jaq/HJAccDyFom87efTbttpoNFFRZy4fF8UB6ONYSx8QMVMEOmuRRSzVu06rprzkegVFtpqJlx7bdUw8ey07QCLMwmyNQjhlMoiHjZ9YVHICAkCAZ4jLpSAI2ftLpPPjsMyk7u7d0eaHGKMLBWDRKdC5RicgKWOlhG+R4XgSKKDtr4h0BvYHnAuRPfvITcgeSZSkt2qHc/MRVs8lTGYyYaLBkRNlJ61mL/tgREnujv8QGdzpKzZ89E5yNOmhmTqS0ItuOfV1fS16shqNNSE4sd6Zp4busx9wvXrrGsixE0uPcu3cPeBagAUONc/HF3tMvMfS6vXERFcQaMHlxWQogmjFbpwZhaGs6JmoNf65cRPPjIUeMHingIDXi7unN9++RIyRBJp0OZqovPvsUmXJPBj3a8iK9ZWlzax8ZK13ZoHfO9gayL273QEvl8y0UqyqjiQH4SWvotdaDgxi+FE2VhbgQa+eHMuMe9IwOWmCoc59VuVzJ8ZBwf8i8mXmwxpJa74AwRwIWFcyxJVS5gzFgIi0Em0hoc6LV78FeEYnVcyVLp3z14nV6H5aVGUtjK0uOzJ9pngecl9VojURycAaWQufVlQsYmzM6QTZ6vQFnwohyYuDqUm6dmxavXj6natltkSQ10FiUYQFgXF8CpIsLnKGdUlk8sAwAsbZfIEz8H/hGFKUjNh7vKdad3doUsREYILVsaB6+J9oXSYitVkucxmXgLwADoWUw0iA7GhrSBRjhnruBCK0HihKbtPtPjz3zS3IaUIjShpRcJMwk9xokCD8sxSymvrDCnSdh8R0ALzgggFnR1EIB5n8hMyKZAVFHgyF3P3aW3dI2FN+QAwYqYMRAm/jgZ2DG+JA6BaHjxK9C2Xm4+tIK0lLkrjRm8urGdZsiLsBb5qLcPfhAEvkTf5I8DMGkipOFsCd6gA2YPV1IHcJDYkJw4o99CcePARISKNHCNISmpDFeGn3OTwBowpRrKGs8204Myi2jVsnEhbRsG4CHsijEMEODk3FmAaMxSVTjwgy3IYC0ZIfAk5Z7J8GGB8w0RvxUPT8D5QEzHmCoSaJA5fkKaewIT+5o8jPpYqj5X2L5wfcEKg8nLxYH8bu04MEBCRnuBwDnNUiIo+ILDG6ejfmATIa4EDrkWfIiJI7TMsns7e+LdcZALccoA5mTI8/yxk5JIumL/hATUMVQ+uupQhbucS0REOI54n4W5iys3OlWIlJFOPCgKPI5zXcI4osDHmrhDw5/4BseuhnP1AsOsALBIvJnKEnd8j8+dOyGzLzuizP1WL4E+5fsYAKSObAtAcmpegUBUDN8vZ36fU+oW9WC9UEBFR6REZ8rSoaf8DsYX8/9RGzyJzSQCyVVdqfkwfAoR8AMQH4C/acoZ+na5kqeo5VFDdl/how8CWwMUXiofQ9XoIHCAXofiIFRZEe4kYYOFG38xDkACXFBbuBqr68ZJQCe0LNWtdqbZ4yApBVH2gIkEPnEKBlgNmBp2syTCek1GjIjC3ITcwN6wBZpWsdrOsrzijkTRRgNUTWRF3dsIvasnYMkiZm0Si5iua/GO031gMGJVzbW8lQvvvgiU93GA515AjtgeJIIPSEhpwMDzj/KQ7lwXY6G0ZRQLtl0oVQDA2xED1OogA1KYI7/FHlGGzUYAJIep8/BkkkIp8iEqOAMrqzFgRYaGNJQ9l/2dsj61lI0Ykni/O/qD/w0j8j3mS/SQd253MZGNqoRQvh4CKM4MTGuR8mALjIBgWCI02c0wkyBlJSCgiv2T3DKzQhI4khym3DdBpiMdv8cKGiNuYdIa6/IvV9rAK32T2m5kS+OJXuYQinwkJrCsJ/Ob/WEDNn5nxBryJg2WKL5cgCpCBGwaSKiuapES/Z6LZyRuY6xFEcmJW6Iy281zHlfVUyxv3jMMZ4nrfbrJZfKUVdsovfHXM8z+uy9dxlOVdfX6OArS8sokbYdWR0PSkwaD44eM3/odFpoHGxiJJdUNwjs+nm31TW0MCIy1Va9Y1BKgCl0Ue0m0jFRfufmNNUmgF5AyZnmTKcLA3UGz/RSzzx164elxcHmQXlo3GCjDZqtcf2wXNsudnoNqeuttAwTGukel9Fq7wOOYdeC42+Bx1DYu5wycEfOutYMu3lRNUtd/7xbrPEuO+U9pKfUkiVH6XpYrBdLto6nCaSkk7P90pLKReUGMsW2n67MxFFTgX7kAQpdnvxv5HdrDvsRtRIlBXN0kEH1ke1amIWb3FOfahy8bMJQm5vrtMdrzYwaR3+y8cgU0ARKG3TWfkjMBEdcYttQ0i+ZABYyOc+qWCSOL3ySXBm1uo/PRSIegZAvznY447JIaGTSRh1h5YBU+xTC5A0Yl1IwE85+IR5wGQHkqEOjRmf68rDEFLR+MKyxxWj7SzL4tJbF5LNfFj2T3GhpZb1QroBz3M8uVNh5S7c48tnTdJGKAHe1UJeasNWIHLc5TjlFxX78pNs6PLd6vfP5/tqFxd5Rs04/NexWxJqBeIJh0njUf5BZOn/1sVXDVEVJlcap/c8fXURyMEnQoxeZfm7EY1GT4nA8G2TahcVzq/vpxm6rdzTuZ+oVXYmK/Wm292D/CKsu6FxduXQ0PsrXq2yfTboIkTX0VIaJIi/Y63petf0S2obuI5fPrdcqTM7PFTQKrBSYwVZ2th9zhWO1uPb80zfrhXJmOOn0h59+en/zYJ8u+dq1K7S+Tz7bXVmZPH3jxsUbz05LdZYJuNKsXkuXobrXrmeu8AipmTDy3DQyMy1wgRN3ak4qNDR2VyQtR0fYmjUfPmRRoDDJYIICoaN+j3EBixEHe5ukmszUfrEAQCQ1WYVxJtHSGZJ2BdCk8ft0cMTGKLcBdsZ7jYeV0ir+WaYIf3gBlBznoxKljjp1YTCnzpSrvKkfbM3slkwbi2geSzx1A9+wG6Te2RXiiw7xhITDdr7cKUZqBhWPHm6nZu/xGOnFi+exVdrfP4ISGqu/nUIqE5z5YqvjSXzJEUXl8q5iEwV+NUzU+BwOyzoJbnIhbB6Z8IUy4sGBCm6YKQhZ6NJ8RuEEegpkEw+50N7zRdkS1jTL445w3TXvbNOlsRSJdaDRrMMt/pM8Wg2NOplx9aEaEU4ze/MIm3t9ChfzW4yN8rTSmV/Wb9GIVuTERMkv531cXGUW5DUvMwz0toU84WO4oswDyPEMInVJKdVBsF7gcFAmbRAS4cG2IopTGVULxjMPO+MLDKHivkeSyKrVhcCDFTZ3Xs9R6QiOY11PAgmmCJ4oqZBYQviJE68sJMlEjwiZsD4x98e5BxpDVPAIbezQLHhd9rh0tlKvIOLWGzCs0sA0OFV07BzeYyWINHJrO5OUNt1aOl+CE3RktGy6sV+cTddq1cYVNO3uw897o/urF5bQ0gsV9dT0IHy9JOxmk5a7V/liMCBcVqkugZgNEz5NFX731puftfozLtAvF3WUiTMu3JCq42ESQGAiIoztUVlUg3FBrHSeo5LgnPkxe6SZ+Od9maKi2l9ZWhr0Oo3DfeDRSfQgJGelCc+5tWX8nOyhxXEjs1qBelIWKZWPj1t4TAKr3ikGk9hci1iel0/xK19aZNF1mGLSNMmmjqrlxcurhUq6vbC4DjOKuiIYqwqR+f47u/3dXKqzkh8uZCeLmXF9NupgYZ7FZBlpkEWqBg68oMI/SkMYX5VFRfaZEvQQphKpYOw/0f1DCodoWE1N2+2XGUaDmYv11fPV5dR2u3HnUXM0vnH5Cte1plYWGVktrK3/+Kd/xZme//a7D9548+6F69elLW9e+eSz+x8ejQeVc5PsiEnFObtD8N0H97YH7fFPXmVRNTMaZgvZ3qTLcxfjUWZAKxyo9XHVdXuvkc5WNSrKZbgKduniWqZSKHIvIa0jXe4PJhtXn9pvYSzaonIraa7V6x7sblGwXruD+dikNzjq7436A6xOKqXitNntpzOd/SOGO1p3ED8y6fYAtUz5UYhFG7ll2UcdjWtp3fjBjX1wI80QQMOVWaVW4xaGSnbc2NkdHu7lMqOrl9drq4vADEu58kLpaOsRVhTn1jaGJWw206yaMciGWt0lTR/D6Hgy5lVg/HqtBT7nsbpOj/LF3nC4k+rfvnF7Ojji9QrKtJquLBWqz1ypfmftBpvTWMvn+/ndxn5xkNpYWebc3vULG+yMfrbPdc/FdLXMyHmxSpMtM9de3bi4UF1qdLuf3vuC7VIMSJHAA144TGPo3Pnt737VfrjC8nSd01osXFZrhE+WVO8sn0NtzRY9x8MBHUI9x2s82RtLa3dGsxUbRe9iCS0n+aEtItUmSD7koxHIw7tg+tpRbrfOpsXQoLFV95SAUa2kHaM+tDmkVHCE8RrGcMwDZL4ZVJO1TfjYpaIno71iZu3KLcVaHyc6TG0aGaIMRRl9oxhRpQihjD2Mw/lpyyKGgSjR5kMK6XaajL17LCWisYAGEh98+F6+kF1f0/FtLE3pZDXGkXrRLEynFdWbc2iIgZGuEubmbRtycEyOG0pTWCrbyX9Og2P3wEhWLOr2bYaPRSTrsPlUfzpYHhUGU6weh7nRoFAuwUzsqukI8TBKKS/UOabN8SUWUJiAEMLhb+qIP/CQaTA6LfRNKrKVES0KPZDNl8ajuRIdIpSbllXJ56rwpN8iv/QTczUCcoGwQLOjVL6ikNNW8Ya3h2Ahjk6g+WvrI4cBf6nMYyqMV+xRrLS1dN/A4J1Fr2cSwm78tF07diQziDDskBY1xwF8CkqUO0pKQljEF8fGA1+HhGlQhcEwhxbq9Qrmz8xAiQKY+tVqV6+3u70NSQyqaREEgo1UODHQehbDShWB05SyoYYqHDBOFTAwAUcklaTtTxuxWVojhoGA2cRYan2IAoOPbPF7uPKlUokyVpAIv6pTlqQWzuCYajVJ1xE20WQ/kAT8EG+VLhmIjMoUqAGdrR0n8lXFeaZQC22aKXI+Q/dBYeAyPDzc393eBALLV30tF5DwYpTeVKCO6PINgZXCMalQ7nNWRPgtLbyjOHyhk2JGCeI/nhCJ4lzO2x+9z64AKnXCWRsnfYYkTCejHFcvd4ZaoOTKfrGcRb8xBqo2qaWYTFloigQjQGA2LeTk8fUsiMozr6S5sbOHKtBocF6tJBrnWAzI0F+pyBbjCdV5UXIO8HC5fmbAIxHjc1W2YEtdRHWCfgBzJaM7RjBqi9hgzcQFSQW1/oJ1G4jhAUNYtNEZHR21d1a1JnPIdRRZzmJzyobZn5w6Mk8GOnv4EbqoZDo3gq3WFQ02nHtMIKXVFWHy4FH4UXR8EQ84yrQPv0Y6BoPk60p0G9oJMnJwmPJrtECgu5CRgxBIKhZS8BCFox9QJmlm3FqxFevsH9bBs6IpjBg/V7zvN4546QABdmlAoMDEvECNGwrVi+RY8uU6YkZe3KsLKt5f5lpJDiBlStm+2Z/2Clm6+e3RlEHYGosVnGTabFaWLnVZvsF2IS0aeL8CjuhQmWZFbDJS5xy7gHJI1bafrS3CGlggWpCVZHmtBmG7+jUEjmIS61sOjDMslm8qz9U0KdYhxUCvAjzSP5jJ090gbelxPVv41z/83qOdX6WmLFqU0GDqWjTiBKukzZntWTgN/pUIWA0mAyNoC4paVFxXEhT86ndRKTwAobV/qQ76mnEvPaTKVlCAnCETpOk6mJNAHiFSC4xnT4lYeVW55jx5CNFAWrWuClOROLuAUkN2tTKQurvfHb/+9v/95z9Vp9Y7oqTFuSwbOvsIufHCc0HMQpwRO889hB/3OLyVIpYvcdVodvbqMeqoV7WuVRxTibxXErupTlEhPa8kKFXxSl0seAmhVAD483mt3QNGVRV7K8tvYLdDd2pX4HQWaXqjVJpzKTOpHuH1igVVwh1rd4TDVedTYLu4edoZVccwAUMgX2OpeyIAD7eok0kcs5VcUYbgjIoK1OCJUSiL4FR+G44QjZ/BCjhD8whgEpPgjiGIQiHVafBS4AfP2EzrOyPt/3MdIALNZTzAaUJG39zTpfL94RH9Oh2/hanLVFOyWokwhnzVZWirHDceCpJJHWKqJNR0PCBIgMtLOHgYkng4mC9unGdDe9LUvbceKOYYBmKZjTA1Wh3P6DzISBEFZQEwP1FA0BDpb0981hfd2mhoG59LRvjSX4LmzHSiwYSYv/g14MQZXf6HYiktlWQ8UaS69rg6EnWhgYQ54bSmwi+DnwMp6rgjBAeROLa7rBbEMV/YReV7XuTuBIQRDGjQGXy9o6J3D1QFz/Gsol+BpBNgnoWLoatCTaLjksYFFhKnRI1b0iLGQjbTv2XLAeLnqfDFnCcVE0tGuy4qTMyARDL9pwGq/YODL44hjfllIuH8ISMme+AhCSIbKhRg702Jwk+sN36nExElxC6TAbOsvV555ZXXX3+d6y7pfkACfjwODOTt27fJd6Fa41vIy84ljK0JwUGq5ygdaARbiJQ59kqynWmzttMiX+6qXl1ZvHPnDj9Z4WGtGdfta4JEvqRiDZp9/karzWQVGOytuGmRReT9doOGgAEK1CK9wMNF4BmswzHKAydHXTGKTMF5/4MPWIBeMDu1jfUNcL731ttEsQ/Ns5wXzq9giTYeNWnv7aYYDipi4bR9VSLGhAq0QZhXAGDuyAWPQ+KBUbhoDcZDn/D1hE4k5XVs5MLquW5hNE6eTkpdA0wWRMFDfmJ6hv/WU09TFmiXZmAcYwCEa3wWC1UIDGhVqIRzYhIBcy+Z8gNwg4lEcR59FrUqi1WBzSEiZkIDgR5uCIESDcgYE9fFhbpXvdJaOPQjYFjdIdIMn/CzEo2j4I7EvhFy8HhgIMyqJqqdZJSnChQ6JSFV8CSTOJ0eZWgDVNTk57+/hg/MIMThgScnECYROA1AJgO/0k8q3PFUcy4lkwNmP8+INRz6HIePRiQh3D0nfiaTfLt+dAWCSHY4Cuisi+r4LCY5JF8gYTVJXAiNOVHRiI2INBWPWkOxcBk52ma8PUCHrK/UZDlrx4cBJa3nHiecZxyHCAbFYucHdRsdKxDI8IR1V7lIUA14nhYazpQEAzsp2wCTRWgvVhxgFEgBKQLX5KE20RYk12II8Mx6zYaRXPyoOIschPhg27qUeEyiboFJlYZbNlzSWoELKnoSY0d0bGFWQJ+DltKVuIeXe43Zhrey7W1vY9ILJFkzsoIkHMUH2MtCuyYcv3IxB0sUq+4dj3IPXCLeeQ6IJzEgwUMSX1ZCgW+jCre3EQ+6Cd0S0OfqMV08DKm8Z5Aurf7qV7/C8BZLlvq5Ze87wEx3gBH37fNrgHV2NtGfOOqaW7NdTsgLFuEAJi+/Clm52pAPPqNg7df8w0ocJjOzVhOqGATQ+3NxEHi0AJ3LsT5L7OeP9G5Ha3ub9SMO+aL9eh3bjLeJFvyB83RzIIWNZF1gT6BSydkpN1WK17TRBJ5SuUAv6fMJksBbMmIUMWZrO5cjC2qK2oIexg2UhW3YQC6BOFhOCJmLpbbN35/0uVGa5HT6r/3L/0YsbOFrna+Gx8oinSbf3mTAwvGPXnoRZi6WivS2sAUaeOKXkpazZVpQNVvCNpA1XKLgMPRDNgWBGL7cMklRkBCi6uMZ15fXckVaH9lBDzkiY14RmkvT8elJ2hQVDd86jTY4tTskyYkkBA/OxQMPUZTRKxE/eYGEfKOe3QAIBADOOzuSaZXEED7pAzDW3Pn6GpQ4jBMT4P3n/IvPqAVAzD/LAWDVG8UF+CQsAGQN2TT25SVJ3dDv+E5oSLiqQT5jRtqUysbkRysRJKSOUmYTlO40pJ3GXFqgqQduMtRyJ9NMOE/TICOqg0p3ACZmhJMcZDxdSL1fvHyVuQlSDlcZKfHVzMAc2EAUnH7C8DgKzIT4lzDKYrEeL6C5T7/+VEdGoAhfmEBB+HqgSwUvLEI/4TjKiEMCIRI2Ul7ERESYLkJeSKulEk3+55QamO2uCVSO5OBEFQCPxx34+UmmePjaMlJEiadKl8qQipBTBbFWBJMGQtQF/QvtDgzAwDc8nspDCMTFgaKNhHwJdDBoICRuCEoLME6prMIc0lefQypH4hhCYAQZZxfCPTsYSC6gd8ohnlZGiDeqgBCmOdonfYEEM1/3iMlam1KJwKZtcEw3Bn1pwq/nHJtjCCkcuf90AL7uScJYqiiAWOQBhUbaABM8cHQwkJywfihIq0gmEwqxEgm7hfOTQhEe0rrH0ToMvLxx44anDWD8FDfEiUi8PcRbjwdS3QR6dTO3oiMjhFQEK8rqDnurgPOER0gC8SYkoArUWsYSNjxCG41wFGyIEahI6k6gdQ7wNU+kDPED5l/Bx1wVkKX3PzGYRMIpweM0GFT0sVi1Pjw4CAqx7ieQ5GGRFAy0JoeU3+qLJg4GHJAkdxmm2QbNlsw3KjOVwkKAZceXtHAMjWuduxSd5wtC9XR2wz5dJAo2XYmkGgBm+mCOS01ApEsJhAz9tK97Qrn8J5ni4atKN/rR/AGGcJDQJXiIA+P3AuIh1stLHlIv2QwHywiE/ri3jTgZ0gbk34oH4lUvXkbrBwign0LvMZ7BNAeqPGsv5p+eqfMEHjgqfrrseS4UnNHjm2++yXgSzpB7kMzTWSutOeenWC3JUa3F/tOJFOJ54aFQQc4J1IDY4sUT26UKsmfh848nixLHwUIWOWEGh+jHeIa5wXHFS2ycSH+hVn98BhFL1AkYAfwx7uSIOaQFrzMuZODDiBAOJORGNCmZ2BJ4zW+1FWhlyY+JixGNMhK8rVyySCp422GQzoYB1pNbEfmh5goGLzGx2DNCgO5b0UI/8Nz+ZvWaYFGUFmiiUX0x+9gbMqc/RiERwk9TxBX8ug+mZ/1xp8+tsx0G1FRGX/Y1PEyuJdoYlWHQ3hLsZ0tFW9wQAaEmY1YKPpST15BHtBA1ct6W1df2/5170QTPhFvcYQIz5mK7zIKN9K70xzduXP93F29LPfFy+yymMOIbqmy2JSAfwwABAABJREFUNOwv5bOv5mc7hdQRYyFWyIsyb24bC0ozDCbzZUhDqZjVDFEnXFsmtpn3eGiVN+yr2sXqtrj6mXuCtMlmpVJFUVDAVF77gIRKZ1QihkRlFje0Y29scAkmCfpSOdruka3VR5XjQw2LEjLSyg8b9ahvoi4tlA+vS9NCsFvHwskEhK0XnZBkaGjJRBZ7c/gn3GYMhoymauwgayJliwic1KWTqPHALJ2uWRb4NYrkDqSELHZOjJGhwFhvuMzYbzYDZTFnhY+0iicGXtigKZTCj09yjT3heW58Zuvw4Cjb6cnw3lnHijBCzPG3QpZqGOZtCFgocv+tzK21VZUuc0Fxetbotrn7Wdc/Y6/GTgzbUrOc2oFk2PLNZoY9TRgYGQ8zs/Z4kEGimABg0cb+x0SrMzptMOWGcd3vhFCRWBYOjIZTadJm0yVVN6JLQcfTo939hfPnbj/1zP5ha/HGyq1zzzDy7tWyjFfI+NGDx3RCTLEWNs4zIM60M1iWbe/uS0eZVTvVD18lKubAKXlgTCBRmN24dJ6zY/VpxyTcBv3T5Uq20Ggd9bHRBhpuYqy+WEUoeZ+Z+TMj78vrF+7R9XNleTZXXaovn1s77HfIkBsGKXVnOIAemI0NFwUpYGgNx7nWYzJmrL/7yWcc7C0NJzeyvO2cTvVTGJnf3rh0N/8Oc+mrt67BunJl6eHmYyy/oYqfkmGrO+0awxlJQWTE4eMFJJmAfiY9TM1Y5dY2MtSgOmaZa7duMwN89913Ue2ksgKd8SGKdocjRxvW8PT0EAMrrm7XELaoWssxXEHX0QpjOWUoQkJoo78gP1FlHQOD+UePNsmGZYLr168gXAd7+6wyEQsMSLg42UeK3oVAMMCONmqEZ9B4MghUBHmZQjOAXSfhzv6NwkUtS2KtvZDKVLAQMv2TLSp8Q9IQXfzMKojgXS2gsdBi9Rl7ATxDVKvOkelQsGettgZd0WBXmj/JdNZRvHmGfE1hRE0VDgjei4S+gii3IPCu3dq1JeRjOudYYaOCe3LXfg7slRIn/Oq/YADX6VSBt87t8POrMcYQUdFgsrVu/sYx0d+4I/V6ORXr3dWJNPx0bls/rhZiQuHk/bFEOnygM5k8YqwR5ZXq3yGrRgu1vi2q2nwrmvYHJBDo5RKlhpEPUgKhmIzKYCRXosB+honK90ypQfeQIf5pmev8iuXl1fZ4l+6HG406fe7j6qwuoJNYuhMtyI+zjKxBTpDTo5GPjglhZTV746PPdnvTbpVnf/IDNBRkyPY5yjRmrfNf+WKE44FOjIoguYzgQ6BgXDGRE01INouiBSmi3ySOpsTGTaNxyDDTR4/Ae77oHByo6BxQoiTBofpRRPEOrygZ29lyxhZMmkYz9aEY4pAKeriWtDgdciKoOB2fr9dyrVHv8UF5OiqxMbBQYO0zNZjtfLh7tNnKDBhaaNJFOnByYz7JuyMtraqwcnCDf+IYWlTlQpHbvrI6JY2oiGJbGR0r01C6edgxZeJBLwMETvKXrmcKvd3DnY8/zzQGS89erZ5fH2YpwQh7zuFomOLOy0L+le8+f35j+Tdvf8yWwP3PDilRgUsbJ5P1lQvXLt+qL2jzcvXKhbWrF7OYKLNgpxM1ZCeqGJ8bjdLbuztwNd7lz+cqi3VUNVWIABhtwHIFdW5pY3Vv6zEXuCyu6Tqs9rZMGfCwpAtClgvb/TayNOn2R+1ubzDkWEd+NMlg5pyZsCfAwKFSLOQneoSHvp4KBQsDUxgnftlNnQxJMHqU3KEtm+3dRqvMwI8be7kiGi3Z7y1yD0nFmJ+tljnDVOXiT27+1DaAiSwFMz1Mecy4AzxitN6aZzxI96GF7FqetwFnR5v7TPmurV6Eb+m+Tu6XU9kLS6u0jn6rc3v94rPXn7q6jA30SqaSb3X6VQxIER078sLJoIPG3rAne+dnnv8Oc2wOKYlu5T8pltXLz0ZD+uvDo24qXUzXK+yOdLn0Ri9OCA8/oY26pmNFZKmF5ZkuH/hLLlX/yfce/fPvOq1OpVqE8g7XINMQ4nZEK6GwVAwWkvQtLm/0ivwDK/Nr5FAA5sRXE1S+UduOegFRyg5CfFmdmhJOPDQ9QFOHMob9jf3t2qVLjEgVkXBCaK3YPe7nm8w9BvdWEM2r48ATf0UzuVM6mgd+ek2Wvx8tf86bEJkhEkQtawBAG6KZTbiBRTeAsv4FcpVFycWCKZdd8Z4NnOxvHmqtRLv/sIB7HsaM3OARQkDBBjwpCdu5H01Xr+hwXavfzY1zpdlksbRYW15au7Cxdu6cxkE+urCxh2dkpCs3PFHxox5WMYgBwV63purUjuSMYyL0LBfhOSvq64eRKZoTy3Gcp4JjOGSeC1sZMFIaVES+xPYaY3Be8Rpyl00fLaY98SFNBzKYOJFEg7XjDuQ4AZCLrXHDZzDTnBk50x3wvzLWKE+lRDPgt0QqPx7HxzDIf6KSwQMMXxyjPh4EI4dLly4AQI1AD+E+r3E8/iXQUJmwOlL7EkumfEWDQ7iOM4IJiBOamo3pCZDu4esSyMBZ0khW6pj0RXHiAQk8kl2eVI2WPLTLMdPMtVYr0AsTQt8gPFqgd1Ln7SJUtEsFKC1fB1P7deLIBQakOSmdSR3s7TR3t9WfCKmBR58IeTIIPxw4EcLPkK/HJWFcJk/gAp6JDtdv4MEFhIZbOLJ53mzF4FAMgVPAmPyQvRZGcYTACs+asghMvZ5YQV6OE39+Mt7YWH/2xuX8dMCtnTrxa0zmrl4xnIMSVonAQzMYlI05x6ZdZ4z6C/nM0qy0UuJwLJajRXtVqIz5mvWpnpenOvGVsFJN6p2YrcgunRctYDWnb+gvkCMyZGGdarSFV9HtMgAe15acjxVOGfVGnCc7Ob1+ARNQ0aJcILHO5KcHOlsw4rS0NmCz8x8MZ5BkAnEqpsSJPjGqhSRC/O6A9FzgUWA7UaR1vvFwM00e3lIeYHRoRn2QbsiVqXtqwp4xK8VoTqkBdTOSTXjL8MilDkqoVpqtnYjXFBjrhRK3cDCNyU7L3EHA6bJUmaQ7PKox7tey3Clf5Iro7m47p4dbOZHE+pnETQW3RhqTLxZBFv8jNfBJM+zY0XcDRpQSmmrlJ36M31k/oO/3YkKbsQhoMS1Orb/z5BzYo1yzNA8q0bdyDx1Xtly9uP7w4WPNcLGMNh7z4R9W97iY6/J/K07USqWoFuCDdqHHLEC3arU6RSALRrN8vbx/Yo6GxCx8DZH3TQwTKB221nx57INW90+/fb907sYrl+vpfCE1QgnrPa2kCwz0wIi9LC3Yvp7JZxDUKJ3D+GCE5AEbyB2bBaqaPLnRFkFpVAyXrOI8raTfkACMLJiX6onQCljh6F6td0jIEzmGrE94oNBSzWlzAKdEPIqdEMKrRIhr76RsPHEBOkYy/+vEJRmB37lgXItkfZ4g9pHQ01I64BH4OGb+F1SBL/NQ81kUgnUs2AOPBZ364TCe9YnIZHIAAiTjdn5iWged3D/PVGE0xmIpct48HZgi46CKn4E20oIBZ50pqJQQQwrCcShHkhiuY7wiypUIUeC6ffsWNqF9TECHwwWN/uWIwidI63rRlbQ6xuuY2CxPpliL0PxEj3VgRIk4q23prSc4QMCDTUp7JisG/FDIsDLQqYaC9rSXr30HD/IhRub3RqpRLtqMRjEDP0n4ujb3YZBPA5Qwlm9YBAzOgT2KrwcmvwAQXuARJ9LSuaOCmdnGTvNPQjT8pkcUfmYv9AfdoSw4mLowydFy1WhUzQ45cru+XIdpLPg5gpBj5LH6CoFxJiodfv86PV4OQkKJAvCXeNhqdni+YPDkfGE4VYZC9YqjLqAPCMLBBvGUom54AcbFCkRB0BOx2hojSSgmZd/+/CFRCCOxdvRNTZUl2o4dCwV5XBCRAozQxkoND+uA2Ih5CHt9O4e7kFG6svbCCy8wPiAq1x9iYvZol2sxVp7eeJqrRTCT9j6AVGATXrM15onDgFlVSA9mta8ZNRZPZnfPVR7sqVYXNKWhCCTU9QtceFepIp+QjcunctQd1tjEgpBJLLIKrzBQIQRgum26fuxBRgPdeE4B4cPu1j7Uth9sAVlm04qsrWMmCbk7JW6+x+YzeY37R6S1ETSdqFwoDghVMG+rqjVRyIQZJDiwAQnMhXMc777ODBD3H/7j/xt6kvUFjDultQgY4itBIEEOiGV+Qmyz01SDZqXfmoDBKyke/0Kb58sXGPgGHm4mAeD2089gHEEGXB/ksS6xAABGCBg8XBj/eOdsCelEkvVeTtuxcJNkQpJRZH0Cg9NPHbEfAIVBY5AQSGMRe/ZM+Ewnc9bXnBdE2XnfaTWb7EdigEjbCDJ2TgBf50z0xwZqDhKH6FeS+GSsVYUCksAOH2ft4JGii34k/iQx4w94kuGAE+50JpJ+tZc0ASf+5FAgmRgRF7U+VYsjnpTfPNy0JSJICiQyhAdPjOnr/v2jEiIqNH/frPKEyeRG2rzZEUWIf6EGYaO1MlaEOd5siSRcAObkNxElF+kZTvpjZzHtogPry3W0xEJRzQ0NcKJsYE6GgIyfGLqyP4dGbfIIjIwOtezrUcoortcTAhPCHaESGKR/CTwhIQGV0wAYHtQmfQcEW88sTOgSBifMqvSemSEkX8rF0ITCZrRKjPqVThM0Gz92uyhDCRocHSyZGgBJ7Q336ZTl2uVKRiOWfp8nwlN3R2jphYur2K722kPKjpUKqUBOGjwUH7RgY46GxzUSsyxiYTkABOKnzbsnfCOPU2J9vUjCEWG9JJujFIQFODoj+ib2ODmJjTUtOOlyuBGAp5gBR6FSm9/73vcwl/7V797n5A0PDOIw1/3Nb37zyvM3sN9M6eGDPjcpE260aPvUCSBTEBJIb24564PGJkdYE5MTxcBG+rWOvXCAlS4UZi7qfuqtR3qBg1SM8TqDDkymx6KmjjpdMAxZmaFYdrFYpSbD6ubBEZmu1heRRq1NGycJQe2qOmwlEQK0PWynrPrNFpGEAM+eLrnkyzrD1J/IvhgZpRSQaKWLRqf8BC0EI8LKna4Un21vkwQ87cbBL37xC7JARROi8YDubildWtGBJG6yYkmdvc+//Mu/XK/XsT3cseuz7neaNDGEhDLS0dHJ5gdcz1AGuUsC+QJAvssry4xmeQUJYFgB5r1hFykiLy+v6InFw/krOo1sol5++eWrn277oIWfsMWjgHFHFngoNB5wEssXR+58SWKxEkh+4lccHusRHIaf8JDkceNwKvQN7R62ACzpYVWC17wNVYDzn8foSSwZBDA8RqAodKo8yhLKmwz0KG/ghJNE97zV6xvnL6uWY4UEPSyaQFu01mus46c5jZ2wnec7Gq5SR9jdqHGZpNE5wBwW7Sk2QouzC7IqlJQ67Q01iqOyELb60hJajp9OUvDwE6r86601ovnUH6f/VPCxgDPKfiz+j/vhRPriO8UkMaTikB+4wQCTEKwVxR8WjeKiAYDfxYzrgATP3iI3XNtV0UQJRVxkUDEVFw/tAnS9EGlR/GT0aoCm9LSFKvaEhMEvwo4zDkhXRACj3mmSNEwqSLUWI3HMfEnuOEEIevvOP5CXhJF0xw0EIGWdcAG5exIxEdnOPyfWCSAiIKFxSSZtocSR20/lgod8+eLHOWZvBSGXUAqfacYw1nhdXxnBNu+Zoo7IjmdWv4GjdCEvT3465Ey0pKKhMQmlIDGvaK0RrDFEjZdGpCA1KelbfaMl9Kh2QnKgSEX9Sd5MbxNFICroqaeeotslrVCdcpaXZ6i4mASxmlAqndk+ZDAlQleM0weE8xPk8BYHzfY9xgdFJLKjIDjCwIZHFJpzD1+i+Fr2oUKFw+iJKOJnoA0kEMqXQinckCvQnP1UrGPwwPAlXKljl2SLo/IYZ7qW2ywLR6WvFTtOrRwJhHgMEZEiLxg0wR/SARbyJZYaR++FhkVC2AB+7zuA5Keh0sgBB5/gmO9CcbEWM0TdqGFaHcz8JIqSsKqzenMBeGZDXul8fYbowE4DAHjAb1RJLXg4X4/yn8TyE+d1xJcQXAB2mokPCT2WJIQQK08Mzk+WAnjwAHMMYoEU6kRvlcQcsvgGHnA6fhA6sfxWRlpoU8NheLm6tkic0x8T+A2yOpYEbMd+xz/ISCSZpHGj6a9//evLf/V9nl/2sgd2OtkkcqL5eQJhMiQZhXhZVlEthOSB8zEh3/wvOG0oxN+5gx6krmvc/gaoSQ6u0wkJhwWnw0NIzp9HCL+Pe5RSKBIOsfXMToRHIExigfckiaGV9stcRoEzDtOB4gUcwY6brpien+oOVpzjxyDDPTL/1DRFQzrfmxiahHAdoQPw9QeIPW3F7vBVGlCZHPj0m4VUBdlUh5E7jlEttVuwO2IYD4hOTpAO+zw6ir5h+VVAEdHyKX8FaJEE3an2YOJI7yDFaUtsKq3KK+azJUFfRDG4uRBTRiQxLq/Q8Iw034omZbNLrfEzzzz1w+rqrDHK9TneygRA1xY7g+AXifO6J9TunJjONrjCbTb7ST63lZq91uE5gUxhRXYifXjLNpfy1GaXXKIu7Heqqw3/zINcgYVr5qLFPA+4L6AW1xYqnJVN60azyDH2AQKTGH47H1QCr2KRJPEgazzJghVK0QRgnjsQwNLTakhnqSKqxE+ihME6Y9VAjDlbqaFS2b+Fn6OJbivrDzQr7gz1DGOvP2Gu0u5rioW5Od9214w3WLhDw2rRlb4d4UuPOsP3Hr63vJBnXvpXP7jNiLnV2KXJuYWy8lOWQV/47/D1JiQoNjZNhYsb6EBPCG0RqJGOTQ/OGx5XP0FJfcRlvjy1c7g45CJOlZcEJMKQiTEo18GNkI58ps/tT8iLrRGAyWwFKEaKE7vr6RJPA3Mho2y2vTVRGRqxacKD9oUZXMOyceXSwoXzzKWP7t+p5LQHQS6L1TKTOp6qZ84wY1+lz4PyKW2GYGjHOm9/xD+uACeAvPggz9ytMT0asMSzXx0ub1RWV87zxM3Ww/uXrl1ayRcG41G1yIx+AbTdTLNR3MfuJ1/ITaZspKvrFV+0Qcybubl0sy8imJ/LHhCvZsJQW8IaZ8y9GUVqAfVFtoifnD35gtBBLavbDOurhRqVnCstVOoLmOTJrokGZSMkxkzcYk6GiDmn++iIZ4XpYrrAwuvOx/dYjHj8yX1qZ42DAFM3MddoG1ec8TzngAuc2dNtHDTpQkqVTKVWb/WaWvO16rSbRiHEuhx4wyhZUspF2Xy12SPZy2Vbw8GswKvU3A+u23YWF5eR8nJ14VK5+r0fvfraa695e2fmB1ZmhOQuRrP7n86x8g7rOC3BeTrYglRRm3iQT3jCwXmM9m0gBw2hDUvoONQMK6Vw9EKITmwAwMotFjFsGGAxzVrAjVvXEaLDnQNsTcBiZYpWfMQxSFB3p+kl9QNVDuBfANxDJjj8vpmUzfByqQm/OjETRGutFAsYIB1PElvwUxAAvGdnJmL4xWkI4P3Xqt7yKVbsABqGIqTCUJRyDYesl026ZgGNh39T7thmmMXVSdrutbrCQ9ZStrSeiHJhdr1tOWnMYgVxSMgVtRalT5zKiiAMLgOuOcFugMYHGO14hGFeCgM4+Qlld35SUoOYUxgAFH68CgiIMpKpu6or4ZzwOZ4Q5QhJaIVLc+1OiDoDOs4CdFQqkGRDStNrc94EDERBpf+MeTmny2ID7Lfv8dpCUYGaBT9s8bjKEaZRLmjyWMhXxvMP4Wqusk9R67OtPu4Th5+oTSCt3mnbVBALZEHsU3ZB0Tg7GWVyndmkvLqcH5ZomGUeeyuXeOy0YB23ENgYwOQl5phaKsdIdAUQr9F8+OEXD4eyXZ3wAjkCreYmYgO7gkeEG1ujUvAbRFYvfobMAuaB/jNKQkIGLKo7NU8kv1ws0a0/2N3mHQEug8dMChD+g2CcfhikN3QufibEzQCxy0NOWW+WRrIFc2+5jF5IhAWQUjM60vEspbpYW7y1vlEZ9B4+3Dy0o+WFlZXMK8VxKtd6MBk3WWcpwUaKPsmOuUxZV2VjQsO5L7ik7fnIjgG9YsRITolWwW3g6MX0LzWIWuaOfWoK63LUF6wEkhNymUG63Jk2721yYur8xuVcfQGLUNQFy50skU1HaYpHqt3D3tbW3rsfb969e3dz96CQq+ss0GTaPtr/8N0/PLzzHhuHzz99na6HvU76INYGmGTagasUnOGNXh7zOzzc44mQoCQ4hYMBb1/sQZ+anlFJOHCUKS1WaysLWhb/5A6teKVUZ7I66vW5TjZfKq5wZwhDEPYviyUmnIOdbbqUUi7SsdgCcNtL87CR7vagZ7mkdVvM09gmoCRQBX/Y9+VCBB53u3D1Ikvq2D5zT/nB3c8YI2VmI8Y3rHNnpqNhc0zIuKylZA1IZUhuN0vqIRre9JBBA3ePSkBQocqFOpZs03gqC/XecMTF173ecKFUyXczZbquXNlejMheWT1fzhYf9nYRnsura6uVaqVcu3alWtwvP7j3BVdit9uHmT7GXjraXMznBp1eH6tlbB4xJbUbYDGagwOdVvP8+lr9XPVif33vw839RrO7UOiOBv2JnqTnvW/ZlHGdNIsmLIxyrYpG+LrMGCMRDbLS6Uv16mOGx1NWQ6gFrYnQVbBiAKPA72M17eiqK4zsDV2ogMQJxHWgtVAfi9KSNRTWGMCkcpLqDTgmkBRJ+cle/LLWqmWd8bB9uL9cXnDMBi00/MQvumwKaOH8tAbpP+L2DgzwuuFesZGRLSCun13XxSnsr26UR+dIN3Ln+9tvvVl4laou1fN5HetOZWCyDguAfzZdKHEEZJ4prVtdbwnTDg4RrhT6lWFH9z5DpOBtgalaLvnoDGoxlQbcckVcxAtYqqVnTqOVKtDGTy+simCMc+DwZUCPjFG0k7HR1ZQGaATGPW+U9ASvAsI/xQO1UM4/14eQBG3MUFhP5tIRMDNEd1Nl/MTCB77ESqXbOiM/GcTCbb6qfXM6qeFqVn2rEoJ2/pWVqBwwzmcUM84LKF8M737MVYligE0SGEztkDWU6LK8bvfKlUuA9XvDbleX7ymJBmT8Ybiln+RywoVA9LCahFlHWXFkTgvvaaG4kMoIczym5izCdS/08MtbCLrUJhKCtPMUwuKi5l8vMoVT7fPeEMaexgTAIj4Yk6OKtsskj9Ngv6zXhh38wNpUY2oLBilIlqt53pVtHOyonjxXg/dRQYxNaV0nJJpCFBmYEwOLlfj5GrWqNQKMSOFxR9dLrezuH2zv7mncqAk4nEFNqcePgTKddq9S1nIhFWS1g9fqHuwmAPbXCxTJBrMCD+TLSjXwi6XUC7evo4d5kwrTWgZAoONfcMCAXJhjISQKIvwf1Kt2udxpITccVTazWyjnSV+B6TH3ysIYlVGFtFLLbx7KBE76IBiLBTT3+UhOZpnKmKUZ7Go59JHmPQxG+hqaMeQ2Gkhq6W2MGVNJWcAPNpz8TicKxazrBJ+gHL/DnAgUkDnqPTPNscail4bguAkVHktFWSi3j/+pkShTzzdCaxsYRkv0IdyRQA+SLL6hnRgl2t2VKgQtTOOQVLvXX9YYQrkxBFNRuASbVWM1I5of+37Eqhly8xn/CgTnsCZO86RZaTKtFTMD1fO0Z81tl652PDqXzR42W+s9PWE6KIwYbDFMEGXoW/VG/CMz3bbKP/K11SiyQBtraixS9Dkm4c54rHtQdjxpqvGDcRikYoLN/S2pEpKVc8Y9VBSiQU50XIxjiXr26rn9Z6796t1HAlB2+g+aSKr0Uf7m/TY+5GKraMifhiaaGyKHE71vMlqU0STkwUMY/m3kdgYO1xVwmjhvrt1U5d5u8x9/8+5Pf/rTxXHXNua1wW86R0wQA20VzjgSEUZVnYE9DqLnib28M6pBMF2llUvBsnE29kpckIFYq2iYdtKdDjEMlsSkmVGglLCEiEP5rETQBXBXG2evJQteiXwl1l/fUVyAXQJpPpDpGsNDAh4oF3NoB/wPUGiuASJ4vJAO7YEhcYgyDIr8MtaaTANDc7aKETxdB0i8tMksFPdkx0hakXYfGWNulCa/mCwxMcDhBxUaPonQcnSemliofSljHJ8kJCFwg2EE98TTRNHuYqKBRgmUSOkJJyEOvzkb7TIzM3g+BujAEZMdjiQhyj2O6rnnnuGSvsxRT9Meq3eQOH6BxakcCQFoRr6MzrEcKXTbQGIFTQitkS8OyC+pk0AMq4Gg4pE4HONIxgS8QQdJ8AGcbrAuLScS9MdO4JmcxYMbwAD2BWj8wBwTOE8sBCjEecPwZ7VIpoxcCfLm4JgmoE0IAqFwZ2+f6p6MGFp1m+0Ou/1cNcGgiwVoLj32mQCTDYDp9aCZN2uUS9wgFW5NSDnMZu326LPP7nP7Aw+5+jjStKYIwxm73Hvy64XiC3ng1IhHC9BRKk/IlzwsSPUCMXwLqWikzpIo5+4IIWHISFjsp4l0SSW1UxyeC1fekhcArPMyhRZmI9LrnXBRYpqCKoM/DIjJJb1QZVcQP8AEMiwmOZBct8bXqbKEKi8eYoF0bPzEAwyU8I4Qnd/qou6krtdrLNHutJrYuYgIIwOxUZHTaWbyhC9Mda7QY8OXdoRTuWxUoSV7cwgtB3mQc84uEwUeiozNAmQzjwVE7whaEyN3XlZyMzqqjEfqaO9IF7F4+JIXBOMhFTfP4D/a3cEWb+e9j5GW0lhHKbNpJrAjf5EPJORFKs+XWHQIPwEgd67fwY8EO0L8sS5WLkzcYRE5EdtoHFCuvckYCr1QfGEmpUA+0QLA3Ll7R9jilotfIzxVnKjlFzAkZ9Gh223DQldfwOCBMDE1VjJJJHOcRgkwJEGvEk4qqMIGCjpvPH371q1b96cZpILCeuUCQ/FhAqnwP8kB7FHkCzBoiyXdTqh5mlqx+ANOHEvesI4DqTgwEx4SemED5eRIrCoizhpxQzxUxbiyVqBJAoy4o2TSJJYFuh3cZKQciXdUxOK37BSiqtLviAAPt697k+Eh4Tw++MChzJ/gPEf/PgksxJ5ABZGBsUn0EZ5Epo4BGCWJlQk/jyc/ViJH6AnFDXNeiwGbB4ZvMtxxUUXEnoHX0hyDj4GSgacxP4lFAfKbeZCXJGbK68PrZGDALAoRJk0A5hO5EIuH5EiXkMTS6zzwn6gRtJ/MS1EdvSO+hKDB0kWdxfEcQ77ymBx6TXHHAocSuDtUcst0jS47cQg9kZ3I4aeHJGU4DpRAhlwEbY5YAvmCGU8QD4aWrpeClva2guaz7muOirQUnEAaJgscpMJPM2SHjRyG9kIGzPGGGeVqegw/t+3T6mEOmrPfOUTzAAkl+5ub2FWVF5bRh04/TBMHzDFOQp/YypUydbZ7rNEv0fUHhZwl/CSdf/3wA9dzEKIWbxEUHAflFJbNV5Bjk4tiIZo7N8B/596nbKB2xiyh9jYPh+jD3kTjBOYM5Lizv0fatXOrVGuzefTGG2989N7vsS97/indZgsACH2xA1SkIi+UPAXH707FeYIjOaxgcZkujORHgz0IY0cZ8CwnoOF1XtXHojLca7SafDM8w2uZkhFdCb0Dd+LDYaAIJzlf7IctU3kpLD0FpNLXF8cTyogROmnJArbXlhfxd+2B68qirgHp6lnezIBnhcyJkqhXsuGBlqC1wKCvSRcIsWQ/8BcazJY5v5CnUPTfZAQMG6WUDhr4Cf21CkKeplf98Y9/vFccvPPOO7yXTb5IHhlBNnTyExooC2z0UQF4qAUypVLGSz08S0sLtLJ8XzbRnBICHqlkHY6BF9KCpJGEJWa+7dEAq0MIADmPZVMsAMDgYBTQC8oXH3jQBnxJSJSQ2ECar/MhZoi4DRJ+Jp1hRhEnw4QfRB5EEs4UwJD6+QkFdDqRVs/uNEKl/RoupjYC9Z8hHUhCCB7EhlEZWym8S0n1wQCJjbGF2pXkxNNgElIVxLIATaVwzTD+SZ5rTMRhpxYYJAsJJCF+TKWpQcCA9ysKFUhN5eY2whAmPp/lII9qALNXAWkDVChCCEl6vjzWIb8OTBInuVMQHB5JhokoYsDbIgTiwWEcgJ8oElKLnkTwaS0K44gCwDSnxs8eiAd4/2LSgMOPwyPhl3KNHFnEXkkjfod0j0cxgsXDG3v6GgypAOOye2qK54IIhKWEeCrlR47RONNx+HfObQcGSIWkacUtIgo3gj0NyD3QkeP3n7bvQ5Y2YrdlGW8YVDDYdCNMXGow4BybouIy0mYJB1sgwMH868mTNERIEtIeUIGBxTW+6FEWoBk4Uilwy5N/na8XKgkZZWdBp2OTkJDK5IZhydHRpwapQplH9WV4ohC6vHyd64xY541EBTwxjFB6qQmhZj0KRSYMhpANRfxcJsnZGpDg9xp1riq9OWC95E4DYUCCQ/8b8xFCBCY7mkEzPTh9IlGWw0kJcQyOn5mXiVmEhyTekNEccD5yVpshXzxe8yHEKTz9NfoUTMFBFQBCQqcwGY7wOQ0Euvyr7zLxCKn8p6cNX09FFI607uHr4Q7mFcEkupivA8NPG94oIxowP+kb8KNgIRhlIVTGdIgnlh1T8HhCdAQMB0w4eXdDUypN1nITPfBwNMYYi61TZQFCcpzVOLyVplOjW8dDOExx8kCYdMpU9KsVk519o3iLcilQiBeKQCNPkNAjpmHrqKpPLGiaJIQkxCI4wNPzikLLkYS8xlS9d8T4xDEDrxxjAVOib8MZnrlMOkoCIYcvZHAWDS5BnrrabyPHr8ThPCd3uEE/y0GEH948pyEQ5qL0FznNkYFxsCS2Lw+xkiqhl4tRCp5viYuiwrDNP7QUD+RLpkgjnbjgvp5TWRJ17WSfmTRAJmO9YNFKJbUoEX+yM2gn14QslmZSEEUGtuOF5lBb0T6QZFRDNwaytisi1ABSUhMReQhhIQNA222jMJbK9ioNmwAMTh4aLl+9VsAlsOUyQ6vb33uZii/ktTCHImbIZfdhNZnv6dWdR58yFOY2ZBw9g/4YZuvGY5I8hIv82M2LO28ApwU2rRgwMowcN7kKmsGSWfrgER7rCPSFYG310TZlocADt2gjgtn0gF5BBmdk8MvYEkKlbmrsN6TTF1vjp27f/nF9PdUc1DqomkzREOTYzcLEw2pGXMA5zbL+415sCfqltgwc/jyfgwMfjDTK6dm9z5iImMpQiDHbkscfcoX5HZlZZ464gYixaVkPRDxVKXH9getKAuXISXVneKgxqhpLUBtYmM4TRw2OpWWrMlU99S6ifbk54j+/qcHsEl+2XKidwVjNFdaiBx9ubSKEGNqgcw9beuSk1dENjN0OW7zcL6W0FJP/c1qKZfYoemY5Tfx81yidkZ935Pk6PFfZ4fedKwxghSMz4Pqedz/9Ymnj6tW1Ivix/iHYXXLaH4fF0m6/rar1tq1ecZJARktpUO7ZMGYA0PYkEwi4LKWfGT7eWWIjE+2PvRGSY6M9roPC2HzI9DY1aA90MotrtXgm3gxctadCSGE4LY6k6WhodP/0AUrvO3FxC+IpHnZImf4xMf5w5zEHV5/+u7+4f/9+MXOByVv/ziaJGR/TsWXGHLrSddJQz9yNXr531C5wW5cWcBxvpj4rvP+rN4tPL3GVRKZe6nfa3IBXXahdWua5Sq4HwZAplyoUO1jVcSRhqTKZDZ6+9WLlKLP1y/1JQ6ihEUYgw2zXP7NynsHNI67l5ll46mqWHhx0C1ey+Snz7wJKopDhmeApCwddDMPsOUHWvmnpnEuuljXr7k5Gu80jtIepyBIvRk+6g06rzWXQ4Jx1BvnRrMIb2+MZJwkw1P70v7y+/fnnmBCfy1exKZ+xiTTDHEzWRLKfL8oKcuvhZ6lxD7k/2tr69Y4WBQoLVWaufqRI1SkxVk8gloPA9mGGEnYkKUefd9TTadytQbPGrk2ZAwV64Z1H4VfOn2MKzYBxa3sLYwcGIdwkCBpuNFYzQWylIaX4eMl6ob7w1K3nQfraf/m1bN/sUDZaBgqx9eUnwk8srVXypNtaZVghP0xG6mwlniVl9A7Dbw1ibCFm0O1pCG73Qd986iZyc7B7QCzJcVDDehu7NsLwBEe+0FDGIJmpJ++MYUZnGhgiCedSRA+Un0vFtTSv2T57QiTEkReOUsNYcsBD7IT7jMwBb+yFB9JRdHua3LIqwTl9W5THjEGlnWmVZDDk1kU9wEUqGe6RkTYCtCxL5fOPcPxiilQVympeKFR0KJ9vWuin6nTu3IbLlFzU2I+BWO9GWnOOTVlFKOI/hHiJHM6/kdJK5CYY7RMIj/PzWELfPqbclilfFVkuwkpyQqIf8R/XOfGv+V+nWd2U0sco5vEn2BB1zB7vvHLNmUhxhndOXCIyGZj0CyRiSgL6j/Sa6krlFus9BDmBLdLzxroYpWoKTovrKExOMGDpRo8DG00KoA2p5EvjhLGoQIkYooWoI2c8QUxKtmPT+d3DVnmxwgIX9laYiwpW2tjxJ3lLSHRFFe1976jxX97/5DBbGsp2mDcmqHuNGpJcIBwSQWikxlJkY1APAQCaMc6Om4zK7KnwUFP8BiUhuIBnocoJFe4vOjJhI20kRNIZRjXYrPGiDmyo4WTQD/VHLI4QxW2VmPSxCKPCcs89OtQPimZk/1Jm6yiXqtBEZ5O1SpXH43qN4agzLWRWtR2cLnePyrl8tdPM85SqnrHgMtVCya+WpZHSf9oNz/RLwQwn8IEMMTu2IzpGFVl72fFY/yg2UgH+tZ4wU5ql69hZPz7IHnVLFy7kl2rMEREPjk+MB+PN7a3f/+Gto45eWp7q7E6VC4xZQn3+pZf5crUP2oyb51FcH3/6EZpz1Gv+/u037n3yIedIfvTyyzrenm0bb21iOc3ubEudwn+4g1pnOspSqNcr3BTHZAeNAI1kYVIu5qpl5qnieH6MATLkk5b9dCDbfW23c42geJ7JVEvlcVarXawLEDvea5wv15ZWFuFpZjLiVmcW1MUFe+NhxAmeLKvt65SItUCZpjJe7HVrWGTrRFFmgUcCxrpymm0FFn4LtTI4uzpnJV6Sr2RBw0Op0onsinhfXvVb4E0RRjocNhmPP9nc/ejRo36mhM3WUra0Uls5V19jiLwz7bOoyfowfURtNlnlQlsGVI0GvbyuF8mmL6yu/1//7n9YK9Xe+Jf/So+ZnmLIPM1UCgxXNFakHBzG0ihK/DfjUW5I7WNov35xkY5iY+N8vphjUEi/gFWdXKfNiyci3Wbj0IkRGb3NfrvNiP17z144fLTy2p09uMd1pqoFdbPIvr7qoVl2px9nbyVqDdGFG44NtDDDnVLZIppG/pgKYSVEbfKS5TitR0lsQA04MP61Vqak1KHonI66g94XH7/LCKq0dE4LCmaYQjww86vDlSKpz12fqHII5wsRwmba27+upuVH2hKGHcIk7JJJDPbodj//4u5CvXyj/MxCtcLMqD8Zc4cEzZBteGSJe95BLpGTg2ypBfiqnp4VrqpYQX9NixAbzeoZpJmcriQCITBiEAYrJd0nHqGyvtvDj5cLQPLzGYT8ngTM1AijJkK8jhQXO+eLSh+7pD8O+1P/QgC8suVgmIp2L2BegzRSTPgC+1kj0to6wxhbp/VlIBoUCSmpkaQ6ZQFOQ1vjCT/xEMn/gOEw03U/X6eYjNwTQvgZCghnPNZ4Y14zsAQVMFQIQQgtGz8Mrf2lU68mwoEJecW5Rcjsjw1hjUKJr3lMBhROQhxZ8I0psEQxIhXGHgygETk8v/1Uq8uulGDsQGKx9jWCVUBC1RBZhlNZaOy0dMLgsHsUbxYYeHCOLCLJ2CIk3iLivBxMpZj2S7nSe2+9N+q2y3rBmzwMKIY0bPOSxeO6mGJrv3PmR8FW0mjMpnqMLL7FLgiJsCnY3uf0REgUzUbAlrVJsWqN/ofjHMgP/slAFnMcT4JwxvCUC0cCMceSEU6HBSR5UO0ayaTSJYnJ7MqFpeUaB5D6pGQOSxLsGaGcn04/MBoVkMJw4nGxIRyE4Jd9Onb1+XEtVxpeWnzQ2kyDDCjlhWbRcqfgoMc2P/B4WpKLqfQvkkPmq7TiFO8Ow++B1jK1HaELRayqjWyNqEgVOV8dcr5YySAnYLZIVhiikQzlipPZX2k8qKLnYrlUPsuE/lfaXnEUliB9jUYaJk3G1vFJr0ClgWw+zCHsdJyhEC768XRGx159TkHeWjXWSVxwibdGMx7S+jkncVVzkmmHRWpOXMFs4WFUhojnxzOeZYJ7KgJVSYM1lBRJpLPPzw2SvPowzYyLWn4aD7JMslLNElBTLlZFBfUPurmlTAGLZUZfzGGJ1iAVFosbzjSqAsr5SIMjSNBmFBohqjlRa6XDKzARw1Qa0WKGBrccGcGqeR8RCLslTFS6+InIMPjVnePM7Gb988uVeqE4nLZ6bqXoyb7Vr5fRSqQqgAOy/qZMyIBEW45GhCbESI5mxfVl30r+nldgQsIDl7jnWq5v9rvpTOWf33jv+qW/qtUWUv2jcX+4UtPGvy5Sh90QrOm7piriNVxVDUR+GpGFKUQOOJNMaVEkSINIzRiIgekqtTVeYgXg4KqyyHmsgGIJ9ziXB0vKWTNpXe9zqWtEY4jAS3K4ajxdqi2wtMerMCRkiOZJYvRf/Vfkm4vIm5MWEez61jngkNEC9Ffjjkpr8vt1oGPxhSZzYh7pjDfmMYSRSBlCiHeYE+gJBIMHuv/nP/85ln2TqoYIva41SzP947ZGzC7YDX7uuecevL/46aefNra2SSK1Le0p/AFVyCUE4nE/0kN3WLY9n86IWwISjAzJYo/FKiGOMPegojzeChWD2t8kARSfnzgsFbkaItVnBYfL15KFdf0bYQN5zIkIp2fKD/YwWbo1o58I2Ho1103HCHjSD8iAHo204rp2SMIt34gqjyUQ/HxhbQhxv7Qkep8ZkIm1Ym0ag/5lMsOACfYyj+CigMZRCxi6WA6oMiUilgVo8tJaJS0jmhwpF6oOPO6SxODHsbKlKJsw0JCcAAUYs5xjjCm9L/dYLJKurun+LNqyo/Uv2ELCZEiAcYCgPwgnBDy0MDyiVApdIyw8otwmS2yOmR1K1NycsIDTkQAJB5gowSgtblhCyB42NW2H28Q6pLIz5ECSIXkBQAirt3SW15+68fzzz/eq2q39dz/7G0T5wX967be//W3BbpzxtP71HtGN1AgJNNPzMQnPpmosZL/33nuE58pmEVPIa7C7tKqNMvS/OKt17eJiCdnLDqpWRll8k8SqW53BpSuX8KeGRxDJtjGpuCASUlerunHMWmQkSwgGUczW2M88t7zqJaLgbnVLH8OOKzCEU5XYqjETUrWaXDEjIvDuJ5++/fbb3Yfb/ESOyUXSbGMyuMEYjdxhEf7RpIEfAvhCofqwIVs2GltYCv7iIpkX861lS8hSKVb5IabPUAX50cPWA8zAgMEoH8K/+93v0pYJxJKOFRyAyZ9c4BdpQUoqgAnBHpOFV1QZ4cNR+j//5/8MYTRALMtgoDK1BWVi6cb4kjmpIFDYYmchohnMMMeTUEHUBXbQhN96+imyoDND5ik7IRqTmXSRRYzm5F8IYAVkaZkjvDJgJy0Mh5/AEcUCNGmJEp8xSsfxUpYcsiO1jIMwnFu4UGRYzeq/O/x4VAv25CRk44ChFsAg2syujRtJgMEB75RrfGzCDxmA4cLPZAEIj34Gj+AVFqKCx6o0gMcJowBFziEtMMH7GMj+ngI7qTaPQZtMeggJYVQylp+UKyB0D4FJSA+0EFHosUkkx8t7slxJyOB3nOHnN/D86Ri+ZqZIOFIBsHI0ztjfqIk5Eu81+MKcE/whFVIFk6VAQaLbtrgKh2CAtbylIb8wmnbNZFBKLY5PZrNryzX8mM6eSScIaRzg8xxZdKNrEyRvj5MdQz2vlTMTm0onxmHA4LKNh1Y2stt7n5AuEgBP6yVFscCiO3c+gZ7kkBzqIIEBJshpWbRx5j7kSAMjOYE4Ss1X50ZjR/dLA0TRghwlQGst8XCUzZPBQBsHHpWIK5R1dyoO/cNaJM08W4zOpJOqO7DFTVNuLPyQLwiThbIQFUdkmMPPX/8SCF362DDdARQkFsn6EntAeEUnhTki5SGcQPJ96aWXGBm+/cE97KD3bGM7VShBLXdzY8qwvr7KyTMuFQDVhUsbf/Znf/b4C+xa7n7+yR1O0tBL/dVf/ZUUgTlwwjeGMU6SB4rJONONyXAHhgYCgSEhoyJy4Se6zuUBhakisEtJjRs3IBi0VB/cYxUZyunESR60NZD8BAkwLKBTZOCpIBzHVPBTI1QTtSqcxh/kATK4ScR0rKzCAYPkQsIU0+mkEXiUJwQPyh/IcrUM/TyiQY0z2gS4Y2oZhBQBMhgtEAUMZBD4yWd3+Xnpz15kuH5pZfm1115rNtpgY5MZ/BAPThwU4sdRagq1eu4cMIvrOWqKURNkr6R0U/as0wUzK/OIFslJ5V/KznYv/7MEP6ppu/p3m/+JhFNdIxOtPmm/NxYzACiX8jMHqYARwtdh3GPfSDgBhFwgGZFCMJwzDpH/GQ4kwFvJJvv7B+B54dxlmENxoJ/RPaXj9UfnP+lpj54jqVgmdp7D2+vXr5KKqgdho8kg4rBrGxVRlrE0BgpI7vQTAif5CVpuxF5dv4CBPMsXkOR8YC0U/BnWi6wZAmyLpxRfSYr2RDYAQsKBCBssgY29DafZC0gu7vF8hUK/T5EV6DMPwB7AQMYTCmd04dlx0PgXWGNvlGP4+a14kB/jv5Bpqc2ULXKry/nEUu7f0CBHMLaBgQcnBvqQTKms7G4coM3OSDKVOHYIXOxVKc70h0A8AbkHWhI1WMKpRzyEIE40TFSE35POTwgGwB0AOO+JgHfOW4hQQqUj4av6t6bnMB7ukI4wGQKkritBoZkFvfIAlfghdgGpCVU8dCEKYhjmOgbSuoevOcmA+wI/KQUN3BNGUPEfBw5JQi4hhFSIKzMRmj9tZzLUffpx6q/7FyQQA7SnNZmf03kaC/AE2keRv/zlLzEWUlobq3tsSOU4+dL1aLBsrcYsSVS5HouHVPAND7k7MQimkLjespky3ZbK2N0G2FY4Ff9HOdKBnHojFajwkSk0IN18iXVsoiSecSjQOOqk8tNqQZRbmxAGd8nkSWyOk1gflVFUj3WExLqHL5gdPwgdRqliwvC7g1ueBDBHHtL6T74OmZQFkDtaT+uQgDkogbQyGEI4tYRQ+U9CLZXGjbQhx8yXn4x2pjWdLqLmSA4qEs6Gaq2sRfOThM5hI0b58BPFwro0Hs6TqI+zGSUIwQBCejSaAxY/6Zq6PMBIRcaBNof07DwL/F4uonAEEuL+5NeBgYRWiHcMSYDg9+SOhy80kLv/JAoKaa1GmFdcpFXOzDTg/KM8Tt6JJAE/lKgu7CZozl9BCa8MO7ATeSLhH/UTzHCIJGQHthNpT9DAMa8///M/hxuE07oZgDEJgFecGz6RMEJoA8okktg/ry8VzZxFncHbOMnpHM4OEbyN/H0EBFDIAj+xPrg6O/ETQp3Pzh8wBI+DOwPJxcE8MEk2ViJxbzcv+MmsHGlyr88ty2ivDopHi7w6XOoGqHRDpiwMgAY44epiBlcsvKs9kCoaatOkMJ2jLZOFD759+Eu40BlJLgKoAtKyD7BxfqNSXkmnyr0GmqHn72+6LQDnSeiXKe0sXX76ue+tnrv2i//w/1JzNVsPhvuGtB8VByLmRRZJiK90LXs7dilWDf1Tyq+c32CFrtM5Qqr6vMcNHFgkjnStiCfHPAnThp2+bIwJgZAZ08VbdBLwC4WKUuWl1HQVwCxVwfo2lbreHt2+fvvPVjZmnUn6sM8sARNTIP1GIRucASZI/+pWOHzGeLb28Ndt3nyp0b0wyzWGk26392hFFs1N7vpDbY00GKXkIZV8Up8K4tYkvi27cbVZXSBV7u4R11PkR4c8e55LaUmOogCq+2iUIK/LwrRxg25j24tBK0cec1z/pJrNSmM2WgUGAbL8mUx6oxS8a3Zl2cpNzShTrhIExh5g17RfOOXWdX6eSVReTQIZ0Ndu08bW0VS9QZnFB9ZT0KnKogY4vwqv0pq8cYiVkNKCFLTbmPAGDMm4JgEaivlKNlNM9Q95hDY9ye1vN1I3L+ud+dTJWyMspy/5GP8jmkQzUsQQDCrFZmMSnNAFmWrek6rZaI8eHy30M+V4awFqdVUtFkBsNGd4EhnqsQ7GZitTKJVbnS5XNJO8XlsBbfsxx1iRGWYGCLJ6MkzM2DpnEYFdMdUjcgUnGOlVsSku/eR//DkHZg+n3fUXbjSx/+r2rrz68n99//dYOpMvaA1DNBaEjaNOv8bZSbiI3SnNF6qG45XFpbUbV7vtzuizfbTSuKZB51a7sVasl6/Xf/yv/xLDVK07jHqEN+9uN5r7lfMXzt26eLC1x/A9P9Le3cIsN+lPcrvt1WE2359wa167ICuzzx7v3Dpolse8V5MrTXTXBJWZHqRk5TdCwgqtRntjca1SKLeyPZaki9nixvq5Mg9RsmhUKdIUdx89LmGynC+lB2Msxtn33Pr1O48fPz58fMBC5nJahtVcM14tVtu9rnWwA87uwlT8rDkhIQvl4uJKfe/+Q+i5kJ7cunmVWzzffffdHRNJlmuRZ4k0bQ1vOjVAqWDgkNZd24cotIUaRlHEpjuHWP22h51SvZwepne3dt9689eXL/6f2X9ZP7c81oWnbBv4tERtPzsbol14+hgbtVImffvqRj53BOaXX7iQS736//n//oZaKMo8kF0YVZTJERVtgkV2qj/yFWnon3hJS8FabrA3qWheIGRtAlLZ5qG1sHD/7LNPMwfe3NzWQMf6CUSGERWlEkobNtHo8LvjfB+edqtLm2VATwsCikEV+oAhENfDOnPU1pxL3IDt+3ZlGRj6+heywZqVIwSSi3TcSXGbRTOkKnjUJkRWzTQYu9mQBW7ywmaStA6D3Tl+OhWvGDz8pAU4cqfB/WoTilVZVHNy7jev8869MVf55XyOIJJ/IvLnQcm84lDnmzRDcIBRCw6MJ9Bg/gAVeRw4/qEiInbwgj+KOg6uWHPusXgy8jJGUcQ7VAx8AgfxTrNj+rKvibkmlu5ihBF+AkPIl2H5pnFRGakgE1TQDPJZ1r9o52nudaeQmhPRHZlajhWv5+aMQ8EajeLMhPEp9zbZBQ4Wnup2dI+Qw+tmArNmVXOwx0h5Z0KNYmkZFb0/yu5udzZ3HtGamp1hqzderKifxWhfGZgoag2AWrOLZbZ60//tjXcPs5pOc/6CUcV4KGq5XTc4+m/w4zR1sQIagvkYGkiIkQWl9TgaF5mjG2CgE/BIKYAbHSUNwFwnc+PmhY8++ojmZhjQ9qxb0Gy9txK5w8GYO0OrlTR37gvGVjCx95TOMbnljmMVxdc0OPJL0zPLaFoq3e+Ux8c4tz7qb1y8WKsscG3hQZNbHkq54hoWAf1JhQZextg3swqH9IoPprpMdsc81TCqYjaDq2RRLGxOo6j5UjCvCDQA9KDCuLjYiKeLi2YCqMQeVznbi8RiOMMtxiLSitNcJY8S3mo02MHLrCz2WT/S0h6vlldAMst1n3nxuYs3rrFw/MZ7d9lebfWkSw8Ptn7z+g63OvFk61O3rmG7sFivrK4uXqg88/LtW7svP89xOg7MZDANQBNTB9ZFNvcPC+ksnSaMyhZ0/2C1hlWbxkBUpVUwzYURk9b0EQi2b/OV0vCoxRBl1O8Co5mmalL/U/H8lIZn8YuRFPxK6fbwRQLzuczGCguk7NLTS/rkipsslK9GYOmlJT2qPOy0ta7f6zdHj7tY3rDtuljniC+9Oo+aDFvdWW/IQwrVcrFbYMgwaU30ljvXPOOQS2jAqJ96hxL0sNCzzoWFHN1yvtg+OGgyIElnyiO9OXi9tFpNVXv7R1QTp4t4LvBg0FldeOp8rlBttD7f3KG/uLxy/pVXXpkeNX7z69eXH372N3/zNz/63kv1Quaf/+EXnZ3t5qDPwS9OuA2H3aITwRYJt6dmp+uXzr3yzC3W1LY/vQd1H+9sNrOp+lM31tYus2P53rvv3qyuZpeWyintgnBKjl5msVbhjMLSdER587nsD15++d99fvgP//APY/igEwySbaSNr48ndX80nWu8FYTIoT+pNERFvoRTn0DTVm8iJMTo6BQzn67kM1IZCueXXLXIoFS3bcNJtVZGa5nZ9t7+43/8R/WDPBQhp3EXZ7L48kPJjP8oDbycAZJs9Ls8gbC/v8slMJjD86298Cw7K3fvfsJXkyWZOHhy1aCQ0H7TaqGRltaQg21mhjupN377+l/8xV/wIoXUAldEo98kYhj2VqETxw+AtYioISTr+5IEhA0JjBeg1SlDP+EURJqK3TkSaQCR4toNZS96xGGTaA8Qncecldez87JDLT8dTghjTga9ZmCB0xGyiG/HUH/zH2pWOGyfWR7l+vB8hmfBWA9SldlNQZyVhzdEO+UawUlgRC4MISnhOAwe+Sbp8IspwEO4L9d6rIN5vcH+RBKVe16hIDdzUAPw7kWxFJ96IBDdzjyLzTMaOs3WElJBTlQCq1HoxSQtjjgf9UVA3M4P9w2zBMhpBUpir5843lgEGA3BThi1T2lpCoQgjRQncoYOHiiCrsjF2wItW+ULpAVIeuEKv7jEndYCqz0WAMpFXTj/AQsM8YQxEp1aRiihDUhFTScc12HA/+Dux1g6gEEzGU2ULUcEk8xsvOcSR45CrkJlZPRuhPElUGNRO6vHyNOyE09RGbQIKpPsGMATJYllm7C4SGyv32W82uKt2MawUKVFYB3MNGPKLJiBJUyVvJjz8y9kgZZbKHNKU7vL+LV2okYET+jKMWrRoidRAFAWFjHpT4d9zrKwgzi9du3CM9cv56dDLgYnEW9fqjjIEo3JlRahUYbmAYsrOGadRglfC8tgcQbg0nqF6eVIxyDoA8SESJXQCyPxJBFhCo+xilT3F8aIyKw+yhbSmYauXeFlI4CnHByDM317PIH+HWC6HEdCWi0Y4SCH6o8bvNc1DAbMkYuQxO6gh9P7UW8OIBw4DZlFoVWx0e8/YyBLGOEMmEkZx5t4RLii5CAkQGrB2hrzKVk966STFoq5BkXZmpZguaHZaa+vrXjjIZniONzMTdDTEVpRjQQJpDstcHJJq9JgoGtm9aVa5kjKrMrANMcJOQR21rcF6wNeEWjsP5Veoh/GNjU7y3FklbpkJQC5pt1ZRwBvGG+oFK4TaBMyYjeuuf6BTdDjd5XBVvV7xnmNVjiza+8oCIc3IhCpMpwtfCMmGysUSKsjIT21ZSRjwX/zl6/8r//rf8jZM41DGyMJTuWw6hDCYy4SmmNhX/YDNPoHURIZ8hdJIOcRCnloWZRXT2qnt7Z2OKmGyRQjEFoDLHdVZMRb32T5KDjhNG42EYKTHuVfenAccfFPNB2YQOs8kcjqfCTXnGbVgt64s7l2u/OzHz2H+tq68+FBu7doT0+Pel3woDqoNDxiDTi1+KMWiN8dVPj6Zxww/+v0WTqpEf00537XQvPQOPaMvxrJUKNqUCwmoYIYNFopZSo64owmv5h21xeG3UOKyfBOFIb8zsAo5jh7iXRP8qfTJibGTjQjgPFP/mrNLvHzpNe5fzLUfn9JlMMHADwUxL+KShDk5NL3BTKiYjgKaiUeYOmaC9w0RafLqgpDz9LyEql0QtscfstCHwL4SZfAiFmTtwR+Yh336S96AQa5Q++wmIefSmKIT0dAE200Zb9AXwl+Vs41YrHjuhAp53TY7pmWTXG0ADMjApvnSxahpCCkG1usLjL5GR02AUetgoZmDyYljitVqL7UgRxspNLGi5yanyFxUuNCx6UzEAOMPw7ML2ZoLFQ9f2MNf0yqWiadLjDg5YN9Is6TFIq6LxjVCKs3Nx++9dZb3Y54jvUiMCMbcPsmwcwOnXHsB8xQq5JaXViREQoG4RMuNZjnopbCGVbdPomjM+a6FThJl4XDowEBV5VobCxjLvo71RpTLBWXys/bRdaYVcpMoMVrUEy2dGOvLoYAnh4dtMhGzIPorzPLS3dm1InA8FMJY3UGJYQrZCLTJEzhnJmGNqoGz4ivO+ApLw3SEVIuCkjUwFYMKQIlAoBw1K9YYK0DgATmFLJKcpYLp0WZR/3Lv/wLxfy//cW/IZAiA8yFB3hIAioCCfFFCs/UA/mSigplgYBY2hoVRom8FogSnlSaBd/leoW8Dre2KOD2TNZnKbuq0jHTIikv2IDH4aFdUgoMN7SmmdWNaQSSRaHEK0pcCZHhqaVmt0k/4vxnGRTbn06744INMA0QU1ywQRXJIZLn+pC3B/ceg5NyiQataur0sAZ23qaMDMepc0oMBewGQ1gBHuyM4BXHl5XvF5KHWAyjmiIhcxC+9G0UfxI/re1kEAJVlILcqWtWsfF/74evcgWKWyAS7lkDj58vB475Yi597do1bt2gXOPJjI2fze0xdtMsvSqJVQlg+utfC3nSJ+QCJ3nf0XKR1T/Nmdy5tojj5LwbyeYEF3aCBEHzXIiFXY6WVAS6pHm5XFqspjRPIB54mi7woVB4SCie6/ZL7TM5HkOlCSoAON35Ys7BPMfw1QDDiik8euBLQw6+jgpukFTyZE3MvyFt0vPNopIYkn6wOVXJwBP+r4QxklSfoHLyAs7w8wQSDw8ZnYAn3AFOpHL4OCqk/oaegByPS+NpRE+OOQ37p4Z4XrCCBejNBAfoOwLqJD0hPATiwWkIr69cUvL5icRGzuxzR8O+FAWKmFeoRjYV7ByyKEm3hEai1dPWoql4hE/KxZe6vvjiIZoqldam7MAumgC95akPpSBrpi5kpyTmoIpwkagHRXOoNqIAoE0x3yA7lKlLAi0ooHJsfFEt6P8LF86zRL5/+ADN6cBgMBhkJkrk6gQ1hX6Q0Y45ypjhZL5lQRRtFaKKOfWw2pg0ayD7yq9A6+PIi4TS0p0OutQ7oHZHupdegp/ZvNZHxPCo81IZSZtnF+54KSLi7A/wZAFdeKhqIPGYXwtGsX+eghBooAvA3hOTWINUz9sbD6EKYlznQ+3fXLhJB/TZg0OGkQ+2Nl11v/nmm3c//ZDu5uKFda5NuHFxA6Owxdu3sYzW8IJHEWYcTYusohgjSSCyZXIZDWxJjiLBU+NvIBWPBvVW14AzkgQeJIFoSyTuKwQ54wIuqwtO+6NvvVLoBqgLul9PJWCreRhLn1W1p301sQQzZq4s3e63KGkhp3uNkB+yc208WdLiZm/Mam2xkOXp6WinYaJiqa8Uq+0yFqfKeUzWfvCFQMijfyRf8gqCBLbhQOMBOm5y3NvZZSRw/dwlICCDfvnT3/6WfvA7L9+i92ElGpiDN94iHDD6vmFH93Ej7Y7BZQk6icXgA6MV2tpT68vUwlGz+fHHH1975UfUZm40xsKdtTByR6woPHnpdqepnsLDNvCf/umfEDDKRds0aiU/3B5Oqb1Pse5LbfCEC1VDKpLwM6odq0RC5iUXgyLwuOb1M0roKyKWt+PhcJo1OqWx48PWrfODXECEhQoCV6+jVTxHRBcp/UO7s7m5WV7QmI0BFAOG3kA3YhNL6ZSfOUpBLoFUD/SfiATGWT/68U/gvD/kzsIHANmSpiQ4/F4ozeX1sI6mo3APlmqirPGAldrGPPaTWEAUhfO8/qivsvMFwZhdYHMawAPO4DdPzOVEHs7SRMA395K1Z0p5ackU38tOuHSUXQMNgJfX6IlK7WXXDw2q50tmTgqBTjdJDEQfx+MA3hd4oIe4OJFRsnSePCSk3omt2MV0KC7U3Y1r10DiwqA85EgkPWNUCbcH8XU8eKhp/0kI9Olr5y+DPiatJ4QJkMSiFV9bdmborBDHGUrBTwJFuUtEQjDADKrgRJyFMPMiFXTiKJdn5GBIOwQESAKBdGcNQV5S8WW07xKLUOPZ33yEwQrEQQmxjgEPGCzTOSXg93yJGnPDlM2PgCEhX798hrbDGJhzCYT4SWvKp2Jay3PL7lplEfy9QRdsw0EHtGAAxmnGI69pbA+BECquuliiIIRQcN9KZBDgNPANZLjff8KftJ3RgQb0JI7YUDTzOOu19AExxJ5wwMzLP2epoCAb3Z5uSJOfSEXuXqfy2GQKTwAKBJDp6bTAOzERWIyan0lKCFaILdpIhIwDSWxJv+MgBEr534VLP0450WnBBhZl6KUgwwBuYMqdECAh2GWAL46fMIdqQrocgC/sxTbdi0wSwBAVKhR14ZQgRtQXLZCEcY5iBQ5s1v70E79WNVAyI3Wv2VE0HPUk5MJkDYAbazfIVAiRee0wRH0TgfhxeHBOPD/BzE8fkBIYAAzKG4INU+PxFSwXDKCqlxM1o0TERuGu66yOHBtz55dffvZ//8MdQMg3CB6oHOBP/CrnuJjWHYFW5BBq9EYLX+TL5TasDV67cRWSGo0Wo0FGt547hLkGUwFNIRsK6R/Q4BdjjWD/eiqqFY8z0xN6uGFw7/xL7f/jP/7jM2sFzs9dW9WBp+0HD6gyTjlQy4xNwKMmbAKm3G1N0gXMv3Nc5rPc591iMtYJA0cy8Cv9Du9p/UsSKy+fqLIgVaPl9NFXYksCkBzk/k2Gf7nf6WHd+2ywiKZTsYxKaAOk4X9akhKrO2NnSBsrnOeQ7Ktg9if+EIh+ws6Boa4wRzpNosRPk38255V0bASZeAkbaJzCol2ETC1i0PH2r18/erx54/pTaOGaXfXAAUaST+ymrZmuwZn1G+0P3/moi/WHGoao8RLl7KkupwupxGMWSZjV2hgC48RcbsTkk0Uc3/JIdRGi5eVzDIIpBIqG0TPd0tGRniBDcHEInwqipstfDSmVXTbFziqXLEEzWfdloDArYIY0Gdf7IubyaExT+R8vXB1tt4p2M0vebIrtTIxhgDMJNioHZaAorxYPUceVSi33WYSd3s5xheO0pQ0WjIwVg+WOoi2BG+A6TgzsFH7SzbrTCvas3C/FRG4ybY15VZyy8B/3F5iOEhXsjqRnJd7N5Giw9UmTWQkLybtb2/e2OfjJPQAZDiLB2onfLs3qnWxqNIlyZSfK4ZfRxmoq4TJc19adxAMKaQm2Qpirpew6Qb/QQzuflEWdK6vNEjusrrPZhWUdA8lkNXYcpCCEh3CVF/Yo6hgyW3qD3da+FiRqyF+et9XbR11G/NzOBqQ7lxDnFaVUXZ50KryFqVojP2HQY7Cm+S2eSkeAeEF+mNrfb5d70s7ML8HpK4tG/IxxFkdAe1nrXbgbEq2kJWhAGPWnalipjNP57qjYJyVb1CLJHSXFrIrvsDPgdogRk+S03jSQFXC3UVutN/tNiNl4/jZ92JSbdBHsIru2UYm0h0KVcpPkYNI9avKcMdYxWFL4BiOR2KryRP362vn87laHtYijEbN6RpelSnXcZ2UBi/7Spx9/OrChcHowq+XKteXlpfX1Lz7+QMsuWrDNlNjuT2UWx5lSurg8mOans6atRvazufc//bx+u1xsp1ZLmnpnBt1md3TQ1iUbUMbc3kanPCVauLB6fmeyg9zw7AbdOy8O379zt9LVVtBOJksNsn6MrF7auADXWsMGzzlg1s7BilF/WEilh9jv87AzpozaFFXxhxN1S/0ZhtOp9rg3Gk4fPPqi2+9UFurDqZkvIYWm+V2J+cIB1gG0+0OWkni/2ESmbzO4whQryBzPYaIWatU6tcuC+qcffvTWW29THSgVlIDLPC8PU7MYTxexc5sNeC9xY4NbZTl4i8ZKj1Js02T+9v/0Smay+8bvP2OFRlvoUCDt+mUuEloDwe/TSLQZeonckJDeYHD/iy84d87CCkqMSezW1mNu3Gb2QpsDhqQuGAVWm1hoy2kKioqjRDAZlAq0x5TAyZgfYL+swMtFXQtPVoMniuDyyTCeXX7Uz8As/rwAcRuJWlCg3BSbVD1Nj0u0NTQHo2YC0gbMC+muaTlQycqhNUzVTLSUEI3uxSXHL8USyzm14VlHBHiN2g9g/DTJV3I4YItR+eBepYA8j7VvNOi3cnkZPcWcBgcOqWKEx/4Cg0OYNUNzbWMyYGgF6QDu8a/naOECiE6eh3mPwk67JIVzjiXhMKehRkxlyE7lTKcdtv/+zssefReXB0Wt/fGT76nMVS6XKO/p/G5eN8SXmT2zPlsO1vIgzoBAxcgQ+ZHRn48Q7M5Weo9iKlsr5CeFPHelTbC7yBVYGKpj0sqVf+qDWLOV5PoSrcSf1bRiZWt7+3ef3N/n1h+7AIPmQwao4UAt4q0sEQ2yw8bWxu6EYITLqABNCwB6giE1TZVpDNY0otbGytKuzFet5ZLEcLqMqSU2m+3h8IuPPvqADtAspFS54GdwMQ7nEUx0ab+MGdot3cBbwhhVfajeTCPQ6STE8cMkugxZ9WjXXNP1Ynqc59rlUgmFgoHc4UFrlOaK4dVxtkbsOMOVQmPUPg0aoxkdw+bOQN04yOkdTKX0L83QAspAjHhHpVDtKISvkUDuAiGdxJ5gq7Ljle4UonxYemBljs4gU2cMnW4fHnCNxuY4z8bexvpyo93mfVtQ8ELMtcuXLt+4Mho99/DxJpdscO1zetrvHfWOxkeHj7aHjcE5ntPD4mmiGzYZmo1HusQbHjLq2D9sDA7atTRKUkskg7LkrcRdmlzjTH2yyK7pIZXLAIjRBg2YG3kzlVq5m89NxqO8KUjKgTUYySGeolNPmZKO+aN1+V2q1LDERNywminqWlo6A8YSNp6g7tNTlimX11ZUX6JKKGAJVQijueGCcKqGf9ybzP49VQGFg939Q8wCVnTjU6XGW3/5g6MDRq6MSalvSqKmrgYgdksEzI7qYDJqDXsahGSLWLr1DxqFmi4uq3FRUoZrHVjQZqR90Np61C+UeDSg2u5TyOXhqP/5F2vD4XPV+vioPXrwxe92Hz58/5MXv/PsS7evHu33uByP+eHTTz+9vrH6ePshV2ndvH372rO3+unx9q6esmxNev3M+NztG9Tg//7okw8mzY9/+xZJXuk2J3tbo8MG44T6Up12kZNJFiNGBoUzvaw3S794bePpCyutvZb6oLxt3qhCoqseYDJ+vhKmeOmQuT8/CUeK8Hi94HEwPO5oqFwxaStUUQjMovrQjTYwVSA4+YLBHfim2Jvb6pIJd6wtrdWq4tLT8xc2ZOxcrfJihNOGGKyvrAoPp8LGGLpihDAkEM5QT+wruD5BcMDga93SI7QZOx9pH1cPnBBMHe3t3v34/R/+8IcUB46xvUEirPlELjmw92GyjTwzWsgxCKTgOuUom2j5UR1qcdI//hOOn+CM4L6eAwm5A2v8EbvwUGppCIvi67Ffgs9zJ9WXwHztKNN7ZpXAMIdleuZsYIbDjN9khWTrRIYNbusv7YtWqD7GNk4CK7xcAHi9wCNCqB2KSDkDmACESMWMv+b1j5ShyiWLOZsRO0NQAMgAfhyjMhxDfXZ9GJZ7voSTSJmbkgQJv6Pxg2E2AEbBKBBfTXYjSuu9NP8iMRNiqFVnRArQKgmzM34jwfQF6h+wf0fVCQXVJrhEMwHQt1QZ/VuMPpDNV1VMIssNvzc0wmGhL9KRVnm4ZZLSyZEE5x7/AoZy4ovj7Wr9wQQ4n6sVMuNh97OPP+CsgQwmYbmQCwmlUBUYtvBFow6H/WGXa4Q0yASNZ8T6i1AmsqZ8YLjz6X3OzRw2GAJQNcTDFQqFTeKmCKRgHAHUPI6+gIloEePSuDoEbI5k0rM81U0zo5PFD7ucAEig8UEDP+EPNJApnNEIlrGAcQKBJLA27K5VK7zPCmMpKUnonUHtIxAtn0uDi0TlF2WtboM587xW1EerRx/JWlknjMqrC6mHPXpaqEJD+LBOuKzunCcizBB6jtGY0FZRuCaJzjU75ShtaoQKoQ2wBCIBVldoaOAj6aA2osIZbnHekFXR7oSZCHE1CsQjv5oGcR4IHsKsiKY64tT6C6nIkheGvRWPsrxU3oiguGj8FPK40h2AQAvXLfA0N00KiQCMYnAUdwq/aUF6zQKti0UzVkioDNsptkElDalQVAeMlTEt2UqtvjwrW3N6Ky0IsWuSTZXzGW6jnBVmLa555hRZugwl3YK6FATio88/Ly4U2axlcWvCWISVFRQmp9AyU54Ao0Y5qAKfED2rYL0TyIkNhEsDUZsCUFzGWKRyJvgXyWG0QLXCKFjKN3qK0JgrGTAXUoFD+CUZVmLhZE+UGwDaLz5z6eMH+4y++pzmYQXDOiSv0QjLn/DHtKHVumUuwqy/YMgEVsjDUSnUNXLBkb67dx7wMpyu8me5Jb6OQ8sdXFagV3jFBxQIrYyy83M61gX07LKh8HFSQKpYLfTBE1wka5I9pdVgFB6onZCvWNpHzFXXNY5a/z//+dfLN198+frarfrSlStX7ty509rf57IkdA040S0mkyKYzpYlL/Tk/GujRxCqUkQxUM44VEHETuUvAN1Xic7xnw7kfr7B4+FAJgNtcGqJWYkwytkqIUfkiaHJgCXIxQU2TkhrG2T0+vGIxdF96ReKPfckFG2ef7ToEMtPnIujLaUkwEOhE2GR16OoB36fAKMAHn46FSFiqKlUOrPT9BHijHYY+6ksDNKqPEG6o0Iv6zj5413sHerLK9itcFmposxEtNVpswS2+/BTlt4Wc7ID5e0xsJnNLj2saMDFWUS16HUt8eMe1YiPlFKyCDDiSDjzNFwsqWlWcLAugRjnhmF1+ZC6BwxtRHIyAsDZjR9HScmIVTNkdKpXVRAvCXEMPBcspxOWRx6xxVDEssmPAANOfkIhrxxgJQFC3B8hPoaYDyNdjFbg24Vl2Wky+/IY/O4xc8hoeYgpKYGykTKH3+nhFx530oZaTFJPr86Nr0ZBKq++0bFTyQncFTbOJeLMSnQ01MSPm52FWSCkUQhDIZSIjwWoGgVY/82KP3nptgpTEPCEkT27u8zFrBcBjUaQ6CB++lRfQeZAEnv11+olGaDsoV9BvhQeKSfVr1dw1CWbzMeDP27CbOj6lUiojiHkh9Hp7SJiL/nCGsKpSuj0AZyBCQmxkEEUGwD8TPHCExyzA54whEXD999/nwOwW0POzh789Kc/YW4z/niLsQvANtSLaoqfdBUkQYaJFRLs3ew4CZnANMKRAUSUKJ4JAjM0UPV74x5rIhdefAm2E8K3UNPSP8iJhTYaS3GmKIZShJOcyiQKucIvi+WZnhy8wz3hhUJ3yazameOPRp1Bh9KpwcZSRBLwaEUbJOUF0tLP0fbXUnosx4cytEDCl1c2oL8w1vOAvJ4DvDvoIRw/lS6RteEmOPlJf4CHctFP6MHGSpXlbBgOMP0c/MHhl7SaBhAxPiezFk24ATBAKXDYGsppdG4gBhKQk9YFysRG4gu8EwBDmH5furTq2VkuVrOp2fe///3heAGrJTvpSAMh8gwHKqgCM/niAcKp9byYjLqQ83XieZcV1sF2HnjEgJTqYG3LaeZLcsAYOUDYmMo2B0LqAoQEEutVaf0ilx+4xKot2EIzDVGzAkbdju0MiqEwqQQTEGG4QxhIcHiUHb2KEQbTIrSx+kukltfLThk83JlzAubET2Ai1sZ/TwDw0xl7OjyEnAAIKB0gjp0PJY6Hx2Q78xNkkJCBQsgloHWEye8JhMd/PoHdAe8f6fF8TyR6gniegPp2fkIAspGcckessMFMsrTuj78adNGqAZ7YoqGfnvVTSt5RImwIOdNsYABG3jJsCdnDEuilaU4mDFkdVJhWtd2MHamsL6mXiAAEL8EIwrn+ggUCMACAAwffuNeIuBGkmr0meiJfegYt/cXdz+7TYHUswm6Uo9RQa0hMpVjBmMS4Q3C8BSXbF7MmGq9eabVGgR6AcCADmYQHR+noC8jaR7pwgyj169YJOhh+0rr6ooCwi6/dtcMhmENU9AJPEeoefBmvAeytGO7hdy0E9/CT0DUJbAwcIPxMR9aE+9frhQmf08NPz8ITkhEKjSKgw1V9mQwGs9zIuZeuYGP7s7/9CYu2vJjLIiZPQeoK44z6HTbkcD/6QYfbch/e2+TC7nZDlwIzYEBDDjrqC+y8O3/JEMnIgsFtir1QBesx8ZMKDc4X7WWgqnHvRsiI3FvFogubcHrHYB7nBmMjwv1u0FI2DzzsFVqbzsEr/ChZMPtxZokryp9FfCVUu1AWrRZrG8rX0tIRkxA/IU4SIgGLumkFMv6RVNtKA9su4C/k1QfRVsAGVnzAgARgethKSUszeCg+CMFTSOt2ZkKgnIzgTLqnu5V+3/o9aUlDpdRkSl4/HHXoenZ2H+O/du0pesy9rg7ak0ohT91kMExe9FCbew02eAatPUb162vrLFVTNVzVnWro6hIqiAu+CeF5gx//+Mcab9iL80RBA8RA7cLCIoOW326/SwjhZIEHgllYxOOOguARS60KgHEWuXiTiihKHUHbH37SCSGzycDgTwKDimkqqPgDXfzB6ATIgC/Z3928dYuGT3a0IC3aWBPT4AEU1JotMEEH3MYR2+npLmwGLcIvHvNyry+haiwd6MHjPEEnUFhGOGw8MyzUUGqk1sEwli9JqIWc52LLqV5wRIYo0PHT8SQxW7ccd16uZJPRX8NP0Zxa9/BFQKGHv6Tmf8/Ufn6jDL4GDQ4CGZSRCQRsgJk4agBK+ImUsvuDP+aJ2OtMsxGHlyDKySknSE5LfVrKlRfdDDetRAQ6NMAWFaX1Px4CJGBe/CTnB+NoQYTWh4kArYmqxNgf2kge8c24F2GzPyHcc9TXKt1LkegNlKnjgYwkTn56CIFww396ck/i2fGNqDWVZ3WoGGSVcGQVBzwrZdCA46cnzDKkNrkNX8+dWA9xML6eHWm96wEtzR8Ng5mRU4WuQIFQcceagaUnFX9JQssiazTMzuYWgeVigRAqncblaEN2eDyVl4UDHGx6vfPOe48f77McxDkBumOur/C8WRwEGJWJIDmGE8RbESPcXhBnF2CwiBCWvfAA4cV3Pzhx+CcqvWa4fHlJgTPTJKQgxOLhS0I8ykBeOX56SDLW/Q7gX2DASXNnBHKU3k1GBT+pnNoQ8iRPwO9ZBzB+EhXoweN+ANzvsXxFjLUaTxtSBVSnPY4LSMcWADytU44fF0pBEn76YEgec5ZvzEbTwxDja5JKHMMDiz+JyrF5b4vNUcBGctUmV2zEaYFEPnEaF5lmRloRv/yIx28YUkoG1G1YFgDj6BbZhaXXK/KqpjFHpBgBkjiD5CcegPmQI7Tx9SZmgRQ0ggyY8QCT021CMn7k60gIP+08O5XFcoEMHD084TgGHnS4ND2QeHanMfwpIaAlW88a/CLUagw/Jdb/saPI9+9/wXiP6iUVNpgGIwnX1oc5Eov50UIZ4yFbdDbFRiBlRBMASGPnJ9VBV8uwisqNcjWGh1YW5yzdQioG7X//93+/9n/5N5cuXy7wxvULL7BbyDrk9tYWtDH8gxKyQOcAjz8kx2PFVAAeaysRe7Xkbcs+ROGBDAMI5CjJmU4IExuBnoCve0gCNhxk8OWnEaAzlFz3xlDqTJx/eiB5OQHuYVA2rz9hjxrjPCMnjt8uxKwhW5yYol0QVgpYJ4BhxjP2X7QpZ0stcDdisY0OESLelJIpKkNoldVYaZlrtEPONuRlJYcxt7JDNRvr2cUhOmd39ermF1YjGTRjy9SbcoFje2/v4ceM0zQtYY9G3BU/WdsfrORL2Wln0u2z6QhCr23uYpHfqM1ZJ8gt8YRgL0yqaSVP3zLOFZCOvk0tShg9jMedfqvda5IOGO1d4QCWNWWfjSDGG9o6JnNafsaOcVHyuF5doXCBEInqslOe3ByOr29c/u7yeoZh5GG7xsaIUeX0mFZRDqonqy2YIz7Z0rlb7UG+IMwhoPytWO91nktqM5mX8tlGn7QgmD6qRWAn/sRXs54IxoykRlP5ojkYP2A6t847KEwTxdBo+VhUjbLr+0f7k7T15YeyBOdaIFrXe588GE0Xp+nKACNT7oaDIxhA0YytQqd2S5p382xPGYfETxokDjD8WByQOyZHhCAUKlhB0jHh7V8czQlI27mlpyIACxEQIhNS6pbLaKDziVwvCFWYpYKNSwHIBNPKyQgB7Ou+Di4LzBSGPaX2JgoxYIMA5RI7D4x/6a8BCDJaSrC9X0mFGofLhiJxlMrVS+GgWTgYlrmaaYJdNsxT1YHH9ngxjmKdg/sLxY1+2q6PwF6M5Qb6v1m6lteUo93ocfqbpqNFiPQUM9UcN1bYDX1UTbYkDs8GXZrXYbe5sLa0u7l1p/TxzqTNbO3RZ5+znNH45zcyurhTsjGCHVPQRdNUlZErJbQVJ4sGPw6g7Tcsm4YjrjT7YmuLWWtpsZRfrF1/9mJ2rZ49gInDx1886DZbBZ7qhNRxHzw77d1nl1an6yt7e9vsp1EvE91ZxqAVnZt5enHlo82PatzrnEt12DwYpz9+vE/Xu9NQDS6UCwyAarVVDLJW67U8d6WzqzweN5tHqO+HXzxAIR6WZZXfOWpeXD+f66hFY39YLhTpsR/eu98eymilcm5lWMh0Hu9OBhNsaaVmVCokSINgaopicuBOCn46Y3+Io3awBePq5bXVYqnGmH5sS8yyoGAOaZMIbo/WvRtZxF4XqovbujmfSsSrnRT8KqwtkiJ1dncPI2wtAQtC8qGFgF73SEtCs2E5Xz23Wn7xuWtpHhbj3JbZJaFB6HdG6UFv1PnRnz09Gu989NEmYk0motC0V6LRO2LvqyRT/JZkSTtJPrUPbovsUIW40GT5SVeKuR9Tl5s3ZW/lE9pAJJCdbotFHA4b0oJ4PIENNBw40ejS2CwxM5pivRkVqx3NKFO2oEQh/0d6O5rPGg9TXA4rWqOWIm/srB1FP7R84KWjE2DEpjZgRiXkhTk0VUE4fngZJ+evUkFcCBFBsfNuijGeBcxhPN40SwxqejX6IaX+NZ1DJukxjR2lVnMLiIzy6FcyPAB4oIMlvwAoKlS84jTr8ITBE/DgUWAEIAJciSYBvo7/TMxPSHg2h88CPsarswDODoMDXl9d3TyZzdcWhhx4inggnNaNR2kDq+y3YrePOkh1tqgeBMNQvi6ZZiTEbT1qNZksWmjKxB6/zFsmE26xx89JHmU+nrVG6I3sYDioZlOsTnJNPif6B2NNhrVvg8mRFQ4vOu3Tg+5v7zxAXajReF2gJsjdaPIPTQzHFJqpBUvqeOiJ0D+8Fks7Rc7VAEyxoBiNZm0food4wwz7CCKJVlXb18YH+I0IaxesnkA5WggRQAlDBk5DEgRoLkGSLuuO0jTrHr22tWPWNkkeTYTgBdtKtl7g9wVnsH1muZ5LQbg7slpJjYZ7u0fcIVwsL6cy5VSWfUHYwJ1ymFGmsU2DSKyeNeRnlFKogLkk++c0V+dze8OM0b5GjNZYEpUnixu1oHhNSmsWlADyVRApduoxMYjv7rdaO0c3Nq4tcGsTyjQzu/fg84NWo1VZ//2Hjzcu3eWJmNS0u7O//c6bd+lHnn7pGSZOXMqIJry8Ubl4bnXy6oBtyHt3P2CprlymX2jni8qSWmStmitwybHX7jd2drQQy0SCxVtCOM0CjAork2ZCBIcdEH079GE0y7YGHRMVXF88bLD8RzT76bBIgJQRP54BJ2ewkB+PxfmJzvdUCyUES3eEwXxKCpDt7xE45Axeu4mk0aOTJzAocxxjMDpE68X6vbEutKXHqVWqIGR1DQPtfL3O8j/jKyqdDn7Y6WSLtulo69T0uUwHMBegD+RMFJwezDKNHk/jIVnZaqnGYnG73aS8FV5qmKYLnUF9kr6xcWXUHz3af8yyDp0xUnqQVjuqlrlNe/VippTp0nQ495M/6nWGB0eLq7MrTz39weZ+DRulzPT85Y1eevD57oPCSj3TO1o8GmKUtLK+Bu8+3X3cyk7zS8vsvaSKVRjwRfPwlfWVp7/zHZakX3/rLfBfvX6TBsK+B/eTN1p6f2xxffJ3//ov3/hi6/PPH/A+XIGZAI1aVaGhlw/LqD5KAUK+ku94nZoQKCfWZI9fGtrrD9WKZd8k02dOguipVXlw9CWJ90f+G4RgNiEliwhGHo3o7SfnnbKpS1cvY9pGpcM0y1FSjVQhSYwFlAVNQ3MfObcbWqiWQd7vF5CQvf3GwcERAozEghlYTn4B6cbNvpxN18lZJO7teOt3v1xbXeYUFExCVLgwQDghkd16ZlwYdOR1GBy5JRxi5PDZS+z6a+HONBWfCC+X9ZjYhCjoaziqAEolfnLKiD+eFcQob2GOWGaeyP81cH8TEBtmcDd7mgEP128gF/CPXkZLzxizmP0Eo2VHTS1z2kOc8WOqaig+4vJK5Wv/WBNNCIiVUwrdixlRaZoWvxc/FNl5G0oSwjF6JLnupeE6iH5na/uxdmjW1wmEbQTiSKVcTGIJD86xOUD0ZUKEnEeyKDEA2Ad7eJwqkMkDFNY/WqLkB40gGtM6Tr5eaTQixBiHfFL4SJ+junoDjt3gEE6uG4A9xjFUlibs5AXBaDyWVQ0hYVGgpmyxJHhBgCSEk8nAIMDMCBiglliVs3dEsTh7dO+T2YSXCVQAYAwh/YdkirSUjoFq8+jwwedfEEvZaws1HwY7GSGJc4BUsUcMYWzL9bLf/e7LjcZrreZ0pDdh1CtKq5heQYuIPpNrWWOaCMgK2P5BtCPEgxanvVNqzxEGImYwkJ/QSRsE0gnGw0EswhlsMPKm8yP28lr92euXx70O91KzAgGjOKGCmxSt9RhBaALy4+tKQxmpEJHzKuMHqVjogOOoxWKlPC1qR5ABEeDhHDblIykgzg0bccSIEn8ROv4xI+MfdPLPc7QtB4iRHvMgDUEQK9/gtuZvow6bqInquSYxQU7kYV4nI4Tyk9IlAyWiyp3/rW7EeXEdzOIDnaiGDQV0DSuvUGWBET6olBK1NmWptMzqyKkglg1I67EwSvKEXGi2Dud5JbdVqZYKBRsd8JvWAoCWPtUgpPNUbEmDjhFgEaucMgXGm6p0bWlUuJKZIzZjtYU+axrZXI8nkjLTzmFn+8H29dolNkmQEm4R6ClPJANbruwYGcBiDz0MPtFOQ1YNQmkoGpwkJIyTnWNAMjnHfJqnl6LyP/kPSXBwj1RYakj4oUHZdLiSZ62WreihDOVAQ4f7PsZ+Mr6vG8PM12x9JYDiHTyEWokolFCh2oZRRViV4ec9sG7HVvyh1VauPCclwnlJ05NajecVuL6HgazS4qhH4pm/40iigcTiIkMLxoo4FC/DGzcgEBpJmOTJ0/Id2WphK13/5Qf31+uv/+3f/u3NlWKWZ9Ww4avXrzzz9Bf3729+8QWtmOPmPC1Jk2f85s3cBDLadERqwA9ylzT5jXKyIIQIvuTMT4nY13W0qQhYrRuWZjRqZYYCniEjVLFSi04j6pXXvKulEe+AWUNMvljjucFC/tGu9dNITVJhYm4xcajyM9WtYpp+JDsvI56oZ42Bj/0l+tjvUz8crxctCYyfqKBIPCoEaDSrypZAgNK/8iC2cY5xoBOqr4cY9yOwgFaYbVF4aJcz0bcgkVzjRLh3hEiuJ0+WwELAITtQwuWLr5TiZscAT1fhqYh1KPoyMPOKgPpVW/YFQDTYl0ksnQQDGX6CxAkWBvSDlQ7Ju7p0kXFndqCDdWv21CmVRKyELeaAksTMiYhJVLaHnADmp/defJnvpKe6IcTxfP0vSdQdjgayH58dYT9bKUa9o2fHl4uemdJ3Bpz2bSJwEMPNBDTRzkgTHngCAf4kYEonVTNco08qX4CGEkLCAjR+onCE80UGCNGbF7Z4BzHeSlO5KrEoPL4uWGRK1aCL4SGNhExHvBHJTXuzDjA5b8YzqRImw9DDZQeUi2MSgRUAq3Z0lHkuACEW/KIn5jl+pxAyBRNVk/k9xIUjTg+8x2EWFCeM4+yv44+hFAQZFNaBCAdAFTGZwOS5FHkupox4xgIOVMtVcduAkdSXX3659Px1eFLSysY6ybGlXWm1QIWjyNowsrxIomZiB/oIwdqVL2wkLXcuoIUv2EXMwBCI8sXz1Hef5sv2HakwxMDPKIoa7/FEFg86dbgubcgMBwMl9aM89JePikMs2hx67g96ql6Tec4bgYeD2dijAczC6NoCl60XcmOuvOQgiCZaDBCJPWw2kDdnBdlxiJhrSchr1JY1UK8v2xCKRloGpiwPlQZTdkEZ5YEH7UpClnVhKfaAQDK3gx4fAvpAFj8rO1ev3aKn+c1bv4MqlIJYwb3iPDxoz9/xk1T8dGd+PtZETVQoF/0SqBgQyoHFBpR8YSaLSggo9Utyjghdu7wCtSzkJ7BJXgBWHc1ymHeNRm9j50URLJuTHyBVlviLBwiw2bBHScyvKmZki4RAPwIAK+hZuaVaiy928QtbwEC6CLU7TbpYMMBkOEMhPFfsqYGJfkZ6O4oCgFIKzMXdEijr2IEZb9Txx4H2d45ByhEYOjZ1sdqQ8OLwdU8S4TEcx38A5vAEJ/3HoU7+CklORtjvL4+F6pDqBJHJhMkowpM/Q3L3EIuDD3AQTxLSYqhm/Q2pkv4QmEyVpDAAJD1nYkgCnOmHQg/3BZ0zYb71QMqFo+GA2QlgEiG/iZt/aX1EuaziQW/sHXRo3bOujl8wP+LrgyS7JQirIs0Do8G5STJ+Ath3BsmEs4zTKduAfMfdIa17VB6xDNdpHNBSajw9Z9f8EctYjvYFIegltnnUjgpVwhPzKXKeO0qB2QgrR2BAWcl662AfeNQXIdS8gwpDLAkkoWlwaQ/XbFEiNlgFEwGaN9HupIhwlMF6WAHaLz2ALIT8jHiolDE/WYCk7GgJaACecNZg8NAsCYcXhGgXyhbsKC8DdCiByWg2HHkRRXJhjHs0lxSQgJNn1qwU0QwBnAlZ9kTzL3GhcAZpGk/7pJHsBVCQ00NRZBQ4+Fk1AsbW8XUtFRS+/fbbRD1/8xKQ9MicofnN27+lZ1k/v8FI7NlnbsH2YqXCqZTnn7sptNY3MS+kLOgpsFE0ynvQk/kzJRXdtocPQsCdyRTQaJNapl6I8tENHAQAseHrNBMVnAcO+5DZo35BztfLqCg7y4KBPgocLHS+vjSAwRFgS8vLUMVTGMRCGPDcEUsWCCEYlsp6onCcKjCEoFvRzMoWa3j/Fl4RAhhiKz6biS5X2IGBn8RqidqGGcDY2ogWR/AvFKtQSBb0I5TXSeXUUabdJpAkagVT3dqEBxh6HfzT9RKW5rnDfQKpCOcnR7VAiPBsP9iHztEW+LI/ufy0evBimr7v8+3HhNfKMmkv8cI1LxjstahWrBFhJrsFMIHHul599VXnKktyDDMm2QIY6ECJ7SDA9HNWfZRL/Iwri5+ghW/O6hALvCrdJJniOGYvGuGI9pkSm4QEGGwh4ZM8pUqZkQCV7lQlwZwkQo5FGS1UDYEM76kFnnCm0mEg5y3osbGkhlkAqAqg3OdZtmJCKwDyd7/7HZw5v7bOWN1hQAUk6yh4xChytNlQICZRrnnfejz2q0sa4E94nEtkIY+hCbg8yr4h7ETqb+cnfMAhxkgpHHA+mA5RWyAP54C4ZCoR/ohRvqNhy09OBwDuiFW3bU5R7otRebk8SfCf8ASBTIJR+TQWIInlbA1t+TsvXgc3PwmXcrCr0pR7LHvu4UsgYBSHL8UAMtCFBwBCHBgPzvOFdAfznyEKyIDNU/ETenBQsvnoAdnBQOSzUtL5OdqM8rWVa0dCKpxnFGcnArwUJCcV8hiTEf0FklCoAiF+NCH9ofv5CU/wk1aYk52iTT0AQAc+fryJRqITpdtiAEyg6IjZFbI7FeISKEg0zHe+853f/uZtmMQJJJaSicNPAT25pQ0MDCjnHnIDmEYKAbw3Son8jLtnyhc2Ak1BEEuhtbojC6K8or/7Z3+OrM5m6DYFwjSOQJhfuZA3fHBsFBD8WPw5pAcKyBVUXNHiKu8i2Vlh+OMAyW/Algw805/MIgCIBluZ8RBgIAw/4TrEYT9CFqdqI0LjqULagByPEMQ4FR4rjOOVIraTHOEF3jyCwxOERf7YOVEuUYTBHPjMfRmWl1Cp8pWBCmLAKSqOLrVi8ymrBxWQhjDkSClZa7snKjXaBoQ8SkBaYKhoJlyIBFsSwPfsplSvRH4qk9SItxDy9dTNmzdl+qPCapRF4wePk+zEkwUOnOYkCU5e1CISBfRUJMdxtIefZEQqDz/zCwDag6goCwbK1lGSNVP7er142AbEWXImgm8SSF5WNJVrXj1Gg4KMpV52p98MubyiCZaD9/RyKCB5bAm7UimjAcoVtqro7ZJYxQSnkiZFW0BpAMNgg36TMtL86W19FzDK26AtH1Wlry386le/Anj5+8+2Hzw4d24NTUjU1Vu3rl64wOBzb2sXzLRlHJKAi2tZuEDlX/AHv3sI8XbkP4PcGglf8bEk6iwCnOg37oUQPIAhjaiXZOBpv+TEZMYxRPSchjse4qlIAnxIGJ8YSYCeJssjKT4elnQNADvlyDjALNWoWGuKoNYgZj7tKmLwHJdT7YabEPlqC4HZHpJvxCP28J1kDApViQgx6C1Q2IR5zF2L2u/jOfpUKWObhOzta6NFbYzrDfXVrqPkAKFiTC6/KR06JfxuK5c3yLwt7zPMJ3zKs68iTHOvAYRncryoTVX1rbFVNQEhT3VsCDH/MSfAHTUOWK9BK6kBizMa3eJYfqYKSeK1Qgj85uequpXZje6E6wdfXT43a/dSA+7wgQX64lRcDRrtP0uiBDYlDh2qMp7a9Fs7l1rEh3J2sAyBcqxpZjdb7mvOcDudafeHm3WPPPk1U86TgfzmpSUeXWAfiSXcD7eamdX+Mxeq2rPjQUEG38b5P3zKqL1SpvtP13kRlVTTQrbd3W6NeDuszOIyUwVuHYLCQtGWR6lCqY+5dlMac7CGf4qmIHAypYPAtElKAW8pba26pKENmdtaM4222z9C0U+GPZIUdAuSVRA/qEnOirJ3KOOSEovcbNyDx1at01xPWaqNF/vaOc+OuesqO8KYhfUI3cVk9FEDxk/oIpVJH2lVd+YkhEapd/bK1JczLFz284q2Iw/RdkI6xaC/uteo8bio5i2gIpWkiAKO7NmfkVbas0MIzaS45Dxfq0x1aabsj5gMFmb5Ab1UV3dqsaSOzZHaC30fQ5Ee9x1pg0HnZrkuissfuYWjvpBfqLT7WoJnq7JWrtx6/tna2vJ7/9P/A0oIxLHn5zfDwFvucoPJ6b6mNDJKw1J3Ol1cWV587jKTQ2g7d+3qdlnPSY3bDdJyP2C338V4nd536fw659ALWA3m8412gwWXPbYBNreKg8XW1XO0YCaKDcds/EQ/3Lp+46NHd2iGNBvayIgHnbNpVpK3Nh+nh1oe4tJN6KnxNHC1+tyFi6j7a6urV1bXbr2yzoNRH31+r3vY4LZl+oOaLoHI1FeW4Wp2ovY7K5YwXlYLrRQXL623U8PG1q6qZsRO8aRQtQejeBECKzCubYX5soYfLbJ8X1+6eP06nCwXdWSYGmRtm2aEOCysyXDtwX6PfmgoQZChj3XvGnxk/f16dWmoLBAzk9dzm+N+T+MJkxXWscHDs4qcDS7nU+dXl1bruZefv1mvFQadhrYeUyyolZCB1BijCh3VxTZuNO5hTvHTn77Eyv/WVkuliFyiBZlmg2wcxJh0WTuy6yxhCCmQScIRJQAIwcE0/BSTjlCZmgtRZvnIBT5FSs2l694AwQ9eGSnYNp4ERs7YYSYKipQWkoQDhOPAAl8pMr7eYcwJJ0yt0q0bMOXktzvSQBFDUoYMhkzorGgqBpu2gCH6MTjUyKuMNXjSV7rcCAA/uUCV2qhwWaR9PNX8t1WTflrvloR0GLTKHNi08fznCV/crZ4I1s8Yr5EkVR85pNe4GgCMiWa6ECdxSEoUO+tPjNoQhccxn2UzMudwlGlkSx7/+kZ/XesmknouCV4l4kzvHfv99X94uVyuelz2U6thf6sOz+tizspjKN26+XCQaaDsM8VSvcKWPgw0OVU/a2glITw96i2Cr5eB3kMiiGDpJegc50UwWaCxZMg3nTpotqqLS8VShbv99BwTR8jiEREU9FP5h188fO/e41F+QTRjO5aQPSfRy8I8Fh24t73D+UpUJRqDKyygh+dDISuSY7UIm1FrhKG2AACcJxB7PZau2u2+jQNBnKxltWs2Z9mxZRgdmjkJactgEB9MJEGolGZ1xZazkycabBFZKy70yEQrR02GaZDAsCbPEgvzreV6fbW+1G20UKtra0s85krfylYfuZSKJY56MbAGHo1KiGuNTCGDDTRbwoRz/JMLjunnLV+XnGPVCSUWpY/lrhbgHg93Ur2AdHCaJ6yvMl6jFVDD6xcv8MbgMDuYFidHzcmHHz+8ffX80srKQnm5h0F7ZtY96r7z+P13//DBe1cv8nTMiy9ep6QsPQutjR6xLGaMK4aNJ+UML0Cl2nsNrHYL3CaGVtQYdjbQl/GGBsAMppn6M4DV0UDIUM8AM+2QORu8dd6zkEqW7DLWVeVqCoRHNWKODVjOAS0Wa4wQUH66kaxnm5SyY1XngtkOT1/THw16nOxLMxDQhCGnBWt2F5kmcZd0Nb/IuIEx0og7/LMdDZIH7NOP4B2di6kINDC3H05KhSJFQUSlU7i8WrdcsigASWksinHN8ehw0B+mSvlqbWFtmRA78YeZ3GSQGS/OeO12esSdJIctzLZRZiMGcDQKKpMhiXrFWb55iIRlZ7WjvaPsAkOS3Ie/f5PN5s2OrurqLKf59qcc+4HAHPsxF9fXWZZND8ednebHvXsrmD9rspAeVkv11ZViaeFBv3t+Yfn8zRscWhLrqsU+dpdD8QQmsKxDL0zH+uffef7N1/6ZS/Lgaz9vHLY2AvvFbUiznRg8+ieZZxQ41/WESBhUDlWQO7ZhNGhknIh4xYH+12AjJQx+GMhH0qQhpb4nXV770GgzxlYWpVkMVaO0Iibawkmmcr1BGSEMEDprbh9ZXVtkTwd+Ng4bPNmAAHAvMOMgkJC7KtK7BC78zGZ2Hn3xzpu/RdTJF8yU0UhV4wIn8xpCkjniF68SgSFJMlBg1h99uR20xCp2Aa0T4Gj5RhoYJjjLpHFO8e5Le+EThMUZPvEvzQeHpuILEGzgBBu3/+ExtmiKJsLUYnUfjoMhF/jVTRxftZGyEDQfkinTuEzye2HlS7gTgSBMRM6TYEOMqKDrGLmxs4Lw1BdrLIXwoCjDOdQRUZo3WLMDg1OCRxWLUpJQCTMNlAaO3knmEkXFgdS51YCKAnkqI5ZqDME0Q0JRqEakAK1EADAmpBUPeh0+TAexuKdzRLdPOHdpMJocQYFmgEwyxDehkKTDHrGdLECIJoIs+houz/V257kDgFNBxE/TqrbaTsFARKtFBR3tb+epMSSIpm2Fk+Dbai9UsUPGFi9qgdbBtMInmIZVNLgn+fWCk6flKADPnWkOG5ad7vC99z6k51WEJbcs+SVGGvcibkt/HHcIL4MTTFt8RRJx0qBfaMRqHMzkK3kzzO7R0haFm43Zv33u+uUC2nc6sNarcHXTMsdWjdA7iIjY4afu1ZiM5dSgYvmqnxJtDHrQDHAeG9psvTToHFp/EKV3ToIO/GBJ8kp+a6RIO26apsOSlPEvxzlw5kFUrAohMql3AR3j9JxIgEWgQYrQREaW3hSjpwWTkCntCTCnmECPheQQQqD4YNkLwKQXxro/gDmp/pMvse7wB4ERjEHAbU2FMFVEaOnpsRyaDpv9bn1SY+sAGkgLtzGlYxDIoTrmqOTI60RAol/yeawZunAlx0oUdtC5CRc/VFnl4F2irARgmi8wIOsVClhp1brjg2a7/tnW5cWN4jJn+DVPBwYakB9aFaKBSYAUvp3PICNzKrTzwRofHDHZMBlg7GFCp/GhHbPNcCIG0SChJxEjjYURW5AXQkycaZsYc7EIoB4+jf116tK51R++8vTuf/tYBtz2ikBg4wlPxD4LPS4PIJWQnIC3NT4zFifahAgtZ52afhu1ItvFwQSHzWOQiD9QaV8+1JI0OBVFkmqtWCqzRwsA6aIM1S7ced0hzTZg5suiM441a7pXOx5X3tnZksYyteBy5pSMzbJ4b1r9D798e6VevXr16uHWIVirdRnqkevyufPL62tt7Kl3drGZ0J4dpgC9IR2QL/tKO4pc9T4qsJGEhyMkeJ0kIpxSvg4RFyIER555oeIYt32GR0TxJdge1+OcH40qw3F8yQPWANhzmKWDJJZ8n5ABNUiM/4tyiCGhmRAVJenMPIiajsKR12Qsfk+WDAwhUamPI1SsJNUIseQBPoktClSvLJfEf9oPcXRR8NqJV5/n8hUn9LrxQItkOV58coF2ptPKCFFjimnjJ87yl8UfTZcrDrSPWtZwv5nK0p3vVjRlcu4AGTz0mvila4wGaGPSqOWq2JGKYTZihFR7KmWW4Ce9IHldv3YJAxCWFIkCSZAPz+tEZRmC6BMgI+SmRIxFcF8wrowczLsiH2UmkXy534vmVUSvBDBfuu2Nyg1aXS2vCzdYmmYvDhrI2nMBTNrZbrtm3ZAmysIcnKGxCaFrrDhjz4JfLgFe6ijfeIREEdDszCfhMJBffP6Y7FjqDWlTGU3JyBQauIaQLz/lrMbd4pUrQVTFNBx1O1FLBoRGDn5bJ9QKqbOLL60upvGb/4UST2zUqIhMirAWFmmxsyiTSQtJ5ksUNLOE7w4/HlgBnx2hlVTlQvHCYRylY56JasMj2SuXMXJpLUuqDyay7+Z5H9l5rcvmBSQUn2UY8CCNhkeyChhpubMCYoABG6M0IOEPIZgbQwZLMECOLTBf0v3L3HYEQH6SYwHlt298iH9SWUYAFlPngSyPhG2jXAMPLY0QLw6jtyA/QsjufWwvQntkHqq8rLysJkC55gJgmKZIWFisvfHGG2MWw9tthoDsK2LuxCrMrKINRo7+qoAsx5sjiqk7O43kArVjO3AKGPDgx8P9JH/4wx+OjhqAs6wFG91Cn5/Of0y5Vy4uMuJ8794WgSQhPK5hB5l/ASAjYFyiWIhJ23F+IAiH25S0WKvS9m9dXaNo42HbozwJAMzbYREahrpr2z2Ps+mAY+N///f/MM8m4SMhBXFHFqxCoz3U4FKy9yc2FIRE+GEvAuMI8HsgCfFYcvGkVCrTftmeh1oGk4TjHMC/yZYi/PFAkHAH1pBLUwQJEhSQ6rSzBkFrF2b0Kl/PJYL03sR/GNvjgYERczZK6VJP4fwndwghAX/9GyGPNU/4GTyiJqGuPfxE2i/RzwHePcr/yQ60p8EIdOLx4EJq91vYHCCgD5B4QqYhMCA57pkjPx7+/7u/vHTUMqqJgTZi/6SadB5SEjysyLjMI8/MaglkF0+FtOU/m8r68MFG4bosSM7HoTqiqQ1vu5/RzgsXSzqg2tvTOQ8WCvmShVq6TaERNOAB4LpV1D5+UEGwViPj2kSZ0LovbFzggjkUEdcIHO7tE2KrZlGlBEkGQ9KpSVqtW9mVET/L5Xy3J83s3HAAT4V1GB4oFEJrnh5r+DVYov2hNAhEUaMQWN90rjoYOG1JQLoYYLYngaEvUh9hVgMAM8AAmMKCk1IAoEVPbnhi27iOiZb0IV90mrhklkFKrgwi3aLIU474EC7YWD94IF+ch3tS/wlm2EjnhQfrW74/+MEP6Clef9QFGJIY7kMzdDKRgP8Xrp2jd3j7gw8xP6dLstX859gVwDKGmmMJD4dH7LLs8JCcwlLG0kyXO/kYKUk+GZEvt7oQ6BSCAD9WOfyENpAIIJYNl0B+UgS6MxjI3jA914XlcwQetY4IyRSsQ+SNInqx8Rgw8MNPnwhBBlQx/IXtvNKA9qZ0gml3WXOhDpk4oY4B6/Fk4GQim3w5MZBOR7QVZUXIslEgDGKMJD7KmvCF6gJ1DTEm86KWiRMjisWFJUoE68BGpwMNBe7GoesZRrJKEqLAQC+uVCaHkMca0E5f1hucoyOhP3wHGCyi06cIYCBHogjkKg8wl5bq0AMTqIKdmcIxFCdEp6iMYGqfClU18YJTtwsSevA7H35CaZEHak1RtkYDNhYM8EMbjnDw4OHrqPCc6TyrpMhaIsEKkY1qwUaOFgI6jwItEPIHxxiANUSrAtNIISJBAyjJEcqJVJ3aTwpOoKobQwp7GYVBF4O0w8MGRqAIM7HLS7pbAI9Roq4ZDFbeGUcBoPDf/tt/60R6VCJzeT1fPB4bw0TltpLOUwTgedDX8wltkudxq4+z+4q6+HqZfDUU3EDMGGTigQTYi9DypVwkhkv8xKNSxy5Q6NiBsbTHK9i4RwoHpiYCKQCbP+JnCP8SD0hwUIXA0K4RdW5/Jl+aAwvQ5OLt1Hswx08LD1QBEJDjdxHyEMfswkA4gYRQXPuGRJHHgf2HJ/EQcneBpEXDOPCIMNt9dDxwmEcOk2SAxH/yTebl5Ilyug2TW/yelyehpnBsuoiM2US5DPX8LOvylkoyHxccZGICjwdynBe9cXHjPFMhL6aSn3KBktijOsIPHtDiQX/j5+jS9vb25uaBYmMkysz8Lj/0IGTNfY8eT6TjJHfYxbKT6DQmUAS452emnXiSEEKShOzpTRTu/UIroiQBE6tNFYDWUzl+UHqO8U/RH/we5V/Iw8PSkhMONroetrKSMMHvKIBhgTEuaIiMPCfyeRLYyWRP+H1m8hOB5OghXhb4cAIgidvIiz4+MKEiSOjcI2ESm6MiBA9fmI++9VZGJqTSM4TWqIn1mQ4TdWqNFiqZjBscHBPTbFPBiTEKhQHnGADAagipzo90dpzUICHQctSOF4H0Agg5Und+6QoJHSfJhcE6EMgAP98kfsDYFwaeehak1TVDBoc0etRadWaJWCuOR0kFmDj7l0A8IMeZXz8jMrhTQlKR4m2h137/AA4AE1JZFn/qxzKNGWrIyDqmJGpW/jMJiR8nBvnWiy2tYkYiPtsEGe0OMlCp7OaA97/+M4T7TwaQKBk6Vs5yYddMXeCUR8wcwBgDwA125emIf/3rX2shhaUoe+CBwT/7/ajuQpFLBBcqxRIDgKP9I/R586iFaLnkYFkCHqdEyIXevrbiZz/jWCfL68VhEgm91I4qKlYsHo7EU4ev2GSOEA02EoUKMN+ix8UJhNxgJ3EMzkdR/PQ1b0mSVYo8dhM+HYO4TA/Bxo0GstgLjdj3QjGTioEsNcqqORqbca/QssbArrKtDWNOw2/SM/QbpGYjHoKzjbue2Y366gj3OykV+y2s+vsChw2X21mdXqdy+KZzHT7csacQs1BmsV7BZsPExqtiLS2DNZCVNUebVext940JMrBwjtsBWG7hiehUapOboqeTB+UMtiIH6s3JF6LTtawG04O26Gd4Qg2lSzrbMrTVvx6Pg3NVYl+XJVUm3M84vjwrFbBVHWu8qI1gpqN2I1tZTXL2QpGzb+efzZQzzXaR12vVNgCBR/qDZYraigJUdv9r3ygsBM3MgltMFCvlrL1rh5nNIv4jZL2t+93WWjoS1V6THfHdhTIifsiFfzCE18LJN5po442c1/TQptNMrnCzaQmT0dfvjFYP+xg200KG7QGPMKQK3FY07WETZE63Q3N57aQO61KzReSJ+2R6NgEmHjx8FSVJQtoq/NT+rzpz6U54C211m571m4d3PrzL7i6xRY20ZxjskhYc+tr+FZMHUoGKSmYioqLAQ9vrJjTDfd9kxf7crD/ftwGRpeVOhVyxrKk3sxQm1JVaqiROsgctznwN51c3Rvw3eSMhtI3ZRkaqTaIWMbBOZTcedPbefbzW1/iVi7GS6OmQ+jmkd9bNaIGDsRlfBhZweJKW1LG3yFY9Vt/ddidXEp/H3UyrNyjZMkpa1ugTs2HHmjrLlcRsOWOd17fL8nOXrtx8/pnN37zOcufbr/2K79HjTR9nIAnjIYZO04WSjvzwkv3+5n5uQpPjjAoDXKQow9bc7ieT7/zge9Mb9ft7W4OVLJMlVjht2FdCoriAmnfbi7PS7kcf/c//yz+xktusLOTz9VnhHK8SlexVWSkwtkKPDvhbsLZf2tf58WExNc5Tsdx5MUxVijxTi6G2annKI9aZcrbKWyvLvH55/vqgl/7gvXsXK4V333y3u/uYUrz4zHd//v1/9dbeh+j93dmwnR7x/CAmdalxD8xpbMVnBSyp8I8K1VE1216oPj466B/taKE8n2d0Vchnh0UeD1ZboKXc/+IhYwVyp2tnon7v0UN4UtlYS5e4/Dh98ca1B5Pxf337943MiKoxC2iViv8xTZMak0qQ4AVnFh6yAUAblVByjOO5xkNv4A4Wy+Xb1y89dePyuXp+3GkwZiEVG9986ZZYDeoBlsJ8jddMUpO+3QRqk+Sf/eyvfvGLXzTauuEawSZHhA3HzJ52xX3nFMUVB0dIwUbRFGvPlai9qUXQA+u0CCVF7pkgiOy4YeIBnrffWZqpLVQws+ciMlAzPAaMvyTTGkxUTvS70kZaCZ+S6x8ybr/0ISM+LEPH/khTiRaLBcAxYIZHiFKLs6YClRX8U1p3at7AAyT14uyGtCQJ9FbRT1eJlpAQAVM6vkaS4zv1NU1IIU6DkTaZ0DbeodLpdTyebyJbCyaV53sqs2NkR6Ux8iiR55XM0fHwdTen0PuIhA5XA48zS2ZNwjg4/D1JbYj4Rp5kib4tzAmctnjHDWsUZFguTGpl2hX/qATTkyLZK8TUDH7R0JkVGs1Go8852dzQiLIGF9kzUjnASGw1TuBdgYzf6k6fKHbJ1mPWx8SDpoSxL5caGzm66LeY6eb3PzvIPrNW7A6mgxFbegXusQUb0svdxh9/+Pjj+7sMWAgZ2G3+/TQ340GvOjMW7Dgmzxy42Wz//ve/h35uIKa5MjpCSGU7hjNCOQoChiBoQOpav7gq8RRy0UGryRjb0aHzK8l9LJpRAgwIdJ+sbD3oce2f9VlWfD7aIr28sY6H5g+dmOrgdx0Ce5SlZcr0GHZh4csifR5tLXvb6WKpzGkP/lVL1Up1AWscVjQxLYYYXtjhdBJmASSDP8P0hDNqKNusHnpUd0uBh90hu575ZS6bZqxotFvdkb8qxmpcekROh0R5OlJNhAVGuCsItIEohBh0LNc0cXGwlE4pz8CNUly+eukHr35/55/eZVLNgtzgqIXdCv1IbpkDMbPvv/p9Vk45f7O/v8s+wt7+1r17+atXL/OIAsXnEArVwixTeTNeokIZePUGrZ3DIheKUUIqS0eRZhUTrxHXX7DyXpJtEOLE8wPZviaivINTKBZ49pAKHBYGxXp62qYyWfWNVmbBP2QbIJViiVdKOzXjtOfquVVK1510ifXrHH0MxfprdXlh8+59proc7C2xqF/WlSbtvX1sFGuLi6zA0jJYjixXqnqtzrbwO6228CyXL56/XDt/gf4f2+fDA57uGNGn0+WRVzbH5ZJTbGGGdFg2YRtgPc8cGHWfKzToZ9lmKCwAPx50l8pLlXGKUTQY4GElW6GaGAxycJPaYL7b6R7ORoP2pA3mwaWrfJ9aPI+klVnNG2R5VaVEi1stfbz3+Y6m3uluVjsZ9Wmhll6ykR4tJ12vVTHuXZBpKofB8oPJZ7lipTdZhkvNwYj7ns/n1WexOY0p8FB3osyK6WoP43omJ/nsSqrwyu3bv3znQ7jd64/y41mTy8lzGbbGQWgdiXU0CIpJHYVVK2M8KX2JqNFmpSK8z0JLgJ/hGXEnHBWunhlnozjgURfoFLvAS7BRq5UoRz+ri5Vbt25Ahoi2+Zd3Z66XLImkmqkpvT18BjceHoH0xWhLw5pIMeqJoDGVWllahGMfffrh3vbDWep8pVadwAps8ymRunxtWbG2QRnff/9Duvof//hHopkoWxEDA0xD8oEWOuvh0Rrm989cu7i2cRiUivikLpqCRnNJ0CYSnvAKT6TZYoVGiGxZz3K0cLDFOUobuH44PkuapzSA+U/3fQk9tcUqPEHBcJylXK4g4QgYSGj+pCUqfB2V1xT+4HEYKKTMQIsAKxeZzomxYZtjoKiRJ8YffgqnsRmcJKfKwECNMPGindLW2CpjIeP5558HAGsgTuUwGOUmU+bBwONEmKFzDHgJhNvEyIo54Zw2vpz3I1hywjDKKMcmD0ngZJ6QMBSjgTByZJFpErcRere+2gKYSYIjebHMrfUzTl0ws4aNni8weMw+iWIj1XaXsVYLNKMjldOJJqXZUgaYb5SYhKg4xkNroZJGknERr5xGp9x7C+7drcefvPOHInWIDE2YCTFotVG6dr9S29t7bAnTcnm479aNm5BKScgUwvBDG4zF7xMB/KxVwGd+UgSywFl2IljlZFYzmbDB88rLLwyHb7N25HVNHCUQgfRP4zQbZEx7qAh6c930JekWAMAj9W540tyjxeRjhBq3m6k0J9X9y5GDJJwOGmNGpQ1gLUutlUtXl5er+UF+1qJ/oL40Zgav3bpDb0tiFhuAdCxUIn4x0WSAGPxedn2twSFd/FM96m2HFNdZ8toN8D53tmWMyIobAI6fMv9VUZALYZYXcMYiC4Mp66w1pv3jVL9OpGpQJUXu7I8Tpd9GEmoD3oKCSCOZUDEMOVVi/CSTI73mAvLF4V4Ex++rTN5sXMKF1iH1J3IwBBz2VYjq1TYqFGKIHc75Jgt9mRwqQoMBa4wQQH9XrTIYgHVg0wI0zkgUt7h81e9GQ2OLn6LZGAXzpeVVNP4nC5JwzhTMNHD6G7LjfAFiwFuE03K2wyiMTpjJeIo1liyLmIPxVns2rA7SH71/l0EOM9lResa5vC4rWlhesSZAz8UdATrNTdlFNlmoAAyXoINA8ubGRYSTIZlCxAG1IpXe1lWyKZ7Dgh5fj1KRqQxoxqEbmBbK3hlKlcqLoIPUrLqYGOXSPbTDs7cuYdOFhRfJVaSoXimhSHJnzSD2G4f5oUzMJQDFL4jscjJPpIgQpsYwy5a4IDpKQjD0wUyAwYHH8yVzD6Eomu7DnEyqkJtWy1leomLVQPgYw/lbI7bGqPqidvRuiuoOhxigUsRAYyv+drN155NP2SZnF4z9fnpk6EFvRJTDcJKUyt3h8Df39ke/fO/f//RFuFcca52QU/vDAQbiWgSvVSsw8Ny55dXVOiqFkdvm9n6z3SqMdR6aJgkP3XFiGQ+FC/wUZbEjUOH2z9nJTwpuwTBeWovKQJzROwymWigIJJnrz1PcQy6m0mkLP+VFGTJ64XL8xXpusT7ZOwCv33BgbdIES1JhXLJr7kyWFOCsgygJeVzvTmNcyfrldefh/o0uulKcszyOBDVkebjIjJ1WM+iu0EMIvdHlD+g5iehpnJEFjBQHZ1QJ8QVossevQDDntKcH6Qg9AyVYJvmy4vHFSXrUwQhe3DQZ9LKd+AYYSzf/KJU5hJJQMmUh6VyJtSCuf1Ej4SIDJm+9tI5f0bKABRVfuMiXl0FI5e3NMRCLB5FlxcpD6DKhk2YLWN46Ca1/2S4QaWmvDB3KOd1Le3X1KvmmW9r5NHLIwmTcGjB6IcqaZEYGf084EZZw4uEpF2DcAwz0QCoeQpxRaJxT6c4IUI2YXQxrc5ubm6nhgKFPFvWFhcXGBbHCmq7jpBCUmm7bf8KNaCh6BmIFRaksloQQiRiQEQvQUIsdK7xi3ko8i2Fgo1qUxAeixgd+KiThzmRIIn7uBaH90ECEh3XmEad8gZ8hxhegEVXxc6IxIozCH3XJRhEKkUHY7u79OCOlNmpVg042X9QQjrKzK4YEFtbW+I5TYgVg+MHKtA0RBRirHSDJiyg8uPRIdcFAiViqxmCmaManX32Vn1hI/fVf/zXzrl/84hcjLiT1BmVNCT8JoY1VSZ/SEIIjX1QjmPdaLW6b2q4NWCv5fLhLWf7wh99j8fTCn/+QKR9g5NLZ72JZQJWp6DZzg2jIMwLpBlRMfvClxORFLZOQB7QImWFvh5MJTxYCVApO/3I0ppCnu13I5cjLJ4IPD3YAhCMYXJdzvAHy3Zdeegll/f69T1lWAKdbZlFeXpiUFTYiwsA0pyuMGUNqWGn3GZEFGdnI067lYtCNrrWBBQVneCq69Q5PHj9fjBKx7P6wcUR4uSIDc5F9vAGSEAyMda2MYiDO/ckQUkEM11beunWNocNsqrpwTES5A94dqsdlhnzhFBzjIpl/9a/+1T/94ndY87HcoBwjrko1wS6Gy5wWACFr0f6iAhzzojnOKDOjlh6IHI1SfdwfgbkAiCcCIBfRZtopwHvu4efX8YAcMG+oIWcF2g+PDSQpR3eJfAkIGQHvP6EkBOLxHicZ4n5KgYcknhF+p+QYZIxTsUbtiRwdQ0gSUJ0IDwDuSWYaogLmJBJiI24nSnoiCQndBeR4jNgI8ATCkJxwEoaf///ocfqpcXbCaEoYHlOoSBRtauqFAsyZgPw32020AQYlEgBAY6l2ANjhnhPc+DqMoitHRdAw0S2oIDy3b15GY0AbeokL92mPKXua1ZGTEe0UKQTm2tVr6FKuyGcLDZIIJxbnwu65g5wQbg0iVtPyJ5AaKAchNLjWDRwIsRTfcjgm9RIbjcSkpeGVlIwumlRexigRTGByMgyY8Nig38mmOACHgzX0C30bgxPojQ4P4AD3BrpeidQkgWlKznNKWviQ/icceEqAx1pqJKs+SNLOs1oH4FE4P72YoNf8QKlS8BwKwUMgQ0nKQTj9DVr3pR11jrn4sgtsb+nawODA0ADlqyvrBN6+dfHSpUtg44boj955C8/zt69iK13MSf/zExMY+jsw89NDCHQPRKi4cVuLAq3IlFrXMKVSdFXYzjzePALSC+7J8ROVrlRJlc9myHFtaZVAZvYY7hGInydhAL528yYM1On7eh3KibKq10sJ/hNhgAb6Si6+w1qHkQHthSIzmjpM9elBKiYo8Eo9rNVyylauQEUg/SkepwrWIoowFtedTsjC5QR2kQWDE74su9PKilnZFw9IzLm3nElOt8vwozjTs+9gAw8DZjaq6WHh8NraCiEgBObzgbZP0hWNG2EUfr74vXYorEqEUQfnh6wfh3UAYGNAidiMB5L6JxDeA4MhK19ajZGXZ8mJLLgqelrThrg7JTBHGvewLeOB/o0YHo+ZgQlJGMLAnrkgWnonABjmeA4MY5ln0j4YVsSZeFbW7rK6YQw1AqkO7zyPIaK/mFDZsOEZZAbkcBj2whbMnNEe2uFQ9amjJ4EzjRpk7wHpRVAJv3jxvMfyBQhmgoe8uKn09ddfx5yOcYXZFqjq4RjIBWBTcSfC4fGfpDBigU90xX8VLOFOwlsU2BIgx7yCPyvW8Fj/dTxW4bG2B9GJ7JIZeRTfZKDn7SGwDvqZEOCBpTh+wgpg8APjjkCt1Bi3PdaRKNycMjAi478ery91h6ZCYp2MQC2QSVQeTl04Ts+LQJSft25aNPqHFkSLBowyzfMwKfUkBDoljodSoJgV4iTGjcvTgh8A/F7YUI9Om0dBZEyM2IIj1kN80RMAnAcyEyYK2UaiMN0k3JE4PF8cXSLAZM0XbMCgsJF2D3T8ysacw/B1CqkmuOFn7MDAivy9998nu1rV7oO2eS6l8EzZwuGYEWAcoETDczPIPEcTGxqFU45S5bw8xyY4NUUqbvxjdoOShNuggttQ6uTxEwKoghdffJHLXpkEW/dEbEQwxNBUc2ptOKXybxQNNVpsVtsBLV+mT3h8rcPJBj/ASmbMwaNEsCuT5k0mGKVU5hxAi6U4tQOh9UAFWBnBooi4hTo2ooKzjARM1cAQcHjWAcA9QiKcosqRBFR4SGuxxxqjhyTxEHIaeRRiBDvymG9C6yx0/eIZ8cUFqtyvrwGRRFEWDzdCrKN3/sBReA4g9OAA96/jTH5DOHjgjBDHWYMBh5gHGJDTtdlPoCJAr1MCLdySx3QCEQLBA/ORHBoNfjKiolEdOHQ93e60hzy2EHj8sohUMckQN2cFtey5KnOvFBvX4TfKlTsJIRtIvr64TwhEsvHMl+cugQGp8Mcl9VRgIByCPZhA4LnCnEBnAkeO3nnnfe1m/PEObM6KkBS0OKQyhAQAgKkx44AqLiZzXpuQae1ASSkrAKaWWJYpiHsmIYZcAGCgqHzdsfhr+BUOHs8rhICKBs5ghtpZXl1Cq/c6MhSAmY4KD9czETvsdpgOXCt2OYpXtFRgVJHGaUZi3WaDzpe9Q/Ewm2VFpbqg91Q6HH9mHWMoU3rQkq8A6JJslEKIcvENOaN8wEIGHuO5dwh0DFGhVINRjYOH526gDa0DEhAD42DAJPmMH2AWFlq7+w7zNb+kcvKCxxOe+HkCGy9tipTkBN7WH+gRIF27r5Qur/LZAEu33cnx9CZS6vtjZvQpk1McN1AAj59ci/bFzBS/O1mOkNaaMSygqMgGFccEhRlIG6XMU+CmFLgVl8JgkUFC7R1peCoVDZ34C9xlJ6durGisZ8ANZqwhFKoLIKFZBBV0E2K6ntPxzLVShd5rgTONZEfxGBlUylxLN65mmdMOMMbDLAULRGK5yk4wEn1qigplLZY2xg2lSMOkN+y3O5ADhRXWzmezc/3UEvgHGcyi87ZbIgpoIans8tLFK2vrEi+WnjtTLslDpn0R3/VlaF5eeeSYdEFQLFBUnXCUDgZI4rTNBYB5bS9RYenp0x1tnndZKONp9ZpGmV1MXiXZIWcBmrNS2yIve2WEYHkCZ9AwSDz72412F3tLDWQLGlgYs8GiSmJDhX1fNiTYuPOb4LyR0HKMIKltqygtk8FDyT8TYKsj+gKmFYMu13htpkedSjHPBpWMgxllsK+ufo0HwU10TPtrjCz6rXPSgUtJr3FPMLL3CgopwTCMUYm1s9SQpv3kEfs96Al0w1nOEJ6MsEBxxkzluOuM7W01X8LFD2ztbdtmKZ0dtvqDzf1Fbd5GlAuXSZ12DXW7UzpTyQ8RxkJ6kk/xkFCRC6BR5brLUTb8gp9luB6I60e4niHHpbyMFLkzircQAC4WywUd6U3Xy6gttBUSXr668j/8+3///v6jCe/Srq3s7e2sVhZ/9KMf/fo3H4PMKdRRL3oXGwBxM/W0zQWJ3D/NMIq75wsDbnAgg3yRy4B+899e//nPf/72L/7lr//6r8c7R8yBKhwhKVcGKR08f/jw6O237w6pO27IgB2yQaXw6QO7kZy6oxZosxBI/+yNyesFVqlzQCPr9sMxd3FTCl2ZidVfd5zu9fetBqt272rdlvJrhQU4vP35J//13ocbLz3HU1HfefkvX/jOtN/qEM7tSCzTb3UfI+fZmW4OGQ8muQovM5VzmHe3scDGZFwynxrOOOQMK4BkU3CWzvXpQGezRqcJA7mCG9cY93Kp0bns4t7+3v39HeaKcftShaC9+J8tRXmtjYT+kUJ5i/JtOcpDQRkqMpu5sLryF889u3S1zsXyyCdJ2U8hXwwvqWscIaZpZR1Bjpz1oZPTyWptnE/Yaf7rf/2D1157jQZIh0eP5FUpDnNsmbns0tKDB48pHwb0TNjBTAGF05ZdzDIkaoXKOm4dcA6v2GJ1BU5ey9BuultASEmhbSXb6gTU6+FVr++5E5R0lHf+M2pSNsE2LiEZhsc6CdNRbvNIrVgqYVZ+5hwPXqCZ4SAs+I0G8RznAKEgAf4YDcAyuUKRWAEF4/VlmoRuKpRCuGOeCMp0i+P074mQiGSLixOqFJYRfyPyQkgMk+DPHNiw2CdOrh9xkghnMupYrIlSpGFjRWvITmLwwP+u38DP07mEKiPqBDNPAc+5lNTLHUSZRd6l5SmvsdlB6WRC66MRTV1a1psWmq3mYZ8Wn+2BQoM3NVfv71AFJDRWqT+Cz/FILIlPfg6UuN2y+imrUg1tuBd+sc5hyK29JgNZdouazc7mfpNFw4Nu+je///jR3lEKcwfXDyDBcS8wJq6T6csvvAQlPBfG8hDLap6vbJ/B4m3K+hHd0c/Ff7Tk+FCeLNDIWq3D7MKOU8oBIe6z63StnSbZC7lqvIK2kRKZxOXFY+GwhWd8eTxA005rlTCEGxHID4/OhcVOJaIN6/b8EfcYU4mrlVKR2/V7E6ZCvAXAFbNoHAyzNcnP5QkiX0bXIOh2epQ3wyyKe/f8xkJpHiEhX7p4KiSxjacKUqw1eF+GxkZXBzHSWpHHlNNVgfp/wep2eLozvRnPsKTAEyMsZmsJMlvMVRcWXvnezQ8+er11pD4Lo4ditfjK925DVXra6nUag8n2Kz+4/uNXfwiFWCWA6NHm5n/8j//x4d1dMOw92vnZz352fl3rKWgiLuzm4C20QQ3/0O8i1SZ4GMPzDxklpJxjPMnDrzK05XgrlV42bkxLi5eyN1p3H9JSXECoVkqfLVXZBWdNhVww7wOed+0LXKNVzGEzg50K8JQGlc70hFUyxlq8gIA1vZqSmef0mK6MR9WVBU1pZrLpGw9HvC6Y5QTVUXtYzHVb3aOprn+BPPgwK2T2Dw/6E221ItoUhAEvTGMvghCMCJGOXjrTGfSa3WGPRWnmpoPpuNNnYM/zKNUix3sYi8P+LEOgFpd/8KIFz3TMZlXWgdLpHnvAXAL28AsWT6v1BdZSd/cPsLJudQb37j/E8qfMRm+vv1wurqRGB512fiLC0pVptiAm4AcVnmo6t1Kqzio6/tzKHhHYywzR6Uf53voSJiw5FmVz3JrpY768pheMkXhVBY4xvTi3vv6d55492NlWCSZTRjsgpp+lVZiAScwIYppijUsSh5SrduhIbDhHjogBMNOZZhzjKYck+TV3/BIXaFDW/UUItTjIhjeDIoUCo3yiUb/8ly9uEBN6KzU9a48CUYuVullYWOJCGA77gxNxZVTASIoafOH577z33nsffPxJs9FhEAi8SmxDCOqOYSRnLO7fv7u7+RjRwoAA+ikLgihIk1UMr8jnzp17nMP42c/+jrkJzRAMnI1D9mjc8+KJ/IgesTTu+RIgzM7ooWVVZ2W3ZQsbaoIqgUde47q0bnCOEJYqrUWHKIMXJRAGKuOhem4gIzK84fnowobfArMKDXTisdo8SUkyFyyeSYUWgns4U0e+pJ5IRS5syNhSc0AekPwf3P33k2XJceeJXq3vTZ2ldetuoBtoAYAAOSBIDGeXpO2YrY3Z/LI/z49rtr89e+8PmL9jbNZ2yX18S+7OGgW0IoBuNFp3dakuXZWVOq/W932+7udEnszK6m6Q3LFnLyrr3DgRHh4eHh4eyiOOqXBwCIlpaq/zKB6jSB94eEKeTmRIjifgNA8jafFIBTedzwoUmDXEXVtj+McqD+M0x6NcnS64zVTLiu8Mhl7+OF0PyyJFKeywNMvYny5JdW09Jt2PSJLKxdkgT7M69YySdvvT1Fy1r6kgGgu/xFfDeAI1MtS0wgqSr1VFmxkkQTbqDOlCI3k7giQyYi1BFWm1qTzBzDaSHe60qQEvETXeFlRKOSWaDAc2BGczZvbgzrWPP/xw0t6tFbEI1V2mcAC9QV6QsLW798nVa50Ots/nnr70FHxiWgW5Pj9VvlqDm33jG9/8d//u39FqQM4+Gfs3bCRznwCXGf7kJz9hxZ8GYsQ6yTIMojgcGTl1euXSU2c+vbWGYaw3FJlwq1iMkCNgimE1oyr21uPyz5NpS4FRP4vR1o/Y6VMVG/pBQmn9KVtckNHvpFLHG8XnzxwvYXmk/jISG/BSF/SMiA084YoMK1oUi19qj8RWux6FXFF3iKli7MQzIxz+ZqVccaHe396BjArXFImfgomSegEUsO9UXgSPFTf4Soc+ZAJL76YG40BqppZQ5ZdI0RrUBHhV2si5niGAshIu7uGcCf4meQTcxCxOpV9Dk2iqybjYD6rYq1+ypnERyJDR1QtaX+Il2RBnRKr4pqfzSjfoK1KtkiVDnpAoPBQWCkjIq7UXbFyxE2fvxUKEiH4Y/kwxfaemNdqhNiZqdKhaULIcRnfJAAZDJS4/zDNKoq9K8ykN+NrJ5bawx6+Wp+VibrtN8vs37qzUF1eeOjOcpIdc2cgUkYdow7oZ4RS7Vd2wmfSar2mdzWeXHJPCDz+JFoQ56KSR0ovQhxPg4QQaBDyJwJ0/3h4BA8D0lNasxbF0h9EZO2NnViq9dW2UcnZKGNzFqzd6M40dhceLEiAJIcGjtUGTUctOKwY4SXnkAiAe4Kg1LYWj33iGRXlIIxomMMRma5sFaBlpielwSeX3kuIBDFll74gYOlYqZ35hnvUutv1gkPMTEF+zIh+UwO72HsoN5Iwe0dJqucZVPiGlEuUKu/3h3719s1s9850XTkhRT7gvO432xryQzen19Z1y2RfEtdLFFfYsr2CdPZs1WK4AP/axbDkgbDKws6ZoshbNAnyFDSmlCESKK7Y64ZsKzObBmS2UVS4z+OiMdcsHNno4TkfyhEwlMiao+TPaodZYRsimS4vzO8gt7DZ4pTnKIUwwTrwzh4f28hnO20gEYDKm3te5fyiZyoOzXV+3RtSb5FEOpstvxTatrowF78rUujSEHanVSQJzYof549mZdXtwkVE/nEOOaUIaumGQridUmVSk8vG9rh7CEwFXXpoHcM04CNTSyISHRM9snhizIhx02AzaTi82iM2bFQlSInK066ApmRJnMkgP5qR4CCR3BiI8gRI2hUlJkBdpgUEiCSdWJJhbWVxiCtpojRGXkr7zIodQ8k1qUBWY47GLxUjNZnQkjAqGDxfzx9/8mQA4Ipo8k8CH/OSSSC4iIYYiqCzm55Uk1jCVNAl8CBWvAMMIYOAnPJnYHjJyrPGERNeVndIZJw4Q5hl5VHhCiXCqV1KrwU/lEUsbw0ttgYcadqoU612Oy549qTTgccDQQMxj7/Yg+f7LE3wRqehsKIk7bIf1fJPpPMRr3MJNrXszM2XqOUZ0GscIYRd93O9j2WTKMYkv8gMPmFcWT1gLZ2CsPp0YMxMA1BDaEGCEGQCNPWQvJvs70vPEgoixKUkkaXW2VwpXrlzhjB4bKW+++eZiucHn4IkN9IPEmgBqTmUgR57UppeCKDz+So1op67b5cnqNlFkxESoP+Sqjxar3pQRW2EoBAMOIklYK8jaiEYs/JglQ60daYEEMBMoZx1StKenodOMVXae6H5jo57Tka6ywWiZVFW6f5Db0vadN99kAnbx+CkG4qdWjxNOQuwRCsvaiB5xBJeR6EycHI8yzz///P3ZFVHbYVI8LRueiU20gu0GuZBEae1bLoOU0lJk8kV7EBux7iixAsaDSQ4lLpeUUhzQuEJFpl4aDZ3W8dJbFAOSoLIEJzzmYJVqNqNbfujEQEtjJgm749/+9re/98NfUSnlas2zIoWrKY7xYFvN2hZNH9tuM3P2mbwaPsmBNDR4xWf9xM7z5Q1UVDGxhPCKB8ev+x3c8cQF9bD4qYb4RGeoFBs8wa9sjFnRiMDIM/XAWFxSJD0RO6fNnx52wB8rU9IQm4zi9WC5Ja6OIapfe1FmCQVyCM+hV8O5z8w4YUStAydgDmD2rI98Oh6ejoGnuyOBnxRIkpieJ4F8ofBAxpOgk9z7DJjA7SfBfG44upT2qOxMpPGILZF8RqmR4Va7RRsZTW2SoIHjAXeILUKQqG4HFVpcnE5eG9sQQKZoYygpdPSRt8yki65gvsr09aOPPuB+1VlG/VecVL/OH8yvCH/77bdRoejSJAD4B0MpYXbr0Gmvf+0N2j4qC8W7vbcL8gdrDw3P0SNB0MIWTvK6JgcShE6DFcKl6ABJgUBKBnm0evUsxkk8Sh53QK49CMG5qsHDTh06ikwZErDYSl4MscDDWAxdh872Dst5RUEAIIQzp/QdfDUPDM4TEOK3okkB4sHtUx6TDIzXhadyABFMEosgHo7RBbDchpjF4EI17PcZmL3++utv/vIqMJ4FMCrXLMdOAIsL3/rWt7gXBCawAXvn5s3/8rc/4rwX/S3ZQSFcpaejvONWX+fAEg785EWRSQty6itVmyMER5QuVrRC6VXzH5nuQuHm8jL5+pIpY2Z4UqrPw57U1G/1jc6QacnVLtTCbJI8wY+Gh1o8dLuEIIRQaOtCMwSevBAenr3dJsvQrQdrZMeUBvophTp0X5i2ukZGkF6YAAZoAxswTq2Xj1ScCaITBAw02ZK+5UAYeFZOnaTqOSuMJHO9FKUjLUxud/dYaCZrrLZPLi9jIrQyN8eFJ/c2tpBhqh+CO51tSkFG4Ez1Btgbzhd12sk5BovgJDlSdi7c4RVHFANpSOVkL7WA7TNktHszeHhiZdUg9ts4PAcDgZSLtSRsnyGVnnd9e9NLp6e1ZWAoKchVyzaYx6MQH9Pa3NTxOG0kBICqUG1o1uN8kscmt8LmOIEkibn9HifAM4g5deqkDkJFQh3hefyHwsJVdAXTDThMuVizAjkl+u53v/vciy9xxaQECSEfaqSEw4/1AHzGDppjFmsPHxJC8ak7ssPv6wDuZ+pIHf3lX/7lyy+/zGcJId7pB48BCGGSSCu+N0RbiVG8HMXlGZcmatqEOD8FcdCRb9KRhXfvUOfhgQxeDYlmhWqw5GXyoHLYkMnhQRDyEjLDg8djw/NQCGAOTHaScLu2mGcgw9jJm6gCEpckzNESIgCbVRFiwBEBnoon9YjEOrw/QYUHYPfgF3YL1FMoo3EdXtoCTZ4ConyIxQCWWESWJwkhwMXPKffAKBfDQwgQiQEUmUXVCk6iAMbj7PWEPA/gEWlexftkC0BDciXHD0BIEn0smi0wrn9stxE/wGACjoyofUulhJ6Wp/D70pgVykNgqyAslnSmoFQFnAJE0w5TY44C3PnkA5RzzTQwSIjl6dJO7igilnjOnFzFYIVmDQbLGp8IAQA8//bf/ltWn2ky3FrNiUwMn7EwRR4A4PwECuTmzZuoKU8IMRSTKJ5qj+ks84t2V4cSbJwqaonCAUPl+Ctpocvoj2o8LqVYYZCqAkoIPEN3GjhGJJBHFjy54xsweAEI3QcuPV1TTglHlPKIqyl4AIEYB/TAx0MAMPq0KoIHhbOX3vUkn/EEmzBbjqQyJwo9l1BYQtgSBA/h5iKU+KHsEH4QEs7THTgjXywJDu+BybQOGUIAkN+wEQUNhLgnwDg1obMAQKSSKFYbDkAIlIpaVoyYOJvFur4rGDuiSOgEOBg4ae9cA0ZfSWowkxCnKzMMT5xUE3ZgGDsAA8n8Bw8hfgd0PqV7n7lnyZip5iOgVKrVaiOixdU5jaYsBKrIVLdvyKkSPZynuX0ZIEqxLBdbQice2nglXETCf8YUxopAp3scl1JbLJAkIYp9f3LHQ3KaHscCbvzk/SMxHELIK2DK0Yg5FGtREecPRfnroVTgMRdJGjUZUoEeUhkBwTFGqviBJNafCTCKNuHI1OuvvsZ4nvs6GEKwAA0le60m+1I6woUzxKQlnOZJ7Wh8ZJcOEaIdOkrEki/n+KnLXI5FlJ/+9KdLo+fYTiY1MGQP8fLY9zNsWCUNaQ1FfRypqA2YWeS6WJvy0AV0ejqlrRyobu0gsjiv9qL9KcmZFcfWoxABkYmRAhVkVs9AQSrXupAXX7oS/EG2C48PpokzpxHafxUnAQ8ZuUUza5MwyK81y5ufNVVCWBvn6TAF7TnoO+Y4bn7jaVbP7CEoRBYUWMPoOa2YhTVsYjsD4woc1wIorRDQGeiJI3wkVmaGmRIM7GNqwxl9JkiT6QDOKTuUA2wSu9VaycqWtLD4gNEFOz5Q1LA/X2a7ilON8zWkjXDguY9PT9uWTBdlT7HB1ZC9/iMBZtdaUwbBOzWdZ+ciNF/yJ5zqpSxUIqpMd+xiVZ3m/Fp30Gyyy8fgHZyNoe5m/Vrt+GJuvl5NDfMNXx3Nwy3lO01x+ko2srIWR/IwAKKunYc+LDhU8aRSKfdd0q/QZGuFvH1Alypnk4VKqFKpE23d5zWYaUFqKIPm6a2GFOjEBJFXkDhkNDSExtkMkxajU1NTLuArVLhvbpwdsCWzBwUcEEKtctIAPGpuciyNUvQ0VQY+amSa8Rrm1aqMUIOLt9YtlfGQK441q521KsVxme8mSyA1i2DXLoOR8qRvtWBa1VCw4eEEgy+53+I5oCE9n8eeZhPKfrOmINrA1A5QipVHAQaEngq2hOTJKGVsCdC4Vi7JIwiEw4pZH2OUml8dpG5cubs8SFW8pgOuhGfANcKFWZ89TJaCucOZ9WTt4WBUprzJBdVTTudGrW6NLRUMeEazPAYLurWJ5flps9tBqoojrU2P9rrYtkz6g876JofPz5w81eMrfd0Os8ESMjuZMg9HwcFY0NJARCyXxrD5OsasqYfVFXU4xCRHLVw30Hfhy2DINenrd+6vzEo7V+6wRlJNF67/6r328UfHX3q6t9vuj1k1Zj8QBcGNn2rAjbkKOY4zuo5xaCqSySl+dyNrg35gh+VpH1WTThYV9BCmQLkqlBCM+dCy1YKWcjjTgGPDHyRoU/EG4rqd65s30h99ShQwXGOPrj535gztl2s6GUIdW5ovluqVWprPNp25eJGRaGq8CXA9o0WBSW6MdfRWuykNYzmOudeLzXDInoz6dknF3d4ukDCL5oGskzslFClJh6QRS88fVT7lEAznJHhSq8wGK6Yby/1xZq+Ta+Sm4wEqhxo0Daa+CkehqESy03Oqy1gpDl0FY3wFQgYuzVc9c9/9ztd/8IMftLsd6kgsE3LxhO+h0PG/8u//PZ8M/dWvfuNr0PSmLGIz6kZBgceF1Df2QiEsU5dcdWw6DROajzxxqY1LopqSWv+tlo9L7GY/xh2HMCjpCJMEUoiTntqMOGTZKA7IeYfq7SvaFrIcI83hqaSvQMNT+iEewYsWURupMoMxAiSeQoKTuYGcXg3Y3qDGBlK8QAIJjcIo6vFXS2tlTWw+OXScMOKEyIydR8UAcaj9Evh4+D79B2D1EqJIldR+h5A42KHAx5B9fsA/H4PnAW8Dn5+Q674EOef41jWQWnShxynkSsuLk6KWlWVgw8DBpNH7LFbRgByk8r3heLOHhUG2Qy1LmhXOSI2sESshkghIspEKlwZv1rFsqO8DAgEhROaQSEoaw1lxGstrELSGQz5+mpm2WzttGikyuN5ODa6vvfn+rU6qwbACGGa6QkEu2UwtU0QrMvZlwShMyOlVIQvHFITVQ8ZRHPV95sJFVo76I3Wh2BQXGrq2nrEK2oDhr1F14OEFYIiEpFW4fVhGGfsAlIOy+JP5MkRReP7CxjOpVCgOWpjkux0WCaDf7WWUmNbBPApVQhUwADA9PMW6Np+pYno96mJkzOCQq1Ppren4tKyJumHBC/7y0QjZk0xKbCqzvsNMxQaBaH5WQ2HFeOILZ+J5TKdqHgJCMdAREIHVCwBmo8G6h1EF38nBtvFGrFh2emcvnFlcZfxGAWAIMFQyDKajbr/6+vM7Oy2WCZaWFjjihtU3+PvDZm/UevqZc9zAr+63UNh4uP3zn725sbFFARpz6liPHVtaXGzwRQA6ss27G6O9QR5bIgkPdu0MHkQ1SpyQWW/Y3twp1TVd5IQQIcwiBInpMhaneZ26Y6DKQaal46vrO1vcs6/RaRrr50plrk5sZjSj6udqpUq1tDjXYHl3/eHdzIwTc1xOm2tleiunj83N17nPlOkm/GI4B81MR5EQSCVftivoF7A950NgrK1oVbpUWlhd6bT2gJmUcst8/rqYYfYyGA/7oz4WT0pozY6Bs/pXKMSSnUErp+WYHTFiHEyLhepK/RiDkcJ4Vs+WVou1brsLhyvZbDvN1wr6y4srpUr50e46nf5Ou9ke9G49FFWlbB2JuXhqcdhapy9KDXqMuVeqx+aKjJEmLayuR+OlWn4nP93kNFoq3UtPS2qmkzyLLqkp5mBcskHXX+Ga9Vx2cdzQWEM9NbeUjm48uv3UiWWYnOtJfsyiCfNmegQG8NrTfrR+98233h5W5k8sH7+zswOHRyQWo7FCYrasGeDUZiKwjmCJoGJd/0dyCH8I0l863+40TTKBIwQApcKZsEVC66ILr+aX57FXnQ4NBsT8SSWkypXC089cIgmlMBFVoKJd5LHPFqgcGgKldfr0iaWl+b1dbXRRoYTTcFgv+/KLL73+1Vf/0//8n/ks89xc3YugOYu1HfQGuxEsnj188ABiUCAkJMuJGTfQiimXyJml9vZav/71WzSN73znOyurS8AwNlOMyzmrEWLL0UsSgAnSNIbpYVSHsIb2Sybw2KCcvVa0ROtWlNSQwi0jpSWJU+dpfQRJnap01kFbIvGLJMwa5bHxACsnJIzYF/FT9Dgex8mrO8+OJ+FJF8dDiQYD0oXmLK8DXRipKBsqjT9AtHxgSQKGMDrSaO3JDszBAcWaDE+sfsi7O5DxBy2dnQamqJwnaNTnmRE7MgCgAQXj9PPqvSHYhMEvqJHhZsxxG7FE3YIV3MUGYDpVFr4CWlqVM81Rebj8NuLQWJX/bm1nA1cHiJJr3qzzAsOmtlHnFxdkbcHMBVM0xr4GFFNkTcLY5xnZU7NlckIKAbOPunEnlQ4i8H0U9crjaTE3vX/9+icffDAbtNGidHuoWphCcvKCCZT/Jh+vv79WKRfZX6FHotNB/bpo8UF1RIVtPM6G/smf/AlL5KRj9XltjSWmTVoZepUWxFJvaDtw2JioCgcJTj14eoqF2ctfvtBq3t/a1nh8MJxxwyyExAVUafGj1Zi7cde/vSqQbmkkqVUvKcNlPFbvtvum64agGXnnbzIqMKApp/vgfeXSyYXcKDNgpcRYJ0yHnfFwP5BXCEgGKsTi46YiCrENZ30jzSJntTDOqzfZR3HQ5zLGHEZISBm7su4XTlU5ZTSdscLDsXjfdfGslReaPUIlLe/hBIPAMekMLXXna00mV46bunUPtUBhBGyW42KZFUbckxZxqaEXs7Uzn5vAW9WHZrhmUMsrw0fCsGRFkRftFmVtvloVC7lwmvO6Brc8RhfdvlaHNI5B06Ip+adDllQdNcIXBqBgNB3ttTv1apnxnUu7jB0LeQyjwUTPREYaO1AKxgXFEpM0a9EqIi0QUjgnNs6xqoZq446yEnXB90jQIcWlheYuh38mXBa8ura1eL5eGuvLKGMOOo1m+oolDWxGUhvsGmOVl0wN1duojGpHHBAimMoSDVFJ9cFMrSCTF1GIN2RSUroLAAAzJxYRAx4CeRIonPo0CAsJ4mE+23/60vGXHu59+OEVmBal48frYv/9sA+Ekiv+Qm6qoTSrUIdBj3r3Ukg5yPEUbTjfEGEgRjsqYWtcLdl9DyodcMpOVOoHWjWSm4xffP45LgIddTuLx465FR2cR6usLi59cvUq13LCZWFWNwVtGnvgw3RNQxFOh5mBBRGEM47iOGC+WNju9/76V1e28qvfef6U6mDEl+/gVY6lHk6T6P481M+oB3tp9Sxrw0l31AiBEuhsvmHfVAMtdQRnlK/VHlrFAlQWCo5f94Sp1sR/CBgOOISjDbMe9MwyQ1tVGPPVOdoPMAiHKRNQCt6efIENf3lpkc3viuGhxIS4o6YAtTXeOOjJvxSESJ5GkuBUInMSMzgoTWr84ukxzmJUBski+2XRpldz8lsfpJvJQcdXvQiP4G0Ah24GLUswhPOBTfJm1wjArO2LesaWO21Tw3R3Tpk/nfXCqs5M2RsJYjF+ZaeK0fHGEiocy1luVcjnS4zwsQO1fLkxn7Tu3N7EBFzrsMDTIWGvcW84QoVs5Pjm9nC3neO4zbTG0cdKdWEVaWLZBjDyIkdlZ08mBuDEjyMKRwfGDJMBNwcleUW76WnF0xwWym1NJ6SypP93PchXRTZu8QslzjjotxZi1ztIQUUahHCSAKZUVi7kmVIDzjC3bLfxekn1UYZicdemPaA6VADSUmWOR5maO+SJX00WE3JJwsBVp8FRUe2QJ7KskXsVeBZOkp6eU/SM6uVA2BNemCrPNYqsC2B7DYjTRkaHwB8P+WwA8MBPrlBg4HjKDE8OwXteoKU4AKNhHCAMUl1yECfkMDfKMZOcN3WJ3MJ2LctrvKg6wuaNoVJgCAFs2bE7eubiGWzx6vUqhi1zmRI3QlJYYot8r4BxmM5IyeHP5KqMERfNwIprpqAK/Ug4ZlK0I3JB4dLbMj9J1bSsjNohO16pr+1t5v0jrrRwejQQ1HLG2NF7F2AFjHphvvoKgLdlGgkFhCq1Gq0ZcAbYF94BkVolIWRQXrwKMgfN/LoY0LAAkOYlLbsXfDtxYwMANBKpqsUcXwn/w299A65Oim24hOgCw3I28OgLBIzvV8Lbkd1BSSAO5IS7B8z4PV8Pcf+TnhHMPrGSKAJtNCWPqi/xmT7wAOBP8sIf5AEP1KoslpmDOSQD4j/6oz/6hx/8EtZx3pwodxSQpWd4waD5qafOX7t2izVoBrHWq6tSqE3Q4qAEB3Kehh52SQ5xvBIoqWCqAPGRxlUgzczgE8XzxF/g6RklnyERgcx8eLrDFwij6SvQng4PgQSIMHsSSHF4DVVG2oDZPQGS1yf5k0lACBJnxSEMJA+QMUDEscfCo4CjkigqGe6vodS8gjzGHwED707vRzmSQ/njMaQClT89Fv/jYP8/HkIRaCAIMBqmG3PPOKbah3gvFE/siNFLyC8Cz61FIQqwJ7EoWXbDw0Ms4kmSZKz7Cfem5J1+f2eLOStTWewN91oDNVsjKSSE8nq9wcLB+x99yAXCfIDQaRY9lgsKkJB/9Qf/igXohZo+8jbc2Q7tFD3FdhqmWJRrbX0joH3cA1WkZcEw0E/zpZUjF4QcJEqpQ4iXkhzDdFQ63MZ+gNG4cKgXz5HvK1EiNhEBJ1CpzInh5nijmigRDnrazT3gufWCSEIchic40b0oMRQa4VCIHsBDcodxYJUlTqTqsPYOkLB5Q7ENJ5YMWJ7z9QKjl0sqBEPWPlCZX1riGj4OBmHLNu4x0IczU8xyicVuWpjNDJwLPbn3k7svuA/06699hWdhorUATOXAzy4mBOsTjLFsiDyrRKeWOsqNl70UkIHHASiUewihmkBLH92dtKjZUlbfugALYoDlPgTTYcFYLxFIRD+XeeTzSzVdH8fqM118ya6KIjsoZ3fZmQYGiGfZsVGtYRUIKpIDoBHOVF/XgAaKD+upLAqCnneqnOFAkgR2EgVKxZoFN+Hgh1TozPRHiL2jpddghANbSAW13Eo8ySDqdbBB87GVZer3qy+/REhrcx3DbaYz0EDpsOrlPlaIn23ukby+vEISsoA5ZEpI4BWBvDLYmJSz5AtbRKFmleoyaHesyvnBeRXBFBtIcPTvwFBx8GGjqa13mAB/YKDJtkADKqU96ITBbcEiiY2UTK/HfkksjnESQJx4y1k1TlGIhGBGIOtb67/5zQchDUZF1D6QSBAw5jG6Y2zJXyikjH/+53/O1vKgP4XJnHAiBDnHlqrREEOeef65//gf/+Obb/4KVKQldzzwn4O2bMbDvYd372PCSaaYolN8LXThWPAwHjoB1PhHH33EVPP3vvl1tNDi3LyhEjbmAoKPeY6HQL3H+kH+hItiLQTk5OKRhIco7SKYS6SL0cZFoOxOm2Pi6QRTNMJDQg8EswPzDDlGME6sARBFTYW0jpMnqXDUuHt4Hgy0AaHhgSryAjLgwYMjiT1FBmlDSfE5Kp6PuwCGB+fI5TP5Yc2KFtRq75GQITqtHk1FP0I8IU6qUwJP8HigKiY4BzKSII/iE0DyODj6dYQ8aRqhLODA71ExZuG1gmrwrFijEyyKiJuw+ykLA26S4zzrgC3Am0c0sJQAGAn9SVrk01+J5RXCmCswxfZApPry5cv3rl1RLnZ2uWKLFJwBgjDWGVAjcAyx77Snzz573pFAAOFgcAmBt3wegLE0ws95SniI5SMEMOcFhLxoLOgoENKQw9QMVFAKKhy565qlfB6Ar371qz/60Tuc1sNqEvJYz0H5kBcOSFLxPCSZtAyjR5y0atE8xzkAJFSBGWto2ngqp1NfGTvlQPOE1Fk/UcuJXPCCwYgUgOcI/oDW6bEUegDpfjzBT5HJWrVwlHMwPWMSvICkiuVhfzzvxIAGSpwGf3raEJvMR5ij1oRXjlj3eEZk64EeHiG0wOB3zIB5CLRRHEcS8iIKB2GqRxsPRGi1WBuVzT0AkDUlgPPAGGc0mUJIPQvVjjHZ2UkqesbUsWXC2XLxHGEprcv9AFAq0pIK0eIbzgEDeZML9kLUO0vmxPaZQepbZUOGVWyK0IeCBPFgYs6V5RHNNu9mpYpUIAebj46AdABRG8+jCcERIlMEc1ZAFY0weOXreNQRME6wP4ElFj+eEGLINKphBRzHK5JP//7RRyxAH+08OZAhOiAMIe4RxljMklGeqUlKJB4UAVAKDlhI40KHSqBcUMhQBNqowAAQcrFUSmjM1yABBQJOUhFCi4ahjGGewTau243OI8aazZFQQSAHWA3BlHlAjpogHJH46U9/Wtm78OqrrxZ9CdvEhlsvKY4ZeGiN2KRLitodVaBasPskKKLRqacWjinpZ24MgwrHGgm0jcbSfuNofddJ239CMNn500N5xYMydHnbB/1n+DyLxxHk6rprhgakKF/VZh8HaCygKSILOvKr99Lik2CiEMJZHkBMWVaRQOuuKTZDVDHYL1Dp3HltE5jRUFvT3ELGYgwfPSM7rkwyp3LarrWtMDMqxLIWO0xJOYeOePpAzVfo6WWAZ5NB1ZzT5Ee2k9Bj9rZEybFvitGBVTCY4CkAOL9pkSdUrVWF+eE43Vwo7aUqiNfOgEMTqfZwutHZm26pCVUqbTqhOt+V5uaBCeYe5CE1ym2JWOP7FdEl+wDI4ij1wiJfkc93+12mJiwH0n+Cn/vmyB2+4dfdVOgIGIeDe/rZd172WD/sh3+2TyNJMSSCspJCoGXBijfvpuYgAmNXDH5PtTU15QY9hHJDt8zM+togkOTBSfX4VJyxa1TI/uv/5t+cWF6lyS3NL/zVX/3Vh+9/IDWI1GfzXBQ3TtsttKSIdk7U5MBUqjTYeEAisAm1LUoWqaO7aQgkI+94oFxr9EY4rCb3cW+rs7dZS/fov/PTLuXiQirUIyDcpcBDafmJPS6HUckdUTxQJlBZH+XYdlQwJkV69GulwtPnT3PBdGiWlEGpD7tIhCyWONZOBcPDuKXaozA8tVaK/p1wSdm4c+P+wiBd1cVaKdYdxZ4IjeNHIGaD8oyzAKN8boZdLI2joJktGNAU8IS5I7KU6Y7zg1ltxBsT36yM0AwK+ynJF5qLezbHbHuy2aIvxXMT+ju/+PX52Vcy3eGDh4/OLKw8enDn4/ffL9D7QSV2cFwlBnZ1P3zviG/HzrAd4wIz/HxZecSwShsV6RMXz2WqJXZmX37xhdsfvcu0dnvQYcb+7MsvsRqyNxtyNu3yzZtIjb6zrmY9KVfLxUqhUi+PZyXkkLIgae4R18yJb/wXx2aZQrbEl3SIymme7PaA8MVos7bWbfKpLw4/4MQvdTDabywgIcIju8QJ69lg1MZ9OmNXHtHM2InabXVv3WthdjwZjB9ub2KSVqhXSYthP9h0TRZ8o7ctljHG4/xDz8bfQ9soshvmZFwHv9kGdtJJShJy4XnIUVBC0JwqhYkeC/xAsq6wsrC4yAXTe3v58VB/ut17xP45RaG1kYokQIowene3mFNL04SfKT37tNQZdQUkAPCTYUm1VvyTP/769773PSw6WA7ipA4YWD/hefWTj5mQM8MH+MaN29xMx2oGyb1FsE3meJSZJuBR7dB08CPXjocT51wGYGNu05/SHqRT0sij3oFX0Q9L7CnyfLgiJsQu0mwRjMJpwso92pBTwbULC2bTWF5K1yrKjWz8GRDG/MdWQvaUMu5R1sIgyXE4kRSTQXoFGiWCiR00xN7412EYrxhChVrt8BvhjQE9R5CqRNJVsQsJPUD0xy4ZlQwnXsw3F8IDMJ7gYkzKHUieHhKXFB4YJUYVUQ4QwELy39Wzj/8JKfcL+QSAf0KwOhFpCT35DjiNsDDfmJa4YBAjGy7nIk+z2YB78IIBeio7HA13eqnWkObMuIAmrgUFryNkbp+3SuryENHlXZH0uQWEJ8obNcaoAx6i5cDgGr4/ZpWQr5xq6bDeKD9q7a5tYaWWmVXKXYTf700zpjBrYZ3u+KmTl69ewRxyZWXRR/BODPKG/DIzqNca9Wq1tbfHYRSmwdjLDrp8SSLNHJ6hJ/w/ubTSO3fhwcbGQQmGLgvwCkiPiyXWFvU5uKhg0Q8wCKo4Sb7ukgDgQGNyvSznWByh8No0DGCrfWl4krDjhf0vw9nStFfOTiuZMUdRc7NhjuM4W1vzLHTOlRibcZKHVKQlyWCk5XUUGhixX4Oj9LfcsycjkG6z22tyaIbs6ctgMwkYrAOvWVCsFX1UAjlg8zGqzA4hBrRYNaQzzXa7tbu9vED+puFZGMkTo3VAHJSo5JPJmXNzi8svVir0TQwDtHzQ73cbDT5FXoe2TKr29ttvvfvOvcmk8dSlpX/zb/7N6tJKv9vNTwaUl4Vc7IN3N3ZktCaUkBldPQHuNke/WCodTzd3toudY3AAAhngsjTsMwrRAJPZ4J2rY/ZMMbmZupzTqiiSDSXDgQ56zoaDBl+Wm065b4JTg82d3lK1sXphtVSvMnuBKwwMOk1OiZWwqEc/Y3BNB40SpEVQebAR9t268Wk5X2DsOhzo3FiVbzlMxq1uB9vqdKNYqpXbGMqnM0MGu8hqQZx3a3dagfkl7ZjQaOyMldYEU2htA5NpuVgqzPKNYmVnQ5/fwdJq3O/UK+XmzjbCdfL0yX5zi4tT1x/drRUyS4vVmzd3mltrxxbrqdLw7ImT83W+UnCXQ1nPP7VYykwebPMRTroz7NGZpdO8sJbjmBdXptjdWdz+bIdPGf6t3V9rzUaYMc7Re07TncyAys0W0uNZ79rVmwvzK2XOE/pc05QepaZSqBG6QsYq99693NzZ5XQmNcb5RZLBF5VxouGCqkaOFGrsSmt+PMTyBBKHp8W9YxJTTpqZ/CmFeh86fJhPf2TCGY08QbuxwfUXzP3GHkSDYv544vjy6ZMnycmVDc3BuyfE20Z8dMUgj1o5mbLf8Ktfvn3q5Hl2DjgDcf/+Q9TIlSvX3nnnPT6byTyfrZ033ngDxjJagE52jvhgoxEmMUWZoA1YWX5w9x7KilesDsQr3xW3BbscMzOuJitXMfz/m7/+P7ly+o+//acMIdIZyc9YEzLVjP2Y19grFscOsNirX8jw1+BJxh7yw3pyDxiSSYKfusJPJfCfBoU0kookJAzYFJ3oEz1cIUgwLtbtybqLwm3RAZxUt9e4E8OTfbdIMlSo/XI5cvIGRmMrEzb8JgVedpEqPpuoaA1Rbp9ae9UDMEWY8yS85jmlZ1oGzQMB1ODebmuusXDp4tNMm1A+gU5ioZx7jyi80BkRPJwbeoNLRp7yRsrjizvAIHiL4ukjEFLh8Sj1icKpMRH6AeesQP7x4JwGkuAMkwEJUPUCAIEsmfn8jjtEeKU92dOXXNW4cA5JBZE141Hqi6TeKkAUirW8OK+MxrKp/eC3b3IbCWN/VgHp8KAE+0E5DihzC/wszYHpG5/eQYfxxWIt2VMYQ8RRlDFnqlVMttmGi8srdEU3b99ha4eVpk6vj/r65u9/CxlD08L2n/3sZ8z3G/NzbAZQKLLGwV0rnHoQ6pjm1uk3T50+9tKXL165coN2zBoxMLaApQVxalIdFbdPS9cLCaUWDZTGlD8sgi9kGjhp+Wh5mkCEh89S0R3mUuuLi3Mr1Wxx0uEMC6IZL/iKjY6WJ/zUu2WBP5AtnD4+lE8zVvUX5thOpk+gUyQVaxTTPJaP9eYjTbKOdDb4kkmwVaeyoxemnHkmS7NUnQY3YU6qS3YzJS3ljGVViZAI3FsBb6Qy8ccXWfwQZ4EoYHXZaFYgWCogMJoRWPojH55QkN6mPCtHaMxxVjhzAAEJ3IY5VCKB+mSEDcKpL8WRXFMfOWBUWzH9AEjSfHPXrhynJoGxL0FREpvdcJZpzEqRX5IpfDDBNAzHcalz6o0SqdRIveqXK3rY7rWkfC1UY93srJRPlyErM2HGCBN3+oNKNteuFmarC/17m5S00+zeu31/+RLbmZnehEVMWhCFYn1cRWO4IpKtM2PZzYZVhFgHRzOBtWaaIMpVnUoCTsZncH3ERdGSH4L3naETpHNSuCJsuskAL2MgBXFaPZM+f3yxUUjt7KeOZD4RcMBLdsn3Q6/JqCf6WZWJFaxrXWrRxEwEQxps1hc+UBUme4dK52gh/9jqKtDULwqBJRZuBOWkOKuBjM+Gvd5cpfryCy8SyzI0p5CRI1SA8UpL87E4UdvSx/yxdGFlYdyCrWx20O/9v3/xwQebwz9/4yX64pKtY0hFwFVQyeCMBgvfrWFan0vlmFDvb8eq5SoDweCHZgbnmhMxFoxtn71OmdKz8DzCQJq9LpohK0tZGeWM7TRedP+B4UFFiSERZuF3G+x8rTErlqfadycv6a4vaPUsFEc551WIgdX4o+1T11tkAJAWaunbTAR91ZQ+X6y0G1rdYI4lZkK8c8obcVxhQiqKCE/VJap3EQo0EQ2PFoifQFjmT17lp2yJnSKiCHeHHxyCMeEGPI5RQpzWMenwbCfHK9LqXlkY5D58SIgHIqEH2hmTLZY1aSmMtOW42ddxp+5QJhhIGG5rlzvCy/MLnDXkA3G6h46+CmphmfCQsfyaSLCGLUqsg/GqijusZM7/kn6vPMfo5YUYXvH7azIzgHUwyfgfM0p14fBJSNLy1Z3/8B/+Q2e3yfD33JmzGuJYrSHIGkvxYuMGIPGQKdVu9SsLNZqR8jJZJjaJOemnqThaf8JVqWbZLbkYUoNaLFZJlEsy6RF+wcTBIdPgiWOgSVAejh5CBTDgkCRoEVOiEpLwCiXOTwKTscrrQIjjtLRxH8bOefvRIy3hRfssgQSVCMye1yEPvIUtBMY80BQIsSTQCSBVIIkBoOBsykQsgxWikNXnn7rIAOhBu42ZDFYD2MvcfOs9FkAbMiVWGUmFWlQm1qboCZH54/miVi039CVDLx32Mn7RJFQxQmIfbG/YBQOxZIQyRUHTQGyORli6KCh9sVcwtu2kUDP4Ah7nuRPivHAPL4RHC6/WmmANhGkcQWHHsgLw49JimTngc6yiIy2s7DNoMxlx1encAwBusEnFK8ZTTNtqjKnyWi1iQM8dG6Cx0me2HtnpVO6W0Vxb92j7ffskhPPAAJkkGDCn4fEnBXSak0nws0dd6nSYARoPIiZAnqGN7t3zjADGQxZ8bZJYNTRzTkCAgTb4zPL07//+7//gJ7+i+aCwHZK0OKzdT6azyAB1Qb6Ms6vVcqlYglcAB0jNQc0kBITQFsKpELiEgJOLL/oQBVoH+Oc8KYjjkedxTS7eSJ9/bnYkx7HHBf04J4kQkvMMyT2KouFR7SRIjyATQYQEPAGQwBi/YYjpB8Azcsg4qd6S4bwGtIeikuFEiTyjPJkcmKQDLOmICq9eQC+pBybxBLBkkhD4RTyO/4tAfi5MksjPBQ4ApEJ02coxSrSgQFkoIy4UijaFOmp3/ENzUdsxsIDmcO3sRxhCGGrYeIi3ZOrjQH9NAtNkaCOluRLa8vrly5gljrmjHjEzmYzaUpyATVx6N4xnAaB/BJswx44i8N3Xr3zlK6wl4afPxaiTo9Pexgmp1Gs8cdqOip1ITAhA0o+WAz/kAXvsGEaQLHmLSxjQEeJdPxZl+JE4x8ETALKgUUJkjE1MwC9sZo3lZKNBQII6RUGRBHgckKgazCVK5Qax5Gf402hUPNQdaekNKRSsIwRHZcE9XiFYxZP8k5sS4jxfSLQge8StlewIBoYkyjudoW9its/VqF46D2cWZgQTpjEzeZEE/qvLsFzwQF6wKIQ/H3/8MYXCsaLHiI79AKGyBXE4QwHJhRBRc9BRCrCxFUouOESRO8gp4Lg/8FJTcD4sSSI6YnrVWVdnLet2FXKvqfP145xGViDnCUPgZHd3C8/ZF14A+f31NSxYW60O95PCeRaXF0+cIBEXZJGEERcn+cZdWTQvrx6TnzmT6T1yhB5COINJLfCxG2eReKJVGEar6hltkK6Jvljv1eD1zuop/ayJKxmRimIQQv/kYKrQsfbLqQLGGycuXCCEV7qnTqeFn6tXoXzpGXVGrJdAeamou1bX76+BDZxA7popveUciR+EQR6EQTNMQJi3796lgVxYrVAFJIQGAMDPOhT9Xb6xit8FSGIdO7KjZW30ZxiG9/pZxjmt/gi0dsMcCCIHOJRQI06S77WIscYKRwYoxtQIDrNrh/dwnog/aYG1p4JJSz32hkMs7rHnBpMSsjzJMv3Jk9hLwg021g2DxMk80cPz5YVw6KH6+B4mDGEgATY+NQ0ArwhSc6+tc2m5FG08ZE0q6KMKCAGSumMVntphzUNH2XI5GE4U/AMJMzWeBAJPjmQH7eAkRy6s/8pXn2OE7JQgDoGwiNDEjzJNqGJPkog/4FV5zeLVEeo15rP7w+t+skSdUiJqnyIYtfsgj6dKkhQwH0gQ5+uSxpMk7gALHver/syBiigFGowHhqjggU4gk3XrqQIAHm9ZAMXA+6wAmI6AKOQN1UQFcTUTyoSuhCiqjKdXsT8duZ5asFCUZ8QTv2eEB0eIA7vH/QAYjAjAA28dUgTQ8LXMLkesDRujluKBXkiH9BDASM5cGGHzakLMQGu2RtFQytkJmKPlCQDSzpIsbZkC8gpjApGobtr+rTu32GuZNLfJLpr1e02ZJWmn3xMBqTQwDx48pJc7d+aYLJp9np7TxZvMVcFMvuSF8ke90K6hEw5jXtqYm0OVQ0CpXGbq9O6770I5bZmyIHVQ5UUjd/InnFSE4wGMFgr+zY0mIW4OQkZefIgSQ2yYR0rRKLKjhk9y2Eosq4TEEoVzMALRFYWs9iG40YK2jAwEGMBU3lgS8FjS/fr1V3+CynKOYJJRnhdZeCAecgnYkpDuT0bhDy4ubCTGTqc99zMFOEn/48hDiKOFEx4SyIvCbZ1K8hgxU8FJzCEcdoCB5DhGH4TjgRsEAo8T5y1cOs4cUU6xMc2nmJAh/MRbon1tDzBgDJv0tGTAEIhcUbOeRAit2SInAPor8MAR7kLIq4c7JEKIjEIqtwAjq6DkCSl0HwNbgAY//drihWPAkEQFMSnNx+ZuBILQnfwRI5Ujb0rlWt3CyZ3qsyh19DiEOE4dSaOySBAZYoXOhnlIvtjEtne1euLEyu2HuwHmMzyePAAkX43VYIwieXUfMDjnpIegH8i01x1sYCWg6qZB0aM5oMbY8JNCEWUJhTigSnKGcIztsNJD6wrIhkloXd/sIQ2DCj6vxVix1WkLg42LYIsLFcjZWDL+aZTiWfAED0+rqSnzhdzW3VdeeeVrX3raw0nu1eOv/iQR4VrbN+eo8PpUFj1MSOyXBAJqTzErKpwpVQtULGmdBshwf8DpK2P+Gp7AoDzpFgMYHscMIjC6AkuiCmnxwAqDV6af7XLcV6uK0vc52TiR1/Y09G1pUno2hAihWuC0WiixJZi3xu9dU4EtPmrC0OjJG/mKTGEojtn54T5gFXuKeeGQXUoKomjhtyeDYDCIUAZevm8cyb+t/WMiwQiJlULlMtLemi2Fa98JguhVSBvv7joaobIZpN8j2SmJEU3p1fSj4qQ7GTykYy2nuzrNyFF2tkawPSmuzhgddpmZdHc1oe02Z8PueGP7HtUwPycD/lZnR12OHeiY5/rYSerl2vGzo2KDfT4U3FR7BdoHlFMB3If9BS7v8iIvvLOfx/z7oUf7IrGjjFF3IYaJkxKx2G+/BLOpJq9xNl2b9YG51NEO8HBPM+F35lQjG2hJ7ttic5hKEvTsa3/wrco81DcWjq/+w09+0kVpKjexToVK5zAa0u3FytF0XzrFNTd089yHxX4mirLZ66rjt20J0ki+oQJRsmbD2j9HHRAlhpEFLNhS6e3WNp1watjlssjsbIJu5XZIdIpUa4rLt8ULhkHkDjDO79h1DlEEPOTiHtC4LGGVa6mkmlNT+56DSUahqOnBSy89xdRowhVDaUodyV6EwfGavnCukmPU7omy7JSj89Zq0veFWHdlPjzfz1x+9+qJYaZMa4qGodBAQlWQ7u7K8IV75TGkFWH8pd31GVdZYGzX76l2VL7UjHv7mCW0N/em3VF2og8RiXvih8rr8yD25TRWy+umJLRsazz42mtfpXSFCUasqWNnTjAtLLXGP/vZz+zqCXgjPQST6V0oFHxO5WvUV/3ZZ44//fTozs7777+PWTbLsdhqtfs9ps07m1v3CsNGtcRFyxpvZdkkR/jSfPVuVszQFCd8tNV2cTcGbQyNJ61Jr8nn17O0KWjDUY/lgl3HXyx49UEbJopWCjUD+EKpESc9rI60pSczMd1/OuM2Ufg/kXUev9aLVBBcJIRxBGB06ogubpDpwV4/hMTiRruz8b/94MeYDjWy4lJZH5FOLWpam89xWngyWTh2jInc1vYW+EcVvt9Y6+qm1DT3+IgqZQdleHGSJV6hwV4PPFhEUpcz0noKB2qgmb6QktYmufMLq6nZ5vVWNzufZ2U3wzWcuh1bSCgviUBoQwHMXiREcHJKy+IjuHML/nFq/KyBICkyL4CkaNKYOXZ86V9987Vf/OIXg7FOhDg/nay1B/e2NvKEYnLINZqUCH4W8pl+r0leDoNgg43y0OTzZkVOgEVZFWFF0mPNQtN1YPiD84J3e6KovTgq1aPpAYqkEHLj6Q1HMXKqJhyUeFXjFTrvYc1PCAAWKH1l4JZEKgoCDLM+hhrZ41BkhheUjRArioDwA+yp/VW0RJYiMJxhvlOo3Ok3lIE5cSHORQHOCX/ae4Qz7E5bRpY0aoruF5KkSxTkQFQINxo8ypjjLDJiAjdihLA0VF8cFv0SLoa7vgWR1ZbHHcj3ULIv8PoZmX6B1EeAgBAN+ASqTAZsPSweWqmOuFR4cWE+V6n20Uw2pmSbjjJKV2Czk9Fx0b1+qtWdNll3zBYwQibC87YWg1d9jQlgHG4jvkgCVGkeDl0SSt7xZFnoMMETKovnXg9kgwEKv9dubbC59ejevSkHPljEkyWdrGNGmmurLghZXVqo1Cu/evMttstYJ0L5aosF4kBCRoa2PtdYXF5iXjJg5EEHgJYYagTPSGw0HHVTbZaw0V/rfJgeATbC7CkM0Wuixc2mw2KBozC6wIdcVpaWOfXCIh2n+LzzxKgD1pEzHKbcEhlWV5t7K8UC11Sj4Zlq86RR0cQAUy6oHmlfNWp6Xr6EO8eFvOVSLT1g4KcbL1GWY4xz9/j0AQuoWDDl8mi2NEsGxbLOmZCQOS1waOnCaIJNQ6e1O+y2yqU0f0E/AIADGIeHnUk0JHdMwDHWShnl0QMSUcnphE3JvmABL+mhVumNGvOoUJMKhoaimW26/nTIvSSTNDeTYDTP/B5C7VsIae5p6dVzmQZXEfe1jvD2bz5Yf7TLYZI/+Pa3njm5kJq0qUqaFQcYSVucFXbW2uIELMe+ALtg3UbLWSo923kt58HGTm+Yu/cATtGlQj+9Naf+ZLvGySKudWSYlElXixWO+c3N12ACiwvNIZsZowmdA/cjQ+JgxCd8MVtOF0qL9Tm+bsJY9NjiKhctM+qu5IscM+Suqkl/yGeqykuq5Wq2xC7jTm/I1dFbzSZEp7irmY8PmlbXnZDpdH+opZlqZRmjQcijvCxKQiFOHIu0OqWjnVilWxl7k0GPeytVguxuYTriE2KFPLedr6RnpXK+OJxdOnv29o1Pnzp9/OyxY4yNC6lRNT/70pkTLOh8/N5vX3nxxaeXl/ji+6Q3ZMeYc17KrtPkRMPiXHGhcWa0dY/p3DZjotqgwPWEzBQyXCye5booPjuIYXQlVz4xt7hZrg2a2+Vx9nSlujMYbve0xMPZsdFs3J72P7j2wYkv/7Fuo9I3AyiMpIYBFK1psL1zcq7yx2986cZS5dr65kfjXnt9k1NeI84+WRPg6R7Xq/7qGhwuwVtCqFnGvxrhqDno+KC4Zk7NTxpIPDR8cYS+pcEgaVytFCYjPhyU4kMwdKTHlpfq1RqVDhISqEUZ/92DX2p8Hz0NQTPYXr/5D9/7v5566lm2WBhK+XIkkMymR+MBAyaWjLm5RA0ao0FagB2jpqf2ItCDUzXY8tOS/dQ2tWN50a7UdDEq5qnZk6NIpbb21jZ/+/Djq+9885vffO7pZxjJg4qxIhdz41EbiLmn5AqSUoQVeIXqsx1gxlili/EE9gmbuSQO4KyCxDFVRpwLgTivkRBIQvwBIR5ecY4QDyEe6JA8qVy4RCB+fzLKALMn8WccLgBJagzJWD1EOSQ52ICEfA9QooGCFcTkSrA6DCd1J6IQN2KhBNnGw+oz5YKGO7fvsevAmojvxCAAkEo4MMEBiZ7kFWwe6MR7H0pgKEu8jKLkhMOUCAkqKtqV1Kid+2oJF9fQAyI3wwKOhzh8yIhX/LghB8lBaKYenGyEJBhDCcFBLDIIDUIYVwSBAZXLD2wrljX0FTruKAebVS59T4kbMnc27t24cev6FUpasBOH5AwbBKbOIc3qM3QPB+MrV1mXu4lKq5VLx1eO5/iIjvUgQ2yOsfC0Xpc2CB60tO8pUsSF+aWTJ06r3tWcMztb29xhyEI2VYMlHh/KKZTKfjEI7DQaWbVEEFR5XGXPJf3zjexzz5z/dfOd7d2HxUKF/q7ANpC+q8CXYCZ8thdTumiYaTRQaiJhFNqYGS4Z0U7hCSHSb5aHs5erRAf9wely6s9+/2v1AsvofVnLqjNEmKHWJ+fCryqgIXLPq1gnRroDj0KiEX4cqpLIjyGvNo1tVaXNhJzJRi3XK6eHLakpbJkPOFAx+lFGagd+6p25OX/4lVR24dPakNqZdSY6v8zZMMMAPB5QavgTcNKm8ducN4TJE3NgP1ApJW98PViNSSU3DaaxlqakclFONlQ2YOXnKOAAzl6TuZOPCJYzLgGAU9u1ZIolP44diLvK1yqOpShdOYJuZ70BvrK9TKRd7EzB4QT3U7EGgt2XYYFaxozFAp/EQJaEhQCSkyit21roXPrDDiMcBAZZQPdwzIOuMD1KD8SJyThfQlO0splSrZZbWEBuOQ+9vdNav7XGmRVmtAANdLoFwQc3UuT7Jaoj1RhPakGKRqXwkmqVz5cx4J+t2ZE37SLH3cWUiZtzp6yNRKqAVM4oeGg4hcdZ5CPJjOaRDJkVVi61Xnn55LVNPqPdb7PGowKre6JkIIG9CuE/9WdTYPNGD84SAUApPDvx1dMqNyVU8SxzsoKDjOuBHPV75YX5ZT4SMx1sYLyv5UHLxSqcj5WUyqoJiKeegVczAYvoYV1FOaIZeGAfQAM8ffw4YO1miyRQiNbFvJgoTEMI4fIrPnfNffFgQ+2yEqHiofoghQUAZIVjdLV6h2svMKPkY1rqTbL+xRrb/M68t9W98ev3rzxY4w76i4scAUzlRj11oNbjkwX0mTU9lKnAbphFdYm5Ng5HJIDRyJgAPTTHhgZKxNO5R3GhxD9NMORSNXb4NBzNDOyiYEbIhkwZeE/BEXmcWKMDfKomRrbZ5cbIjFf4TgrhUVO2lBpNO0XUKSHmhME2mP0VtecwUbRKczhEB5qIRnfgkU4zgYXvzLPML+RYNIPIG3mJBWgWmNROrJKlhqREOIhB4W2h0OTa8iSEhOCxN+OR+yDF2IcBQRwQ/QbgQ+H+qkxjB30eSC6EHwnvgTGnRACtF6U/K1aIYr6nKZ8tkIHBnDqPaUM3cG1v6zu5VDkZ0l3haODAeI1Ru4gm25JeRlW21cdnkPEvHpWs3biM/Mp9bl5UIjRTHIPWtjbjO/w//OEPKenJFY4jrLJ+x/4PYAyAOGkrbsTOmUXZCYA/iI/HEMJrDKWo4HcPIVrYYu3XosAfkkBDe7eNtRFaEBgGZCQhIyWMCwSFNEjSenKeoj9RXvwQzARAqWwzHzzUFF9DZ5d7oVFiCF7Pq+D09EKcSJt89aLFsSpF7Md7tKMjYf4O91TAo+DBQL5qpFYo84t+OqEkPCGFokZjbvKAJ+TnVDHgJIRCkVchrwk5LZLnO++8Q6Gqp1eZqGx0dv/0T/8UKyTuOBveuQ1JYgvqzJokCUHLUAwkrMUTyEqHMrIekXBWTLh1jkHYs689i//2levUC8lpO7fvPfDREmZtWClDiRfEPfW67gD1RXlCKAJfRMRD8wCMKsYxnqOVMTeCYwBbuDpRwHjCETyUkIqDZoXo+DuBEjZ3JLE5oQSMIgOGsiOEpXbKQt16AQnh0hiDUbruWAeKuWKLJ2SI7QNpA2alwu/DBcuAJJ7RZz8BU4cYCyEdPxO248eOQXn/1hrTNr5Aj7gxgwO/sohVsIYDetXTs8IjOuyufMCoL8ahnjvhuaLNMRhua2ip5SFGHkwRf/oL1ThfgYAhhkripAJx5Xd8DZ8jIdwFAA8hPN2DH/pJjgfWOTAeyEYMwGMAURKP5Qn7lSSh2z3KWXEIW0gVcvQQXqPxgJXLecLTYi3HkNIFQ5x2aZEUUeMeH8gmlhDQ4omeVj34gScpngilQUYJDcaGI4oNaR1VBG9jEXXBhiHkCHjA6bkH+M/whCQBhrQhObHuiMXjMM7SAH/IY7H7rQNUIeEhyN/p1TMNtfk4DQk+/E6InwgM2YEPAPFKxWH3ARceLxGQiDRSypal2ntG/UUyuWfzeMjj2YM84A8eJyAgAQ/EsIPE0vP2rRu0bpQXIfCdKA0kTL/BExx6ALWJ7XOr1a/V9E0hAh2V4/cnapC0mGeqFFwdmEotzM07n2l6TGOxmvzFb37DJa2eNvlM0hnCwYZog0G7a9hTZ6csYmb5Mh3rejO7rc9OFwGPZGlmxBCZ4ZArH1M7QLK2ZQCSPaJcbwDG9c+4Ur3EEjk2HsrJnIYHdsiGWA9EKQU/xIATQI8Cm5eXfQVC1J/HqfDgHBKP+Ghl8VwABhVhPInllS6JjFB05M6r2jgDULQ/y22XL7dTYzq142dlHM2eq1L5WMIUHZwnhBEC93djloL/0qWLXAyaybS8pjwLDUIGKSzQRQxItMCkOYaetoLAbqYrSSjBbg5KcKQCgPES8xN6WEJ4ZZDtfKA4jEJhgpfUmCA5Z7iFzICNEOQZbYOHq+C4eIGhC9303vYOsre518QimK0LqvjWwxvAUBYQ8iQjZEaelA7vT+zL6QR6WdyD3BKlsnPKSJVoGsamgoBBknWnXFlBKTWwhx6QA1/SvRvjTK1KSN2GE4wQyH1E8+Nzu3NaHaNP5YmVEANjSkRy2gV0spqs/sg+I6yvVesDj8c4qTPLC2BxkGJNfKIOXDvWznPwgJ+CPzCbR/yIWaGTZSrIIAVS21t9NlfeT73/5S9/uWrjJeZq4h6zPeMJtYbnxRdfXLo4QBLu/N3fUnBCnBt4IANHN0LtEIjTkpE5+W3sYcGRVOMnlQPwxB9xL/Lv90cORt1Ry8yRGfRRWOj3S2kCBkfC0xifDBZyyUxqRvtlCEepX3j+JecqVu2Y2hPb6faQDQplGA7Q5rhAAp9v37wJOm7ShV2QgWqiasBNcQCDA+6hZF5AagHR/eEPf3j18id+PoMaoYtWrCmEMBr3fMMTj9HMr9Dq5yjn2XleUIg7BBWQKDwiT7WG86YHhZ6EtATi96cHuj9EPR4booBEzBxbggzJbXAWLgrxwDcyc8gEfAQLtliO8Iqq4Dwtz8AWBNVjrZVJOxFCO2LArFaWyVCzNApmYdQXCXHkjiPKHclhFBhDLu4hEIBDgbxCEkgeDwcYJhDuHrLAL+E3UE/iuTt5BDM4TKLCD3K0BHJSrzZCEkOgRRw8AHi1OvFOoRIqRlE8HT4kV2nzOaT948vvo1q53MlhSEsxBEYXRo+gtW4MiHWKkbsBScRZKOZ4ElorsqcCnoEe8FQ3MOzHsLKPyoW9K8uyJGXZjy6YbwX//Oc/v3X7U3DClicx00nlSUIgqRXwcF3722+/TT2SNbwieTyajWSGItLuvBIAo+Pl/0Q3QEywEQGbihyrzUA22M6ebaD/Z9M9IDF4AVLFMedJKBTwUKtLNBRrsmEAvJo7IJAqly9NOjVWEMDBRvdBjlqG+gLOaQAQjz/No7ygB0fMfqyNlh2SQHeHXgmENmcCfkOhcsHnkAWZqb6i9PFvPDwI2skp8VSeHL87x8zTMfNkTcEehxuIFSIqjqcVcGKVwwE8BAArsnaXzROVHb8T4MCAkbUDEw63sQ/yEF4Rc2qT/QjGAHykDinKzrRfwnQbGPpB+hTwIDlMz5EKAt0hG6YPbLE50dhBiWh41kDCOy8I4dq+MSZHKEwMyB1dH0LcE1Ou8FBM/IQLm7UsZWOcYXL6pS8VfvWrtw4hSb4qkaFyJMkoD7EcDwVHr0obU2h4tC5B8RHdfF77xK4ViaJVcvkzPS9lVbVFema/lp0Mx0vCH//4x19/9TWw8Rlwogo5WzbJpGH75vaWCpvJsnHuFCJpwBCIuPIkkFdqp1TQKAjVBh5CyJSpsnukE+i4Ox2G9FTfH7/2EtbQVAQwEU4jxbF5iCf0yW5Mv9hOZg6An8TC4MrUMJC1x6KI6E8EEwutIbf6NQwGroeFW4V6u5vNGI42MzedtgD2L+vBAlqNGaGXIhMDdacPa6yoe4WHEMXI+c6Ddnni+b9twjlVlDPWC3BEZYr/bJirAZ62vIghwoVAUOaCTMjjxkf+9NOwopHKpreR3iSjAO/dLXgQSsYlUThLjODpShunNsqqgI2qmNssYk9amqRtu3Uk+5fJaJgacfWG7sXjG6GISKFcqJVq0+Io05xVukPEms96EKspjBbDlEVjPD1XX1rK5Eed/qw/0fKGFcWv4FCuT3YUIrh9foWgJ3g8lSc1YpTUhMOVi2YL2laTRjBSBKob8vixa4SnboV9qqWF1/tVDWHb+mQj9+LBNbPumfL11cy0NzixuLw4V/hvv/ud999+5+7169Q6N2SoDtOpQgmr1IhqrxRlY4NXQtX4Tazx4zE65AGGRmiQ2mYvcn8v29PTTnPzXiGtFdtyId9p7axvsGw3Wl6sanFLXWC6YNfoOQaKBwaEgCebXfLr++myLUJp8t0pFA2For6KJW0k1PLqSulPwU8s1GoZL80yuhQCwwahRbxMVIyToIycC1is5UQ5Dl4x5lG2jzlsbAuT9PDOOrc/N/iosJecIYUP5CzFiJFDNtUpsICI+XCK2yr11dF0qlKrM5CkaUAPEsZAVGujnB1ud1necEq0T8navRGkL9ZTp4UUxubdVH+UHZXNEPbBb34rBXflCpO6Xmr0ZvaHrz77JWvUasgs/EoVlfMYYo2nA/iyt9lO5bPTuXKLAxCFWWZ1DvP1cXZ25/r15a9+9aWvvYo5QeOZs5VG4+xibb7ZrM43+Mb9/bWH4N/cXIdgzobBEnY/oafISQV0gShkkSMqP8VCOsUtGM7dmRO7fRJLKBtswXmmfNbdqpPAQ32h+lFFGHGBeUzpRjBJsk8LEx4Qas87GoQRgO0De5N5bpXW/q0t0ebHvdE2F1/CzD3bXurbuYu+bgCf9FOjOrdtFsqVebqnPEPbvdSIgxAD0xUQqlwi5/KmF2Re0vKY4/PR0Dnc3abKFpe1yXFicYVaWLv+gAs4yej8iVNrD6+zBMK+Mn9cyQUWdsvVHVCIdJalebZYQW4dRfTJLzCMhzYEtEJj3AFndIclBJg1N13embMn/iCVYrg8HImHrG9IPCVIgvKn0ewlEvEEenjimSzvfvFe/or6RUYoULK1vUGN02syL8UxGFKnJrMpdRpWDKpFc0LE2JkEflmd4mwVw3dr4/wlM+ouTVQIZNxkXWdEodHsasQKb22JXokyojSgx3slcIOB7NR7WSEMLWy1fLn8SdEOY5gjA4B9bkC8w3gCUeHO0cXt3Mh0rio7ktgOUMTnRKooJEKS+DksOfSvotJKF+kT/Bo1HdJCCRyf7z2UC3R+fpr/6hAU0PVtyDnqDe36JuRH4VaDfq9cdm4uNz/fsSknrZcymoxhJSF75O540uwOdkcYi+S4eFAcsLSoVVW+zUmQD3xhuBLydY9LCDTALGgjHRjEOhNQQ2b9FzbO+tL47NMbt1jYmjQ79C8zvlKHS2xLM7rR1XGZKes+mMQ+fLRJnboyd3rINJof8D6b8Z0JmcZMx+hVRqkIJO0LzBh1PvvSC4srqyw4/v33v8c1vkzFgSe5pTtQs0FmvLx0EoChCxnb9KCzWGGM44UFAOf+8KSgGgTpVm19xBkAmhj9OABg5pUcUT18Lzo/1ihocXlhoZCtaFTNfQ9adm80Ko1ahS8BYH7KDIAFx1a7xxYVmg3GVqsVDZuw46GppseDfqvf3sNqoV4roflkGxLT5AUk3+BxIl1gIIS/rG0zsKZHXe3tNOHZfL1G/4M5C30CZm4YQayvrf/gRz/c7HaYNlx69uvctbV07Lj3LSrRZFpjRDefnmTK7db05798B1NfViK+9PJL+WKOnWtGqU4D2Uk7tVPNRy1GVvxhzI7e4qwPG5193UY+a+rSfDk+ZN3f3aMXo/q4RhlgNDYzIXakeyRkoTzDxVB86V7TQY7eUDsZDUzykE1ZKpnsqeXlSq7Q3W3e+OBDwhv5EgvNtXGuOsquXb6JBob4yiQ3a487dzfG60272oWZanHItwGq9d1hZ2VunvzBTPb99JQLp1nxZBAETbNCjgMEmFCwRg+lHCpSU6NztvEB9FMJrDrjIRdcbzbujIetnDZ32+PdufFsPlVi7386HM2V56q59PLywh++/AprZD/59U8pdae79eDhpxeWl+m3x92dcbe42RqxBfLSU+d1LHXE91E43sMq5mRX5jjT/mplejpz+uxC+fmXLw1nWPKu3dd3KYer2fb8pDzJdZCvyUq+fXK2t3Zt/f63B6v05E/l5u42m4P+GIv6XW6Nngw/uv7bVHHw5adeoG/gQCOigphIYrP5cydOXb15D1v780vHFy889/OVd7nGpGvyHEsUhaY5aAGIdkkdIeXGCvEBBx6XTX96oD/FPXJCrWiWIkmJdAWWQTIgY8ChFkIETF9oMFVYol5MoEAq/PSbsZwrF/z0mKaBRINnTT+Jh773k08+uHfv9uuvv/7tb397YbG2trbBkvSj9fs3b33qdtOBTvOIFg9RB003h8Cyq9HrwWeESpKpm0OpEu3cYKgueA2rzNlxUvL95NrVh+uPnnv6Re6lmZurMcomFXXn43BOuZGUcisktodyBDzDRCCEuAe0irWnjca15hWqgygAkjBis5yyIJa8eFFNuW6Pi5lMiP9x5ziT4R7iWcfhoihA4rFxprgCGI5Ypybuuz0dDdo9pKVk8UusaZUqdsHvHsruMaQirW8OoUBYdWUBml0TbooABtkmd574cT7i0kxTVl82/oEb/JMMQrLPRCMBiHN2pUq2ETHu0buFoKmZIxXshlCSmFCZfRKq1f44oMPADy2EwiGEvAEzspFPNvimCCRaq1apg5BwYh0zT2KjrR0/Kg5Ko5TeFvt9BxY8RKhVqKILGdmK3fr0Op8TLGVmc8Usl/IJxv5MGGQJhsWP1l203pq6/ulNxga0Mh9NSjHap0ehBT0sMtS7a6EDOrlZ+97dB8tLq/lcEeXPHKe7u/v3f//3f/d3f4dOYwoCt8lOOVqjpeuCTp97Uhyqw2iI6tqPFp07fWJr/eS1T29jaEiNKSH/yN44ALX60+UABAozo2I0LmsptDxvLxCJIwriyYGyZEdNROT8wtJCZpwa6vw3h2o0hmDyAKQBu/0xlq8Qi5iITs6GaTApVCBUCEnEdsmbSLKmxBMwBC/Is7Iv0Ytnh1ld+FPSTcfRXM4lNdKQ1kVCObYv0CFVoErgyzdZPsZY4QM19NTMSuiVAUI4xQdqgfoVjU4AmGOnGldrMwe8Axjn8KuYCI7CVYQYTCXCxa/iBiGgEs1JRxKcGrCasKdSPLngXKWwQoyHQMQjmdb4BIzaHeHg8VSGMqKTQQziwaYvBCNjqDPOBHR6g2K2rDG9EchBIjVMhlnS/iqFyCV3DqEWNQ6grhnUMGvJ6rNUmXIxNSpm+wO+tMEudgbj6SETZI4jHF8ebT3idBLrB9sb2+sP1xfLq9A1YlY0To0w3WNOTZaSq31eRZRLi1JsybK6LVBonCs2iwm+OhIxSmyBvFBkL4XzCimCeotV2fCQN4XSUV14OOrNVwovXzx78/3f3qJ1qoZM6pwew6lQUhkSSBFXCYmdDbWk3byj9OBAjDhnFRGDUyncjtuDvbSa+QV9Oo9aAIzPovCZFpk/Wx8FBlqP5R+S2giVlkg1yYiWS+Fv7W5vc6PO//Q//k9Iy9VPrjDKQqbY+uV8tabA4x6FBw8DIx29soTSlrYZBvJioXzvwX0w8MFqzn+5okTyRTPnNWm+XFHMdl26xqnN/+Wn7/z85uYffvUFvldRymp0jWGHIA1eAm6qg5QaS4sF1hZgspYbVSCASWX1Kn3Cu0LFW+6envQ4rjec9LN8ToavkbHsGTUWnTE56ExVKAgMHskVMFwQwxL+oNvlKjSi4hVOz1hZk5HSHHSqzaPCD0JFb7oXjATSjVJO8vphRhdfjaBsSRpwPDxNlWi0/yTnYIK3xox2sxA1BuNUKOmTEDwxHIEIcWCDhP28Ys4GgKRHFSKnMJDgilnZUGRLNYYynIzA0bXy5PwEIeMhw1REi2+zZcvVqvVkmmaQhEi6Cp5gYH9gNor25IGP2lBUxcn8/2/xx1IXCVxUPCtk5P9i2YryBPcwGOHGsQ8++OAb33gNm2/uuwk9MaVGPbP/Ywtj+9jJDmKERP2reQ5VjakDTwBLgQQnOpeP4cBz1v0IoVIwaMLmkkpmPgPPfQVJMz6rNUu+r1KRW+ywKmWZzLAATXJWHx052NRgWZimtcs2SqNhtCshoRfB71wCNnDSsji6UQX4I2GUo+FhtOR+BwvACQZHhQUsxFIEKEwm0eqGf07XhvJEIV4kcZoBxtMa9OBSplZkmkcUkwoMuGQRM9EOPP0J5gD9zSaD2prmvNJdlBSG4HdULF9HwgyesT6g8aB5nbTM05gKfu3P/+iV11/faehiELgNkRu721CCUR51997Vh0DiIVbXBsc4vRRURyhOzF6FABaeDgAlyBViwLIFtYNj/xC3PK8PAuDIRVtEcdoYm9iIExLLnXIBjIGK0pilFbeXEgk8ZWSJEz8ZQUB71Ae4ZDID/QI3JuuM3u/uSI7cckgVmtn4pSDgoBSEY5FRnepLLGTKYJqvdOFxLeR1YdWieoFAyIFUzJQg0tdaZwOVmmEAqCipBtPalBWrgTdUM3L81re+9ZNf/IZUdMDk63zxWJLH+e0XzEMOhTtaMnL2Ektfi+UpPMXP5Bn8bLYDBiLKQh+P67SRoC5RpGJUDFdDNg5J2oAzRLmHKHeUhSoMsZ6QV2I92ENAjqO6qDLXtABY2pBUnmR2JPShnlCZksGjwKgOBO8w8sUuGRuH6dfJSIQQIFTiuVNr20LOQAc7hMoxEJhEJSzmSEJaT5J8JnL8/xNvkkUqtVU+bU/h1hhD8WEMmgcYD0GtJ1nAfJhRvm2HSPAA9lgBW4+DMOBCeDJt8Ie8nPnhFXwOQ/26ltt8+JDl4OzWQ0Lm63XguQ4aeDLDjygAzxtSurS6TBf2yeWPWInlbmStSFqDdYSeBVThoZVBP35/RXucOrby2muvLa+ucBPrx1eu/sVf/AVq3LuDQPOTPI7HY7k2AORqATH3PByGHEpO+yNfTAEhkgsCoD/LcUrTYCKbVVacWZU6i1yheV5eFpLwivLkP2QAAQAASURBVCqgsojVMqLdROQ1Swixgb1QhZEIW390KE6bI3Gq8EeY7T1QSyAVTFgUokVz3ZNG1izTiGYjmP4eSAhAR6EPWcd5sP4zzlOfPHsOfl68sIpOAwM8x9aWvDgzBHuBRMWBx+p0fwnJydvZ2UWT16deibpRxMG0wkiDldxJOYMNkuitQI6fC6wYKbG0Qd3ls/o45Kjbp6eeK1eBgQk8SY6caGJO0UaymAOYw0u+DjWctkneHwwuX76MEMMxismYhy4C3UuOYGBVXdKVl/3d6ZMXUMV721v45+oNXc7Q7EIJpSAhkzP3e/HNLwmk3wFABNiTEKgCj3ZSWMhm8ouUm7H2aNoCz3qqxbjXi0zNUpXGTLY/xTcA6Mebm+tc/1W2G6vBBthkoJEzBbRq0om6wlyDFbVBiorIVfMFTfBm4g9lZ8qXKy0CT9HY0CU5tkKt1lNI15hrp9VwpCepMpB3twcYCPe321gSFUp1SawVmfkWscwDEZLdzhBhY9wCHi+pSxGQwHOjB08YALxpi6jhx2zRa4QTX8IZsNjrDhiQGxLpHMtCo5ellXnI4JUSxbBH/Iowp9wiefVM8cAi6pTq+PGPf/yrX/0KhlBv4ryd6NLSR1yDSbyOgXxxPv4hltMiNAoYS0U4fpEd5wjxjkEKNJUCDO55G3n55ZcwbOeTEoR7Qua3ZKHlP7mIDx5lIQIDwP3BI1BzeKjFEA6RDslTaBNd4SGcSmiDOjykAjgg9CgPJ5AoHK/uvHTJjAgHjHACDV6AJMHvSfxJLC6JKhkb/I7fX5VxImuSe3jAHGJJ5X48LvNUEB0N6ggTXeSHWkAAwMATZ8AqVIxhn1RCAPMqjGMDdYJ3F9LyihhEEC76RjNEgsdJZZEFwlCnSALEoH+gAVEknuSelnCAHZ4nOJ0VHgIYHi+mNmfM7zTgN1RH8BYlgPnL9sP76Arm6MAzgQOeQW5EcPxDII6NYVbAuXyPpRK2EXHk6KWzUlvxvRbscnlU6JtvvslMBxtzvjoLBuT8+9//PtqMMtpWUNQW4nz2f0NtetGIIDnZURY6meFkjB6DbFQ0EXHx8aCHOfBBmP54JcqRkhwV5GiN2qheYCzfEqAUyIDDEOvl8uQhCbE+YPEiowUBC60y1CbZhUw9a15JGxOiWDBY1xz1HQ6WfAKDCyGH/Ba5Xy4yDBmRSHR6EzONkUxLuFESSQs0E0uI6yLFmd8eVrmK3yfD0ToSYB6PAqHhVHPDCZs5n2FZFxCFh1iwuQPQPWCAcFWWtzVXU47IFlhhO459Vrr4Rik6xu1pYSx5E2s7lpAuGoiiQaHSafsKkhO4B2ZtS5ila1IhXTyxD8W4ob0uW3hGAvRo8+eXPQ3YmOiSEJYDyajG800+laPhpyDA8BTtxnk4DB4PQSsQa0sv+6kdMKLvKEGCM44WSE5Q06ffuvtoP735SG549BI8AcZjyZooXDwhCPGRh1x8BRIYpwduwHDYSEcJJ1ttfUEKrUkLioTIFALgoAhFcHT2GtUvdcQAhqNCdK9s977w3PN8feFnP/sZc3wUBZgf7ayjZxzDITxgg3L4zxPFcvL4Cejp6xPTzHCFn0UBK5b8nhYWA/l/3LvOSaM3XnqKESPhwABgT0mLh+CJA/mVczAP91dH7uFkSpQlEiQMA49QWdonPYAEhgQBAD0GK8Lr7+pRjglsJD8UkjuR1pCCLQJl7HwhdxtW68mFy3Hf6UwUsNy+cMsMgIRGMyhoRAYgww6F24vn6vywFkCwOhUW8i0eT5Qq2jDy8/BRmAHa3h14nJVKaiGWHCQGql2TKE3H9nK3rHAP66Jkp6yaaGKZksMyRQtnE3b+ZdJbLpb5GFke8eKKPXqgDrcQMxlLT3ujSQNbHa1/wZ+ZfWGHLpAdw9kqwsJko63jh5yiUbPHYkMf94hLZJT9Cz68gkB4qEY9Cw/kNA+v6B+eNFFJIJf9sL7Gthp3zajzmzVGshU9Vq3T0hit4+duES6LYRmX+wruX7+zt7Z9++atKx988qtfvXnv5t1Tx4/RtrHn2dzamZtfZCDEx1xJ5dt4sMUrlwbvthgJgZG0iZ64UsyDcknluZsrNe63HxYynfykzb5dt8OXRbeq1QIapFQtshnIB4DZFBh32jTj+YUSk7G5Bb4A36iUpK+ZE6tcfOiTyfOUu/ZYW0TWpaPFCvXxs1qObWT16+TL7QbAwwPJJ6YpqlEeRzDTOam4hAuBwZOIFJJKN8VHhipt5CSjwuEi2TY6rWvBxpa71bmCmxNOY12bwGdZdQs5NrxIDvIMnYwf2STlzkcmdK3dPd0rNNJAyu8q8vvEx9wFOZtuZIZf/epXF/7w9Z/85CfZauGVN95YKFaZbDcfrFEXNx/eufT887sf3Vrb3bpIVbH8yo56muXXQZerSrEVy2YYKm3v7XJOmE8UbfcGx5+/hPyzhL1YbfRb7bd+9o9//Cd/0pyyhlLDdq2103r/Z29ikXH2G6/A0vnFJRo+/KegtqZNd6Hl3UFKy77w38RAfPYRYzwutq4x4kzcUrwi7AJKEk6GA74Fim0+oy4KYv2uZpVoBhctqwJUObASLXtVCZkLmSUP0iXTcq5hGqbSfQ3d0Qu2SljS5/j2uhhN9vZ2thggjjCY42OPilRvbJTHVOkl6Xzgq9oMDl3RZjt6MFgeTs82FrjlmoteKsNULZdfS6cZQ9dPHB/yGdnprNNq64YvbZVbj6Ldcm2XS23ytDUsQpBeJIFDeflSebzXhHSIIpDNGMSWVQ46VKlaG2EYfBc76D9r/Kvf/va39+9vj4fcO6o7qV0nO51CIWftwrSTc8xjw9M5CbDH3rh2Hbsn0/mIHteKos7VlGAgJoeL82xqnG01O0yWHj3a4IlAYndnAyefI5FU+coAzAwfQKvtfmGXLbwNBSxIyoqEkCx4Nv8DoN2zqrv/0a4F2aRkMbhQ6WN9QriArZv1J69xETTBdkAGb/Lr+l5ltS8z0KIcJUI2MuEtQm5pTS4VRqggCZQ30vDyE2ZPaRiP1T2ChJpycZIMYP9hxO9Pokx/glmTeTC424dO+CxhtAGcCIYgkygnJCbyAMC/5It6k4Q79JqIib2UKPbq98CLRXgI7YoCMm+i+szEP8W5HM6wFxYW+9kcdrbATu2iIV26RrueTDuD4W4/PRznh9iV0oSQNDFDNQkq8OCcaZbPEQ9ok+RBoTURjEKc/+oj5DQZo6/hOgN2Ftfu3mBNrsJJEV3WOsDilXmBEprFrG6vwxQolWvMN46fOHnt2rWHa1sg5rN0Ul7GBLCD1LPABgGJfLDxaHX7BEtjdNzt5h5D9v/u3/877nv9+P0P/vf/42/++r/8X/cfrS3bbfhoNkciskxU/MkrxUxKGq8E0k6B9+InE2L5Iu0bqzEaCK2H5MDynNr4wVdV0b3gYY0TJmQG3FU849ZhuFHLMQLgUx4cIIIkDIrpSjRy6I9n7b0iC5rj4YBU6F3YxxnGPHZStFmMh9m25wbjbUxgR8ssVcN1BsmqI/IRZxixQLD3g9Qf+YqnFJYap0KtSulOFDKZYXWHamUZu1gps4yKhtL9RTmd1FpYWllcXn3UbAHJt3wYtzzabn308dWVY3Mvv/zyM0+fW1hYGg3yH1++9l++94MMa7iF1FPPXCrkqVnM2cVnEwcxCWX88NajzCjHAIqFYkY+aTx0pazM6+g04ydJLqSSF7LHgi0loueaX1xYPX6ctVqiytg147b0vcQOo0tdWtHP8kUAjkBhri6k6elwIAvxbn+82zxTW2TJe9YdbVy90+9051gsX5jLZ/OcmprlJ6lhhxuhp6WqMsxohwZrCHKkb8kVistnTlP1Zb74h/3yzt4MQ/RclqFVNbUoiyP7PhIdFkJiO/JSbPg1eIKrVKiqgRrIcPtkLz3pYNKUSXUq6d10n46okCl0uoNiqrNQLmwO9z7ee3Cvv5WrZ4dcCJ4el2q5NmfqKnwXcC5Xy5dLhcp8ea+tHUrMfqCwXVAPslEecbvb/fpkK7W70ZW65nr3WWXWLfcejddbm0O+Lni8vsoCHPctbi5l96qZR+32O598wGin2umlO72VXqbZ7C2VGyfml+4MHjEJv9zaYrj05ee+wtKJ7M74xAViiQyaOX+5qjnha1965dPLVze5Wd0ctUzl8mSr3hsKlEjWxAMQyFmIDw4OKzrvWXgKzMYz/kVgrmRRV0sWYnauVOb4/FmW47kQk81hH3Pu94xOSvxE6sjRcjKLSI3g1SnAQMIRMQ7zcQksJvUAkgjRUil81GMEc3qbcKeNKJwNldVQrVjKifkzgUynSQFuYBg04M/68N30jA3qKQCZSinutnZ+8vN/vHnn3huvvmY2uVI4HOkCGz2y1Ib39cpQWfgPOJ0LyscjYI03dYORErKUBLoZCgGWSkhwSmb9O8XnlSicB2qUdNARbomUStRZEkfi4e73KJLGwGBG9amuAwZF0Vpi55AQGVJ5yIEnHCB/JCkOhVLhVGcVhZlqEW2i0MJc41GPbkRCJWxzi/DDR/ONxrkzZ5jsEKUhn7LGS+VCglhhDgrVF5IvqpZsdPF/nD1Z7FNrRSEJ9Ph2BX6Ppdj4VXASapxseoBYqdKI58TiWIkolqsgoM2Ql9R77AikUE899QyVIoJkLGwZRD2LBJg8yEIaBo9ZDBq3NAaGGZEummhTDUNw7kS6+cmHELZQY5dugKanM6JwXB7El0kJVzevH5UIDbW+sb7OYSNufNbVjjIO7g2GG1s7LNVpzU/8149XBCcFqxU0Q4+ZBRdxsFHHehMtApWrJWPDGSTW0iorkQ0WX9Gx+TWvODPnpVjiFVf587Gol55/gdE4KwOEwF12Cfmcg9QsZVWLsacJiwHArwi/uGdso6ZNNan1LXEhaKP2zNmTRY5vUlZF6A5iullVvMrEf/UgTqERrFzwqNxWHBL5iwUKEr759QsAUChUpYFK/TFFLlZLnJk2hEKB7OkpCNWt5auSqFBIAp/d4GSNDWm19plKV+gaORc7NmVkqfyBAiSxZoyPuZAXLIJIRIIQPKpf4w8VKMHTeF8BgrHBgcse0ztC6A0tDNmAk2rO1AvgbKhTRhYuAGElgKKzN2EkmPBN0WD07Vo+ANogrVF4xVjV03LZp8QAWc2ZQRIzGWowbgGkhhPYn6rwnKThTdZj3V61xPBHPahApa5ZY4YqO7QAEWIkpMJ8hkkDbgq27VXscCGrgOIt5kvDwSg1qXDoamL3aeUnu/lJ+fjq3o07yCo3dG9u7Ha2mmyr07XCAW76lkbXaTkxDOZEtZYYIoqBWglBbCiO7q5k1ihukDvwNpKB61CqGrZaALNoNVZYcpMfeyVQdWE4DUyNGyaWc/2nzy+/e/shAD1bAgAnTt0JImftheo55NQXKyNBEmXKaR+EOFLAOFNMyhcnnMBPWLvj0vEMS3n0kr2+ZgwYOSgrJTKqLD+S7GM0n+UlYVMNInL53Nbe7v/j//X/5ArT/+Zf/wkWmSdOnv7Lv/zLy1c+YQ2K/B8+eISe9CuhAypYyBQYM3dOZTEIuXfvEajo9wt5DB2wTlDTZghMIEKMX9IG5Xx4JJ3enOb/4e0rH959xDL0a8+cQgdmRz3JIW1dJxqMIymJPgQSHuqFdq3dDA3nYVcolxiNGf1Qt88XGDTuybyESwykOX1V1lPtn9MnwtqLWgs1Lt5HVuG15dW13SbftiJEtw3K2Y/lq5x/dycmWP2SVLzg3SvzcVTSUwknyMjJE7DEgfpNBKocnsADeVK+gCKZ6rP9ni9P9wgtZbA0hyg8Eo/3KxxaUMJchldUFQmloGLHK4qAYwi4Qj6rZc3BhJE68bYEJqZDPmAAVKv5xflFaDBpULhTRhRQMcr/Sr/iqlWnPz87V+ikmAwmsDRhJIoyY2KQsYM2aB7Cv/e97wGzt7PLjKlYLDODggm0qJ2tLa7EPn5iFW6kzH48rltlKNaZkhJXTUjs1ZoFr8YSDwEYOmEUaNlaEen2ut1swnLOQtHIWdgkkOVB2EvWHE+4cPEEozEuNgQJ9umEs+yvlLbNo05DK3f69eIDplhzHuJPAvB4rSfpScLgd7AnBO43niQAIyTGMbIRNXckBoeHRQ7DKxpE5Uo4K4oGBEQxJALYwTDuxTF2Ja2NYGfLK9yT8dW9xUXU3FMo++XlwlhHRS6uHmd2Mffxe6+++mr/2EVs6NgdZFefLgxm1peXIZWFYgQAKwDwUwtaVijoE/YYHHHAnIwIB5iRIzc8gB/VQ76EU2skJwkUQhhgOG3dqklGDSRRmsirelGvq7LY07WKnqB1IJDjARL28KTsPFn7Bl4q0WId0ilxf3gCgKM3DY7XEAsqRA5UxFJwXtm40LC4IIvaobLVWETw+yohpD7sAZXDwzFYQYeh3ZG5OV7h1fDRjtDYPZ7c7wGTqTdaE4EiMc4Fj/oPTX1Mbi1K6h9ou5ZEUVbXhk184xXdxYgWj2MDhlIw/vjOd77zySe3MeLo2YEOjssDjyMLR0yvTBJ3IXkcIGoBC6/4McSjaBwzEBYfLhgAZScWwj1fpO7kydNMn27fussuMeHk6PghF4SUBXj9l1MulNn8xnQKoFcrTqQ5FUkqORu0kSPCxvNxsp1gCBSsFSE8o4q02owAnlC1ZCfMjBwPdnYi3vSJ58urIw8iEiUUEZaNPJGjdyEWmnnHEwdHv47WXwJywII7MtUhJOHVMQQ8Ifyf6XEinXWOykIiBRuQQ3Pw/3M8MR61ferd80UFcR8EUTHnVQXkQki3K+v7IQuh1JHPJhLZkxwXeCLPZ5IJEhFgzc/x86T6UIy03wfXdQM+l1Pwyiohkj9MnMmIKVf2KBMWaB6sr9248Sl0GQmHZQMw6LFy6Ip8Pv2KruOdu/LBz/4fU+L/9T//z2+99VafEST20RPbgGTaknDAO1tghgc7GYRTFi87IR7oAEQp3CYb+EEQJ41ElFgkHmCasHSjyT+beSAhhFjIY0qvOwmtgwAALDw9O7AxovD1MyAhg+EEzsGAJATdS2HhEjVLuEf5M5AKpBBGEyHnHmEWqF+PVS1gVkx/BzYLtlVgUmYyKKU//MM//Mmbb6F+ERK0h3+oB2MTerd3ftsg1bCfY5PAC0JWzz//PHicBmGLmy38B4kaspZQDjhUHSXy25NY+8ChmYHgi+wQsLC0yNIwxFA6wChvOZUlZH3tAXqVEAhbmNMiILyl41itcAq8sflwnUXqqjSoxuQUEAyeK+YC3am+YeDsYgArqlj7t+/OgwSE4KnO1WA+gZSxsktW7WG/Ta/BUhZ4RDCLNJGKNr0HuoTzvDwAP3jIhScOSkgO22HI2D6lgZ0mgVzpAM7coO1mklC4VJXVzKg5pvYLQsBXH3TXOWM0RpvHj8/RYz56dIduK7u0SOzUhtlkCrdxtLjN2+tAsgDNEzJ4ssdJXnjAOev2CSQhh3IK8LRef7i9y3YvB1756EWjolSlXA1qB8MeqXAAMwQC/vKdG6JWn+vYXwmx8nkZJW9e2MAV4DE+8/AQGIO5fO4HkxcvtEkopBejjFQr9bgPYfiJTYY87ocGAmE6pcDD06mCdfbqIarTx9MS6OGORIXlNBWALtaTCa0AkWBYDVtQQZaPxAx4T+hP79YoEa/kw1mBtfsPmJC//PILDHhIG4EdpMAJ9jAAjIZ9Ivd9cRNzeCayIPRUniMc5BUMDuCM5Yn0Ek4gUThPEij3V8IB8CRPiopyseoIkJ6cJ6lw7iFWfnsNAMETMgohAJOEVZdkKqc1kE2UE8+TzoWyI2ToRiYRDHqfeeoikkPTQdoJNxUXNUMnSU/Lj5GRUNkLOyEh1j0iAEiNfFUEOess3esEhCSKjF3Sj/xADDF0aBBDFPSAkBAHo1DuBxKcVBDPEOX1BYDDuMezToLBAfBQ/Csfv6c9Erv8zRsOOZEva8a0dLDhtOQad9DYGJKK7CCJroDzJ6yWjEa68RytO19vQFWcNVB8M1CUE0VnBHvJVwptPKad0rjwCMhGnk5q8kkWXthkoKBNXqhxmhWYOYr6yTvvQzP3VRJLFtI3mMX6IqWkF2mXDIMHnDhf1XG0FJAQ5yQNl7bGJpZDEuWsUxLPVdTK8epReMjRkHhMFOXcdkjHFsoScBICZorAWR5UMWCPOzAAA6RjR7EYnogespakGDGqKZs7JJE4AYGMEAUN5oSWQEeukHiAoUyt7ZO/02mRQoAniVCxtofm4UaeEILBOeOZxsklsUTypC151EEAjYhctpOxniNIoFAYTH3xRnZofrpjblODkzRQT0XWEACkkpgL2GhQOD5hTbCTADAh3F1MCIu2SpvSya1hUeGYLGASQT5gY/+VdmEJNcQiZIoVTDwODFkEj0M6N7R0a6zTT6xLycuXSj0J4V5SXsHPEwABEypalRCPQiJhVhAwCG2xePVxKVKqSG17DoefJIeDh0OPevdMicHjc1LWTagpWMQoiFEADAcZsdAImBF8FCIXoXgeRyo0z1/91V/98ue/4EtLp06euX79OrtiDCE49MsG+ZF4SMX4SJKg8+hp7vCHXadPnVfNci2QjZkJ0VTTXOCDY0MLcf9P5+FTXCV//vgyIONejyiTJ9guSYDMQL29quAKiVDK6wIGPKqMjWs0UrujCzzLlblDkHr9PIdWRIZnbc1QPg/2d4hXuYzyXGlMrahrIbXn4TvbjH0IsdnK/poxLPZMfC3cWenw2tcw5zKK1zOIczKVpPVyecwFj7+G4tkoRHYmOMtOa+3SHQ7H0+n0HD3bOPMAkupYN7Ot6U+qO1emGaRXGkyfuCiVcXOrmS1kCxihUlt89J1qZVcLhuSxZZ5x6qiQ46RkZrK0XJ+2duhI+h3uZhn3e03lPp2VS0U+cZOdDle4KpuvfzJHjCw8U9VD91PsU/QFfNGuwudDRgMOmzA7n2VcCXNtmsqkXI2Bf/SIVlVIcUA6LE4Hk/aH1+/SAT+opI8V+GbaErXGt9NhLOdNJayZ3NwiK5VFYFi6Ygv3wb37fGm9Um2QHQMaY7hWJeEHmAlk+lEp6/MmthsTVbPXFNRKDIwCvkSPIWM2M2631nOzDqnLfG1zNuh09qwXRsOxOylquW6QU5Z/+sbLNGCr/C4fmoDO3FTHXfnGKM9ZaqgPkerIyyQz1cYryjqUFENSclVPZPIDbUz3RJJ9qEGp5WjYIUXwBGmMQgz4cGCAxlPno78Pd6pD5EGtBpTQQSpxg1ctqaT7BVbq050Kfr6MnmXzcYjVUrmCTRpLc66V9QEobDW5uKfT4UnRxTrKZQjFd3a3qODZtP70meyppZuDndmJufSppRu9nbO52jYfni3WOly2XGfrvLz87IWV5y6mH37MFiwiQMXxPWj+ZOmA1Vhqtnz8WGm+3me0x21dpfziqeOpu7fYdkWJ1yvVjbVHw+3mpFDhMi8Np7hRaKeZ22zfevfdwdYOdxxz9I5Oj+/m0o5kA4gJel5TPpZiINvt8dkslJzI/JQ2ZtISMc6HGlF9AYKjiqh5EEBmr9OkOqMZl0wjbfvdthyADAohqkcu2LLekfTqD9Vfw/Q0+7EsFHDNB2Ds9NLHb3Ch6qBfqpXSeTrzTDed6g+s8VtrypWlf4xM0ROct5+I1hCqm68RpsmFlWOzVgd21Lg9bmWFvBgu0164Gms85BMlqW6ri7mfrArGE0aj3LqBTFqtIi3CnbY9eRJCu0TGnBXNpBPRlrGBMqZHsSUGbRJIdvXpXa18vfji+ZdeusCWOFPxVqfPND5l92JvrG8pGZueVIi1Qq8FcjD8isT5Kzw0rMRMmaY2GpcIdwsvovA7sMMPR32snrkY5OSp1VOnTjCP4uvArKAN+lrjcMss8hIuNQV6Uo1g3B8Nwyi6YsRX4HgO7W5KbNcq5WIlrzvBKRrSyLUqxIa0wmPVjT0IHqX1P8GIyAwXw/CDAMkmQP2YdoX3nfICo6iyMglArfbApNq/qSs0BqOM4IDzUN4jGBjlIFOL2Gu5RC/24wv6cYjKLh7IqRAi5MlO1Mb5hj73yeCKCbWmDA47l+sDoc5YD0r6DwD9U18ep8FpilqB7YhjYCoVUq5h/szlxyQZyuQHfaU5BFfl0Sth+8w9eH3g0RXegqzVwEL0PDIcSv0ZlAJDAVVGa9jQYDIAy1IFxm6p1NaNqww3sWwq63gTNiLjUUarh3xtjbQsPkKbfasA6wA1x6X64qgzvHL5HuubjAAfH0YD7v2j8jDHogOn/PAywPj09q13f/sOmDd2d3nlaxNsqhZouZASi1MoDmDKHIEnm7hm8XihlCRR9QYsmSR/Jkqy0QGhOKZ+hSZDRrQyNKaS0WrghTmfP5R4T6crpeLi/Fx22gYxtDGwx+QbKGypGPOjuvH3O22M47h0Akq4SEvmKKgPFJRNjba3Nnrt5oXj86U8V9Tx+RGy5IPWXjj10uSCowZVDAooaiBSiyzS6VQrRibypbvDLlO7kyeOCZji09WjHm2BHQV7/oUXVp96ikX8X7/3EXppNFWvxMc9qOD7D9r37l9j3w+M+TJ3WU9ff/1VNg5nqZYLDz0dWeBSvdTu2l5/r59ncDQe8jkH6IHZiIn+YPUUI2bxge4K0SzV9CG+xdVjOjM0S3Hqpb/XIt9KqayJKAZNxXxjNDeYDvkmH/WLwQ5dMAat0H9mZZV+aG99c9zplXPVQk4fE4S2dEk3dXS5skPrPlofYRA9GLCgKfEas2pTxtJM60HciwwemgZVNup0GOsW52o7bGv0B6yzlKsV7oMeqPfQtd1w1QwcqXrVrHiKEZf1oWgYFCajYp0ss/53XM5RPMoO/kwFq6vbc/n0CwsvVOZ0bQsflGDhZ2Fu8dhp6hUhytXtSH6rvQf89ljW1vn6jLNB5YUKkrpRGnNs5tqoXW5Oap0ZMxw+yAI9nNLLlNKd8rBXHTcnfS3R722VBqXFRpajEHMnFyccftubrM7NzTaHJe587w4WipVCKVddqLMycbe1e+Phzd1h8/SpiyzZVGsr1AUX23J7Zm+SYTFgs9NcPnMys3aTi2aUHQZP3u+ksfyaccc6WgRS4QZkAwDbnVHcKBMvxexrS7hE76u+MSh60lgqniwgIdNFDNIxC3jiUg61oIzs6R69WAuUhzph8MIYnsYHSTi4bRE8XCdEYx69xxogBlAIDvUimXHtEWcy6Y42B+tsmUMeZUSrIQITO4LmTY4zT6TFr9LhoVzYF2Zm3Jb+5tu/YSWaa4IuXjyHWEEhTCBT2BXpIQbA1jUTKH6KOMMTURbxEPwBTIRqkMCgWL08mbrzcEICJOSA03NSuTRilHLwVDyJdS6RltfPcAB4LiIyWYkmFXAsGYs/RkdhnZsRbtkUx3GOB9lALnTSRO8ap5ET0Px34iMwimGbT/wiJJj4MHhamKuzMcDEhzEeFLI3pS8bSbWRWn84x6ZOUphFRph+KtbZTo3GnIGx9JtKaWQQHmQs8Iooh3eYqGwasps2NmaSFXQ62xiJeYl4Yrulp4mAaLb10xAb85l+WetoDOUYtzjVRPGGvs2nJyjxa9c/ZqEH67/FYh6Fqk/JoJ+E2cAZAtNzqU+1SRVlkd7KfvD++7s7bZZ24QaffTFQEqVQ+yxks2bE3EPCiwmPmZ354jUo9KpRp6YYlBoPuhFVqhm+LapSFPWJxjRhxPHZm2iX1t9VB+ZzqZZ/MuyfPnFs1D3NCnifgxgmG+DJUpfYJap1KAXh/eGYcyv6bAEmzROtv4d6Acibem2S+srFU0v1CtKOGhCZlp+kzpzkFCpktGXnP41a10te50BBpJeCpzsCVYMSBCIjVN6D89GlbCU/rZYm9AC2cRZYStq47pQ3fr3an1ML5wjhixHUEmbRYCcJkOph9NSfKzNSE0UqnnBeNUwvassUvEpi7Y8gtVJ6W1BQTkx2WSYmSlqLIZokChBnCjDk4i9REO+OhyfExw6MvEastCTCkXCOStiEUpJNpFCpDGbhrOkU1nzUGkMnDUCRKDUsaQ9eNSrg1IsGRzaWEaO18FEYj/re5yKxYocGXyKM3io14iS31KmcDK5ZG8mkizk+CMyBM0gYDXrdKTPOyerJY7Ot9eFOEwXx6AHX9D9H50nuE6adWD4zIkDcwM/ygqpHRcBBr5dCT2UeORgr7LasT+6EMt3ivD9hMJkdG0/uT0kpdWwOSH7DEzESm3LMtjEob3MR3ZcvnP7oo2s9Ch/DO891lwC1bGikJyVLqhFVXqgVYTYtE6Xe/4EGXqh6npa7EDGj5I85KW2Xe5/Z+ybQsDlOkYrPqcV/wJnOpxqtPKLHIe89WvuL/89fIXW8eqZkTOWoS7ayByRiia4sYQzFgDYz6M96g9TGVrNWbzP2K+Rl3qeFCKks0QODcGzNgwGTesqCeTT+X3xw691PN77xyotYQ9tSBLNUjVf4LiQ5Ih2U2Gcd3Mai+bTVhXdOcE5yiq10ajbMFIaZ8e6A7wqM2sxXRlgz6HIhmhK50AoD5UmPFCuM9WpFRrCByOaKi0uDzXX8rBAq1pIap9T2cfaQB6cyxM5hkrFxjP2aNKL/xe5kMufskcm8DgSfpD8WryR2mMurPz086U9Cfq7f6QEseD43ySEAzDEw03j+99/ge9nNzs6PfvSjd9+5rW7SdoqcA7Q/UkUq25QL3RJRPMh3NsYIfdhp72CHkm21gWS9ledkVpfisNkjwIK0aiPkX9B5wY+sEc8F3kK9aieuSPmPqhfgIZiusdXv4ZHJEmfpL54MNhqMKVE69JfgJJCGQe7Asxtz7rnnCCSWZVTwOFVOAMzECrJyukYg08RDsQ7Dk+QuQniYEszbvZMM0+GtD/HB42B4mF4+99xzIMSv7WNxViNOtX5TQIQoUKrHYvGZc8I8Kg6L+uCDSkORh8AC/Gd7nACHcflBMFAxfLYhyZYjkbichCgYnmxMYIbnMBkBw+/AYouUbeQ8dyYPTDV7kx6jVaad5Isxyz/+4z8eKzcYvNbPLBNYnRSQ/Pupj/GzgAOeil1RhIdcqAIs3EFKwsX5BmhhJJ9s+vS9D2Q61W4zFAYV9tTc1oHD1heRQBi46GNcVUKcput5PjLL4FA6FbQWLH+kmayOnC0xcyIxtdfIn6wIYbNuCcEAIdIITiOPZW6hJyGvgLnfg/Q0ByrPyHHi91ePhbe4SqMCZjQ25fJwECbBPDD5VKzlHpLQgghkCk1dYKIFtUZSVGssQ7xy/hKn/O7c/tgpIQs6Ofw4zwsPYeQC2/CH9ktsgCHWwXyGTOmpdw44Wypxm1hKxFlyPKyk4HTbB63GBrTvvcuN7h8gZoK0AoSs3UP4IWfhavWXLl0IQ3DY5SwiF4d3yqPXme4hvXSpzuY8V3Mw73WDOA6dA8AYjScF1ZNRPHIWTapV+xEGo40+ElI5PAVXF2pVNrHRw4LRjEEfAcNhwwerETz8fKaDJ5TgAFC0MZFRGiFoCwJs0C8mA4DD48CxsoyKk6AE7+e4g6yLSkGg43fPQZh9hIwweXFIf3rc4yH7aT7PF7I+BJikIelP5ptMYkxLBvxX8kNPKAKtHzIQAJRP11S2x0KKVMFIX5HmFgFVdWJw7MmRUtpgsqSfUQBywYEcodD/2FnIDNOS5qN7tCxqFzAiQc64HfwSaJqy8tHkyNOyoIOl2K9//eudZkcEmHh5whjxgV+wcYURAAizTewlvagRgOgDwOAjdMBCsiSRITB4iFW+CZcM8Sie7gHKYXnSlDi3STgen40HGKff2AN/xFvK7rFEQRlZ0LnQTn0gxLBYDdas5AjBrwtLTGMDyaIA/IRRYAjsDnk54bwqxIiTxxKTLy561ThNmxCIB/0XGgN5IApGQRlRUqZjGX1861vfevlr32R9/70PLmtpI166ghK+twXi6XgKBro5SB2OIopAhSMWXaVvKvR17kpkmEv6wYODD5DLk5kGOhCHn+8wAl5I6xZCljR8QEU4jEKwWSJGQxrbNcpiwZROHOZQ+/BnryMrPL6LTdpes08xecUhqTwJBBvySPdNYYklFTj5cJVgzHF2DDyIE+EcdKJ0OHHG9i+9ILwSOBhpgThaTlImUUkpl9qXVZwnJIoQ/ES57fPqqfMUlvq1PNUrDTNTzwiqOItK0ahrOMwXFRl3tsYdNjUf5HX0Ld3QcGWyp/tGfdnRkZMj4TRK8KS51UmrDHY5no2LaIcUnzSAre924FimrkvbVrOy1+vutMBMh0t3PJ2tEVIo1qFtnCnCq7WWLgOhc4HzanpWOspCgSka5cI5Zwh0D2mB1BpgEFaPkAQqlTsfFRxKSxRFgw/0g9Jaklk+Qiw1cggyDtj/JV9/AYkTEzyEB/L2EzzZx7h03B/aUpABeUFsSYE7dtE8CJ4ajkTrCCyel+rXVn5djJk1/MM//MPp0ye40ObEiWMEggHnRAJLKhyv+ANXwU6I50Fs8BisXolF4IDHo6dhINaROEIHc8yEB5lJYsbv+Il1gpOxni9pIRgwnp6jJyHWSQtJ8AS/p/3cpyOME0YlDUjI0TFAHvxHjxFCE6a7YeR2/rwubAFDoAdgwHgC5g4KwRblYn0QQuzwBOIAi3M3KbW0nqmHk4hXTxIyImEgMkShRYD0cAAMiXCQGj9sxE9BeBIJt2ny3p1BgwErF0so/QyAJVbuhoROUs2kub3O14bWH9wCACe0ntieqAL9sitidiSJmBR2oHbEcIamsIys+qz1sNyLmoKZ5OtKKdpZsK/OACwCbKHZ2UWI04zHs3AKk9kFf8wKApRZDBlRTRFYakBTXb92By1EVsDQSrgM345OCQ1NXJuJjISNJSCUs0qBADjgZHDon1YGPLRhauKOqDhHZW3poqg4nF8y1RPn1YQHIIDJFA+vnkXS4zDQT7/ivarDRNgTP8lME8FHeEO+PiLyxS+j5QAwYLiQ3SHa/JVnyJdyO5d4IrgOQCzdEX4c2MjAvFEFWQ56iJnisAgAgKeQGGeI9RDFxc6Q6OEYCHYYfyV9APBwxkveui2X/VYDYx2lwCx35wlZE4WUSsLjTCGS8YMIG6rbdUfjAowjZMzK+006So0emamtVE8C4ITFClhE0eN4eIxVpEI2rzyJEiXWir0UDkY4uWMyFQEIWiwiKV48xDqko3I/T+A1II41P9coc55vG+M85/VBqYMUkgAcYh0Prw4OmYEbHmXZKcwJ89wDBpJBG5ynlw+UE6a0canNG+Wr8IMuztlGuuqDVFiIIAuXGSSNEB/5e1KnIaARBuXFl8xks8JRyHPnzp44Ji2EqgeMWJ4gMY945StajM8IBIyxyo9//GM029e+9CxnKQRAzdokF3+cMMbgmj9ik2rHHUwAEtlA/zAkJy/QgpzVthjkiN+oLIbN6QQP9vV85cuhCfRwXvHs53cEss8PIruoPYQmokSm/G1Tx8wW9/FEoyICokP33hGOJA1Rj8HSLSufPp3TgJm6s50eigQwqw5+gYvkWXkGe9VoukbmRgqbcMSibTxz54uezgh6ParESu/r8Q7HF0SB37LDnfdqZRiE3Qij/NVjl7729W8XVpfzxaWrV9Y3OrVqRWt2kMTaBNs/pMpmazzZUpCfq5ZYqFWnz72nNGEYnc4Wio25Vf58l6B17QYWUR9kBnzxYNkWg+YmDKSzg4y+KVSaqK8om1UytkL4yQvnXIoW0SKhicqoaHPJWvVaQPiJSYaLUnFPQiAlg0MQ2er163GMb3wcHZjsrMlzyDbrlPv5RMmn09GtQXdrcQF/rTUYdte3hx8yBcpVGoLPj3IFaQQaMvcTIMC3765tb21feu0VYlUq7RFHEqoaYQM5X1jf3CVGtQunkIagZ+NVcBACgMOYtlzMDPY2sqNOZjqk8AwU7t27SzXQH5/S0QPKoMt0vvLs+QvL9VlrC4SyzfIy88NdRbRe5me0b/rkyaRg3EBXEGnqz1ihQZiup2TnETr5Kp+0SaYEjDvf17K6ieoljvnMX3hrYu08B7TBB97T6Ye/fvMUm5WanFu873E5o/j2i74FPOvmmVbNehPNjdkOy2cz1XqDTQNWOOhC0RhggyFYjVGzXABdz/HtZ0Sd2XIWgzE2i4Zm1z8p5ZlEVWqZrWp6l51K1kBLVULe+09/3btzb7tcZRR4/qnfbxZHnVy6/MqlnZo6ktJAZc1zMXYN7mqndG7xJBPsrc4uh2WmpexOp93nQ1IL9fEeolW49d61r1x86VRjfrS9O0pPgJlM271Bb/PBjUKqx1but7/5ld/cuL25ud20e6z6uSrsr9kX1alVqEJPkyMl4Ml1zDxHZnvBaSX8un2KeuFCa6RuQgWzvy955hWnrXbCx0Puh8EajFVHxaoVCE8MlPBax8PeBOpy1O2xd4tdfZ8PZ+vSYPKi09DYhUufB1l9Y1o2fKxO8nmoUb9xbIWDpWhqBsFczmlIlQvUqyHgw3GlGWXxa834LiyTf7vR6au5xZXiCsbgz5w8vT7pFfuTNrbkuSw3evJXWWicvHD2wcNPaX29jrZzMMLGKhr2Uj6/61ZqEpdT34nagyewgmkhowqFmKWAckYbmlENYwMGK5Jm2iPX5zFQMN6yGU7STE7DdHaBtDY+Vuxzz57d3Xlw/8Eewxbry2Q1Q7gvf7husRKixtV5E84dpezXrq9vbKz/n0zmaUl84un46jwskrSKQKkIdxCJh11fPamybJovEfPnOAmEHm4bRyZxaF1fPiYfANIqigZe0IOHCjpz6rQn5OkIQ7u3cOqEQRMTkqghexcLYXjY+cfT6Y948pFYmEcrI3e0OdfmsTkNBi81zZTMQ0Z4sNqWNjUNpwIq930AXwq04ChR1DXNNJBFtAh1bQyHHcKRBH9UFk9miJMAxOII4Rll8MV+DImSPJ7wydhcon+3jJ5EzhfH8jiFjtPD7cBC2g6K6/ZnXYO1stpmbxLzL2rFxrtYKrKNz3C2O0q3aDwZfWoVJLDf+LA/LD5ELVm4FkqGq+Ikuhoc5+yCBcYSiCKHZTkNd/X6h+DEwAXlnp1oKIBVgeHRLsqUe/O0haKKROEhwuPyZOn8sSvX7txv7dE8IIpzLMQyEBj0J+WKWofGGYqwJ2JN7oBynIjr3TFClGSjGBQNNJH8MAbueu9mgqHxlDmwuUcaInbGhPhF2stysufBKMGIjIhv+hIULK9W6uz64ocMGiMqFE8BU1gu08AYOJU6PVdYmu1Vxlr3BCtPU9L0z8NyYdrTBbXDRn2ep2vpIjY/kGdLA5Npt7m3sdfcmF/AFjhHe2TVk+QgFxq72VlEOtOl6r1mVQT+W1/BAkGmPFRvvrnW6j8aX3rmEgusO509ljAwuc7zufNyQyosbytinEyZTOZrla996/XXXn2JcyEffXiFrdPd9T1w8mVbZT3LPH3q0lNnVmf9PYzcxdW0FuIpEhow15pk9kanqzUUF1+Q4i5J7hakmRaydAbpeiafLqQHaV020poOWVmeL1cXGvN0LqyJdPeazIKWzupzlIzKmAtwwhbp4krk5dpcY66CZT0MZi1vfjR4+eTxufHg5rVPam2NsEpWt9x9ihBUGAoP2E4Ut6d0WdKGkMm3TMZz1Qr2sSwn5DrDbmfzdO0iNKx1NhldlHLphzdusShLjhyeafY6p8tFSOd+Q1QiSHDUEZUDeRSXr7mDE/s8KmLEWCk3G8xGfXXWFG5anIzZVlzvbO3s7DWLgrw+HdxZv74x7nKpxfl0mW/hTtvqF0qjXH+Pa9lpLxzuwo6h9MloYzE3G83Xdrd2t4vD+pn63iTTLnMMS7ejT3b3xvMLfEyaXLoYIOoGb8YI400sICcD2h50jnI96NtNLe6l0y1MEPKpWp5buCflkqzql86eyddqS73m/FyheOMuLffBaHe40xuV5+HD3S1dJJUuNugFOOfEXsW588fWN9aQX3Y2qUqxknGKFK/0ABw31kj2vK2NUtUedoDedkWOWiPwLF7SV0sso0ZEqGzZaMv0ZayBAMnKO5BM5ZXKRi/efJUp2Xq7Fk41F0LwwAcJu5wogSziiRYxBmO5EyQY9oLMwFiV5LMSsiMcVAqxO9ya27uEGDL9Ro6sTNqxGiGEwT/Sha7C73YrKh85Wh+tQFuhwFwHez6RI7s/vp50f2Njm4U2xk7c1w2HXcOBCh0CgOfFKw6e+KsxDzLFcxxEqgJQplzhyQhYwwGNA4jyNVQhMgcGR2XpVExLK076+MHxE4tHoeCNt0w80AE8ClQOgEcINd9UlRHoHvzkqChDGP0IwGEMGa3FBi3EMvFlEEOoIM1Rc/xyuyvk4/cc0Y1BYAb9zu72HiHdThM9wFYNxiLIS7OpO/EolFFFxWhkATdhlQhVq0T6uFFUpHLZGnQynicXcicVGeGxYqraGbpYGQP/RSOSGgMIuYIUaLNhkvDPBVLfzRM2dyQBlRHALhGLEtoMMwEXcva7GHqy/kw4wsBT9WYNil8g2Y7EygJIpno8KQP6inOO927e+PTy+2wmFVKjYob1EZYQ6d1VjyBBHESaiEYnjGVhqwahHDFuuHPnHuWGGMb5EiXVjooB8+B5fzi6e/8eJjVEaOya1ZaecQPEIk9yCCKmX+AzbUAhiFJYcLagZoT7I7LT9/hoCcUojELs/H6/P71w6dnxYMbJfZuGptvc4+t2RPR6lkNvkuIgTIUjhOh2m8fBdQYgFJXZHHRSm4vVCn92954uxIA2ihBIUzGCfJpf/KUoEjiZtJBAaciA6ZC1CGL5vI6GwZSUktucHRgEWFO1WaavXiaTK+dGeAp5BtC61j52np2/4QcnmYRXGpV2LHJahgBVllu5lTtf3DIQN/izvga6PKXVgrGDioMPiEzAqIJANNkLQLKkj51LFFBJWZ2Kw+Zdt1QhTY7H1IYyw3ybuRDtQ0RSSPHBG4LoIYl+YtYBr4ysD6BILvG8Ki/TxlJRppxJIvkybJYCccT2fCzbetO6dGAgox9Fo/FBhwHX7zJwYIynESitVwtlQ87CMs5AnarNEmwxHPcc5rBj7vNFBiTGsNMBMlKaqxX41jtc4GLojl2Ptl0clU6v9O4/oB4Lowkfuz6GAGHRzsyPLDSPU2FV8Lj56tVKpMzM4WHHHgKIMkoUCtmmrMmYX1FoyxLEgIj0zlv1dI7Q2iNvUs4AAQ9r2SKfDhk4js8cP/78U8fvvXuDlAPOuhmfebqLKKFaoczkwdo1fa0KC+1yXgnu1aIFjZisyMt0AiioEZqx1tlpwxSS/yz6CCf1RHpVvCQfRIZLus3RifUWFQUQT4hH0p+ihaRscOKc+cgBwbPFNNBK0UhrWCpPBzu4DKDb4wzgiGqmC52mbt65Q8s/depUoaT7PxkziHWIgwiipgyhPoxEC2XtQGd9gfn4/saDjY3nH269+qXnmIOnZl0I4QpOUqNfQaiVLrSus8FogGb4j9kZbagzy3Un2e0U3yZLtWxMy0xke69z8bhqocRpCnith9MtHoHeWQOTKQUnTyGuX6QJF8srKwwVuBZJINZ+KQB4fNQRcYl3SmSMipBayGc/ogXoQ0Cq4IM4goAegrT8DoIegrDXiCxK6zV6FMxnh+1zSkz7nBwNRJq6k5Jt0Vdff50x1q2NDQb9n65tURY29yjj5+I5kiRSURxWiMDA15Hw4wgELQ6NgF+f6InMCj6H1COz+IKBnrU/H08CMarH2OGnv0dwGeswvtGcytjIAIi+X11dbV6U20IYUgQ8q2xM2zp7/QUawFHOC86TAYfHgwFHiLd2V1jJpMRisfLwbhP8tBVFZbUqhCpmpMFow1EBw9wGItlNAJsPC1wlgZ1S0NQIl6KN83KJtfzRnaCPR4eoZHNkRWyCGMFGr/Evr4YwCg7+fUgT4BAOHMSAPkpw1I+yUbvVE+fjWg0abfoXUoDTIagLAuEDH4uM0gQg82iGb9v1VDHqSMupqZQuXmi1YBqfwUS8kU9iycMzcgodMwSzbE8IyLmxdNTrYiaTyy3A7YZlHTLFLoNhMZRU63VwMrtDhLhckmray1eZX5VPnv3Rj36009JSBTSAWcPFwNWYbLGLUHNxWPQbSu3vgAAreKtOb01YPIEfdUuJkgpEr7ETgDmGbm53xtQ6jjz862UnLzwsWPPp6j/7sz9D5P7+7//+x9//JdwoWlpWRiWNIkbkO36eOheZSjGzJa0PB2lBakTHF4HHooFYWE0SUGkCbGnxyE7NBEzd/FH6B+QA4EjCqoqnBZIQLywA6l/lBONRliKSWxGAM/yODfFgt/zuvR3AFEUWnt5ywUsIyNWmZEeiy7HhK2NlXL/Pd5bXEFWm7qvLrwL5WN0KzJDIgxN+C7E3qRFYlM8UwUCUxaqmKJBe4uVaB+aZrNMQaPDhjTz2/fAZ/FSE0k6kSZijMNnY29KJBMyj1VjM2poJG8gRXbBpUmel5mkF1yTWkXpexhC8UU4UkNhQ9hBOoKrA2nXcDR+tCqJyRYNbz+qf/ozo+TwEDvZ5UPvxRzJ/P/qf6kuyK+mP8Bn3DuGmv+brSwQmScJPl9Tp6EPBs/isSSije5It4hDO5CuocIeIAQMDxM1799iWs8GrUhyCOfRKe3F9jr5F0jAqJAl4QM5UAOBhfzqp0ZYi6QoIA9khJEg1qUhOeBQVT9X2Qw6w5Wh587SeS/IJfxB1Wrubtpn0qu2r4ZitNyHeZdAwQGJaSvN0qPJWRiAIeXW0wAOnkrKS0u3WamoyvNLefbzBcpLU+HTKtfJ4uBpCawqGZJ+wuGkRghNOH9YbGK84QIhy5HRMqHfG5RohcEWdjUAYe2VKGoowPZKRLFd8sALFTNvsB5kAHD92msWIh7fX2Nvb3tyirkv50vnz5ykvC0mkFH6GDjDfcuRzb6LcVHFBGyGzIjfW0TVbv8yRIvjWHuss8GwsO2V6EIqG8FBSJvkY3y0tLVKVLKuiyZutDpznpmjwgITwbkcXg0AYaSGJ7ruR0nKwb3e5RgLYFJSmiIyCgKd7JMTI5PQvmNTtEkvvjKe8WqeDZu3qww8/ZIWGWoA8nWGyWgCAaiKJ8MTdB1kwinCq5I97hOD3KCGnW5yq9YGBkmLOjGvn62R9sjfjucS501RqXqaE2eGozQjhwcKAasq3ueO6Ujij25n3OgPKu9OW9RacIDs8OBLi5+nZuZ9XdyAnr2ezKfp3OhLw9LodYUZLcJyrrK+VrLYG5MtXHhmr0POq38kXIXtnYwNsGKMjn6XFOb7goq+P60r3EtzAYtPyUjuyoQfNL5JtC99XQVAXdwgkj6iFkx7qwP6k7tBgyIOIt0J5KQ49VVISGMMPRYVX40akIgUa8weAZEL3A2wgopmCU96A5wgPVlG6AFoOaj2hvyafXh085eIIldrObP3mN7+5evXqN77xBqcVCcRxCNo9CB408AQz0uKBPMHhLCGc+hK7TeNBs+XBAndkUs0r8B4OHgMmRNwgLbHgD5IDJCExgfrlNcSGcIfxtCAklQquubwc4Xq1BoLHncVED0KAwfEOIK9gAA9LSwEbgcRq5QNgW1T3sSuvSD4haB72qJBkLshBV7AAzQYPq8/e5CkUTQy0KAvLlXTKBSe0ce7K3uiEt04Ur0BaksgTejFwenIHAAecIdCBw5NYL1pARQiOV8Ldg5+0+OPqiKhCVbq2VAJzAODYtyMtuZMd/CY5YNDMqImp6Efvv0u7zo57tNaSnXwiK4C1XmTFIS8tjfKM6xbcRAH/6c27W1vbXBYAA/YNR5VOjkzhJz0FmufCufPkXizujygibhwQmIh1npynw/irZ0o98OpwXkZeySgmTbB6FXsl9iglaEDbEOKVaVMHgeFsCmhFM+aQncsJJl7eqVFq3aTE/G5qW+CWyikBoXssLHoQQngI4RUHWq99TwJOAAj01uYwAZt7eEK2Fx+AJMIj/SGQhODnGULwiAaTX3I1P/FQbyBWigCvYMuOyBAInIfz1GWbII+LSV4ImENCLU4izbvhkbwd0AkKBxtgJEyWy0jxdEa/NTQPBMxTKbE5T4jXAfypUtNva+QgejyWV1p6rbw/szAaWR6O0oqSiV0kYhE0CorD6qInd8yEsGjtUcLPztN4zHACwUC80B4sgrA4yOCB3tATKvt9t08mlIv4BOG8kjOE86RAJAJaT2vdlAQeomGFLE5mMAqAGGE76FQirf1qBKC0kwnHy6rX1uiRDwIe/Sa8kVN+jj6ZiwiO54whnBSg4zWEOPYQzqv7HcxjPfDIJAkAeQ1xVFIfKR1KlYAXWyi1FUL00+pZQqCCGAzQyRI+7OsbZo7WOG2TBcpleoW0sA4HAOfbMZjYXrv7pS996dmzZ+At/QEYQCs+2ycr7VW0oVR9+xzkSMjITo+xTE1UgMFDv6NxETeOmT4iBABcsiJDILmwxswTyimCh8cpPN3nP+FVQPg4tPdwB8PZX4o3TqJpUBQv5esOH0ijLsFoxzzDougt4oNmTLbFYE9hWkn7hAdRRviSP8ol2v2wYFU2ZaA1sItnKiwSCFskdf65OnOLQrOsTLWL4JmwyMqizysvv0H1z5qyhtgrF49lz4xbXXqmuE0lc0/4fVdzVlQJzEwxknwaHrsKC/NwoDtUcfpa6EsVuTOPg8MjdXI17tTNZOZt85krLSAYKxLCy1q5iksXcUYhT3b7PHfW6Twwo39TYV6vGBlJJqb+kUXDZDDcqaee3Gw2B3b/6U69zAz5bbaRoXyihRv7LsKEU6AszXf7+gZdN6+rxzBOYXi0ywnHbHblxOLq6jKTuQSRkdQSQsNxiVc12XInl4aZjKov8WFE1NnYZlyjMOtuP0yPWSXUyIRmwyLRgFW+CYPgfGbQ5RwpCvap5y40MiNu3WaXCWzRON1EWXcOmxE07QwN6UxQ7rbbqe1Jmg3fDgWYPVKu18ICGjb4sl0sjvtssiUhL1uEKi4nr6HMHmXlleQ7iEtpcYiVPR/DkrqhLgKfiBWcDZf3itQEhj8oeoZOmsVia0ZIrdrg1nGfEY7MrgT4LFdFTjODZneR3T42Nk1bsePEH/dz4Xay4+XzJ+70dk9iHzXQNhs3R689epCdq/CpnT6VV0w1Tp3pZvO10ZQbzU1yuHhyXKwxv6VjQBp08KvVa7H2munWGAjmqvUT9YWVWREJgT4G1YPRONMdL5yssrT6CBN1pLqu6xHGw/5cvTq/wjnizte/dOHMYuGD925wzHl70ASm0K3BH1oiT3ZnqBfKhZgMzJJiZJc/IXWqL901lB5X1GHjQ/OSHP/AlibRn/CHAAQkPZVZMvEqvclzUj8QJiethVxM0LBL8wt8g0BzZX3cWJXg8FpFQW+zHlDK5ou6W5P1e87Nn7vwDLbCJ8+c/vYffWevOf7FL36BxiGVjseT1u5Gp7agMKfF5xE3pPANx5PzCyxePD0sMgiw1QltQfda7XFdiwh0IdAPI2vzi/Pzi6jvUW+UqSGI0iX8p9eHGUxQMIcmN+2VRuoxErpyodidYU6tkTf0Gzci5QmXIiBSMwKz8qutWYMjd66JoluTidYsxSe/z5xe/eiDGyzQ0gqAxXKAoRyiaeXTkgTTcAYaRLn08ow+NcbleSpH7sb1uxfOnsMKvjfQBp4yZNPUlDnRChHxRog98UGkoDzMUlAKSCNC4eZAEYHZa4xZL8lwi9x/JBAQuD8MomrAzzVouXJ68dIJsHHLHo672BgI9rojlqsgFMwsVRMrs3PhksaGBfjd8YqtRhF7HbBbd46HKJ7ySAxBoiL5TV7MxZXQ9BvVKph4kKQE7p689LyPOcrisM6PURz+jVqBiQIV/Ts5GBXy/Z0S/q7AXzAXKwsfepeEDFm9W1rm9mdsA/TNcmrB2mOrR789bk8yE4w0bYgmcZKTDMR+C/jMhxqgrBMgjS5baqeoviMz6e7d40jv5iNQsUhLuEblckiBlIeFeO1YLds4G22HVqxVFz94/2NUq2AwH9a2h2xJSOgm/764wKvwmQC5x58ixXzeY+JViPUgHpN8OhIPkTw+wRlLIj3hzOGJXqJFqLW6HRrDXo3TuGeOq3F11SDIuEGDJztH0sncUMlOLqdn0uk5rGWlq/VHrozFpceoAlQH1m+TEbcel1fRsWn6r3qpwgfgUcmoB440tZs725uPCuVsfb4yxjYWyz0uOSTrbM4bsFOLMiG5FIUpeyuZx9CHG1WzPGOV7Y1t1muY3kNhfzRkfDwtlK5cvfnL336fqU5jeZ6P0b345acY9ZE7SKZoYlrreHL6+NKZ5aXc116mFmEjJ6LJkSv6MTSaZnThpY7O5GbYAKP+ew/XyoNhvWPrqbrNk3Vm9Wi9gnDuyEp3cncojo1yLIjk7DMU0qMZLi5Pp7d3dwfjHgP9mn22N9tjBaTYYFjDcIETXHuDzHBwggtAVpZn/eHu7e1KP1+zb1TwVV/yUldB7Zq8wEfnOCRjEUY0TISSPiOc8aSYzejTs7t7lGh7ZyO302bfjTu86PiHkw59ca5U5GPoQ25ITE+7fEjDWhnDadBybp5cGHWgDjNT7kshV/YhB12GJdlSJ2s7c9lKd5wdctStXN3rtyd8ibOQKywubj7YI2mlX9l6xKG06v0NWmwDngxHHW5kGZeLteJ8P9/rZmvz9dXi/Py0WO5PCyy88WnedAPL0GxBslfqszdI18mJJe7X4aYwVqncNAEZoyvTKCK1Phx9svbo6XoJRc7aM+GshPOhCGpWH1QYckF5dZUbL0bVdDE7rBW219fXNh7s8sXCItbiWtEbpvKZfOZSsXr6/Mrljx4Ui7m9VtObks3xjdvw3RjNkywonYQkXi8Qo8xZN0sDgTxSwUNB+sicsTlJa5W5kyfOYuVm2JQGj+eFB+ch4YmH7DwchLzi4td9j4frqd7Wmz6VSCPSxbROMKnICKdO/eDHD5UkOEiADA5YxEsD8Rq0DcN0VSWjE93xrat4VT4dJyILNRfZQWusj9Sxq9vstL/3vR9evXrjjTdexQCiWK6Se6/foSHDCjJhCWMyRgClfHBg4GnUima8IENZkhcDK6NdF7mwYg0YUUCwEYRHQy85VQ2E8J8kPAknNiyZgR8kSSYT66jweDgAgCm5unqvQNU12IAR9nimyWtUH9a/M8sAhfFDYDgaOMCggQAPYUKGh4SixPSPckV7DHW4BGoZUXPAkRkWA2/AGISzWQUAVQadpLV7pelGZK6L3gEGPUk4FSaB07oGdpUK53YJ/kQAEujGBw6f6BMt80j8nA+EUFiSBDrx4BySp0cRArwjBx4X/BGdMZccIU+KTRSFwO9sFXdhJ4Mt1bRqFlMnSnr72sdYB7d3N0lSskO3OuUI36xPoTz46XT50SKIKs4kUwHiz43rN29cvw0nCGCnw8YPUC2HX3/TVL2oDxBwRnO+MSfx1hRKYzZqxiGVXeyYIFJAsrUAlVSZx7EO6RcDErafkJYIu0wiKDJOVtqkzXCUvj+32Dh97tS1Tz4FD3qW3OxSZatFeyVHmhBlhnPoGf+4EgvQzM2Z+IDn4smVEsdJuSVWX9RBPhlaqnbIKCYt/EI8gRHJTqEoeczRyROOZPpOZyiOFV/YSDErF0b5DGspDBSsw1HV8kfXBIBOf+BHOMVnalbZwgUmovSgdlg0Nc8HOnKpdXpDCa/aCMJEWqYgZASVKoLVq3yMK8x+U4RRC+Sm2jcYq3EBizByQ+VLNXAAHWACkTannKfAhEyOKCgiVh6ct1ydrBDDafhg8xhFWosgU/xBCEDhghLVDvKr0b/RZjFadBaRSoVNHLUum1ATWMSM5YndVntpfg46nR5UIttKzG01mbXNGKBFKZxUq9H+NMseot5KzUYy+IuUIJfu5vkC1qAwLae526pQ7qRm8+fO3G3ulnrTnd3W3s7u8dVjaTYqaAqImopDAVkDYl6j82ZQ6LyliIbf+SnhCDwHJumwpIJgVq+Adw57LK9oTtPbKr+5/XQ2xYSzWoDmK18r84Uzi3O3+SpRXA/7oAd8ohDuUb+sTjBcEx9VTfvOSVX7VvB+DPR4lIWTZj9qP3HsAxhvgPHXZEjSLzGVUxJ3UKlx71HOUOnwx4DzjHbsVlD60kdqr92/9/Auqg+DAFQfzGRAZ0pRsiYppJc0tMRgZjxOF5CzdFbb57c3Ord+8MsPTt74vd/7vZPLC+DPjfmUFZ9ssFnnTKuLOsXFxWwTbiwYcpIIdbjLZ0+GSEOFKTFn2hB41BK5tHuTEvJQYZSUK1nPhk6Ecm8ugjAHToQH75AlKAhtLGTnFkeba4ToUGNw4FR9KMjUUhSxzy8LOPQaAdmPjT6TAV/YD4EI8hcBp3BeYyrUvkgdkZb4I0K/SB5HwVB5w+kIG086eAZGFwuzt99+O587Rsd/q1rVAnTCiTQ5PSHS/HpENRPerRnzxjwJMXINGEYohHsgTxIiB3GbVxs2HPuYEyi/qBckgT94waWQ/bDPwoP0YyS7sbcxyMtiqFCvwh8S0NXRxzAq8j35Tk7TTo25udqijOn9yuJSTcwJGR/MxDmgVmE7KgdqGE4aV73Fol5Jym4uZGDXjCMtjov1dYo5m8KIzMsCKt1w1N9DJTOfA8ZzV1HVwdvCICSaM0ziA3nhSAI8jVse/qMmfenZ+iFfJPIk5C+PNSGvcGotjtr/BTMvYBZszGp/dZHBz/ajl2g/WcIHgMqpoYAR6YNFs+KhUqwQEfEQQKchw7HehMkDCUOO4FOt2GCdifd3v/vdn5X2iEXjsPwx6Orb8fNm84LksVDoBqdkmiBEdqwwncpiKAOTmZVhI7NSroOHoTAt5aOPPuILM6szTSFQKxAAAFErJ1dIm6vkkJOt7Q1oK1V0sRpNiVr77ncvskf326t3oWH3XheYIVN7Xbqvab8dSoJ1ooQigNNJIjl+QZiDHsiANp5k3u1w4zQCKCtm1hTMIx7aCkmU5PEfEkLSXE2GyYzMDwGAmRBgnC1CZ5Mcqu/atWvpzBhbDMhgm4oPMEKbAzi8VYUIUy2YBGK5xg1NxRv6mE/Y1SQLUKFtyAhszx07gy0DqbxQoexgDrQRRTgwHuKv+LE39F1Hojj0SrgD8EpyBwsevWooc1BWDS2Cz944p6S5VYpPrwpbWvd10onROpjPc3eFZw5R2AGDEwBaK3LIIIpMgeXJHYXsulNrcIDKJYSxgpPEkyQ4J8CfBLonFE2DM4VGozED+Cfpw4gTQgYV5Os+iuMenrQLnpTFJIF1nwZdNpSzrYLr93T3ohk48q7a8bQBFSGQjSOEIge0hGujwMIVaAM7l23NIwjwIlp4SIUHniZfgx+EuPB6yENGhFAKnoAF8g6BffZrSHUoI8IPhXw2nn9C7D8BP1TRoKi1zsH8kNgO31Mb8MFQXXlEZChX8DijwutBBNGbSDKG45HfHFpOywEP7iAVaADDIPlx/jPKBvLx+iMQieK0NKrgxo2btFY/CISAUWcgwbH7KZx8xs5UfUTEwR/PxcMi+TlIodPJ0yH99SCOJ745GUTj4QkGR8IbmeDPmc2OWgWHbW0o45YFnouKYA76+Q2BNGXnNjhhAmn5FJ1TCGQlVyUW5CT3QHoG6m7peAOtMpvaENwwhzbrdDqR4NzPiBd7RYmCEyQYw1JHbtXLku4go20n1jLYOJzmF7kToFgvw3NsMxEkbuWmh6rO6UI2bkohOfuJ0mA2YvHs/AlAyJ3s6eOwZbbMxSU0C6noLvBDCeVlYkcsL06e7NTMofDBw03YlLRer0ADS608S2PdW93ptMDc7useZO7QoKeYjLT9j7UqhSIPK/h+w3Q+uJh6XqgSJxV6mP/j4CF5acpjTlZRTIcZ4xW1702V0ndDEiHobTjgYIQQqwG59ivlWCFyxa4QU/KzErbF5a4ZzmDILYta67iIpb2w1QwkCSnR/Y1d/PSktFxZe9CVW8ddXC3Ta6ycPs2zX1Uvxra0mtukDRm8el7u9yeBgUI8APCEbEbss9opNHm+NQIDGRHI0jJlF3w6TV2TtjtURTw9NwcZ76zJ9mqWLgJZbFSZCKwWawyfmruyx3cRVVpzpMW5n0w930SYYtRqjCTKbQARvKcyAA1ByYLa1EFic0JrpQj4o4jETzIKv+eeiP8dvCRHPhGwUNefnVjVao5FugAJEgk6lMRB+PESKKEyhwcnsKmuBb9z5ybjwFffeJUmQHMAXvNh2xUWNv4bTsm5CS0soZhg4JV8wMNonyQGyOq39IkDAEOJAEPwePLq4STBOTzhaJ4k3zw7wg85kngIeNz5ayCG14AHT8wB/43fWBSKbw+TErPrBRw2pAgZOULgaSAIHq65I32IMNN3LC5o28YLwhPWlUt8wLUIWuVuLMJ0AFJRuryyLGI5qtwBhiwAcP54cfxJoGN2AJ6BXR4iVOYIT8aSESFeWR4OKjxeKJFljhB+iaI98nTJoJ4JV4SYpwx5MWwsReY4nXD58uW7Nz6hrjFpIXwykgR63+fstoTCgbPkQuYeCN7a3GY4imgxXXCYI5+QZIaAugwabUDWEM8CrpOpZywJnjwm/zAyFcOcrRkejNUKbDQEhTycbntDFAva6MUaggtqWPO+fPkG5Yo5LQzOKeoLHntCAnnFsYRHCLMoZt/YuBAOKmG26S1Ph8dD1CHnsYcCQ7nwqPpskdb9wOOA50kIuXtaWOfhlvYAk8nd4UMuwEAcmIniiVNIDAbbeeXJRRU4VpqpQS8ChADsfp/k8yqguNI9C0cVsvN5B8wIaUHoCYXKLIgNB6WSHAYJdwweBUlmcqMww6+kwREoMiDEShEyAgByPYqnhztaJ4AnmHkSCDPp8d3vT8oFH5gDCw+DCjMewg8wfj/54VLqHCcLsHElA7HMZxEDbOtUR9z61WqdnZtnZje6c4/kdMeiyhYyvBTgJ63KBn7TtHgEYrMwBcaVDhCQPEni+cZRtpxi50ptPVw5CF3EE29HEh4PwROcyNYmge74Qozv338ACY+DBXg8ihW/IUNe/ik/c7y7JwlPSJQqriwP0dOhDSCZJPgdYXgFPBmi7A1nAPhcjyeBgd7lgYBS0E3R5KkuerydnW4ufRezCRQ74yVIA5iVMU+o3K3uoIRwWCUx8DN5xnpOyP3N3/zNy88/w/h2zk9yWFNVQismsjHGriF+BQkBbNI45SEXyAN+gBEenw8h+wSLLF/JrWThoCOEwVVv4yHBxhtVUzLtQfDf4U0fyQzOq5xXkWIu+gkQscdXuxNJiTgMSyEEQFfkJFOjsmqBI4J0aC+olyRZnkPFo/zEmgODxC44X483E2T2EBRs9+1qVZ6DkaznzS3Tv9exftl9sHPv6q23rn/M/vPt658iBzaYhDolS/IhWpS0PJAHfoGhppIZ7+Q0zPVvILVto0Rb1Qz6bd+yyCpFOl0d6dkYaBixMFCDbIy0AFeyOyUrZjIhayyq3MQ9yZPI7wt0JiXaawPM4KGHvMREPdU5xWyxdFY9TE5QVAObKO6U6APSvxx025n8mn0elP06/igBUkqLF+ZCiRIytyrqu185OmyWwHzaLHlIbvv4oi1pYuUOATQYskVhem1DLCXlQRTGUXj5kDw8n3Y3MsM2t32Rls+5F4q55l6PvZjVOl+Gx4IG+93RuRPz5VmvOOxIWdN2NWawnRIKj9bmwSYhPIQLscIEv5o7bNG9RmyQayFNm6327WNI0b6VgLwOXe5EPzVk3JM/NDyXf8AVas5hFGKakKok2FmymC48fLS9wAYWZVT3KbH3ygGmb5a/nbJiewXsiaJPDg+zmcX5Od26SAtxRcCiFczjIvNiKbMzmrR6uQmDMxUq0ODjg9qZY5VTK+lub6vT5NhFoVzqd8dchrh2c4NRQ+Hk0rnvfrNvNk0LmHV1euqGMX0qjDOLJb6Ty+VMXEQHe7R9LRXJUD87V5h/8OnDGx/eYvIg8y0STHI7D7cvvPDC2TlOf6g33dvcnnDFW3taxYR4wCfruYtxmE+Pq6n0yTMXlgZzt1O3T5TnGFh3RqwWdXb7qs0dGwNiyot/Y8jB2wmmWCoRF1al03W+Pk8bS2vW0SmlOZ8MR+B/dzbgi0L56gqAk96sXOJj1jLd9d22qCnGnAHGqwxdM+Lbe1O2Q8BkqtyUkHNQK5DITXo8YuxhNdjr9ovHywCzfnLjyh0mBhgaFLmc28YQbr2CzFDdGSbU7IWMxlmEeTA6uXr82ROnV4qVzV6nRGMpNVhh4RulueMnYRSjAYRjZ2/3448/YeWX5QGKykXIcB09sy9VkhPrXpjwiyM+u5dMAKOVGjkbRoyj3X5r/kRLFyBoshAxtvCk7HBSJaXrs3CDQb5ld37u3PGbNz/JYA8GRjEGJo0YcwAI/VCrMpqaBQEOPyH0ceDks0+EbO927z/cOnN6gW0FDd7QCa6FDJ4HmCGKh/1pGAEMSyFxlODARoUEDiBmCk04Q5J4P8pr0hNFqM1ICUjT7s+VlZEAmGFYo7YUHFJIpes53QRC38xhFUqAx76aM9zbbVFxGKsSgl0XZWCvR8pfCi12CmGmiU2o4TaeE0eJKClDRaNJvEpSaIEw2n/tmZBbJY9Ypyh/NaAv/DBs3i6QnENODI+d+y3DOOgL/DpJYm+cMuAMIV8AzeeAaPylby5z8UahdvxYt5Dnpny6jYnpxu5YRgS7ZoY55u5dCqXZONKmeterSexn5OGkKo2VgvrSWYhslpt/mQ9v3LvJaqMUgYkoMlPgJAaax5ZNZMTzWFHJFIFhKHnh4pnBsPfg/uaQsxu0PNFivbDZxfNCD9vvjYuFGZ1ScAEflIRA93gUz+AhHL9DeuChJMlXlzQ9E39QS9HVwJmqgU2SLUaAjbkgmHEUx6XIB6mmQsRiBLuazdQ5STGSxtZISEl96xCZp6dgstwv10uzKr3BtMLiCd9ewGALFcxUPDvbbe5srN8t5FmHZWEaW0dM0dgXjlaLvPp48r11NVGd3BATccnCmvLMDDpb4/7u4mLj+PESKehrxqVUq9v5yU8+4I6QP/ijl1lkbHeGrOwUirr4Ymt36+6dTyvFgvRwMTVs71TLFdQnlojkF92MwtL5lM8wyqX0yTs+eZDdbQ9bbYzCJ7VMkdJifESsJtpoXY5upVJ7mIRi6WlbejAx36jSe2VL+Wo22+m3Oq1Bc3d30qswwKqXC9DDteG7ex3UidYmWqNqpbI0X6pXMs0rzZ37O6tcMDDjFJ1aGQbAKrvxQHMRqCI/qo/DHVSc7XSp0WNrZveS51nnyGRadKi6SVSjzZGNCZn1s8vWyg6n5fyADowRWF/qjZRUIyOkPCJiWos1eXT5MNUbpQcsN/fTg+as3c32R9niODdhRDUdMnjjpuh8u8e5tGE5W+Z4Qrmv3mTt1g44ZxON0IrpElZzWMo2GnN8FbffSfd3cvm1yb1fPcjnN6rzDUaVLDDmttkVmCdto8jH2hvDbIuxAYZBCGMPI0HGVRowqvw8scHB08OSlLtQ5+sMNKuD8bSVmW1CSi/bbiGRDF6RFa6EXyjXsMSn3o8X82cunuMbDHfu3HnQH7108fTS6bN0qdtru/Vi+dJTp7a21yb2/QmQ48Rkyw6P8g659+1gq4u8QfoD9vPHOTu7RlNhvOIYUFXqtVqjzn4AnEbDOGZi7M8q8rFez5L+sx7WzUX9DmJJE2aFIvq092cgputUSffv4mDp3AimAhhYyEjSx5/R1pRxCa1IdjLVIqWpAYDRHyR8+933r9+8/c1vfvPppy/CQ+7eosPkUg7akbafqWBMKLlWlBvm8WvIIU6DTcN8jUtyANKoeQKgeYhB6HplbEVtyu1RIo/6kj0iMagwTs9EdtCqPHNgwClnq9k4OOrIPFxY4liHDzUJvKrXIXhaP+tIeGNuwuSIIsg4wLViLEWQ4zg9KUm4CoY2sr2z9emnN+/fv6vtk2yOq3KwFGG8zaaQdQKaT1CiMvtW9Tr4OQYAIvDb0EI3CuIX5yWtmrzBG8sz6hpEo2Vt+WoiZn/CYX8CcznB45D+6glV3th5CLEEJtGS0JCrykK4owISmtE+VB9jSzjDx4SwA6G/AYDugDbE1xRuX/v0+kfv0udWucy9UhjzLR9Fa5TFlIUSWOcpa2eykPF0HMSAVAFZLeJfufppt4fGtkgjCEoRGactplB1hCRt7mzXyqX1zY1zZ84mAeTXfUucxJHXZg3RWnDU9ykYCqjeGXMC8xuohUcPWxTGD/08xR9XBLpvX4eiqbPnX3i61d65dWsbKQYzf67bGRpzezCpxGRjPonB0+tLksup0ZefOb+6MJeZIPgKwQHpzvkfh2jMaXIrGgAI1eTAiourknqhJcFvMoacEC4ECZetsGeegn5JuLSfy5vs+ckDDYBRrQ412LBNy6LW0AhH7/FFDUioc+RkRinIL51SO2bsIXJi/kg6NQHxgkuuRDxUkZd6Wd74xdkc3xY/GBsoBHEWPXZZAUnw05YJJwsKjhDvF98WpoXWnHscGL9wk684QZ3QInhVnpatAoHRrfeAqciRU9/JNEsVSbyc06aaM+choGMGMRgNe8MR3w+j2EQy0WIlGZbS36FZIYuNJdFsm+tII0LDCSCtZPmBVAExSNFXk+hZ+RbEgIvFuLt0lueUUrMwq5w9tn7/LgVpbe8qnjUYuAppMM1Zyc3C2EEzqKL52BkU+jNt9JkqAJi0so8GqU8evNAmR+gdkMA5K6X4rPo2httTwTGv5AcVT2RRkFQFwjLljNb01ZcuXfvwvb2Rtuy5UFixT3DIDzXAHdha0rIaiIgyzGZbDUPIJ9LhjkbvVvvueRy3F9afIZYcgPdX9ydDAljweKzVdZTQaQMgikKDzNJ9KmbMKQilQ8vmuLebb52NGLaktlgfKDw4ffIU/RpG0qp3zb9VXzjUF4Khw2/wUJsr3Kuts7ngxGUzudZ4+o/vX7n2cPuNV17iyqZivoQkMxMnFktbNSDb9WmOM9DQHOf6syKfHUS1RqYhpnTgP8NbPkQ2ofEWuaAcg1PlrqURrf6pnUA4JULOecrOlvOLGJ8szU/ulpHbvH8LlYEOzk6lKL1aiuQkkhUL8Qc4Em+HvVK44AnB9hrevqhHVWtLHyQwTjwxoSTaehgglOSzaNtHIvxx+05Sm8x3Hzr2UZ2s97AHJTZmMhibYDXzwf/+FwwFMI8U+yJI+/1MSiLYo1jpUTxRsGTkK7auNEFPCFKIB1nBz1wUSGSOEA+PhDjQEpF04IeE9h61Ol5jJ26bxFqbF5Bqk1h8ThgFhw/kf+X6FT6YyKsD8MRvt3EAzJsM/djlWJ7joC2fELUM44eiD1AYcy4GIEdygRgjTLGMJsDv9UtaJwMPa3z4uTlDMOrUNEpYXZ1brpbj5DOMZRB07v2lphjUgJNxsOC9FzBO8nrIOYWekXc4kKRAy50n8A7Db0ir3uwxJ8oPSrIndMYSFeGJk7LgeOwxJA4GpKfCE9Hm7GZ/olqlM0EwQlJgcJA9HPKxAYXDh7JdEYDfeCu+MX5lKX8uP4ef8SuQgD3//POVu7skP/7i0zKenfhhL9nhAuYO4LbRAxipWD2xvIbMOtY/XWeqhtEFUWpWcCCdwpi38NwcGzbb25s8h5Pea6+9BhKlAoKFCb4fpAVr2X/7cUKIJBzyECcuuSCci1V49gs6krM56pLpTpMZYptvxkE2o0jVu7Ud5wQrE4Ro7DiQmRhZUHLSesvygjzpCWEeBQ0uTkmxddoAwEMx8QAP5pdeeokj27TEN998k5ExxBMFAWBwbJLDWbRCQUJM6l544QWYDHmss2M3lDfLdAh2nJ4KggGA/+wKgIoVavY/PWvPHTBe/RlIUogNsKANv7cRLw7DCjzEQzPCBE5vp946PFPChdAaHkUkPTwGEprp9lznEAgM+CHDa5PjPE4GkCB3VCq12eOjsHD4Obsw6G8xawKzF0EE2nquJ/FX/Hg8xJsXwB4CftcJUWxoSglPiDrS4/nuR2lQRFXGTTGOoLCe4z4l1qJFgJXdiq9Sc0yf51yDryMyE2QdZIh9NFVJx0EI3ZQnEWLjpzwJR+7WspmQq4sCN5E2+ksAiSEJCjW2ilySvDjsib8AW2GPADgy6khgD/R88ScJiMQsgT7EBg+RSX8C9rD3C4KRzEiSwEAAS1TeAJ1Onshh165PGU01IPNwzyzp95Av8gQhYDRPslt/+EBS3dJ3ETT/MBoOIXm8IC4SJMe4g7VF9IaJCud7CvShQru+Tl2R0FqK7PERpxobrYH+0EAO8dPaTcjRPTw9xxB+iMJDrxAAZ5IOAAJ50i4oPitUtAZIgLyAGeToDV6ZeEjDeMtiLCzrmzzqjuuRHDN4QO6ZKpVpAGBQJoyIVX1afBY2YMiOvgO1j560gyxK68oHbO4cm5hlTknNeRFCIExuNZu0TcZy5EItGmQa23MYzvIifAYhxnGEXLx0lmE6ip0hx+ajtbfeeqvTagF24tjxr33ta5WqZMkzoh/CMxzq48BQi2PfA5opDgochPAEF0jCA2GAUagp3ZNtFiK3Gb6oyyJRJg2vBnyEZjjE4zgZIUx7I1hEV0vZ9cELvrpQldkgSMidXHjiQi74/F1Zx6Nrj+WZhPRAGALBOzsaX5EFr/zCqzSXUhSLzFAIh2aocnheKQW5GLz6lICccAcjFwYb+TI3mejqAADQdfTI+Ym+1d5+0Caj1y88R+fS/vgGpfvXX/k6NwJv3L4BzSDBZnl7sk3CRu4UrKATpV4ePdJVeMUT2hLgTgUyKsyJCThogBNJMoIfSsBPdnraXZm28sk117Ioz2e1G9HbaaqeNHyQ+Q8cxrwUUdnUfkOmO0vTuZMdqBhhckX5Tn9EOK+kDdrZ/Ps0eJ8eyHAPqbQ1RqK4vgIAzKGKIYAQ1wAhilSHmEzWHhs8lkqByZCA4bM9ZMo8lSwoO9rmSMqPwAANVnoqlFgEUiOu2BljIiLjMNGmAsbmoLzaHrAaBV7GOX/7t3979er5r3/969zdR6kZt0ASYuD1QiWCET9VSSyGKyQ0D0N9jZSYJQm/MYElTKIAoDYRDvDwSi7M7YweYpSpGGujI17Br9C4Sh0VryQkiYeTJMIfg4UoQAJm54y9Kt5T4SMLJ5giaGE9ri8AyI5lFEKcbCXTmqw+oHLj2nW2ORll0F4unb+AEAIMW5w2ikASmgmaxKvATZqczxylwbEAbQh5RLNoDIZISL4gwSkiVvXwysOhlkD3kyOe8OoenkkP/oANePwqVGLaZRkJxFMFhIDhCHRWq6Ll1P1h1cHzyvVPOIc3bG0jZsxJoRB4T27oIvoJcW54FjwDDJLBPAVZhW8U2vLSU7kedFQRBNAF0BAAhv/1as0PSYBNcw6Th5CIV4gRGbg4FK/3pQR4cCLSgPZh9RpwhnklOIu5HPOFXu/jO3e2ASdbCMdj9SC01Kz4awdavOyEVCslOi+EgRBgeOI8C14pHa94Qo54wGA0CaeHuwdIrxdeAQC5Sx38sYG8kHsUsZ4LMMTCEsMgAXCACJUnS5Sd8IQ7wCsSihr1SmqAytNyAScjBHeAUOdOM7F+mscCqSYnLyIsUAgzKC9EOm0ODDbWXtwByWKmeGsyyRswwR/wxB6xyJF4CH575U2OWFVQFEhFqC6SjkyBYSYbAgkhCZ0dwx62Z3lFtRgfZMsMmELsyQxODLKOG4LVcq3CHZWXkdavvs/6RzLCoWkZfLYrFXpAhJyusLBSpWaJAhUEgp8q4tWmJvgjmXG0Co+dJMFKlywXkUaSaNYlNyaEPI1ktSD5Y5xgA4ljJpwYkc1EiPsbGo033njlb/7xPS+vwzzpCYw5xRuaiJ/CGDsAkq/4RX8CwAEBi1OICeLJk93jOA/BPo4fgJDK84L5VIRXridnSMa4lFWCVotNx9T6+iYnDvWVjnweYB2nhWwjEw9Eohmg0+xe8EpHeb5RRrMUo9m/+7u/42btV17Sxwk9lieO4oIBPQ0N/YFuNcjky5CRs1v42fFwInlyIRkcy7NhgVRZtR4qrBfN6In0OfAoT7RojEQpIM5fH0/uIZ8dC4zMFhJALmT72LzOY+VuzIoiJcr7cLRP2wqnhm1TVsuqABiMwPBQmCR8WLBOBuKPFmQtDcnEV3SW6kRR1AxPVIwLmyKNRNuXiG5hY6eVYExr1e1lsh+8+85f/9X/xmH5te3h97//fUw4i1j0WdfoWcfLK14cofNSeywTVSuF3ghXkSwaYSF3fe2V4962TcR+IGUc5vxSBY0JinYGjy+dALOu0XtquaeF78ZItV7OaX2nZjaGFX3gOF0yrcSlK0D6d2MPsEw81T4asSAnL2L1OViztELLEg4S4nnix3CF57buk8r/YHutmcncrmmRd2AlGnAIlRviitWFxaVSjQ+zVOtz+oB7u9ulnRpu+HyQE6B7guNm9ey0PB3qvAm0AcWT4Q9NERXMUwhtIb6S2dHlCHZzDd+NRkVTU7RGtu40cpsMlxbmVrjUcNidprScx96z5ameVfyH7dSAD3gtgoeUvklClg34bBFtxEYj3Z3fBE2LRqyUHJjE9CbyK+l+MZ14Y6MaP2S737MyDFGu+D1ZrjvSn6kSZpaE2q52tMvdsTWTrj50nJ6WMrK5UZeayVdr01x+MsMsE9rgGIpcQo5RUp794vaYv8xowh/fFBbx+jC07t+Bwva1e+O7m0tP1VA0D3odLA24ISyVy3z5336XGtzut9eziJwW7iujzOaNe05xPz/OzBUGfB4apouDEy5CfrSx1epPuXzjk2s3WCBYPpdFxdRKNZJkcprPX/7rt8DJpG45X+1PMoWHs5MvXaRD7eSGm482ucjlRPXEqJdnWJm61T1ebeR7fDR5wv4srKsY28fZKSq4UGkUKoVRQRXBEgIjSIbpULLW6zDVZ2+YkI3uDhOn+/km4VkMydKc7e3kK0UsdQfjTqY4D29NiLxAYkrs0y8Vwm0rtAkqjlfvBtyChR0PhySqTAvloqSqWmi+lNtr7w7b/dX55XOnTq7dvvsJS97lMrM37txDlRtaAGHZkFYh467UbLXeOLW4NNjYInY06E3H1V6Xs8B8e4fbt6bHlpc0ILC9gReeepGlh7s37zOg0AnppPg5QfGTHGKBUhVbvqopSuHKCsp9A4aCEstmq4VLDGkPKjvobcSD35NQQiCHg2g7+czJYzdvbSkKCyQuHGFiaCMMYYjbLP5cVofvaJUeq7ymUy0LFsq9/uDO3c2Ha3sXL7CXt2pNXBTiAHPnqPCTXCGmLuUnCxSKAi3coRP+kDCKeewnQngwPA60iZlxwHog7dzEUerRSSQVxNNNthyJ+T3f+hwNlQMMGmU6vCdpt7q0MiyjbWzBg/vbxFXCgIw38Ayz4UTNOW6e3lP4a3JhRayI3eeVOqI8kBSnO/ArJGKwdq09grIjLl/EAfkkGp4U/kXQfpG0oXsBeGSTk0y1UltZHuRLVARWOmSEBUVvPNzsTgdjvvgrMw5G04RDNU/+q5ZtmOtUhUr31/BMcoPeh4HXsLnJ5tDO+h1gCnkauz5MZnxWDebNqIAb/6CNdR1CsGbgid2PCQnBM276OXvu5JXLdx89bKFgaHW0P1Qds+MJRvQmCcgA9NK4MbSnTaFLPVwITSDxHHJgJkQZxB6H9NdDwMnXaGSSDEr4IQjmwEFoYIfFO1Semg4zktH1hXSVTOfsxJI3JW2cy5qyVsnUclgCyx6Cjlp8jwYJqgtsKbj3N8+5Gi08pxrcdDEZaRPPbCS55XN7/T4Qx5cXyqxtM1HRp92lffQXcUndZHAqudVyNKGJI2h9uxutUrZ68uQSa6Ro03xJHx8pVgr1uQY91/e/99ti4cS50xeaO+133/zoauXmyXOn3vjGN1bnueli9Muf/+L6x29t3N/+ypdeL5RVapo6uAtZyRvfTMTP1nixlBt3JxwWm186NSm2p6M+fVNJB4dZJtCwvpvGxrn3KMtQatYajLleo1zL5Uv5SpmBDKYNqWppscdFzOPixXOr9Ko9bgPt97s57RZXakV6w/a4Re4l9fkaZiCNGb/12Ho0JIw5A/qZ+qdWGC3452XgLSzDqgI6GdxISqy5QzPrWGXWqJhr2HduM0UtPWPmUigVc7VMeWGhy72CLCIPOPsk4y3ZrjGWBuOM6yUZVDKm1jdDsmCYDjgKMBn3Gcjkp4X0cFzNadQ6m+YpRWpWhRtcPp2als+UT7DC+z/8yX9Pr/3b5s9YcLyweOzyg2GnNKpUVh5u32m1pseePvd7f/ivv/HHv8dWTSmnr3+/8+tf88WIjb21vfe3C9m5ap0vL/ZytPlBesysiZ5NTZsMI1XGfggF7eVTj3rNtW7r9OLp3sbG7qizaM2xtbudLpcLZfUso34PCjlvVy0Ulrqz1F4vM8c3HuZWKmVovs8xf/T1XAVqS4Xi0nJ9d3tHLLSM/Bmp2XiSjxhiqUSzIiA0VgmmBv6SYHp8MLhuUXVwcVZtrlJmzUh3hYETuXqy6gYRifb7C0Pwz31AM/0SfMAjAfoijkGVmKDNBhY1SEF3///l7b+fLEuOO0/0ap1aVGWprqruaokWaKIBEMBgCQ6BmV0j12g2M8/mV5rt2v70/oj5c8aGNmZ83Gf7iCFAEAQhGkBr3dWlVVbKq/W97/N1PyfuyZtZ1QUQnKisc+NEeHh4eHh4KI84PKljlcAXgHRlKkJgSzAUmW0WXyOxjpejnsrHBmewBe+nn169c+f+66+/ytLb4mJtzPh43EazDBnKYXnOJ1B0z8AAyWesp0Zj9xobu0jNEHKCAKsIqGK7D5rMMZ9Bxw4412rr7DDW9JaGP4BRt0P0jzmRas5fQcsQRa1MSTQ64tUZJa2o4kgXKTs9PZFPKswfD9tI67UJDoyaM5hYkJBkcmQSL30ZMwgiIxo3C0NcAcG3VXqdNmsxZ049Lfu1ou73BAaElAgquLOHQO4q1uIsNzdpXVK5owPwqxWwwuikAQ0l/Cd3W8J2PBagvJ1IPGQBDZ7IyfMo4D0QPsQl8tR6eiwejRWtYBrp2BgPYIdnrcwxAIYHMJLxNxxptislQ/hwjAFgqZipVWoH9+9oa+ruDSaqbI0xPJf8mJknaaWOlMxRKi2VZS9R6yBE/OWrHqN0o4liIysZv1NJfmpHTTQe5DsWxwCr2XZij7nZ6fKEw0xeZFequR2zNeHnDT+t2IomTSJ5MCbzRO4JJ1DhsI0/S8WrpY3Yhf+Is3VQdXc0xtR07dSZZ55p8snEXhccxiugTXYYbZcQSCy4MVUVOJqDGe1ko1bcWq4wCxlisqgTqxyhkYDlx2k+32QNB1LUf0EJ4aZJZuyKA4mXkDq1gUJKRBJLpTCPTcJwUjZdKgyzTGpSulLKCgskTU1zUDW8SHOpn+JGx5Q6KRdpTJ4hixPedIWFKRfQylwJJlIqnKlzNVRxmxIhpV7v9DxagleJgKFGnDypX/FcnMeDF6cSmUcVZ504bxG8jeGpYpq7i4SLMbHkzgPuoiOVGo/UujVd7ERFtcSIro5hEhlYoUUPjoxormI0js0zNSspHg1UnJcQpDuy1XtxUAppku3qJHXYbCyvrfIxKjgmcFYD2bWScQ/6EqkTfo6qUzA6faqWL0OodvLGG9eo6MbslA92IfLs43BX+QB1ME23pzmuh14+dwb1wgkiZprrG9VZTVnLdUbpXmGy4cOfcnpqGQXlo4eOeRhTxEAiearA5oBkp4NlFT51oXDjiImb3gxeT3ksikR4eOXAFb8qZIqvqPW+8uz5X314bX+/4V95ASDKIP6BOQTZwkREgE5FwFvhniF3cM/LM/Ic4RivVLRHxViP/CZFxSPmMCdf8TvmIyiOvYgGKQxxlzrt9ga9PkN/a/wmpd7vyCJBC9Mab957sI29F9NnmjA3puNIS3aqBLVVLZSpGjBxQLTRUxoyM95QAafIFJDZ7Ic3Hzw8bPFJ7RefvUQ40grHMaFmlbExKrfgPEEcOWQxSe2LE3uM2gnDBEGY6U46AzGX04hT7fAizboYXutLCiYRHq2Gu+tz/Us6X9s41d0/LNnFqtKEpgH4jVpIBPu7/WiVKqSAjbyGd+j0Nw/x2Bg4QMUB9hulFpYTAFRy4TyS5AlfPC3PJ4SnNTLQBx4J+Nu//Vvlm19BP9IDMaBhTiWEhiv+fSRZShsTLWBLFqcSiuCfg/TXEIgY+Q6lduuRAVvcZBBifqljcsGfLGDIV1GmGvBQCqk4I0niawsieHBgBY91OegctQzmS1idNAdNYknCk8kxnuricmVjo7K4StuYZjX9Ay3TAygDhldgoqImCTrmN2KUlowY9eKJcok34qABkmhsOtDBZI/DklDPnGo0YtgJMNUBAKlpV2TNYigAhEtbx44Q8ERvMXkeyJM4MJCHFJB88vMQNy1KaQUnh989cZX6W/T0WOVuDiSQNBdITECCH1nCaUp4kgtpRZXNNv2qRrHdRp9J/AEtzISTzkywBnrcw4j2o48+yl15A4ByRXtcoKbs8B8PCKGH4SweolhkqcSE+ZjM34hlTkiSVqvDEjCGQtjnrnHSFXe/AyVWKF0fyYB4yKeObCyL9UE92yfhg+4e3d7ixinMzfiENftyxawmLczuIJKejqdb5jKdBtuEmSGyZwc3nADxI5NZLfOJy026Y2JZgGYRod64iTF1Pq/hOJ8JBwY5hM6iPuH4JbqOjMg3sDEu9+wXDoCEsjgkmd67d++v//qvsVriQ4Lk/otf/IJpGAwFxpNRIqqjWoxaHAmJIhccliwPLDtmuchzo9sh6szZM2x639rV7jSMws6Fz1ldvXo1EE9CMINHDdIcr9abRzLvGkYFjxtj5KGzs1ZMKeApqRyGQgOAc4KB8TKG/gOqsrk8+643bx141kgLu0KeRCTYQMcRlssVatw38KyRqg8DhCoQgBWfSidtuaJv6QJDLFHu5vyzLGCUei3hikDtJeGXN5QiGX5iYBIg+IEM2AMloSoBC7EhiXtc2t3v8J4pU3EctBs21QgjDAYTtC0Ygq000uJfmUORw3lk1Spaghr4j18xwcUT2hBw3AOqEGgIw9u8JxRzPsLeA+vmwDzcA/HPxZ6I6skDfw9sJEEm6bJRjGxjepEJpF5oTXTX+AMfRLDVpJciGf4kRNLjcOvu3t2bVCDtHCRg0NOGeo7BG9qRVmkRkIHDC7W0erQrbYFXBr+Enz69Bc6dnX1HgjQHaUeoiCIXJo9R7NEfRxsN/uI2EgUaJGmTr0dTR29eluNRFNDlCQDaLKoVs/KRdnghXQ8vr6tHzsxLko0tfsEX4eqm7Y4zwomFX0pmTshjcYUtvKI/eA6GWvRh+MG4olLSPezTUYvkDkNSYNyhvxwVBQxlBJI2BIB5RCGqmHEdegw5AYxOj4xGmQmWIH/+53/+xRdfYF/3y1/+8vWvPv/qq69iKIvwNLrN3/zqV2c2N4ChW6G+lpcXWYNI6AmRwVmfxm4bwvLFqvqjjgyXVtJVpLFQT0M/ME4hfADM/U4Y2EqsT5v1KIzFgAAAFqCFzcY2PHGLi8uQPRxrZZAkCAOqAz+CYchVPyAUzpgbnot4GzOb2OMaDACc91Opoj50A88pO2tLYNAFKiJYe4RByymXqPuIyuJs93D0G0lcsVMiyIMD45FOC3X4njW9dX/yx3/8x09V17HRo4P7H//jf1ApHCTK1DvwjcLC5936LnY6f/V//B+vv/56qqqBKPNnaPujP/ojauews8c9sB8cvsXzqa9uOjE8nQw8OCfGdTY0MOxhCYmeGr7xiu0OdOKor/LyGvLBPSiIx/7uHgCjfA1qb9zYQery5895XaDJuf2FEvFhYwi+faNNcmNO1Ao8R298Tgm6HUo8JKoW1Y8ADTiqDfyuK9AJIMQQjhqxQswex0MslQDkiZv8LMEjfI8BJgtKjSRIsyXWKx+BKRFM7hSJa9BM1MlC63Sx1CXgIq+XhSx4x0+bhv5AGDTABGj42c9+xsLrK698BUlg80UKMCP7LNbUkCK++0laIElLqwIbzZkQOIegggRIonBEEOuQsIq0njVPYr3y8CBj2hswBzHuCBfGiE4VycM90JKfKHUC81x8xO4l90BoweMhjhn0oMKB1vlGWvw7O+ogGGTiZ2qDIdSp9Q1Kx6qoU0ISPOhJBBX5ZH00YMaDYyXLMwID405CwGwxEQHu5wkYLHJgnoTw6uFGmlRNeA04iXIwosDsGJwqYMjUHWl5JdZ54nicEgdzHe4ZsQmBBxhK1G3XP/jgg0/ffQvggnGJHQjwYMtsNNgo9KhSc0o8X/zoE54wDXn44JMvaOPc7UeI17shEZ3uCU+nkyfSQpNHJ+yNRugfblUWbWanAg4Di9JavjyYqUa8BRsAlldALI+H2HMm9oFpALgfqoDBaIHc2at75ZXuT978wqMIdw/4PQv7jeqIKBogZGPkATbEA3g44MXE707lMMdrUn94LDHmOSLecaCDwAB5nBKPMnwK9UxDoCfwcZcT7AlJi2PZ3D2ePDwTRdOQgSSwFsgA4B6Fm3RJ3vHEVFFegInVyhgw8TwlJCcWGHKZhUQLHdISXgRg8Hhd4McB7JjdM0t7jDCPAj/yH5IQ6EjwQJXHeoiwJ5CQBG0MJXn70gbcQ/KRZySZK5qMbBU8SmWoIBWHDThpPXeeQCIChGSNzZZQJlao6/MbG3SLXH2CSllPbYGfzsdx8iQJwFDkWRi1PKJMI4VgHCahcoxrxkohniP+YACHawAljgVGOE2BO528AYcfAB54ePVUSDLDg5/+9OcO+ZincIrax4BEUYKzLKBcBB5tp8QmGfjl6AyCVEa8XryY4TWJIYAZDSopeVGndHAytoyJh8BWqwth2A4z/qQvgMFA0x1AMyMQM5PTdzsNT5QpP15dkAD3mHqqaLFGEqgRyVYxJy937t+iT7lwZgOZYWEPkYi1xKwjAN6Jh0j0CH4QEogdNE82gxmXzqANFMjQpIDBkYTemeFN6mHHsfEkPPjnPI+JckgHYGk8WmdRqKszs8DFG5CLZAmU/9pLIlUyHM5YNGuioown73Ok0Dhsr2nWuhzj3NOFmCfdKK0vjkWL8YeUeAC5SOKLVgipqVRqrSfR2VpbZVVlu4geTR3qgjjuz2kKdpoZDrj/RNun8VjelBcf0gap2ehpj9IKr6dx2LOLXiJS1C05FVp1MC8Aundd9suK4Z5fMPSyIr6dl0JpFmQIzR4CElUbamWnajdBLw6UTU3DtmxtaJVt2qdgNtS5Act/sr4MVMnwOVZPebsfyoWezopFKm7BBv9A93Gl3y+Ub+/sPagtIMos8S6trlVLS4wFqwtr5GhrJpicqLzatGOrTfpKDGH10CuTV/LKTzXlo8Hgn3IvDc3b7h+kuDS8Wjnbax8wtqTX1J4eCLjsmC+62nIMd2xRLtqRuNRXT8DWGFeaYbdDxiX7fEeNr+6yzFdILVUw6LU9QOuFuCMsZrM1G5Va5EUMdzsADCPYMMdqQAvQBTADKkUiycFjsmsVFKGyBSCvPH+CEGcFdG+kfXjx4uMhrZhkzq1yVulWMpmDDz5a66W58QpUY7aQsIq1S8WwesZfx3qYwRY2DdR2RvbOzOLz3LK9sMT0H0KmrIgCh2NSn0pXqmXMAgYHzVKmwEoXufUGfXosXagkeMGuFyof/PLNZ14/x2TsEJln9b+cxSagk0+1Ji22rkFbxvigULn94zcX69xbrC594cLmaCHPVY1sxHHJFLUA8WjMvC5lHp157ux+86BUWyleWH6wr+2KvLUFDHezox7JcIVRaTKYHr6108zuN6pc11cYNwb76Z00BRJ/OsN0FzNrnJfJhyRwAbvgbH/MLcj5kXpfGLOQZ5wt/8PGAcNLPqdOuTaqKy8/s/5sY4Pj0h/Tm5Yrw+yw09grVNfEh16P4aP60kc4xUymSwuLEI+0swWojtwMN3wGT/31xsMzW0+x3LCxMWIvfjIY12oVzLf/y3/5LyzB04UDj7SaAqA5qFMZT4YyB0ZkWWKw3Lmfr7Wzs8TqeaXEcgMNk0YH28fDAeLKdIp8KR0jD+yU293W+fNPXb16jUOxCK/MAGx0LllSo6UWZAuk7d64y6QCKSLdPu2UTovuAZYK3jYwOMKTrfANdIkrJQVS14yj+0wyaTTQGE+bNXilKepuWzYwa8uMaVkgYAhEMblOkC13faDadm7UQmcf0olOhlJBhsEedh8932RGe3NP3YMHu6dPr3O2jJJCiINpO3jWTUARwaG+/HY/NbOAM9n6Igwhzlpc9DZLoWaYADnBi84RjLfx+Jiww8Eujqngh3VzKZMhURYG4xYZruENZloqYtRT4avCEgfbl3aNRP2gwRp1VksRg7HmS9zFZY58nReeqUTgX9M5/b7iPeMWHaeJdTJnIL3gwYOkuVAlwX4nf5KTj0rotBELMCeHcJzjYCGpcPFSAxu3vDYp+xnN3K5utxAElujQvJ4qqc8Bg2B15bNyCps73+9wv/f1fIAczXD/5uesRQ67TXIupmWxhVBYHc1kt2+f1CMGCt1eZmonctzmnT2+tbVN1lQ//uhtvuLJ1X2Ug5VNxtnhy3VCG4uZk8c5CX2dgPt6zCVZTR6CN+d+ZWuByepIFtN7Z5hDoqj58UL1ecOhZi2OaJc2k2FRhB5BkZKqjk0ovYfug9AWMhlxbSXhpCRr006MByj9lJub2VpkEicCrYHJI0hrTdg+c56v02Jvn49N5EYDdCFDrHwlc+/ene37NzCguXDqdHY04LMn0gdY6qI2dE2/bsoUHmOzt1lvTYaeB9JoVcyFdcNJ8/5ealI4u3WJemMVfSwbo2yhXIDGZ17cfPYrp9/Y/zrnE3/4w1+urHz69Tee5gNQa2c5qDFlsZ0Lne9vH1Rqq09dvgQruFWRIqMJxdJp4cb1e4f3dax7dXH14G6j09K5h25N2yGZIt9gtYN3UGPlnbBhz5XGnTZd0qSU646n3EzcaQ+4STpDx8bFwOlsz6xO1N+PdNwE7Ur33h/0KC+TRnZXO/1e44DPL2c4k2cfrJBMpBiFwjurMe4BoR4YwSCbOlojbvsIV9I+VkeI1Yv4JEMq2MqduVk+UjDst5r0StVi4dDGTsVaqc8lhLIzo5YgzmqgiEEV3MN6QbnSWY6Z7DA4YQ+mUmi0DjmeNRxgrpQf9CfZ3c5StTY4kH5744VvkHu+l/6zl/+MFZGLmxe7B529u7tf+ca3FhZX//mjz/mOZ69Q2KEnrVX+4n//8xf/6I0sV43tDj768Or9G7eQ+eUNLB82Xnzm0sVvP/u16b9hx/fam/psb7HfqvQo7KF28kxD+jddRGAqxVeem6PGP9xutk6f+V6tNqlUGq16CxPLVKGcr2LEzhCntFrppnrrvRpL1fV+h8s+Op3+4ji9sKo7hTmxjJE7l1dTimE+9/zp5Q/yXbqugc1lMoxtsJM1ow04i/ZmL4KbYzRCMyaLCmtIWOcxvKEHLTJ50nhMs0S60Hwuj7V7pbyoBiuzKImuasd/TKwdlSPSxZ/IvyGlxHOdEkwmVumPumisGMcKwmXAaHPYTqvNn5LPIUi+JuCjHCy2Z1/NInckFseQXYcC6OBlk4tlom4/Dzc/RkVDV3BpJ+YFlp/KRBP3vNLZu/e3793bZpL82msvM+XW0CiV5YABNrUsh45lDihukQWDDXLERFVl4rt0iGzCDprZrwYoDJ6zmn6grQxMcyUtOmPBNZISYXKHGSIDNsK9FE4kT6qJEA/H48ndo5atToSCaPg0c1YW169+8oZjEALKMc/jUJwm8A5MGFmYTiZSO3wM/x48ePjJJ59pflSrYYp7/uw5VpkBG4y5WVzpMNSAnWxJEq69LtbfzUyHoRKtkpVhyqB5mdq44MkuJlh+QkSLGQuDVqxIOF6TIUDySiqGJI5HdWF8cDwOAAyVT4iKA5/j24c9F0MvxnoSexUlDowHQjXpgtWcAM6Mdu/fvP7J+2wNYt6Rz1B1FIv6V//OipyQWBtBzUdybBgNjzBgLqIB1EA3wzT64y++uNls97t9PvNCywOV5w+p4gssMoyp2oKMV6rVMqMzshY2c8yk+C7LU6kx42oXhgknLmCLbuZVvfKUDoA246Rz0wsryiM04jkwcTjoZ45wWBEw4CEV932rS01lnn7m2Yf7408++aI14rYumSNOMqNmu8fkkCUKMkVsS0yPplpFeuHM+laFwb6+PeN5CZvRBp1kaW9OiQjwKvC6I4okTpZ5ZwjihBH96g68ZPBTpxqEFeg0qrJSGeW1A4QeZsJLJ0WUDnbIQFl/vh+NDSX6Dmr46Dx3xiMcSDXzcE4KcpNxaTClsml4hFNlYJM426aKCmCjTT+lqv7JpB3uolLhhoCtFBSN5Ho1DCIxdgDgFYA5/KDl1W2EiMURgxBZXw+hREqwHQF+JcFJHUOAvBqH2B9JxyagdlMzKdFIVBNAWKfazhlpPQ+NThFiqBE74aXIsBF3tz/o9Ppq2oSr9CCgu2BpR1I7IB3jQY4NI9JMTphcMSbJ8PUADTrEbpjEKE2dDjbSqaVSAXsMbvHV8YE044spX0PIr6+MH3ax/tNniihDzCVlQNOy8QSUmWU93GARnFCN87W6ySlsm1eCnlqz8gvS+cMrOgKkXObPJ9V0HMuc106AUSqx1BuzgqFNgmJ1Ucz08rnJxbWVTyu5Xkfj7aE+9hRlwavqiBGWnCrE6gSNzPycQJ/DCidOmYPXPEAbLapfnPsNCiUUSY7AEw7sQCYCVPVJGPwhJHiS8MFPbFQCGygzluKyb76SToVBi4sYmVMW7ukkUzT8+voml5JBAyVnz4BBODofDplVMirTlk6sKyQXkugomOhJIxtghDYg6BgmZsTJN9AQlg+v7dze7V++fPHKlSsLi9V2L10foCGHHc4GYtbJYBwHH7WEqjGkMY/2KOrZtMByUl9JRAhLFsuIGtnX8U06WUmdjzBZQaKwfLcky7Jq4wDtRP3BZBbagPRad7a65HgqZXeSixmnuCMW0ERYZUgyZtIRo4hj4/eTfoNQeR72FCaJlfEU/LY8OZ/Y8p0Fzr0SoYSxkB2PddqSFDoBASNJPJU/YWf0noAIucA+SYnBhBJ5bAL8iGTPZeev/vQcIR7HxIBALdHGjlccM21iERX6RbsmWkMAEkIGjiVDASGG5ixIuROWeFqctZ9IygyAfRJGP0tL2uVY2FziOUjrqKmZTVuSkx5koRwNv/u7+jgN2Zk68FGDL0jZAEjrznb5oy9AA0mfC2J1NtS7TdvwEE4okBqeM0qxu+adPxScgdrzT59nGMrExoH1TEiiWBGXGo8ltE1+3bfB6FQW0Frq1h6g8JOVYI5UFOk8oTw44r2Y/upPUonUiJlGsGVt8FYu02uMySBb80mt06mrOO5AgmMkD1W+bA1p6B3SUt3AO4Cys87YR+FNTrnKWezxAhgTfv7znzOoLX3tOfo2jARQChQZh0e3ELZ1RdT9zz8HYZnrF0ej8qIsWznEBgy5EbuU0m3Oa2taN4H55EdZ2OPayz8A8zof/MHVO1g9kwRSeeOJH3iuESOtSDzJiXZzEh7uG4E/1niRKoXY9NKTggq0KgKTajPDZ3b69a9//dpHP4WYQUaGMKUFNjM4wymZPCm3KEwZ2mYj71gc0Jmafp9PQY6UkXUiJr2gpeC+HIP5MwhoIMrFlncb7QazCF/IptRQDiTwdoNklRVxJrokIQNWIngae4GSlBJORcBksvNXigNmwnnCBUto3LBCWZhGLIRTZJ5oC7LDI44559WfgV0AWs+IHfFkEdcRXg19PFIvJj/aoWHZxuZFQFJwZswMxIfcLGBW4QK0epEndo6TN7BBAs9upwsS2i6lozfVlNLu+I5T/G6/lAucT5Lm9wCD+DlxUXGO5uaVlSQAknAeYqWeUXiEhohs6Rkf9FMUOKmvZSqZpIXLT5AWHLxiDMoTx+oH8kEWx7MOZDwq6ggBcXsMqeY8c8ChRC4Pc2WcS3viqyc5Ea3DB5wnJidwDiD5Chvpp7j6Hdn24g8n+phYQBXyDZ4Q9SQe8iIhbRB7tPrDO/jpYSUPsYjg9xMbTifwuOOYBTbhY3pLnH745JOrjUaLxSjAuLcTGwfaO6Z7moMEqTYkMTYZdKOng04AG+X1XKLsLM8YXjGeo8M8/gkkAIE/eNyFVMkS0fb7g0mP2a9GO5JG+gieIFEHyijFBin4SO4qgkAQhiyA1Fan5Ug4Kpd+B1UPHmYfoDrcP8QSFiWJHkaVqb/wI/x2Tgm8Tl6SqkBq8IRMCWGDcHFxFZ1DJdCsKot+sFFUQQwOlf6Xf/mXv/7Vh9jc/eM//uONGzcuPvcc1pcby5uQwTEdCoIlGmUfpG1b147sbD/cxWQ4My3xAcOl6hI11Ouol0T50x3USiUWFovjA3J0zQwlxJohHiumujyqmRsC3MyO4cBQM/cKZOOAhA944Emr14VO+j5CmGWSC/qTkFBSPALwZDSWWDTJCy9ROIcJSQgBHIBAmNWm6tMhAajadQpCa+cJDAkPcAkbaXEcOdaTddDJhF5MQmpH6WEXpT67eua1116799kelfjvf/CXDDzOLur62najQZvd3dUIE7tCMKDcyAXcxHI+9M/+7M9gBWtAv/3JW+zyDlsdTh6M0h26+G+/8cK3vvWtpSuneO7+fJvOd8QdHFacULrgEbFGarvdArJ88RI5lpvq48Y56dXCuMTAj1UHxK9iox3u84AYSIJ+mI/MdPN5rHHHixWmgtik0YQp1K9+9XarTeONLuxiPm95iRvkSLbUoT+dGPxIPX43uvJAgClvtVRF/Lz4jyqIw4dnEoyEZBii7DW8fbkHAkgCCuQQgfxdkysDE1d+B91eK4da0zYVmgoi8bOI4fiTkJYo0gligs1uCEyWy+BTXL/wxRefMa776ldfRcBAS2NE0SO31A61xlehKQIeRM7VIyNV0qKWqEFlzYImt17E2knJ7HYyUqFYPFNnAvLAfJ1cAHbiSUuUa1rwh0BCiEpQqwbrbCBQlMdTVzwO6eH4/dVAVF48kOG3P+NBOSDqnCyEDLTHxsYa26v6/KlB8oQMMOg6cysvGpJUUK5bcGCgzRfQAJDtaz4hIwqF33M3bBGd4r+NPB3A6eHpgUCShCxgpnKx5f6A06nyV54ODIzzJ4B51Bwwgc5b+OseSlQyw4Xr17/gpGaq36JCfebrxJBCWURtzdGf8KQgXmXE0Zb9VIcmBfSwPga2eveUUIvHKw3k5IjqRkJIRRQOVAyKUbl0Cs8//3yEmTlZAomTFxhoCMVeEB6dRiqvE11cwIjtwCSx8fr000/TPD+5cR/aMCOBMALxj4fqHbg6Cafr/lIp9KfmL3aUwSZGGicQK2LMWbGi1kcAsTgCgz94oMEzSj4d3p8OydMdgXjgIS0f2nhTKZh4KBd1N97denbgpNSqzliBBMyEBISO05MEAPcA45D+6hwDJ4HuAEAzK228BjKHgVfRYEzwJJ485G5RmlUhCMITu+N4QuzxqECSR4EDj7JICCowgdsBjLHHYk1b/sTitDkWasRagREGG8UuqphYngLls8KxgrJ8CNeBgORZNHQYLQ7ktBEqS7GmCqxCImEAE8m1wxz1a7yJOpATRQ6WC5FEW23GLHI4iKGVuB9IR6L0MQaiDFVU3RF2Yw7AvJILnT6DgVSn6QmPP4FxhMfkaB42iR/KnB4CcZDh0MHDK+EBBbkk6fckAYBUwU+SudeAOQnjYGy7+0iDVzjqkDzJGbYjANBJc2Z8yPe0oJFa41Q0bZxOlqpTKzMiIQ/kUCj6bf3HhZVARVnrQ5eBmWITQn+JSnz//fdZ0b789EVmJSwLO0+I5U5bpwQ/DSgU3JnDK+GcJsFTlDWgJjKEwER5tIkuvvEULeagtmdjQsdwIn88xxOfnioZhenLbIdBVpaByhkPNZ+K3cwXh8Cf4I09bGZZSfRMybbxiJ11DHXiL72rF9tjNbk3djpP0XzEBj4CI5kliSpLN+TCsRxjJe4e6mltqGbtja+FC9L2lvf47D29gq2/sksvrjr99gNmhVi3QU04f49zTVk+2gHPqEHxdjANCkHJQVECOrZY09cOzbRZVAjfoMRfoi/CMnqoZ9XEt2bfQ6/JRDjDzYkQUxYjUwt8S5QdNBdBG/CxvwH+jsYtqUZJFj37Wg3ONHXTVupwskJHUliqkBl6pq3rxuzIpxGoNAmnomMzaPylzQx7Nv+342YF7lYWY1QW5Nb9AGKpkMvnSpV8e4oFUD8/popUGVhSG2IIl8K17kPl5SwvjY1WToViHwosBg1g4yNv5WL27OpSHkMKlraVl+DV71kFKUdumoIAf7dfX2hmzxBsYAQGWwjqn/1sw+CgosRaUzyK8AX0eBtAmR8V4vAaoowl1jitM7ZLy9Kr3dzOzkG53mU5WTYs5C4OpbCrIWGb65yx283RPWCmZUZOCkhzv2NpaQF7e8Y+VC4hlE+jMdlHT1by5Wx3ON45qA0mBX1Hl71C2VUXjFrHz2cJ6Wy++ZXXKPXNwwbriNXqWpGNc6w/yBkDrd742e3xj3/8j5m9xtLCQpON1PFw7Tw3kI86fv18BjnhU7z9S5efyvd09197OqwsVg6bfCgpvfTyxqnL5wf7bSYzr7342t3ewXinTbeDUTulg8vYJHCdM/5MSpsZoNIzJdnjflUEyOpF90kSgS2cGbyp7BSVKxpNPdAeKVJ6uVRlmYnBImWhWZBjO919amXlm5ee0U2Row4HuFsbnQpXQWO4zVf+cmrLJzmr5XSmXK1hl8e4czyU4Yl9FjiuTyMBm7TVtYUbN67V63wS6hQWGxguo3ypFuLZCOfp+OnXqffMdIiUW7PgusxUppA/e2YTah8+3NYXbtWqKAos0MWmk+FIO9Mmsch5bXFB06FsZm1z46Bxk94N5qDZxxM2nGCZ/sMQ5ahDAfCNDsNTE56qlkuwiftkfdipyYhsC7X4C08hjW8mS+pUdBgtyn2PGuSUge1wxtkchMfV6yyOHUIA1tBF0JK+qKvwgcCP1AgDecceF3h/ZdBMBdEe6WIfbu+jGQZDTDNKfGKKhbga5tUx0bFBi+GKH47En1YTUV4WL3/I1FPMvXogCU8MPx54PMQxRM+41fsr2goPOgo6Iik5ogoIRlcj50az2VM7fsIslRK63Yh2/KepUknfAJCBwjSDESITPRYngSQXx68aYYWLA/g93YQe9q0cJiLSfrwKzKvkX+qcsORzLonuFwvOlJ6v0JPE2RtxIMCc5AkwnlEACeEhZM7jAK7BPMr9fTunUt7cxDCpTdfBzQlZLUMf9DONLhyVzmHuyFOtB27Hfb4jsXCFuvPGk/R7GfOp/q2btx7epg1ikqN9eq6oTO4EkwSGH6+FCG/8Y33flMPULITdvvXQ1SGR6xunOdezt9dgc0dIghTFhBkC5gBcZYPuz5aL0mPOkznWzb0ClpAEyaq/xqMyb/yGPm5Kc1UTxR35AUcaiwkGZv2OdkToe7gYn96B0YKtd0KcGuai7ytzsIh2YNtjfshLmpylHysdp2QmnP4bDAtLyzVkbMQRe2md+sF2c/9BPjs+c3p1kZHJdGT7kGTNKktmbFvUaAwpP5vcBp7BAR+U+dMJ79abo06vtFko1QqVZWbpmoyyyvPeF3ssKzz7+iuM5hfX++Va9Tt/+sqVl06999vfEHvnn/ffevvdCxcuUpaHDx+wd7i+uiwTTjQw6m+SPjg8fPtn73R3m2dPbfIlgAU+Y5DPn1nRgvV0t4ExEkdqWDkadIYH3O5no6xJpdRsNztZPkKfZ4SUY/cxpw8YsnBbbw66i6XNTQzCZdGpT2/1Ond3Wiydd0Zj1r57oyYLoNhpPtxvbm+3+EzDKfhKFZtpFZZmcHVq56i8XqOqVL9OdbiqFFfgPGMGBgySfLilToiU9BbS5+poMVo1x8kXZhi0HTBzmb3UKNHMiyxMdlEZRs7MoU1t0Zuk0hhtDSZTPmo4PmRmUvu3b/yAiQcfLfnOt74zqaeqg/JzZ57+7W9/+9pXnj21sHyr/d7aKrsvzKO6D3ZuT9J9bm/vp0YbXH+9usBqZq/doR9c21hv5erlVvPwwd5e//67dJPd9Leqf3bq1NbXrnyHSzwWNqQnl3r7Sz2M6VTnbgFtXm4Ltk5wdfEwPX6YGUyr2dR6qZXOT5v1+r1bhfGKlqQpRD5VKaQxgex26ov5XHV1mSnZzsODbHV5Y5JtjDPXt1ulYSFf0X3cLzxz+uHdSvcqJ8cwrFA+DFngMT0xDVjDGOkc6RY8zn+Rpd6Sz8er/RovZc9GD7uwsozxp6JNeImiifHqi5J4HuNgvQZTymfeGTbhebQT5VQ1NLC9yXRU3crv7VTmVKfdpi5woEEhQAPGjDzNwBF22CzMJrdIG2AUFkhGJFr1EAOwlZOgmWhrxAgv2Hv95S9/fe3aze9+97traxxK5ioVOy1kZjdsfHC4ZFLkNAKHRNiikx00BWHVlHJxrYiTI+2E9sSZnBOFF0g9+SwkAzWz1RmiYlNjzEot50iriyxbhvYkTjMdjcGIfnhoVadaYPOYBm2dPNMULRECRogNQ1RZvKoZWcGBp5b5VDf42djglbEWV9sBdvaCPr9ZKWp8y5Y/D25iQrqK5Qpg6q1YjC6WmJxGa+HGN61EkQXyY3kYlEpNEUhFLjyhlhAhoc3Gy+seSxSepCPER+lcCeBI/JmEIQRuAAk2/O4cIC6m8kwm8XBS4SZmK10pZLusMY17nDJ8cOsL9EahqBmfaDb+k5xWQsFkBWMMSCI0v0pHJPqTk3yQwfYYGn7/oN5sMWhSfVEBxPr3POgGTOYsKRNP7S9M+RKDcwkREryv+MO6dObBw+3F5aX11TUSFGQ9ATVaA4EaRJfRxhHFK02ryojrWcINMAgtP72689e55/GKOL9Ryb/4VL3d2d6uYxEuh4GObotSJvQL+kJCt/fyy8+9dGGrOuljvCX7LSYNmrYr35B1IMNHRMiStD6lMBmdo0RtUZTrx6OCx2ucJ5iDROkLMdX8qJDuc05Z/SS60UTSwBwDgeCKakv2vKpRQTE4ZtzG7D4zLQ+4AjjFuI7kFku7NAJMlcB18kU4yJohs0rEP5NtQrC41WtkAG7oTThNy8gu25z92GkSI4cq1p+gJc/gB8rtqbXQppGfNBw9ZQRpr4ZJPsY+Ed4oKHq1xWv6VxqaMPiNvUAa5YBCmK/JSHxYH7GyUGtU2yhdb7Y3NsZoDzQ0HTW7Tlw2x+Y/eFRjUGs2s7ZwgfEc+/c0dZ3QGiF8xkxysKleqlwANNe3yxbGUz5douMB0zzLA5wx1h4buUQt3Y4EKD2OwsIOiRKqWKpDM13+Wyw6k7krgwDZP0l4IEqSFiXFB0Yd45JZLH7wISfkhcewh1agNh23DGWLEPGnPDOZpVLn+UtLVx82CPdTj4o/6qix5NjZI5MY8buEECU/awjCobK4JDpFJoheBCdQmAxGHioReuRTBRyl+FjIcQDHI3baF2JAQgfFxYydLhulIBe3nXVEqXVob0DGE3jQh0tLK4wtx6kR52NZg8Zy2ewV4KYmDuorKQsdAGioDT7VZYtnmoNi78FKjvCrI4D/9Ch9GpZoTt/ebVzffpfx7WWGwXwIBNU55p4ixJwZlBTNMCX7AM51iSBzXLVA/fQZRA5Qm9MK/60+C1r9ZhCq7odvUwLLyB+n1culWmFlfa83rNj9B0CQZL4WI/RP9DNvAU0iV9zJ1GKGuVBtoQ6SYO4nuVnORxXvRfJnjOZ4olmIY/b3pH8G8QifOBTlScuJHNIAEl5ORBUCVS4jjhA5y4JAY8WTUH2EpoCB0Bk6A3Hc3oR4QlgMo1yIdbrJ10xJMJhS15i3D9SEsngqhzSs0cPyUlPHuZ8IyWIqxTQJR7j/8iSL+FVhIiwqt6L6du+twxAVIB2thyiJpeShuRbjVJ2qU79AFLraU/GMII3F7gce+t3Pk+bBSRAaD7MIVmdieKF/jAOMIRj4ccLg3ecsuyNJkzidga4kZvop5j/JAI5gjvrJyJW7cUvFZxliUSrHGHgkQ4XgvCqcQvu6DktUOiCGriKW+rdnLLhWBJb5WJMl3NH6pkuk6S0LsFE1fESet05lyBgrGuHx3b8KakTD5X/6p19wR/Plgix2eWVYzHIqbOe6DWHOyGi3VqjevXt3sNfCdm/AAQsuuLC93AsXLgADS/V5wAvTV1555Zf/n7+nY+PzjuTonHHZiLkUVSWvOKLoVfGAASd1oJmJJzTGgt3wQxIm81BIEiARHhMMGZphsoStYrGPXYB0N5C5aVGZmhgri0c7UKFqkWHyJR+K77CWrR7MfjBLYZLAnAK/HzynP8E5DAWHKvXKcYh4ZhZeMAeasXCBBvHEDJwpLJAAsIDutxiChxDEA/z4mRbySo7Z0/rSHeA8qX8CPa3zDd6JV4aNGGoEAIPXQM2BSUMbYW0cMOUhCvFqLwdXKsn4jiLDgU6Pdef6YZ1jtV1aFYHZfIEmluf8NfMujpHaXY3gBwVpPQthieXZS8HKDtN4Vk0BqB+2f/KTnzQbINSHdGFUpbrhSeaentYDk35weyB0znl4BTKEz6X18KOoHOQP8zwRcwik7AiIv/JE5SCSUcbWBTv/YTuQVrECxgEDowKJpEID0B5ZElKJPtj2KGe+6iJ2+D3QAxxVHPkH+HXi5rgNXpU0QQYhx2EeFfgosk7E4MCQQTtasI8vMVkEErFEO2GZqJY7u33rUbi/PBycV7+4yqEHrhsC2isjydsvRSH6rSqhVtaUXd3qjp8hP6puaWmRKgb/XJU5WhNziTrwPK3x5koFTZSTfOaVXJSROQfGG0I8/ESyA/Bxj6fyZ4j1V3SXxr6jDmtDjKFdaYCfbTQA0C6eFwsoxYLWX6DEMSg8tkXyEArCkdvQdzcbTXTdQbdOc0DTYvfBtAlIVBiM4lsoUue2PKfcfP6pokcuSTNBJARGyjObxdIWsof9trcp+oif/ewtdPV71z5DM//pv/9TrNsYe2D1/NSZLbT0R1dvcis0dzpRZeBh54DODiJsNqwagWaiCMRBLR0QJKEk4czOQZv+sXEoRVd4uCP6rX+kw2Vpmzs0RGdV9zl2s0OSLFcqAFSrGu4Pum26CcgGOfMNbv074F6EVmtjSzdBo5kpDruk5LhQ4EIu7eFRUi84GenVBcbCCAmOKHJB9YtvxjBPFZLLYzNwEMArCBM2a1bq1OgR3crSczEM9Dv61WRYFyvTAOGhp0L5w0wuylhfXEdr/fhHv8HOlyhyZz2dslAouEoRSKXuBscGxGhElPhWKPAZuleefZ0khzu69/n+9U9gCDdrUTvsMdM/gpATV/f2P/T+DkjIsCePqLegyPihDZv6TwddCnW6WgWD3VqXXVzmdqkyezsQ0N9tQhvAVGi3WMGqPbOrr5AVFhYgadyt0ykvnaGu9Hl6inDtzucx45WdOwgQP05ykObkBUiyMyvXDeflSYkU9iiECldxH+dOTBsCVV6/7s9OZSlcOA0pj5PL8ejslGTaa+mibYC8RTh0xBZDGOVumUT+WFYBNhwSJ6I8libPK9X33//7f3/uuWcYVXIuAvHottqu8InFY9DAyg4aP9/vJJClGDtkBkUZ7vkBUgrERJo4ssOPsPGk0B4ignXfkYDdEe6C4cDEgw0/HqIcRomMZqfWASgLyPHzBAMeYHjSCIBHmBH5nb1dyoKVHwC0axoFIgqSSq2CZPLRS55SXIxLbbhLKyQtjkASut+zgCQGZPihnnDy8HD80EnLcpo9Ca/Eerk8xJ9eimRICLcxdjSeBMzDwelJQOjlOjFtMjCkJXfxHBRs7hb1PZhP3n4TDqxUZYOs+UBiOAG1AT/wSYRzfmLJAseOCm220WAQ3QVGnMlk4WerzSBcOAIaSkMsUQEV6p1XZ5plrfrS12tqCxqD2YmcOSKgFsiQe2g/UBLQ4lHG8TPpCTCeHa+e0J+IEN/aeXVQoFdq1aENPNH5FYOXiLIHyIW5OkxjX9ImCUsFjhaJ4jVJSfA7PSF394RYJ8N4pQoJxAOAAye58/SGoLRTSSbrUoirp1WglQV4FUlzfDmvXPjlAIZZjDKPnu7xWMfgITzZYuFJ1o5ESC0Lf4UeYp3miPUsk1J8WwgGVHHm3EOqwPMQ4qiA8lzCWpZTMguPUQEf0Bru2YNwHCwCFRlbdrNYfIqOwmeiAjADBuSQdgcMqXxGTDioJLyWtfhps2DCVUZuiLRKUcsyJ+bYfeiEsENNKtbnSTKaaBOO0RVCjnPg8ARA1Fo10QpJK3NA4wZPEHpUoIE88Tv1VLPjEUmsY3Lq2fQzoUaL0uIBRmWKUXkI+stxkhZlKTJSma997Wtv3R5wI5NHOfLkk3CYAZ2GVTG8O0L8SY+HQy2BZOFIlNrr0dK7HwA84emQzpaQKoncA8MzZBpCHBuZYmBMIK/OeXDySmOFfITI4aOC6FKvKcMVsDHqIIpLwuiOuCLM7aCRDdb8xCurKc/UETqz0fsWGKF15DxjGI2oc/ksPdFbb73FqGb9qYt0K6NpdM5eUoS9GrnEKUlInUI8Dj2DhBLDl65whOA/XnAP5CgPuaSagv+dnKOdS8Kat/SLOzeQSmbsW0khxCRTJPqYBtGLyx+jgPmWD+3Kg6LCuFgEqCf2kDzkfmIiJ8BMRmQBjeMJhWcPNT1Y7kpZNwoaXvAlR0Vbxw+VeHUmBwGhOKoNNST9F6kW7jorav5EPJHztuv3GbGLiNMeBOMMGYKyNaYgbJbJqW/XRfdtEaOlO+ygXFTluRwI0/qSmk2VE/J8jsCWoUumW9bs8/XVgYpaHSqI/WGe+3by+8FCibLvlbXLwZWNCuc7qSqp/Nr1Qw1MbLjDNUNyXpvCpq05vhzS5mBlY9Tr4ufifG2CILuoPAZykkmVjMubZeNqPIR5TIWwKs2XaYfQz/KxxoLRGozlQf7KSYVjH69oE1vZuHH3E9qDuxXoUbg/+sLZjeKoi1006CxdaCx6M0HSxNiHbpiconiYS6mubPWZCw4tAWll4STzAZviGqr5h+rdajtSEBH+eTB/N+AoymuzoD4os9ybpLcPFkZaI3GYgV1915TlRarOZ+npYyik7q4jOdQBlc7xRVRr5MR6K0FmxDg4zYRNNEujoSwG2JPzCVrdEi1FAgr6aviQ18dQB53rt5i2cascVy7merKHLXGkutGrf3rzw/feK159+FSlks4P0Xc7ucH6qYVOhS/LpNiChef9bKpHfVQrn37wwaQ5TC0s8MXi9gHfzasRvV9MX7/++Uplqbdc+tEXb7388svb6W6tmO2NOyi1oo3VtX6JGZttolBYhqDaG0Z6i/pAMzIJ/TnTZxRb/ZZJD/A42po4w/1uuXSHGzCLZbrnPl/7yenLG/ndgxQvrb0z+crVSaNbzN45bBWX18c5jP8x244OsIt7CednTbxdsxFCk4aH5MUWIs8EoJo7dcHyPap/d/f+YNCu1WQ/hbgjk5CqaKxesHoGVgWxc3GsWICNvdtcms7j9u1b55aWmUGVCzQwLmlisxHrCa5rwgpJ02AbJU4///QzZu+Far6yuNQ/2G802yun1qlsRgLihzoFcMoraVQGtgUNBRJ3BoVqvCb50TgPP309coABLa1MdgoMSuyMgizZUimsvehO6s0uo/XB0IqqXJjLMP2jMJq2oVNIy1TPeMhDyxnuAq/cw5NhKGsoxC4tsgY9ePbZ8zs7e7/59S8gGHRceby+vopUyOgtdt7SHSXEBpxxvH6tUMmAyB/CQypCgv+EBL9z0BGx8dRzshRQWucy00JOBk95xLKYayEBGwAF78O9S9HAy0vEanMCyqbZijGhekTnmKDK+6MZJaBytAGnSDL9Ep4hKuHxsh/B47EkfxQxcxklsD2p1zG45vQ07h9Yr9Qr52vrq2yRcVNaR5aj0/ow2+qm2iyvZfn8abK+nPJ5tlsTiohxNvtIQKdn2HzdvYc1Fpa+GvfZjXLZqWQ1idcTK63VEhUCQzQBszUvEwNqVlcTrK2vFEv5D9671m6hKNT8SuXM2vpSo95pt0Ab1UKisqliofcKQsPQ2WFeNyzIVsXz9SgKjpsvW5zQIQEIqGLpUwpCHcksNhHiaQUWN6WQI1oa1YeuGGlHiaPZFQqL5kT2UOeoqUpmslVjmtxGqwzcAMaIdONxBiyaX0zVL5ew3m21P/vg1+AcpfTZpdF0xPcDNhc3K0ypjIi8fVGdrpnRVqSrbQhiXUcgU20HRkgvWSxf1NjZ3ccIjgXKta3FYbpj1o5oFnbUBAZ8tzH5+MObzd5Pi/+v08+/tIk1ClcoLqyc+ua/Obdx5vRv3vwtV0DU0qnzZ7c4VjJot7hjkWED30rYu729kMleOnP2mcvPMPLp1fdY6lotaPEo0+yk6q1MT2ofjlFfLS1ApbZT4zvDXlcWt5leIdtkctGTodbiSoV9eC543Wk3C4POWnmdEWar10eEt86c3ipppemQ/WB9rHbysNtvDKetg+bZhcxKkY1G63fMHtmGChTOpo7RabN5uYDpgHHwConAyoreCnmGbc4NdsSoa8aW1YVyrlrGkIpw6jda0eBN42DpJBajxUd8nPSRhWuqORw3xqN8YYHvMixkl77+lW9Wx9Xudve5bz3X3mv3271L5y9uX79x5eKll597DlHhdm+sHOAPyJuDwfbhYaGycPb8hdMLayuFWqrbYrmnea8lmGxmpVLb+pNvs7aiI2AkqUwYK2BcvVFa2zmED4tP1zPjOlt9WnrwMbm3eU71k9cp7v4eZHqN8YNbe4sLtQIbP6fL5FtaXKWHy+bLtVGq3xyxtK/NcvY8JmNOKV25cOHGZHL3/g5f11wZF5g+HQ4mtXOVlTO1y6c23yvdrNcHEjWNk/VklYNfM0KiWSkkcppZaC2IUQXDBChkq8EtkZbW1/iaAqEOTtEsiSlVw+zSHiN60l+yeBSoRWkBwvnDLyE0ulazGSUxLQaVJ2N4RHCkVSwWO2jSYp7JnBPhMd3o64zSnQwfVIXOJpden3VbMOeBWCbinnEgueWUwSEmdGJKRoaNH3zw8a1bdy9ePP/Vr361srDImiD3VXkRKAUzHuyg8wVOBbNDpry4hAPNaWvQUg60IwYwwBGlMdtwSGvlFSGH8XgQFb0iAra4JjptnQJgogAmoWNwJHQ10MbYGjAZCcpuT3ikrhlfGTyDKFK5IxwPa6xcT4degiROlZ09e56DbugQbgLFqBlHFnx6lH4EB80sEoAXO0NuC8XIBzK0C7JQldKwazEwTWE+BGboIK2oZ74QF8HpiRq4rVOQL0z2IkSUJX5IC53E4hFOKw7K2V8DIAiDHw8oAXAYe0pzO4RLmgM7AOGgHXY7VE1R5yzHH77/sQxH0pPVWpFvosyQ2+ICeogcKBiy5EsEjidJgBQbTnu7HNfAMHxy58HOfqN92MDORtb0FCFvjDTb0Kj4rAVRympFS97NZrtS0XEK8b1coumRi2ckbkwzfIyBVXI0Eq8457NbrZqQJ8nxyCh5iFCqow4IAmfljWM9X38SxowO0Xjlyplp5+lfvf05vQEBhNMfUkn2TC9lU1waVcAYWt2rdXBm4gAS8CMYPL2aAhVkjZggFHG2R34lJtHIQmQDjMMjJDndVyloOkP784KNEVaO25eKg14Xs1fVB/81kdWInsyUxByRhg02xkGGWRaU0wmHcVnJoBbVzRhqPal7YxdKlhZLBVEijQiIMSReQHSyYLnpmHLRo9kiDXmRIVZZCEYiQ2VNFCVyIogTpMn8lENpUMd6rZENcofhOfPbgkVcTbCXKFL7PjXlkWYZpWm3Y84lKxcNTtT2KLWUheUuPhBua76gQhyA0YDPPu5qCiFHy+drgsTm8sXBsGPfdjKtZRjgGN0yx7g55c5/HQVUZlqnFrVZvi4mUhazmR6LhnCOmtU+W2aQy/cwAdAkEKmxSwAMIXqKtK4A6FZJgZSQO62aaWPX1lABgLuRumFgoG22GSfJV/AoJqhS4VQcAgGTesFnjsDYCZ4wnsx0GWT4ulYqM8gWp3/8ykv/39vbA/uW4shvJbbk6Sn6QytUYz6NLR5oHU8VoCmwMFlNiXLkSA8jAFZAhr/6M9ZVLpOW0sprmZhIWFpeKUio/TitQx0Bo1CEJgHcT3JrcaowvtGGSmf1C9ySCFvDsVRCKAQmcRwro6cjOWppPNaom5ptdXvcT8XHnNmgZKItuVVTMA77oMREmkAJm6pSfS5oeVVnplUClkS0tzGUoSo8T9+8c3ev2cEsoLa+gsbk06XUlL7QwOde+BAsK5DWSqAcisFATJMpCYt7Wh/HAgMQ+K8+TuIjpwR82wwShou17Ob6sNOCM4x+FOlEGYy3cQUedc7Go2F6U7cdQskefzLE4zyEWDyAqEUkXBLeUs8QepRSWQRPcY+KVz90srMsRIY7J8n9wmYxSQIMt9UxWRicNx4M0Ug7tOE7t1lQSVzqTogtkiIQhgh1ai6Zi4eEJwmT2Xl4IFIkHXWPQQWgxx55Rhj1Q7hjJlN6GfK1gCgLz+vE5/FMCZkLtIQzxnq2yScADINoA+x5+lDMYwkHFe1cFLJUJb+QQxshcDAJwFiQAMQddZVEHvwAUzocMD5qRFuS49oS90hUx51D8AIMWEjirx6e9IOEQJDQLUOeRDOWgWRa/Epr+JzUudjf4xU85Iu5Dwol4oxVVUAVqCUEAAorCvkeRaHAGqskym4ZJlYltWrB44NjH2cTRQFJRXeUxIaf5ET95je/wdorX3uRhDAQSg5v3mNAPL1/wDzkMp+HM1tL6pRMWWVAtTge5Wj2QYV0gXXYxaxu56QvQCEyJkMAgIdgcJJquHsAVXigqlLQqA4k7iLirQiEe0uklyVfXcRitQOkN04ytXxVVOxwlaO3LLSgjauofU4dQS2vjOwRJBhVYnmnXOaabeVr8lku6eji450TCQyoKCqUGHkS3ZAQArCNwjAKcxWWauFYsSATlb2DA6ecgqgjNxMVhYwwOmaWM8aEZ206hcJ2xm2ZJe2elyMnLxagy1ltCRBCibb3H/DET6FEUuzgnAizdmTM0RBeRFoF2bhBfRWc91jHj58acSs2ikgg935AA4MB/jP/AefQJsJiMirepmqobF69UTkfYm4YT2LGxIHGOhvPIQYM1sEDfxAMPxTpRXOq+J4bE9TEoM7JnD2TxBM69zqDi32BBg+Ye42h/mf8ejHhp3vI8kmIAd61DcCITJREA3g5K37SH7HaY5PPkO+XZmoAXrdJBE/kJ+2X1sgTIXoE0OORw1gaOLaKDWukEl1OkXd12cvUJmZzWJ0VX8qQkIpbX/fv3aC9FO1W4mSf5OxVZSVcoBZP8DsMKoL1AsymWMpk0cEToSXW13Wh/+7uXUIeQxjYnNW0b+hxJW/6KZH9l3kfhd/Dk7HHQxx3KJReWX9ky7Oiy7WafGPO1COFBcaKLG5hncoNPN1bH1MvhHsUkIGTgjdVz7IAWo4LplR33MTEIYyJ7IIxDkWJcWk82DCcJkemsTxNObHk9yXih4SQKexCuXEOw8buY3b9oAGJZ+D+4t3B22+/zQ281AL6nBuKa0tfw+L4k48+pjv74z/5N9B/8cIllkVa9+5hGwIe6CyUlW+rUQczdareh3Uv6zQJQeOxStm7Iwtlltalb834kUvdATvgFhVKWtKJHN6RCjzYSnfu34YJq1mZBj+1ugg2LyRdqooLk7H8HY04NcKnBlGbYojdnABOZz4hwVkKpqoh4IiHJEfe7UX1wn+N0LQwBwz0OHlkRLm8ID74JYREzl48pApjJCAzpQXKTjHhDJ0ddsowny1bsMHVTw4O/uIv/oIoLlRFQgiEWlKRI8z8/v/653DgpUtcLiLR4GTA+7/8gFK3OuwxZbaePU2NvLB5HsHIsxhTqRTsy9cwHIQID6jskjnGkOq/vH2yBki+hVYXrlbLZfLNVspgOFdcJwn8Z7WSexOABwCpa9hVUdzUQZFLtTxb9XsHTWjg+ihC9vd0L+3SkrpXATf2y6Uy1E4yefimC+R0gz/fnwDfvINPYp0MtRjFSQ4pLBxwhlsbmk8CZh/jzUccfQehIK2PAK37j4JEb3NRvLJiQGVRUz53PTHVEwWSv4scT1a0222WV+ASii4kj2kTXOxXZEQ8IxrjAk/DEQmqGGsjPYdkjPTeewcMV7BvoB4ZDkM/IudMABWH+JEEGrVGYjbEpeJoSspJG70aQ0Ycsz0zIHllyIwHPACn7dPuAFNfRIke46r7nR5DFj0suTeOeU1OQgdCvGnFGOQybkRikT0OiDD+OXPuLILHLJ58EQZoZrBEKvxwz9NCGJQQDiQNk3CKwJNwB+AVQQFGHIxzJArCeIonsXjggX4CfUaWBHZU/vSC8+QVJKQiO16BJzmkOpgyS2QX0nqq5OtxMPB4Ge/fuMbso7F7BxiWqpxUUR4rKpFB8cgoDnHMj3qqgrJp+ExvS+fLNR6U3j70g1rQZX1eIk/u5FPvnE9iWwhTdEoMY+kIRAn/jXUA01bARk9BHZ05c5pw6RsJRtTanV0GeYJafhS1hB9njgN7uD+hEMnn6g/soO/t9q5evQ4x1DvSbDSq50Ve2KLDo5ryFdkExwI24Ody1Gskp8rZYwNYwO8enoE8IKGBSTkenNLaFSguww6WfEKYkFiQY0vGut/p50k98kwCRHnEQbyCBJwOJuTRtRsGYbT5LAk4gryuk4tIyW0YITdHvn4sNFDqGTphhidiFjmynM3TAWJ4vYHJX/Hwip/kOI9yStzvsQC4c3gCcfjpHKn6bEGqgORwG+HkqcvLKFRca4ry8po8WKVkaKiGJkJFQjpMHtwErby4YZbLamwDGBXquQd4NaKYFJATS22AljDKq7VMc4AxJhOkOWSBYG0ZGMN5Amw0az4LcsW6MjfrZkJATgjh4HcPT8L9CbUIAa98jXZr6636bgP/cWcUgnwW4wQ4n0ElZ8ziF5IoQQB1SDIMIcETIzkhymEAEOLYJf2Ezb06FIG298dAV+NJ16WqSa0eqwCwk6cv0dt4TOViw4llFuhG+dB3oNMAaLW6qCmWYlDO/W402Rd+y8DXKuPSeeYzbvNOFKjwcBUtmF2L0TfRt65sbTLyKVQWCQcM9rpRDrBCaHQ6ZqJA0svI9IRlDsv9CE+ijK2iGQ32jqxLzFgXwJ7QwyHzhGrwik9UKjwAUTwItmy8CzH0vkBDjXsZCGPJ3mmJQiy5ypmo3aTieAyVqvVIEJ1CEEft4WgqBXo+OiNta+o8L9Q1Uuny8U5ZQKuDb5mfK2oJ6Re1LtCWuXCmZ3cejtJVQoxSlSDCiO8kB6QBRzWUBI5tr5TM+eZPM0ZUIAmxCMBDiw8JWakghF17nmxE8MRGldg2RvSsdQ51GA37CsKZu/Bc6TIvSq32pAuwsQXnTk3q4z7fqcxmO1xxRxFS2uuIJngigj/lEt2dFE0hZ7U/HKttpPqDEpuituOKtQOjFNCCszCWiMMgOGa3YqEAIjGFNvS3YslNW2RDxu6lSpUQ+7o3v3IuaRhXSX/yka5pBrNJri3iGloGn5c3LzDP848UcZOxyI8d+PHyhBLhsSdk4M9w/yyDHZ4EKvIEhz1LApmUAwUybFb2IL0kt1KfgOJoEASs9jOFabp/c3upy8UQkqgRyfUUqT278LlTUri2+wuQSgfHt5uzi3yHpFSk8mxLVyRDCdumJEY/o49udg+2d7c7q0jIpKlOIcXslsJW+FgUyt3KqNvycqnuzu6bD/+p8eZv1MGz/cbgkv3sXG5hkuXoVLrA55O5M3vKd587Z4vDrdpoLdvodHT7Eo2er23n9Z3d2tml9YVlaB71R1nsD1fLnfpgsJhduXJmpbaICpuePtN4amH5B6/yWafWAZ3loGI90ALf3tVXyyUPhXytUKhk0gVCmoMxuhWWgBN7WySHMTUykeqyDzdhSYLwMhdLFvLscsAfznbDMu3Y5vLs+Pd7o0qp0GlNVkfp5Vz5bJZ1gvz9Yn7abS9Wl+nCSX6igwzws4ILTnEU9qlrR3rlR1jofCWDcpKcFCciCimuwF5dW95+sFOv7/aHWiDm88SUgsk2+4ecm2GywU6KumdZIugUEsgf7u4w62A+W9otTXX9EyfpuLxQMk1hqXDUoMvnysrShQvnTqVO8ZUA7LN0kkFNhM1FenesAUUJsihyNG3TVqIMcKx7oEDINNNjhtpqbFYmUc4ZBlu1hxLmWdZz6HNt9B/iwFQTM8ufAY7sSS2QDBj9g0PST954A6PwOGPkSTheYQW1eeH8RT+fjn5AJASGXT7I7I6SXqerSa+3nXiZNaBSjxDXSIw7mYtK5Y698tirJMGPJ/ma9Cdh/vX8XpXgD70bxEUfCzI6o6uEnWRTr3AdOuXgkVEmLaRKIJ2KqUrRQCUqPoDuj8TYkoR8gbSA2SMknAXFXFKeT+y8b3Ii/GnN5YnTfxngcTo9Ryz3ieryYQFMw06d7ZZqbOcwx+imis3eoMXXadNY0TrnlIenOl40WVQh68iYQfFALYOZm9IR1M/fe4uWK7Mefa2ZtoeCMQUSSSmVYCeSpJ2jiggEg5BhpdeLVwrTbKa1zfrw1o2HZGRRo8XFpUq59nD7IasBvl1IjqIldqE2nHjXQoybMYLmEupSWRpSdNjcIE70O/xKaxh/RE+czv3JkEjzMbowWeIMjdPDk4E8ZmKsIpBaZ2I54mEWOiksYBlHbS09fWH98/uF7rQjvUEfr6Kju6MJG5RDAUaCZrY64YpTRecyw+lgMuycPre5tFgDMYaoQJr60XBCMFZnMAu/slYVqO4ElnAwn3VQ7DoHqcmZixd08AYlLEpzrDCdOnvqe/821e3fevvT/Sl9XjFbb+/ceXDw9ruffP7JdU6ffJVL8goLLFtcuFhLb14Af37YJqmd18mMuvtLleVlbDqy2XbzgAXQ6ko1Pen1682Dw92FoQY/5cm4rztl1Wyxd0Yb3x106oVMD6liVJmbpIuZ1TI3X+XGzQ7nZZbWi2cvnzu3soDyJDlLCf2RTl+iPKWly5XuaPyw099uD7GJ5PASqAe2zgCwi58zxNhDbUlRe9XKLx5JezAWABg+6IGWYUwDDxFaTXvS2WKOnZx+ujcpZYZc7y32sqPMxykEyfyCynKRZ0lMEquRhYZGYOuOBkyCMT9m8XGhWKtMy93DwwunTlVrxXpjb/XUqQd7dye50cqppRs3PxkMB7/57c9ZTHnt9a/nynmOLKxubr3wyivf/va3twrDUj7XmaSf+8pXht0UGwD3r91m82YwOmhtP7y5/DFt//KLl954443qYnnS6U8b4/r04NRhJ7dHk9YVUnxuDMpdGtYy8OmwnE2trKTOXVjuDid3P75/UKm31vRRtY30+OLFak6n6bheN5+pVTKH9cbD5tqpU/utg1pG29ibqys79+8N99vsMy/kyv2D9p3PtWm0uljdub/PyImys+LlUoofKyEZCs06JVgHuJ6qEQhD8vM5lsXXzmipXZ1gJMakshqj3mAqrUbpjjjgYTZB+rWne0LIEejHvnhCiEHAWMwS7Cy/me9kHMl4K52DRapE0pJyk2rWiBmKqOC4yDYqmr8QIFsoCmIzF9JqvCcFg0wKEyco4RY9n6TWRmgMTSAY7/Xrt27f1tz7pZeeZ2132mcmr896I5OlkWoNbUQ7cqpoekhFYBThPh5jGMr8H8ernY1QkwHDoA8pqj/CRY3VH8mBJJAmz5NRRwhnXmYkq4Lz0fEU1I8wAMPEnCfWaqxdspWC9MITrv3hSThNqNHWldbQzMYJIbqdjcXlAv2aRoM8uSeUQhdLlXIFXtagHyK9OAw1OXvH1efe1KGJTHEeS6qkn1cPJzntl7ycSAezdEpIOM5Z5BnxikkwT2LZYWGECTCvniTxJJxAY50VPxElG2RPRUJ63xLj5dH43Y8+vPrx24TkrV9g2qgk2pqUzXcyuYItQ3rtY86KabFYjDOgPWh2bt3bbrT7vf4kry8h6eP2mu1w0/xArdNajyUwkWXczm7QaDBgGbpczKFtFhaWEBuOqFAdw746dLjU4zBPs3HvwX1UPyqfgTbCwLnkJAOdNhiFDJ3Eooj2x0QlSxfAfM0wPeyzSfHy02fHzZ2Hey0yqVVKmt309UWl1y+vn+XjNUyNNZLXjEb3mCCV1j8m0cJLvaJtmD5g56uxh0aeDgNa86sQKkbskhgACBKO3xsFAEgiGKdcMHU4HVCHSA7nSfjOj3KLapQanOOY16zUgTUupK4oC+hJjg/lMeVJCAJpre5Ei2vIIVTLxFYhbEDK6lmKjAasIP6zcDTJS7ekZRCscJXKHDMzwftlj7YAagWX5oFpORt7B2CiAtn4Cec5tyol1OY0b1LmGtQBRolwzjGeokJxNgYySuJ0oMXsUVoO6QKy1e3X+rrgi1q0mY5iGZhyyg9IvhAmPcQPhWC5grERlWiNl+y0SKmSOkOQCgY69M+chNbsD0TkDLY+drjdrkGKFMuFX0uLfTMOMZGTxjA5mWK7p75fckWciQJxGh2YytIggYyJV2qaMlnzRNagkxo2PIIEKQACFjNhSySB0v8gsEpldky2tWzvyrmVL3YOgWcEDAYBmMMDMTpFb6x25FZq4KgHEeaQPJ0Sso7BQkzkSWL2IKNN2YUk4sUx/QZwEub4q7AZzykOkNzbjqmFJyEEHaVZ3oxSMUrVKvHWGIFrN9DJ5WqFD2PUDw7FvNTk3vYOVLHmUKlV6cS5zw6MjsNWBrS+BABtkozEXVbN9KEMmqROY1nVSFcgK1hVQyAtiKby4Pbd1kGdzVEUI4udsJFDJSDwlUCvpkhOmH+l0q0+drrTcVFfA2Z5Eub4yh53HpCXbPnZ+eP4yfJSfnXlcGeHDgxsZDznXA+EQOUYSYTCvI177GyOF6C/1CMOiJ0nOyvefFQy0NPGUhcJ8XwCewcyyIfLCuUPWYMTB2P0lPwj7HpSPQFGr+aCJ2K6SR4xSpAQd0/ozxDrMMefgB2H8RDwJmnAH/NrVlcULbRbR+4IY1iV1IvPdYlA2oaK6YVH8x94CIhp8LzinI8XgMYdMU5oGZaxTccFGST3cCeAV6JYLHPCgESB46dI/FfPzXgu5qQRoNuITspNYU4hHqW3V249YBDvK1yCeAKnzC1r95ACj5SUakRhT4Dj9wER6phs2IJpD8UBEX70I0/3Q04SO0mIIBbnQ2E8AUChJtWgorPhtDjmV2etHgmxDI9gi7Kzi4xJysUd4nZOY1mO9xCicQzOrlemQlFncJet7HERk+06wCgXHCDcAUP1MWhmkZGMmAAQy5MQnpyApFKmRR08x9CMnbT0A81wsk1NyweHDJg5/GhHGu0jkHnG2gyp0xOw8TUeKKE7AZKS84QykPNxKucARTO9bO3Wqo9Yul5wLtVqWD3gYcVnubgMk2tlzXwYj4ITdgvnox0wRPN0EAeOkyS6BXpmfbFBs4hSsQLy3X3d5UfXQlp4CMeYAOHhY4agcgxE0TTavS7LCtWFBWoKuyCz/NJgkSjAfOzlT0a3qP7DbuPVV1/96KNfU0ZQWZQ8SSLhj4dAFTsq+MEGl8BALgTiJwXhxlEdtiWQRQZqx2xDFAUMHQ8PLYM7ND9GvM9DqC4LUNdC7lAbdeqP4KjLEs0Ta3FY9MUXX/DtIF6vfvY5suSYKVQoiCGfPcDuMJZRnEfUsc7A3OeUnIjK8cRgMZ4YQTJJEjKO/wP8PiFauOoyQNWGXJ3P4dU9QLrHMTtMsiBz8I95fULaHoPhXynqMcXxKChHvKvLy7x6CPKGmI18uPwIso6XlxALlGAwE6Nd3L57k2UCtm3hs/XSUY92HKXnq4SJKgtgIisOZ/WZrxT8+B9+BpF8ydMw6wp7GsLhfpO2R9WjAFk50yA9dkZY/BI3XijUUJRTBSV9fJoWRNoZ0Jf5IpzWDlzO5nI5jsCVyfFwQlxiWSuhpOChXCzkCdIWoCGPKJ4EGDNgx4wnBAJPMo8lOWVB1fBEL4EZWwlepb40LfGhGVBy1IznaGWfb9QgdAck2FB3AGNpCDD8l6WHzAbJZIAF7ve///3dzs/R3surqy+88AIeKoVuiJUsIRdvs2wB3vr4I3B+5fI5Fkw9a5aBCKGAQDabPbCyXbG0vJzqTtl67DZlAe0lmg51A0B6scCr90RgIC0CwJMQ+gs8PJEThBpUZI1qJotLlxZJpastuPV4+yE0c74IzFwTARLmDI5q9nSR1XxizA0IhMMdxcYdH34c+BVh6p3sYJT8vgBhozJCYIID8HkO0e8uHp/wBhKedCx44BN4INXDoXPl9AoFbPQO6bPIkZCXLl+mLl5//XWtEtqBHronLuYGgLRwHlseDNK54ffp86tkjeSc2tpa/5N10O7ffUirzBR6hMMKbMC9l+STrORNYXWwoK+lPaZn5NvhTg9qZTBgI+FUYQInWVbnBBKo6LXzkwx57T38GMJ29itk9NTmGmMGJA1U/fjya88LykHItjFWqoSw7u8MpOJYscKg9e5+m3AW0EGeyolpYscxZ8aS1kxi1tEbOscY5IHTqwhPSOp+i1IF2essNgqxegxJYrBkwJf4KbLXyBG4I/kciXnci+RBZYRUo1Zn4xyeKiDcowIGhyEwhOCxpEKSDPSERPkyNAKDn4bAQmGjcYBd/LNPPwM84xpfSaFQJR270q1uVB/AyDDw0IMfSBSTD2zAjIUdUbyC1nUONaimakt4SBowRBFIWq8yF1pH5aQmnyacpJAyB/7Bg3vIHsNRFjefefYKzRzCSOtyAmHA0/BFjxUaVJ4XyfHLsRZmH2JBVh0tGEiFcOIAcxfBRqLiAhM9YxAhJGuj8IQ6hm8AEMuTjCTSsZYgR2Kd5pARniTm4H8SD03+k7d/TQsKSCAMdDypG/Kirr37eAw2gIlNUCFYSN25eRdFwckNi6UsKjjEY4EjiNipIJh1ZDPsZDDe5ruk6Kg7t7aJZwqDVuHT5nAYFePiAVF42A6kNtFd5AyvVNO4k1o9eFQo43mcp34J4ZEMeZTfIBUJHhypeEIbBqGD8VWnShKenqJPvvWtbyJIk96hkaNZgNdXSG4YTshKucRCTnTI1BMmkXgImIHh6cD+il+vSq1bpyx2xm2FWj05NihJlp8op02xccPBP0ewYyCXEG4hs1yIIl/PgtyUi2VKChFpdRQN7GJNK3itBkUJgTfKA8MpT4QnYAPG/U5GnMWMKrAlHQAOA2F4SIXzEMBCCIEImF4TiVFNOMKRNEunxku986F4YbM1ucBJ0kI8AHRCeHAkJJU4aWZbLjA8pcfiqRyxPjJxSkLmnh2BhDgSx4+fHMDs4U6zp3IAqk2pYg7jhyTcROs9kuGQBRSSOw6YKIvIjHUGQ0KygGYGbKWP7/W4muwo/8EGBp7kqXwj8TjCYYXbuT1XBYEAB/ZUIXDuNYT/yz2BPEoEza5gHa3EU+sevNl/LfoHFkY5c3yaU4C0d/pTtBAVS8SDB7JrYaJNeLet2b0xWfKMhxyVqYkUORLIq4fDWDiHHzJUEdYElL0MyHQYiDvQWFFZty9tHNpdz57QqRFa0aonwCDn5A7V5OEEAszQRsCuFIjg4+SLi+BUYFRNlt7ff5cnh9yE+siKdSxwQm2xvmLtJDpZ7nfGCSxOEjwxDSD3kXGkg5TcysLeCDAupY7Tk2gjRptAujIIv7pQbItsQUUKLd2RjPr+g7YBCJL2dPpBTKzqn2GNVUDRLH1KtoS0aRmcaqs69yqqnvsLSruv0WaqkpbFCgNQWN8rK6iNmYyEB9NHrKcE6Y61RtFgzuoOn/HQnkO7aTroHnIpEAkfVdwUl/Hx9B2DitYJM31tC0YLlLxT6mhHwras7MaeKVflEqWBMwN3mw3W600w/+reTSYYXO+ruZaNaRpYdTIts716lv/wR/sovndkfIsaB1uSiDh7aZwCm/Ybh41uq0FilhZJxNwQ/JDFL5uOcAarZAIwtqbssF11JKZrkoTVgcOTWu0xMy1USyluKJIaFZ91b5Q4JaZli5qXTqa91JgPwrYJTmeam+sLC9OWlR2QmTjpJZpkySsu6EkFp4b5Iq0EHc5Kq7ac+EPQ0NpskoIBUJM0y1c0WNLogVzJZ9ywS5ujrQust5MCHPye3CRA+p+kp1sMZoa9waQw5PJFr31VcisvrdFS4+Uqax114aMc8IhrjQhafepCaWGhzb03glVN8WsO3LImXVteag2alTPnRutsRE2xs2AglRlyZXQm19cIlTVOwZv1U6onhVXNVeH+kIOH5Vwxq3Xk2kAcyJ9egNE39xqF55bOXtrMLOVSvUo5vVZYqNDhdPtqWD19WT7bo45oaHYmIC/j20naPpDVNuKKfGo3Mx0UxtmNWvHUyjJLttbZlKhILm7u6BOIg96Q8Xezy1Sxzx3gD/u7GDojMzk+ag4lCCL6IYOmbWXHwzI3XeQWWHlJjflc36jHd1xpBvlBc9IvTDLc73l154AtuNMLi7TEp7Ir7crSpKb7JfczHRBxZynj6lbrACFitkkpWAwCA4ZPEkIblENGt4tap6BRj6jhqdY7dECJFmZP3S7FOglJKRJmKYtLLEZsMePFtbudTz57e2tzi4FgvdeU5OV1ZXsXo32Oz2QX7za6q9hkZCv58SHYStWauqIONp051ktevHKFO5pv3rr+4Pat3VOb3HyJ3HCnOh9RGaSK1H2Gu7y0O6/h1JC9HRMr/Og9pFtXeUl+pTkz6VK/x74FZ7XYL+dj2bC6oWEHOjJyqHDxGRnhCaPZbY21ovY8dZkWmmfMt6whzheDoq5LCEwFS6aPOcqF3S4bvD/84d+THasAXM+TTTfgPEhhvOmKVL4AQ7lDU1IXaea46dGipc+s9fmTMgLl/iPwqirJCUVT7SRh5Bds7KI2wyvYtPMTR0S/1q7xB7gEr46AekYhCKpU5KOZUdIAYB7PTeqGVxUwdm6Nq+7HuEMts/QFEVKC0qiCZJpDdJzFHGaFQ4CzKMYqbJLZmKo5mgVmOQaAOOGJv8L2r+e8F/bRgnqfmDdRj2YcG3N9PesIfE+Z5YOtraWtrVahQqX3szp9/xAdwsea02rR0UkdIzcqnfEhVKtq30YXrD2QFbbPwPa73M9+e//Bba4/iDhDq0cVu+b0th+hELwPR1RDllHy4azmicCz4cR9+++9997u7j7VgXUd9wOxBoHeazRaGh1YLfQHXfzcBA9dXo8AH68yjsNAznDMnfb0sZge2JYhvatGrifXURhRJCl8vJ98k1JhzZaxB3ShFqQDPSvKh59VP0YRCwVdD5UddMBcMdv9dW7XMXuNwf1hPpfiDkQUDwZZJXTVzDEiojHKUKuQR4ZHbJcxDDi9trlYrfFJQ5oovRt3CObzBZQ24xly0YkgkrG5ri/eadwnfQPjzFZaZKh3Vx33uj2+VMZ3mYhi0IDRfL+Uw/YvP9HlJ6ls+cqVjf/z/3oJDTwuVekpfvHPb+/vcQE1V6pulIsrqWn5t++++6Of/PNn734AQz7YXP+P//E/XrjIkuiUIzkrG6cH9Q57jaVSZnf34bBT1uZrdzg4aBY7QxQrW7vSt+SUy2Bf1BqN2nywMZsbZU1KhxDCdY4AwidCuOCV5RGaOSWmmFzu1GMNn4Epp3MKnC3LFvcb2wMsxPPZga6xnLbavSWuTIpGXBDIpyBGtWq102qjr3Wohp5MzYWu2YTUHhruyCZF0iWJ4V6XvNbdluxikA5MTbM4Xigu1jjlyToCQ1gW9ebkQX20Wf4iCTKZyeprDS0++4bg8EWcUXZ1YZlhRb03OL21NW13CqPx+vLSbT4xWd/b2S6dXa/CcoacG5vL9FyNgwa2bnzk49e/evOrr7723Oob3KrWn4w//Pi9RX24oTipDV/+Ny9xxAiadcMjsk63hhv1NrbOPHfqWS1P33m4tLR8epItDUvZXL4xGBWqtacX1gf1W1srK1yykGkcXj71khbWS+XB3u29ibaKW/cbbx3+pv/6ayxijqb1XnvYXawO+osYe1c3lrkEsdHYG4yKC6uLy/v6MObSQonrOIbt9HKxUp9k3njtaw9+/C7jmWxJY1WuoWS0AF0oXekI08DisY1mGTEjyfSkcGxlfa26uOBcRRcBbcUikevtmVIx3WivwmgjEK9MepJI9zgwT/coX3PRK7nAN+9hqXW98qLBmGY9rAMyElPSudSg8DIkPcdhPCt/kg1yFVKBEqNR+7wh0sL0WGVR5WmUgj8ijBdDKyJjF/mpNSLtPmg/CSRYjzNUJNjdPdjffwsB4JOV7FpxpxjFIbt+D5UjE2aGT+wfoBNYkUTq2nwlQB2Krllgx4xRBiwnHKxINftIhaLSEsLwCUgWsuGVtbLokDhiABlF12NSitSnbAiyOeko7vTkzAIXxFDd+3uHXD7T6bYg4N/++x/QF3AdGQzpj9hKoUBaWFFTxmmq6iabTE7zOptZrETMkDBphsKrFB/w7DzZJSGk1quZRFAcccbqNzCWV6J5klYeDWXVF/J0LeyQFEGIrcNCZcErNTrkwzBLndpdHwBQdmGwfktUW326B78pG3IyVCJZa2HCzAIHPuMYw3fU8v0bn3MyMj/he0bjLIapGnOLKhdpzqaD06mycosBkZSQjbuoE9ILsdSdJiNkl82/8/bb9/fb/TGrb0RR/AnfXGE4qs/NQJ1EyPhgYoeQoO4a+3vdZqNSLNDcMQhlw/L6zVu2mUG+FMvXdFhHy/PtmW6fY8mY3aycPr0JNqxByX3Qs/mItV/owWm+r6ZvbdbkNqI8/gEm9ka/SmWVxXMutpjSqV/fgCnkc5cun28Peoh9n6Mr6UltMjm3sXFho5Tp7+by6oW1PimsXmKxSLcGh0ZnY6qoJ4aTxkxggiRY7lYjJhWqmpgwNR9ZRKlRwBynk6eEjGLKky6vLI7v8XUXenANXAnuM0Uk0s4x6J1as610OEtaVZPqUceSbd6XKvUnxaxMo9hItfmZg6m94qTCLKFTpSArEYhoFNKMJkmOWfCC0LCfp53lgqyoRIKxOYhSaKVUq8CKs/J6OvOrfMAqRHMTOCuYyI7YXulZCeTPoSytA4EYpmmKLP7oGXEr4GRMIbNiISRKsWqwzNjpWPkwabWq4Y2F6FwEdybI1ptEjJGZy4hgiGIqxza+1Kuskeij0WMoK5Wa4kIK4wn1P1zhzNQLq9bUdHV9jZ3gdqNtJsQmfs4spdKSF0zDcBxhQs+RiRMM+2jNtgStAkaBWucRjbbmQ0Gk32T+blM+Evit1iABp9cpCSUhptXgPhKhJRBYJA5GdFBOxDOX7m9tLq6UcvudPmf30XvssTujIMAJY+CuGfGs4lTjYEMewBX5ZwNsSRQE4IRAQi0RwIXw4CEKvz8FAT9tMRdPMvD4q0DNmVRQ66octG9P3USLm5dUz7AsZqxosLqKkgkYyvXGRB7IZr3JhJGjh1XOGDWakAHmuw8eMrDgVM3y2hLS0mv7Gr1EHEt1UkWtBvNnCYyZJniJKBW1Zodr0VTK2iST0T7ywBpeu35IJXYbjdNnz4gG9BnCkNK6gTVfFV9ODE73OKDKNExbgJqbaB3CMlb5WEMxzJmVlSqzrU8/BwOL3PTXRTebE3fJleCk02gqvM98bokdIo57KNUTBh4HI4Tkqghz4pBm4DIUh0QXEYcJAApPOETN3xQOEyiDd1cxTCiVV0wcrN8TKQ8AHhuLsmU61S43rAMnQxsghyWNe5BzYTPBCuQZYRFtxDoZVJPht6cVJElDKMuMZq+SeB4HsOMPHsN2QkEYPVQKBexH6m3dYLi6UQWnD1w8CU9QITeB4CSqwGJ181JxmE40EXeAcbRyXMDjHkLIAnjHHAEkZAOarYC2emLh3bYQopEUbiwBBocVLOEwlfBKUXYr1VwFi6RUT/DJfJOvSX8SJukPHCaXkGMSAH+EJ8pHPyZZXnGCVdpHOI8CtfFhyr462MgUwXS0sAiPPSUb+OGtU+WsY9Z0In4CAXMYjOyUVicwpjkGUUx9MaNiofTgkLrmDjyWacgi0IgxCMn7Nvpl4YYRZ25SZtmUCgWGkTSWy2trrBsXutaakJOQHT0LfusmeEQoQa4gCmIh6CDkjT1PAsdpXQopg7FiEZJIQIlw+u6BKR2eDDagsNe2y8SbutRFp2vhslWuCVs7PW1QXgymlZFlq4YmTjo9aukUlpBpLcUm4XB4aEXWojmrw4QjsaSjjGoLlRrErK+dJRbFRxZMYHC/+c2bhHi+IsAcaV02eCNQ/ax7BCAY5hiUqGq3fDIFAhtZGNNICkFRb0eOnKcE/4IxAUxUH2kJEdaJzMDZJJx2dO/k2oruAeQVCxpQiRANUbh1zFjtUmdyZ8W0PtYqS1NKs75R/07ZrZGiQVWu39FBHjmSlXlm8hPx5SRsVhbVO2T/3d/9HXuzWJe8/elnlJ3NT7DZt06NjTF7A2HHPSflEIUZYVGH4K2AiIgVBqJae4QLGSXi55kzB+PZJeAj76PCPdqRzKEKSERhgpWOiiKYyJxcX6AKOSb9AWfSEyCTgTExs4zjkCTUk/q/lIYnRfRYuFAQWgQWxeh/xImsaWhIOwMZAcxXYIQxWToXCUJwyAx4uJGdpeeb1z5B/1Ts9liSgY3WGCgKuT8mJER5cghDBTEo5GwKN8/a3pBAmNyyF3z//n2mOaguho/JhEk/FM7na+qQRgyprKesri2SC3iSqZ7QD3Ig51KSXRRuDdP9xxFGVMWNl1fogZF0FlDDqb8rV67QfRBOhxIa5nE8x0PQ0mQKi0Dl1yJBIyEg4enOm4amUKYweToh3vC9nyryydvxmJkVeFjuJ+qwWUcdPWwdsqyQHdmtzaUFjFgXzl5E5XJhEFLkXTO9AQqZ4jzc3ubgPHoYOsHGZc0fffTRU5f+F8gosmhRKByY7fPK8hoYDh5usxyQvsVlwc2ttC7WsFO/5CxdDQa6AyfUS+HLavREAOSHY+8ZeR1ka2AjuThgAk3XSW8IeQh8U9f/sQyVJjA5/XNOkgrmwHNe2XeGZtvDldAoa6ts55VT4qmcvPAU5XlZEdJPQxs4PApgUrk/aCcvHYYFUOiQRAGDoTELjvTF9KeIuhYfzcz/2qe6E/zCqVeQXjYm6dqsOLoFgoQMTX/729/+ycsXn6rVCnmNVP/vH/4DdfHdH3ybSnQG0iiBZCkXOlOZAhcBf3Xr/A//7u/e/vhDsuCYKnRWa3kIWFzT5XgEgpwLXmiMRFGt48wAnIOJLg7mjBQ8Zy0MAb5weg1qB/0mmBEM4LGVhu37hy3wAMATAFLt8+Ud1tQqVUwRIQCaycWZCYvwhm42biLR+BAxxr4AzNqrKBRC2wez17Uj4RkQhpA5j9dgsh4DwPFA8DsllpGIcRgKhaNEUdqgP+f0QkD9JJ5QZgNGKmhxFNbkSpvNIffgT4aEHJwDx8sSANzjaTFpR1qYDiBUC5WyD/aoZZILg83+oIFWQ2GJndoIFJJwCLWHA4l4gJBAQlwyWeVBeqkf6g5NS3GE0EqhVmDALOziZwxNcg4TYunMRZqo/b3dA+r6+ReexVqWhKSyZqsGBRIowRFIWnIkrRcZP9oAGBxRPL2kJMHZIqoHqD3iA4AkAYwQDw+pImj7ASykAjlhnpDSEQ4ByRx5JdZDiIUhSIsHAh8g8XsWLmQOQAgeHAlx4MEBefvGTc7D7d27CQB3Y/CMYm0B2vF86RNUpAPMPMIBbdpNmExu3L6BbuHyDCY9qvk0V8CruXFdHhlZ9R3hDzfJM9lhGwaY1aVFGv7V6zfoOBBa9DAEIy1YKrJUwv6lJZddNrEYQa+uLks2LPcvpfnxAHFBZlDOFn8X6xIXb0IG1GIEDW3t/TowxRTTmYrPAWECIazk4iEhjtdjTwXCN4tXFdvrkQcEHHcBwhMaYkcV6kIg8A2Z1GLvMQfOY2FRgBMJMS42iWdUBKIQ0sdgIEplscs0QOqQItX8EX5bmdFicUI4gfRYGpgT4K8BiQMEhMnXUByPBS0YQnKP1auVGxgB2JJfiPJYogAhlodKYTRTWmQbRYHUSZqN48TCYYckUVQBhg4wSWkMlswXSMgCgFvqcHxSQxiMVPQSfhSJ4xdkLBLCb/niwYGYKC3pmrM8xWech/AEJa8hykKUyHSPik8IeByAJ1E8HQOx/urYCAdWaadpZpHcu/XDH/7EAmd6zNN6oPs9reVCQOR4BQ+Y4oAoR38NBIfYEz1OJFEgEvd8ZdIKG7ILMEkMHsuTWCqUOlI1xQn5VZS1R0sOfsZvQkDxLVYeQgDilN5SRt9XYBGB0QvYiLp3bxtdxKibkdLedJ9wxBwKkROQeEZ49MpOeWJQR6AXhBLhTzpSAQkqxK/eaqJeassrXpCA0F/hKWR0VaZxajBBO9GtgsrKckQYIIB6PKzcpRQkQUswklfZDT6Z++P9dHgqWNTzaO08WmWPkiXQ0T6cyoDR01I6QnwlKVr5S3CKkEATV56qDvyK30iwJb++Xu41xPoQSdhjEosBFm2sEimS3D0XcpxRYniOrbiLAtVE1EgikklV1c0pqWzHFoyaqqoNeIxhix8M5AoUymJ31477HLAajO1zN5ytJS231hLbNwNmbE7x0+4Fb3e5eoGcEr85RVtdzHmi79iKe7K3NUMnwgd2N1/EN6s5AJxX8ijpCU4b7ORbqFy98wnfh61trGe4ZJCb+2z/AVsBpJD7YqyBgia+v4dshXyGEG7ywqW77Vaj3aijVKz/HWG2Cg1KaW1J2pZFPQzz2dHHhomhGvt+Q12Rxq2OOC58Bg9DBpXSbBwwkCIE+2CQ5OyjQ+yNA4n5D71vfoGUfM5MXyiuFLRuXuDzRGaI6mWfkRj7TgyHk/yxLcsiOsoIuqgM5Rjx3NhnSkoBccG1C8taoUQAPuoH/pPKay1n9gSyQ0N4lES3JfJjFZ7C2BneVnvM0tvZB01URYXjfbrGCfPSUZfbgLl41PqEdtG4rc/sZUa2V9ZN5VAoTO6pKW4iNfZa36Y7leTIBQf+ri1n2+W6aS7BE210Y6xyXlzHYDg11Hh63BtoBNnjarMB2TK5wuCDsRQsZw2hS4ppf9zPMXDnq/R7B7fv3m4yoK8zk++2SlOWTFOTvLbIZBVMG5ZQYk4kSw31jZF+hyaV3QbV1Dte2iHGWdkGLdEvZ5TCE0/EUhz1y/rCdJBdyKY2q+X8Ml8VXEGWRtYVjbhSsr1/5wETjGYn3ct3SiNd3pIeY0VCVbKrinXutDftjbibupw+bHY5NTzIVCqrtfTDPc1A+dYvdAx04AURIlPPFyszRHtv/44on2jqns9voTr50mG31wYtIawiiDYbslBl5IhQq6i6lpAakVRQVqldayl8RIY/787RNsxisHtmyxtZQup7Wm6a2MVsk6VRiryYs0AVAwElZ/5jowqIpfari1WYBw3VKvOoh9oAJXftDcvhx5GvbyfbR32RG10JcniAbq/v7h+iC9mBpvGwWcyIhXoQH2KbBZLTFITFbAcYsao63Rn+oIHJzrpCJfCsrdQx8NxvZImgtkBTZfnmF7/4BSAoAZ6Uj/HPdKxz8dwLl88hqNaojHsAhKLN+XnFeb6C8VSWl7fBiELjmAPzTGILge6ZFXYu4g/3msgdtkdV9oToaR1M10hjDUh1ZNgiXiWREC5m6hl4EMWbSkvCRv5IfNQ7ejdCuDA4J09I8NggrxTPy58B6WPTKTJJYYQhkcb7MlnuMJWxptEtVfhy06DKgY9BL1+BS9v9YWs47vLdAvoszU9IpLqlOAlMkRf5d71NqwOgwGmabvOLa58ymeR2vAoa2VxmouF4zvsjwzaHynKZC4tenRs86aE2N0+jAT797Hq3x8gRjZ/BZmp9ffXatRusH7L6PcECwppglBiaTIvy6uqRV0cYAxAhUULfsKVo6gUB+deSZc8aGqLc+XFdMXvXSEaXatpSzHhg1zRNJi/r29xac2SFURPRhGCGGg9o0ajYs7CYyEVLXLLJQUsUIznQf9n4jbl3ButXmRnSx1GBtm6HqYuzyGixyUBMJmRT0cSyrIDVIdjIa+/h9gfvvrPTPJRy5nMZONt2ZbUSItcuXALm1s1rYGPItrm5tLxUYz2azyrUW/V+ig9QSCl3eh1lj+JlTFfIFKtqdLlSdqm0uFSsDpofNbsPy9liecr5D75uLiHUpwqwIUqlDxpNPiGIuTPdOOSVpuXsUHf6T4ZZPqvAwCiT55DPQrpYwnQEu2DmDipaOted5utcdNyd1Dvj5mDYH6fLNq8vcMglh0xpLMR9WsD27ZpTTDYpe6mqTyD2h5q70raJRb9DjzPJf/xyNsSHhh/HqL/L5NMwvJuiI2V2ADkGAQoaF5TZ2hM4ETsGAVjCsNaTyRbbGC2nC/R4k1RhcWlzMi2wtMmnArmnhHlRpVh6+uIlmRlyRIlLLdLpZxZezNRK2HR3tNWQ429pdWG/3ry2ff3MlbP5Se7M2iazsP3dbe6L4utb/cmIKxeuf/wZ9XvxxWefe+65nE748OGp3N07O8u5Qnk8LQw6rcP9bkZXbUyra7XlMtak+UGfr2ksY6nPQv9wXN/ZL3T6q21N6fcwJspUrjcb93/9JibJrOmwqLR6ZWVULdWxMuIA49rGQmeHNXROByI5tclgYX25mu5oWWrC9Cnz/IvnPvrkze7AxgZIg2aVcCpyEZ9tvMSggUiGlsurK9zYqH4Y4RBLVSfe1hhQyG+KMUITY3MAfwp7VJPyPt5JXg1nAFNvpIGRloZ9rGLYaNsRJSqCC0pI8/t6GPtoTHJ4COU0NAZgrAvi3EBnDityK1NB0348/TTMY3olCa3NWRgGcsfGxx9/xhf+nnnmEjtYWMszYmQo69s5+VwxXysyl+5N9EXNZqPL6IjvR9J2WIJmFgJJaimmtxFqBrSMlBF/PmIKJYzTaLMapcpSgVmNvkij3sQ2LKOxLpcRYeidK3CtxNXPr8Hbi5cuYlnPyqDWjwq6q7c/6CMhVkCZ8HHkAY3GkQx9CRNs5rQabd8hBMxDfL4MrEnXrEegr4ABsg8Ewsao0E+q4w7GhkD4ydAbPjtyD8cv5oNHw105R0UZccC0mh1KhMNPCG0HU3GeAZJwROZIRnTHUu2cxZgwV+Z8DJ/2/eCdNxnP1wpaNAdcay6QLI2tC3pN/E0dOVnx00cCHuu9gmgy/vCUn3qZshzTvrO9v8f6MzWJYAixVkaQBHbtfO3eOcTTGtB0yXYpFqsVbEpv3rh2+9YN9rIZuNJNwwF6WJ4Uk3L1OtpjAy87gIPMePvh7tratm0taC4T6VIRJWcsjJrQoyrFw4H0JM5nFw8PCQB4aKeOFjC6/tFh/ez60uT5p3/55vu0L4YSV565SN0hYyl6EtueB5Ie09Em6RFyGz9Lh8MHY26Ul15NWYh/TFbQaByUcAgLNyLsgcbzRZc4hdWDCgPafLa4UB3k2lTxOJ2nx2ayGEoKCNn5SMDrkQySggvNBZYkhpNcf8zl4HzkhyThZBt4EBXlY6RyOEgIbQQLXmI5xIPTfEYzlyRihTPT4SExoPlQNm8aJDSjN7UL3QMJOq0NINPKxQiG5lCEQIPizKlE8eKpAtCmSmDWAdEXQ9Q6lNBcEhV+ikywWKcfodIIVWNybh4fsW/GkQRmy0ShPvRBixFjWHYzOa9ARqJPdtVShwyXMIJmiMArEoHo6lR0lLXGyGkMgLQ4MJCtNKekycXknHIzUIkcgSKEthgXWoRZrYksv/1ck2DGQ1JAXo8kJlLtTkCqBg0tEALOLOXo/6RkQEgqg1ftOJgQxQ4YEroiiqjBqJZzcqPx1165uH1741cf75NKiOhCqUH1ukoiuRCPUdRIpixwmRIrzPoI1JgBmHDEeMkz8h4JjqPjXycyftOvsjHFSN6QSgioHOw4sCdUOOtD1OxIJ78ppSl2Y3lEZkSEOKF1qZmDy74KSs3xRSitQbN6UGIEik10A2IYg3/+xQ0UFyYvm1sb0+1Jp92jiaH2oDMiSVhVF9SKW+5DQcjD26PJF2FqQzofpH5hOqLPHAxvNq8tLnPs43SxJqYhijxZpJF00XqQMxKMOLKQO2j2FyrSQt52sKTAjW0e17HzdpUzW7tcgN3usmbNyXSV12CSRX5Mvw/s3I6rpU48jtSBlfFISAypwCABFhjAQjAhziX/cULdH0WY0LueCeHyOOgxyXC58alUDBITlKAhIPc4XiUVsYs5FomdAxNIB8AHL9QB2KIN0knUkMMVCGvctYNQikJVeMQB6aLs5wiiNpoQEc8ltq1WWmqdQCeM10DVEbz2wsiP38PdQ7r/xXwJMaXxqlfziwjiBCDEK2oT+XqkZ+RZgIQhOBkTNRNhg3MiPYk/YQLLnxCAYgA/JhcMPkqsQ5HcF02MESTEYWMC90r2CZGSdaU6kMZhkrSOZWVTdh1hSnf/4cCA8JLLYwqepAS/ZTJ7UNJQqR4rbBHYLKkGekxNbXCM6iRrb1reSGinxKK+jUL5WYCGpHGfg7Sdw8MW7Moe9nmuNcVeDgATq5ZtjhDIIK09WAvX0JZAdzT4bZ2jYf1CxOscJ7GJfTxFSbSkBHGgtIeYq1d0A9ue3IDBt7PLsnm37/QMsbHFVKo7yaDIevuy7in2dWEFtj1YEPBFVcaaOwfb2AbWdYfeND+q8BzZRosWfaGWBQEWcWx5l1OvDPQZamPxUatUqUHKRFXKQMCGtqSldFAOAVAL+YwLyY6nUBHHaih2HFlNirJcTc0Alx0X6jclW+bVZ57BXxzJpinb1Xdgx/0WA/3m4S5IwIIkY3IMcpDAZ6xspaDrRRS9cUUswoEEGH9KaUvalbvt9ehqSGwfYAuGunlmBfDaFqC9NkQkl2NyPNm+Tk6O8BcYZAE8VmRlhR9Rh0IvO3mJZiVFMFQjTgAHMWEXXxQBAFJ5EkVJieV5MD3AgqZ6uIL5MAnJEbYwmxqxMWEOeK3aIGmcx0Ng0rqz6WB/F57cu/sAPyvTwJAWhCTn6SMpmG2UCAvrCvajZxh5KCR2QBpdEjywwQHhsdZxXD+ERF4Wy0VtIcrdPmGBzkEbMKgh0O/TDMREaB25njG+Y79errngJB4nYA5g7jUJH0fNZ5mE8UwfhTlABg84E36888jjTPVrsaaHkqG/o9+zeBSFX4qMhF7XDvl4gr8UGwBgOILxSdI8GgZUyDnriezDc8MugITQB3X9WgoXZBN4YpLEB79aecIhyQ8ePsBqddSTzWNqGhmoSsKtZSVg5X3ysoABh96G2nfffbdRb+bsfi1esdHDkPZg7xBtDUI6Nr5YJuwmHpAaqFWgOUKUNQBRU4x+KY7bbUE82ZlqidMkfo8j9A5b6uYP5MidS8fQ5OCjvzu3vs6yy3SqYTEKTrw9UblY7s5VsSKTQaOiReEbmpNIp5wnGFCzhADjAyr8KrI50rrfUeEHCfAoTNSg90FEsVIA87nxhx5h0B3rIoVpjgWp6w/5kGwd02Y0djqjG4RLnH7nuo+RLB8xcb1161bdDIFX19Y45g8NZAFCAEhC2ty0uLi6Sk+Arq6301i7EwXAcKBxThgeGR+UUArQZpt4KAFE0t1BMB4op7wqjt1HDDB+WEoUnRpPQgTTV89VrdYA5hVsWodmQ9Xklo5YZTG+MdnGTUbK1JfOwSC2PVobkZD+C/3cMqsWr1alkATak8ErREZtza//U9uEDGG2qmGpEbIxdeGV1UBqges8EFfYTkVAA56He3cgdWFNXRvEY0p85cVngPTeEGFgEZkm4wbU+uriKMVCEtVBoI9mIQOwX/z0p6xLf+PcOWo2zS66ddOQTVdI7OlqBs/Wxip84LXf6y2trJzu9+/f3oY2MFDSrCZYqrJ33nmHzwLz4YrztfNi416dJMDATwoIqymUd+tg4+IwUq2tbULPgx0VAUjLfSbuQeurFmzkQ3b044FXJBHLjJKoauw16beAEx4OA9onAT6enkIx0qOAJIcMkW6iAUJpu0dLyHFUjwsBJzdGjPkm2SFglN3rDn+U77FuwghQbKSpXGJPUnAOyZPlVyqIJIzcDg/30CGXn7qA+NFqCIf5TiG5A4O8UXG0/RTf9ab27RwMBQYP4UAymoYZVDfMoeEgV7RaIIkinBAcqXAM/OEcrUXM0wp1lhs533rrrWpl4atf/SoKBya7CPHE7/jJSK1eR5ULHJInIxyvXhwXCfzu4eken/c5BoBxzkD383Qw8S32e5Sj9XAPMZSzCvaEPOEVwDwdjCflIpy00E+U1x3F9PA5/CY+CgPeAeAj/lpVm3mfvv8OtUM42jXrl9qdVKeOM1ALUx0b/GeOAA+9OA7mlJMfr2Cmb6VyPa0alm59TXfZ/mUDUPbREYGRQIlOqRQwbyxro5SENHCuUqF+y6bQWONDZpgRIAOdFntOams4uMETVU9ZVpcX8ZMdBEAGJDlVFvg7D4FIS0IvnWNzP/XuckIsEwQIoOtBtXLYiDFMpruD5iQtWotbmpJC4sRE1ZFA7uEhI/eEAjoZ5IUjKnjwe0KeOOJ4BiQKMSYjIehJn3qRVuHmWFYIwMFDDFj8FQKCrTd+nCdM5pJMmAwHEmHV0/pozxRgw+90mqKzvMjPASBVAHLKCzGg9tUDOxhPQuMawTOXI7GOx8MNVo8onUm402NZRA8A3Ecqk0xtj3gdER7FGgH4qU3EHiFEJShWW5fe0ZjOBsLGCcyp8bAljUN3OP6A09GyJkBaiowssStEIC3aySOXXJpbfEk4ow2EFuLIhJ8aITmB5iw8ppNYqlfhEXikLS1cwWSkvAz9HGZPQSAu+PEomY83TB/SVBll/eLDXadBwFbqOMPZr+dlyfWwVz1nEL+Lj4xEhzmnkFdYQQA4Q0gS7Dh6gcW1if53dQQeT+VISBVlJOM8egRSCJNn7iEUAvVD302TV6+0sICf5PRdn376KfTwTUIGUVwkpYQRQuIjxhII97QObfQLu7lQnDjgyC8KB7li2EZe61un6GG9KYOW9mJsEHchXsMw+sSs1iUcRTJroshofU0Xv6Taka5OAhzJ9dEvUs8eCzq2Q/BHnbyF+mq6442UDu0mTgKIErFHZQLkKeypB2Nb82uFRZKuvsPaBINyPvCqnS32EVihVxwurJTzxgqWGqjbziiaBUprLTZbcQKiHT/nn+U09wCz7RBGMoeWFUOFgXtyab3pFfZBuY9ShZjsF6TuudoCmL7dpNxhOSiNDabEfcg/ZlYlddi9vlbtuyVbkjZWjIxlRho7DhzZRD2ozmwHz8mMSGPKgo8tOp7IDk9geRIsTrp+N1kjEOc43e9PiEJQHrY7w0Lx9ItPdysoAix1SkOzgB6aQEbpMD2VkvN9i8RKJ40QXOMeo7pm/ZBmpz1CBWkWlIMfFm10WZ5WR2x7QB5fdE4PdDgX1Y4xQTFTKoz4mDInUWSEz0YJTQIrFZ4lvpzIvbAaArL5yei/rzIzLMyoO9PtqKpllT36Zgf1r6blMkPwlzjVpiTE194EzHVGUIjD7zeMe6mQKyhE5ggvGOfN5NfzzeZHEo+ajLszRdYFGarwjTkMBGRTMhg1dGaWjDiVVxlOynzKRjYak1K0HEqtTfts0qXSuxUNZbB9ZqQ3yZuKZLOPBXcb+Z16/pkOI2QxCfM8SRGyT77J0jrlviSNX8WwrtQWb2EM3Sp3cYlDqi9w6HvWlewSBa9SXmjObFQ1bkM+ufnuoE1vh3EV9LOBvrd/0FnUcmq+qmlVbmj2K2PJPOvrSJR5ZVtNiGkiMRYqhr0hE4DTaxscaju1fpqh0vrKqgaR7ISig6CGW7BZJoUiWhRXBmOtxQTbdOKUeaTKiLBwh4d94kk2IvlaZcoAK78sS6JcqrTE54pGW2Ta2DsQIVzwzLT2xt3J+nprxPJuIVPhGpDOZNgjMVUBn0Fo0iIK2WUnE2kbuJnmC9mcnpz0+jrDC0nxcF/7JVBBKk/Ik1rgQ4O4alU4QaISxI5YnClfSQ5LBcCg7XlyBgKXKsi/zxXdwxbVwL2f5TTKoef6E47wlcJ6atJtN9nz/6T9IUjy6eL9O/fXX17nvk5hGEoIqA2i6oddluGw6KFjaNmHAggkf1cLsqWTg8hI5NGpKm1EL4G86TkrwOzFyLWNfIVZrSE/YOb1MU51pyaJJPBjHsgVIbrilFiWZvgg81K1xNIUzAuonAZ/ggHIEDWXKRwmShThfKcugBqpibc/jNcLFZ5JpE5nktoQ68UOr+4JkJQCP4X0RwDzyb+e8Ta4eWcqyyBnteB8MpZLPTjzgUkyzTlGoPIyvlmmIU95YqmQP8H7WQ+riNiFXOKA2a/jQX+SxaPAknnNUkpS9ZaMdXuWbq7IeCu3deGAK9Htzvr98bQ+nrT4qnemwKDYBZ60znNyDmijshs9fAEcjZgZD5gSP7xznalkNm1LzybVHNJBHLHqJC0jK57WQlhCMbIijB47wx8ycg+6COXA2uXtO/fv3H2Qkd0ceWsyzBYXy2HGXEdsKYzX8AoXqokIXnmSFo9h0FhbCXgYOQNO1RSHhUXpgWPm7wL8n+CoKaSYpc8pxjgYdKdT61unSwu1abtJl1jK5/KlIrepQi//7UfUqmgqxYyT+419+s3V01tas9YIiDNJtjTPBicMlDGGSgMfxIrYg5d610AkDme0ubSwSHd6e2cXlm+sn3L4lUXGaIUBgwZZzWidl0/W0fd0RhPU5wc3dn/4wx+2u5lGszPJj29cvbGypqnON19/edg+RBdD1RsvPPvyV65MRy0qgzEMNDTRbhxianVHmWYuVeZ6X2723cuXevUG9ZiucG05676CLGULfDYy12lCQxnjI3aU0xiv6OoohqjDXGlcqIxL+WmlPC5pKxHCkCJMXYaT4cPRtJPK7vSH9fG00+sPhlxuKpMqPvzAmAC74AIXxlY1WqNvpedii7kzGNaqnA1PFdMThi4cFQPn0LZYvFlQBMHroaNgdF+ikiZgHZm+ClwEISTIwUDG3GCDYB/M07QZNLl6oV/W9m+XeYcGN1y6zfcMGMhUCsXLr7/KGk1xijFcrTdp/+qXP+dK3O9+97sYJR/s1+9v71557kUuUB2ZOf9rr73+J3/6Tdr46WqLMmTLxfVx5SsvP795iguYF2A02/9c0srSNrbPz1y6IqPRYe+9d976+U9/fun8pYXOQ76q0N/vQMM0x5Xwo9zuZHdvv5fRVwQfHLaYkjWHzQH741NGo7BF4+EyJ/cYfw6ztfLK6NRS5/7gRmv39me9pf7h+c75zeWNDqaa1eKNWol1TbZVuEH49u4udyAicmUuA5um12v5C5tLe9ttGNjlEm6+hOpbv2KoHJxGgjFBQVIY/yxvrOXKRdgIVxkHqL0fUZKS5KTediSPeh6HdGk/Am/9o+kS9Kq6b74hzZN7ixpdJjE0BTVJjVhssI/MshCb8tYXIQp9NBIz63eO5PL4F87g9QcMUcjM16AhgLJDLSHJpF4iLwVidkJxEtCelqd2Ic3WED8j07tcivlwjy/LXb78FIYb7BtIq9gKY7HEIb5Rt6N14WZjOqlyy7NqgU0rKNKno7XNo6VkagaNrWvkbHMCSrGDBj94gKESIUSXNWPZwzyGIVehxCj6/Q8+5rzGN7/1LbZPsBkGkj7EiqkPsKArKSBjQobLyhSBsMt2dMJDlGtjiTpivRTZgC+ICGLvHFLrs4El2IBKcAZR07E61p6cN/Btzjk8o32c1zL6CRgjTKKocLBY6dxPEg8hFoe9M38e5Ql54kSvrSnIb/2X0IHQLDK5+JfjHbevfsgtN93GATAczxC5iS1eprBKC83HyAaPWG0XiFODzDWYiQj5TGaUO2yCkdfuPNjeO2y02GCUshKz6QBoXCgtDgGaxrMHuc0c1ELMygLoK8za6Z0nKOdpbmVpEcxUCPMgAnkyNRDTjEgsNxnc7uzt37h1u1x+lu4eBQVSbAspsdYgzInUWVYn+0JxnOEAeekSZURqMqkSG2Bc7c8+Jd9+R+gK3VazUM5euXxuPGhN9tpnt061B3zpKZfm8vMxWwVRmw148IAn5OLUKPaI/pFIxE4y5i5QJY/+RcXyfhnZo3eGz2DTzIT2mJ8WFqstjkUj7a7hzBAKbIo29nhdwCDB25oPOEgtm2VOR2LyiSk6N0EjMGNmWDQiDURnm3tegPgZ4YSKo8WJ402vRgZkRrw/OKtMa3dS3BKTYmRYblG5QUVyKIReLMF51UDDOSAZ1p9NIKHX5krWxA0fYC4D3g0c0ZlgVmrhF3VaCTRDdXhFxoSZfRm/ogGOwBBulucOc5QSbQGZtA0gVh5yfGWCVAicRkf6gEiaibHMnTgupu/GI/5kxV3PQqaqtxPM2AWzY41pNMXMjjN8YIJOnttcylsLpJQ2pfE6QyMiVEfY2GopzXiFIOFlGRNJE8uNKRAbBEMAtHE186gjFPNsiw71R8+rYgKj4gq50poLHmDMbtPkgxUkW4odjzrPnN3YrFY464CSBmaU0wFvaBMew8BRNUYxavpaklQlkpE5vLOMDPZLHhBDkscDwVWQAxMBuzRZuSxhSC4SgUGh+fcJSKUa8eIbN4jFeY6qSvl5aFxGDgYrXYmf/NgQZA+VXoahmpZTLLzTS3386efIDOcI1zdW2fBjSC4Mxl44AjphNm0bcCaVlJPkJQIORma515nVEzoXxJuRMwfi79xv7R1unt1i6x3tycBBKzXQx/fJOHECxziMgvk1p3r8y3Z28zvCApFaiUpN69zQeuZMr9Ecsc44ULgNqPmdOR/wz96P+mZdkdN6NFayAEGh8o7DOPcdQDxOuBDoHp4IKtg8iYl6JLURQJwWAAme1ShR7rjphhAfN3tUDO4VLKg4RNUTwVhi98ctMZJjYhiwEAULDF5bsrQEQphV2AQAr6asah6Ryog21kiL1iDWM1VNHS27UiZi/dWD5iBFhqUNnIxVXpTIs+DFwSCSzpsREhZkOJ8tzPBbA4hTzvdSAQxU3U6HcSQFJt8QftwDpLNVesR2QsgdTcr8YWWpxnCBjVp4xQI0eDhlY0/xMDPRCqBpS+ll/JpgilNauPcFaF9xOs45L7KX9zhJHgIMjux4qhrCj0mOpAvGxtyQDMcagXCmdzxpVjwZi8LMeqPOcvyEqxmxkVHHPx7UWyDnnCFpsQ7GQQ8jy5GvoxgRhOBcZgJVcMk23TXsBQONh4mTRl0yotfw1JdE8TymaCEKMIeDS+Z1vSbxhjDwB0hyYbzF/mq6pVjWRxlsdTtjH/CpAEuioT1U7bAUALZIrdgn7CZ2ndGUubJJGuqV29dw/U6fOzf3Hjx8++13y4USCvHK5afZrl9YXgKDuiYxVxuwEIAgwBDHwJOq0Ks9KTbM69vnQDtNmcgVUjLJ5iMv8KtMSTIZLAII4euwKMSvXH6OurjTboA5c/bUP/zDP+wPNSnFAUnWQBpLYY84JMnyvtlgjFfyQZ1+TAL1G7Md0VUZzZ6FY7jkiNk3rwmOKl3ShbTuobAeCx73kBdlZEDAUsjd+/cDABY04P/444+RtGxNVmmc9CUvBzg4qGNTtr2zjykEAzYxIje7K9yGHhEV5OtZi0ueeyRELgYabTlo8Pirp+KZDCcfYp09UQbHfjwhwhR5YgB/pcZxVB9oA2bPxZ8OHqJg/5q2AAEAAElEQVTwJMPxe+14hca4T/gFMoQGbHPIAwByMPNbjc8lScbiTyJ3fzJkDthfHSBJNiEzEuM0AETiZ23qOMjxkDipfol9POUOk0xy3P94DI+PPY7tXx5Cw6eZYy1G1hSQ9sJH2GhB3hd7ILk4Z+wZ1aaLqxPgsRg9PbxzEy3HzbAKseYPy5Q8JjQUMPYkWXoEMk4R/VJxaE56WPoFLrvktCwEGHkpWiuxKB+G/zHaKBVk4JKkJmUAIGKVRFBSjDheWedD16EUaEoRoqM/QB9vg1Ehjwj70WS/yxvFIXee0opD3a7LmNgVFBSirOhZ+oczjHAgWUxgcESDgVSs2lPRM2h6HHPJJGDwVDzxAuwY3JNn8ZSbWDs9eh9WM0FIWucevOLzffjZ9WLJANqUJFeA5tVV4USoiIV+Bu5/+7d/ywLW0889+8orr7zw/EvgOV3VVdEDvogrFarjOySnl0Eti88sDOsbBm2yw4JabCmqV82X7VokWwDFhEnjBNN75EcqciRfX5i7vHmaVMwrCMeRS6+lc2bNpjZWUfLk6JkSS9YUk7QgJFW+JErITqmaunIXSADwEKsDaPH2MHMRJbdeLyHvOpBkjJXmIRVp1UvbrbWBvbx6WjxRjWitUqZ5kGfEkxMgMkzGcvmdDz7A/41Xv4G2/3/+7u/5HgC81WJNKsUIwQkGwIo1xG70wcMbbFqfX+4Ddn7zNO2dKuAbkkun10hCZXOVR3aSoVvktdtu99sNOkeSU2pqAeB0WTc+15Z0lqjdOIDIUwviEmM2ajmT17Z6tqdFJQailPSwF11djR8kkASplOX2Bx9A/5n1LURog6+U2UiMJWw+IkfaVls7TFxGQCmghAXxdz98QF499tQ1EaBMBPMTdSlg5hUkZIrpENHO0oiHgo1kmHAoSTLcIk94PB7GYyM6YuThlSpDbCgI1DIzgjyieLJFQk4uY9r9+EM5xgs006mGLkgz8o/AUMxAz6PySZYx6T8RHgAQCoz/1i7Y7bh//w7mYE9ffApWU2U8sYemljE2kDYwO1buikF4nCpwwBwcWRgwilrCDLWEMHL0xg6w3yTIojOZDrXeV8AiHltU2jJbLNybx5iNJNbYRRWkgdCRw3bC/RWxlB8DnnhGIOrNOX/wHmcUaZ2kwAprsABq1nk8iQsVMwjPV2Wx5QDPiFcQ8lR6c4SbJEfo5xDyGkXYj/OHdDABIaJggtcaVrpRr3/yyScP716HALIgHKdECQw+bz2CMcZutOiFhgY/mWs4HsIlUUKjH1obGpLDmrR9FKq3PmKQa18gE2jCJZsnky3mJlLU+Tw7TBRhe3cPcxnsZriYi7ZA6bzWEAFVoiWGALHUPnOytrLMvIZDFORgBIu6J3EOZyyJSk8Ir47Ea8TxEAJ5KqyZ6OKndlBxxDLY4JrEwxTSN8jV1BHDDsdMrCPEA7YQ6DgdP6XwV491fnoIz2RsIEwrsnFJA+ScB+Q0N4QNqkIUGLzafZUmGR4YBoSf7IRk/BRHfDZn9RwSRR5iKGQI9VLMldRjQyBJApj74Y3D8Arl7kLZA/LHe0iLIxVizpOl6iQ8OHWZSSyxBuaTr0hmLEYYnDb3qDbNoAQ2Giu0cShtasuItDWu+cIRqJS2Ga+cbZ0XPFDAEzAEWNEJBwz7GQSUa5pDE4uo8wycOdJC4/CQl41VlYysvV4i/D5EjdsI2HBGnvImI17xGLDo8RzDU0BR7Ex0PZBSwAEgUZjclPXOO++CTfyJii8or0XC8Xte5EaO+Elu/BDY7+ecyJCWV/yOXJVimQrGIAKwe+ypwjIKoyqd1dQZqYyBwuPYHL+HeyD8iBcMNOkmH2CxKyN/ejC6G1qZIK3r45dvhbgd9BZb96urfAiX2gchMNz453R6Ljz9le1bD4FCPII0tUCs0++xhBNNCC3UZzfMpC5cvKyq0fUw3LSkwQNXmAKPB6VdwgIurO3YZoBnAR+YInX3DxilQ75lF9HgeX3pU6uvMRA2bFFbIsTRQIKvlynE6kQ8SjjoiHihZkmUiI5Sx/CQ5WnIShax2idjwqF8qQX+tGUv1osp8iFqUVNRKOcQJHaG1ykETNDmdIeCvJHE4DNU3gHwRnZRawFMmM1OFm0AasgBAuYSVcYkMFNsjvWRt0O+/EKXwOFyhiPWNmxTKt2zxdZOQeO8dkvLYdxpB20j9deMqIWtWRgS29HhCW6vscU+feUTuTLeEup7g3TGZvlY0peCKbg3NukgACig8x/GERs587F50e73BvkxV6Tdqcuip1mXHWupJjg20EleKJqYCoejCRik4NgC4wBjt3nIhhkNBxjt9ErjqEa42YEnWpYnHQecQQ8TywVrKR187GiGsFFbWytVilQ9K2lKxdKzCqZdE/YilSn3LuBH0Vp4RIZagl1PxPYSMFjB62lV57alvAZHQqgNr3Me2fv4H+XWxiUP/XEWmoTea0gyx1g9E5DLdZjeZCpS+ykuOGQYNG3KyLl50IB7ucGE2w24b0WxJjDTngYK4FFPgLhyKBeR0OYtvSkmT5LVLuOE1LjDt5Ay08OCSB0UM+z3cQYGsdNeb2bayaTPXbzQsg/WlfJcKA7VohDEwOPhGTth0HdiVQ7FUihzzj0ol8iKJeIMbQdhIUrzQH0wWI6fabGs4cviZnG9sIXZL3Q29luapZxeYEp2p7tNSIkZPH3AUBptQXvFtBLW2THl0EEeH90yAAUjHxVEPR3sMjI86LZ7n31+/frV6z/+8Y+VF1JqF3oVK1UmnGgipnkl7pQrFrFPYpq3wsfmGb7o+gvQ0sqxiDO5ksCj7M3SChEfpJqR/KhlYdhEiylP+Nphury+Qct6/un19+58euuTz0gO48hXWZtutVLr4ZyEeO/M8ACjcJc3t4JUgQhSOCstxmSmzTR2TsG3aBmMX7GdMaCTH+SCY4NQ+Nmn4qDK2dxeu51v7NASV+iWUlwan+mxoz0a0W3cVXWrc9IN4N0ek/NRP1uvt7uFNN8Q3TnYpwOoN2WrrovnM5U0a1ESCRFKDYkIcqJunD9MBwjEqkbNSlYJsdbzgllTdMJdrgxDpHwNmYg31erMcfSeYvY0XUQnq5BILxl+G3jRxglOT3U2vFou1ExLimZXXIY/QmULH5HFowVJ1iHJyyUs+iOtKoRAu1sKBUFMRDPxRxqIsMyHHAMwvIIMbj6JRSQDH+U3wIi3+KmKOZwhIR6VTs5KGb04PE8rqp7y22qbig40imSW1NJTQv3GXJqTc15NIsQyB/+yp2uPJMoTUgR5SMZBGOFeF9ZokpGP9Ds8WhOI4yRmzp5NMavPyO6slcq2et0DBtXZAlfHigYruvIVp1TGmSY3XERx4zPCtnvn+u2bN7mFXGpqbGNuPupJKgNjp9tyj8oeSqceLXaiT2y2BHFg+CUj1pgq1WUu39jZ2afvgBPEUjmMyaCCZR626JCJKIkNXPC7kJAcvyP3ELVWy0uli9ugg6HiAMY2i0F5hC3x4zD+TAT/gb0ocj4rr6EAROazDdYFKd54qF7QTruzmLh/90sypYzszwJJQSiUVaMarHBSRGwnuPxJg3V1XQTaUznirIBeX4Kv5osYuN57sMOlpc+99hyaGRNqwrntkHn7qCCYabY8YNBll1kxrEPz77e6LaZ46QLrDv/u+/8WyPfe+UcGxz/5u/+HTg2qWKR4/rwO9W+t1VSXBR2s26zUuHe4iolXo7dzla3D++X+CI2drRVZaGPoM8z1alipsSR9t7M8mT5TqD3Yb/VLGiXuFGt09T0zZj6YZg/r7cLd5s7o3nohxzJl1S5Me9jqIEK7jTY9KeTRxWRhwzRd6DfhFTZBEII4Mb2iuy6UJFqUxQor/VApcXgq1+u3u/0eQzH4alEm6cY3l3maCwOGEYbVdAvoarsTAK4Si8bhbBMGaLQr5JLhCUaUNoSxyS59Fb9o9uy0w5VYrMHzIRB2SYdDE/6d5eHwL//yL8+cOf3555/++Ic/7jQ6zz37AjZWbdndlnPFlXGq0k2XdzuT3eZg560PL++dgtXbqzpufHa5Bpe64/53vvOdNza/QcUPev2trTNbq6coMx1JuVLq7u+x07C2uHzv5u3a5BCL9Y3LZw8//5xLGJCdVuOQUcRgcXF1bZVP4DFmS5eyteWNfqe7uFEtdelJ94fX6shSmfuBa7Xl5TP97eYXjbtI4MPFVDPXe6t9czlf3yquN6fNrfVaJjeqVdO9jepha5d9jklvOugNK5PuHz138dPL195772MZAw2xipUJGhYI0E8F0dDJAuldLla2lteYGqAHNAqjmdt2Ph53qh2NcB6pWB4VFSM4+ZecwIghFKgZM1OtCIN9DUSmHhqjq2ozGkcUbO8HBUoau9X6ZIyEmr59ZGwyIgHJEgwjFubMcIPleGTVNVgS/NH+mQZOwsCTwDDXdZHeMHM9Pkr5+efXWEl84YUXLpw9Q3ZUNBKIMS920HyrG6kQgkm2WJJps65ESmEHzXIMtGlPglbD+jCvDkksl8GodeS0u6O5G4uDObUkLoi/cevO5SvPrp86fdjUNhXzGHgvGxbVtzoCqh4OUHzSAkADKpS1LI7DL7Q2/sRvyfVKYZEfnmAJrZtXL6wD4OdCZp4qC3cYG7BzCcqYhajBWBcDvCcEgcgy5J6ph4dAPDGkY4qeDsBLgPcQXulSaWiyfIQnE26ouP7ZB29R3RiLUITou0KJ4bfbPjtecTAxfgv48VBqHB67GUXtSeao3oFO2IXNNXvDDz+72hmlWwObeVLa0ZRacl0IA0jrjvbo0oKHP1y1VORzRExS2GKkaph9UEfnz2xh1Ndu8PXuBpRjqF7L5SgIZJAt6CgheDhv3ekN7j24v3n6VFlbCJqDmVOtCSquiBM5GWKTnuB3pcGrh3AgBVOfrsSS5R5WoKhuGdxiFL6yupTtXvjVWx9c+M4z9B2jSXcwHvguLvkqawbTuiHGqXKU0ZNAs6GNBpdoAyYJHif1TzGl+iU84hfyGS+GySMOKgZymFBoTsFTkHynKpupVUtLC+29PZvqRWtEPsYz2bGRlueEZPpGstQKtBqOkaobo650b8h9x3yVAB0KeBA//GZMrwECgY4Zj8pros5BD1NTFEcIFWi/0INIsHYTUMVUiS7Yi6MBAk+4Cm2pogIaHrikXMSYWJ6YoeIXIrBGThiM5Zq6ByIMQifzAbaZC1GkVTvVn5wFwHm4ygudMKfHR80OR4u0nDeWTTTM4Kg4gxfN9W06B6iaSXYsc4QMF1iyOD2USuG8kT4DRGPQ0MnEwKrYxgP63IOql8+r6nAzrQpDV5UdSNLijB50l3sVTAtGNxIjczTOiESKmaJGxVd5VCIlTWptklCHgb1iSpyR+/3V/UotVjgySIQqDOuxth0/e279i/dTdY0Z+fhTNOABkpv8KZSsUJ02cUm1GUk+bCLCsf9ez0CPpwabMo1Xn2UrfLSWHYBUylcyoKEmXYnjcapgOBgcIcgMpYSGWMB4eqz5j9DuotdqdWmIcBVp8mh41mqOP/3kc8QAnbacykS2C6xW2S2uqBHQOkGer+fiEig8OPEQwdOoG7T4tSKmChbxjHMI4cB+Y2/vw8M6W7wbp09xoypHFUSJrbvS57Q7jCH43gfiZz2mCkftqMMdUI2pUen0mQn34e3v0eqKZozntzvE3HjcL32NGBSDqCbczXwhJA6C5jgs+gUDeOYCw6uKGsufAk07sAht4UIlLhhO/5k91XHbGqnhEoxR6sAWpoe/+jOQ4fKUjEr6PW2QOaIsJ84Gso5UQaMwTOHbFaz9q5IpmgagctDskuTCJnVANehKeLQIKoCvtOkw4JG8kjR7MS17atHJ8CKAJ/nqccc4Lali8VRXt6T4LvAiQsneJEt+zGReeumlU6c2/ut//a8FRmGiQIVypE6PE8+OBsuRFNCbtOU7yzrQEDykddooLHetYb1y7tQigazAUdIpZwNmrfdkPJDhBAiVyYmvK6D3SBuikjkGPx7PPRnyGD/YVC7rVDxf+9YjSHSPc7vJxLrNuQLGKHksuVlTtgEuHhKKXeYkGE6YVYoH+hMJVKy6FDkJQyz4vDqeQB44ZW7DPYx2w6mXNMTiIUkyUBjNwVGhisYQaioxmGeRxDHze1qqiSDKwpOZG8/K6Rpj9+Xz52Xp041KKhhrhOy/KaEdTSpyjofpL5uq2OKZxkeUmYqvLK4wbtt9yAHWw564R5dgStb4QBSOCxwhEoNmMDO4ZwrKrWrME1bXFlmb3ji9AitKNe4MZ4HZ+ljrSKLGYoIOFd64wMACBVHQT3FgIJul73521TgvDuABGI89j7QjZ1TMLkDkkq9KZhjsVxZniAyjVc7yM17BDtrDH/+EAGqe3PXdKUrETSz9/m5dU2UvEVFkSgvlfA0WlHCDUhBSbzR++9vfThZKcKwz0DlEO/3Jt9c08fBlO+gld9oMT2siM/pVEBNU5FDiGhU9EkHRZM7YElJ5iT0meoLHYY6E2osjmfE0hiA7j1JC/nOc024wQVhsnGctwoBFZOwE/AgXokDrTS+Z0P0hJAA/AtnvExyQh8THQ0KUN6gA4J7w6mD2Ois7gZ5KxCd4EnC6h9g5PEmAwHYPdITuf0yqJIYv9QfeBs/xJJ7XYwCOJ/EQEiZTMZZCwxBFpTPVx00zZhMkudaw0jPSM5acUGQ8hPe7HbTZg+tfgFbDJGljE/sI/gj/ychzT9IQSD0x0GPpT7Gf+viTL7iPUsM8OVEEXRjfUbHIPD2NhVv1UYnxEMiLAB73KKVJAqS630vnsdK0NqwBIc6+ZywSiA3A7lHov5qjUtBgjL3xkB1al8VHvjDxrUsbhNNxoMFC5sfpIcRLB9n0F+g6B/Yy4vdKjMBMv+G3V1+OsVqLiqyOWEp1OuWCOSoCLQpVcB5sWF2yQXrr1nWsm4uLp1hKrvLhQG6ALcpGGHh4WC4vsVCFyS28ffH5LWrw/Q/fw3GPxIcffvjrnE7Mry8WucX4T773LcrFooAMlA8OKWZnu4HGrhb55l5x3NN0lHP5UEIgNFhnkV7IjRFAoginaOTLeDz46Qe54Lg6HjKSZPmAsvB1PtB3ZL3EVgvXSQ2hFT8YkDGWFBRuyFmSIdAEweac2EHLIoJ1aZl5ghAm91sdAGAGWQca5LE2xbyIcPBBVdmWv0FOCMkJUcOzVMDj8HvWzOV5HfRlJGggepAvO6bw4evf+Ab85AD7j370I9ovjD174TzhpWKe27SrK9psbvenjA3ABpH/4T/8B8LP1GQku5CZwJC3PniHlg5yxtfTbu/hgwfjnu7mzla42yyP5y/+4i9Sd3d/+ctfNt78e/Ktnl5Q19nbJyqfWmGJmXvj4Rt22lxs1Z+INl7hLTNTxsNwD8eiHf0vYUhFZvcaxFB0Kq6Vz5D7pGA25l2J92ZJy6aQR40P2zr4DzZGtt/73vfYrmg+aBjPjL3q5cUNVgNIiABAGDKD0QeByuAkRzWdFDwLg9vJtNBMEg+ce87SmE/ChkzaFRzwk7JTs4Q4mKLM4WGxkCxmhotAxGACPplwR/PlT4xYyZcxDAxP5i7ENhkM1lhfjusYG50zPB0zHuwn8ZPdm2++eWONL4k8g/k8kgNyaoQ1IKrPJXyK4ZDsoG2Jh+UYc2CgrmEMPQ9MA9Lo5D5zFlo1XsWBzdsOsbTWp5++QqCQU8W25GNQeiBQXF7oCKk1PMiDs51YPIEhHhtKQazTQzgeL6Dj8YTKK3b4k68Eg8cdSfDEgPoFYfLV/QRSWEfiCQmfS+iQBALm/IR+Si14ln9Ho/fe+i324KlBF67yzS3gI1swSxlReIQWRxk9T8zuCIS9OBg7f7TrnV3l4jNE6sb4NEsBZFwiBYpKOXVbsNTnC5BNWWinVOXt+9eIQtUzvD+oN2nvgBkSSRRKUbwsqK9BpXOz1tNPXfDMhDR2xM5e4kB+FW4LPYmwyBsTqXShduAt4dDDPMtyj+bg0mypCdSiuOitfvWrve9973sLJZM9/7aEVxxpYqpAlczUs7BYiRC54Bwg5M6rJxcWcx4CqrBa7WmJ9LQ84STy4NkZqihfwcykVeCeY8g4erUik9BdoCrgP9GTJCAAWODRLKMqmFEbEkZ60Gf0AcUTe0JZIJv6Ih2YcXP084oje6KAESdZ7sOZviKUQN4I9xAkEwnnsw8IIcMYojwVAETRpSutOfIliqxp7PwQSxaAxdKrhJ4ptSAwvoFqmgcAgJXqqEsQr6Gaj1jIX7SZw+MpeIOOCHlc8FmUQqTiXEYAFqm6XyiqheBJJgGMcLJQzracTCxHnfjAyuG9poXbeoKliTFFupRYd14RjvZf/nSSwKPyWjEpCBmFV88CspOQvMJhGiwuhDuM0kY8UFK4QogjATM14nVHjGchNitf/SFivJIEVKLIkuFtNvuMWkm7vLRKKnpApbXFepAL2EE9m5OeRqRUkBeNRJDBXCXAGoDo5ujJ7sE+o6DqwhKBjDEoI+Mz0jJ8IjnXuQW5Mjr1CiRpMbbr7O/h8YwC8i/1MN8RAxhlGaieTpDbGie7NR+8CsxW9xwSBloAv0nYRD1YK4Iu45dSx6pBHYYW/TXC19JtNI0USLT4Qhui8jNmEwqV5AvLifUy4yGVbtd04baoUCG0CBFqUU5NRJNZRDI8IzlffEG6oBvky9x1IhpUMWtas8z1ONqQL7LaxjW4fa7u4SsWZvvcrGghiVtPgGxZb8yWBH6+IE54aVShX8tXS7SWLrcAk4EtJvqlEyO7KXVkdxjZ1h0X9ur+WdnYMrrVpt7E70wcOIwpNecMViq4gwafzs0slBc5tLy/3VhYWPzf/vI1+t2zZ5b+03/6T8N+9kd//zfcUq2tQXaZNHTTOBsqRFu2wFB+b/segwkWztSytOMXLXRGrONCY5zVJtN7vFlu2wTRoFvjlNCphc1Nrr7TqJcrcLEtcBjgMQ2wLUnZnIKaJWDVLJeHOTblJmx+47OBiDXmTOpMDqN3s1NT3w5NquxoX9Ql0G9wLg8whUtzMaIWS6cFrghnDwoZW5tWuCORG5wx8s41dT5xqu81tnUXFdPXcapMnwpe/UlHYxaECmVvkx8uq8JBFU92zvW0FqXQOATW4vzK4UN2J9Oj2wUtI2IgjRxTy4xvMUOHw/1cKVuslE5d2Mcomlt08kIfFEHYNSWPaA2brotqi3SQpMtv+8CHwpfawmPyCX1Gg6rQSEbELEQRU+amIldTFPJT/JhryRfLzWL+Vqd1wIQO6/stdVr1G3cYW7Dbrtl1W0PVfk9mHT0dOkwtVMqSk9yIr3nkNK0rLtVODYer3LuHoYEP7blUWVNBGghWOCIhZd9JSvGV8H6rvXPvgVRqDj2lv2q1tHWebTZu1l8BXV4nobkFTavM2C6ZU3erexa5dT3LFZ9j7C/yuWyzN3j9lRf+4cf/P80yJhqeIkmUVPnFEgKPCCGcj52Q3OpNULAGLlkKgeN4wQGmn6nvW6KdAM1y/TLEWHJFJp0H2q1NCLY0z8g2xDv6OnR5Z6Q9w0q2TN9fmvRXuNwqm+JDsbVnzt3++OOlSernv3xzOOlhETYo1vaHqfEBpWCtnqUPLTGDzXc+JfNyBOC8HlUGNWcL18NaQ1R8C/XS0bNrM96KFQUnfkLX6PHUtUcqG8mV8sKDfDoFeCkyUxiB+YADdZ1JsRcBi7BqJ9ZM8RCvMYLogs3OaoRQFaOkavOPcEB6poEGsiYRr2rpljwmhvcIOCBzQl2HhNKF2Md4PLvHAMxFedWHVMGTBCMQUu3hWkrldgr9XRTCEnvGei+J4An80ZfQnaMJxjwy6TzvQ9WSIshJkIQT0VCiSDAsQ40WYhfx316TmBkdEOZ74H5zbs+GKXxPlMXESWmRr7A1sOgc9HYHI27JxUQmYBV+jS4kjqgB8Fj/TkG0oFAcd9E8Nz95j744z6faJLFabvP08uGicUikvQlIFhCMs1fV2RGhIoq6doCFhaXd3f3r12+iSQ2v4TYvlxxgsTTDbGY//n1CEJrgmuxGyWZDcx+9ySCBlbiC3REsczNoFjwTPTowzoTTvoZ9fV+LNuW5iE6grKlGWKP2AX9E09GYAHLEExVcxg/zgkGpi0NuYsppoQcblSwfjk3fbU//75+9vZh+mQXlDpf154sL62fY8M6ltXzD7iFoJjbC6WN/w9Zat9/utF9+/hLjV9YuUOKm2FKcD0O3Y2/LH/8oFB4rnfoQ2d6qTqROomnwRBdr0J3v7e529uss7vAFAu6GSpuJIbqh3Wz/7GfvciS8m19RD1WxPT+zKrq3T2+UKS8WmK7tHtaxd2ZgcP7M2QtbK99547U7N6/dvHlz9+59lu26nQZ/w3p3mimlBvnWvXr76n0WT/nGw9YpLohgba89yWGoxkeiWlRc/0BXtGV6Re6gvZQvchHDpNGE/sbGkrhc0MywjexlU3wQmFHXfpcbkZs+VvEemYs0oZahE+sKS3bL/+VC8VK+UGnycQIYma3ki6Oc+s9OW0urGCdxzcDyCubPXPWSKuS5A3rCLQEQA3xUm1S9/SEOiDIMz5uhJpcyAMC13bAaAzvZ2A0HGGsxktS3oeSLZADJirGRH+MmKOtPc3wga2ntzGbvzhQefv3rX6e+Hjzc+8orX/3B+StgvnH//rvvf5StLcJI/4hx43D0j//06+kkv7S4kc4WucK1OGzzAbdyerKwtHnlK6/S92Hcjczkc+Vu5+Dh3W3acrPdYiSZmww5fr6M6XY6g8X7xqD/wfWdg8MDBCo77I6xmS7l+pVCvlZGN3SwV6xWivRBXX06kvMI43K2UClCSYnN3OFkZZLJFasLpS0Wm7gugjX9wXKNceBNG8Ov9Ufrg+LT1Wya40aV7MNWa22S31pe4ADzqN1+ajX16uXl2zf3NSBkBVeSSsNUA9UXVejn9HGJSXGxxi14SHNCi5q2l/Yyw1UbjyWbX2i8cE/N2XpbPU9qugEgicGTcA819eUtxWbBUkS0L+aL0orcLYIBjS3GUbHYL6bKpeigRsjLPfqGhDmjRL4jlETa1EFmT1OA/soQjz8EVWcF4j3FROlcwKTZQOzE458tM8yQwrQTHFqdVGNsvnGmSvxy6wd7e9v7+81Ol/WL1aVFKkGHhdOs341ZFT/YH2L0UOG7FLohRAhYb6Q1YHkPqTDK9apvYHDNcGZRa8eMUtUlmjHBvQfbMosuImwMCjMs53A/Tag+hIq0AKOI+JPSZr2bmwgTKz7cSk/VkJEm8GZ24MkhBUc4T2cI4cCo+mxqwLOvIw42/IknC5SeCucZkgRixBlzFME9HgUkjlw8IbGEu/OoKJmqJqpR95iumE66HQp2uHP37bffbh3skoSRnk0kpJfQMjw5BaoktmEW90UBazS6sF7LBUC5iIxETcdZq2jZUvnO3bvb+80mFl95Fr4Zlxs29RcSD2UlGdC0JRqUqg7gCcd1pnxxdmNdX6ahvMz1qpUSF0CzdwWrF2oV5KTT7WPlhw7EhJReFrDFxSXmPmwkoDe6PS47ymEac+fW7VOr65zjBGRWGCfEhziUPuaYA8CcOcjkq8cmk/RHsqcpcWM+a5EqGTM+2hr6wjaMspO1U2u/vXMz88ndv/jappL77QwxUlDhTsxUbQCZcWlR9xylkcf+lNJGC1pvES9VKXCUP/5RPvT/lFt4J9o29Tod2ywpXSsM9iHF8Ju8uN4zlmhmAmLEA1xUCP/lNy1HOPNrRKYwmBSwtOScMR9ei8vymF8fc9msXHJDfYGKpwYQNubjNRTQ8ShAfZ+IwakEOvLDXNyLwgxL7UEFMy6RSkwwNvBMstT90j3Ki0iaj+QhCeOZzj2FDoQxJL0HKfk8qbpssUZsYSmJMQ8MZCMUSJZ8+EKarG7ZMdR1z5hFCwPigajzMQhUOipCLDVNh6TQrnUSmI6Awqiq7K701ITv7nIIgG3g0yPNmq15IA0i26niiZ8Kg0ZUjBVHBIsHcookWu8QSIYWDiAhtF3VQpwlbVnm2DYGNHqNFJFzxIGOtHaKKNJQTNPhRH4yYmVotTL+zteeu/k3v+HDzAUsFqYpPqBEvsiguMKylRUaYmkikEXBwQ75WvMxqUAViGQx7EucUzIHFAKtyGKw/o5icxienhadb7uHUoeUhSrCXBi/rLgJQQRdVoAGGQUw4gAjFj1PLN0HfsmhGo6wEqi0wsAbXokDnYBPvh9sH6YzNy5fTKOXZD0JRFprp5Gpit27ECXjx5yhnfXgZumn6iMSAwbpYZuXc5eH1aPqiEKRcac+utZsrm+ewjCCw+8ICU0G8eNECgKcz9X02Q0b4FR1WojBkXrO0QIGG6XOzi4fEMlwhR1MsPXeiJrH/mjNDgCapHMBPx5Loif/Q7gFRo8Y5khY8mXOr61nobJgSTNiqJ0lCiF2uGOdI/Sjrkf8KRI8ZYT18a+W0QzegYXiKBLl5aEG67FHn8qObowOI9vXhv9AGljtnyddMbGaI8g2Ryj8SXeGp4vOZfM8WuVVBQvXUWZGqUzeaekWK26wCO6yYinmHwyAmCZBEhoKDH/1V3/1/X//DQbi2UyX8F++8w6Tt3anT+5cdg8MQu/5AszGPuqJEIrCCnQSNbFOIyU0vzcJPR0MhBi/rK5WeUUiPVCscFFWGwTU1v6iJIkiW6vyd6CinGKOKd1R5wAe6v6If7E8qO5shADzUe6sdIMApkHP1RtXaeGc96HsJSYvk0mNsy4x/4/mo7YeN0u8+EUj37HFr+aVcIC5foZqggHzSHJ0nNQIVPnnKOnTgYfJHARmYRJ/GJ6SynNMFtqGWCBWkSgLMIFgPN47EE0s/0PuvIqqxxYNxQ2F4IdRsAWXW/SNOBlH77bbhFcWKkwOh3s6mDa1DxE5kRqZdbttW/qhuyMjTgWSKbNiphxSoNDJAJ39wJ4+C878jidfbUQ82LMIdGbsXnpIRXSxBWafDSUOc2AxNDDyY8KwUC2jXldWtERVM3qKNTZy0rlhHtq43I/kzFGxX1MfaE1P9CSZCGXmjCHilfPcBqnzAkasJ4/rwqlVrbHHCA1JzMczsiw8twhPCIGfGIxwFqFSqsKHYk8cAxs0c/sKYE581GWKTO+hwCNUJtQoAbVNYKAnVC9IAEgSpsRHnZD7RRmWLAk8l5bXo0mjgjh5HgUMxaFOIQNmwhYMNNiOEiqbFjIrEaTYFlXKHM5/+auX+lF4rBRWkCOlOfIiAk8qLOHJwj4qC7XIOHnwzAF7+KNi54B/p1dwwoGIzrhYf6jskuLxO1H1eGCneQ4G+eGCXg7u3NVnoyecLWUsxVkvaIh6DaummIfej8xwkBzdtcO3AO/exWxJ8hZzA6CoIBaCP0ai5FFUjGnulWAH9vDgRyMRwvS70WiGQMMG/DxOITGNFGfyyF8NCXB8RkASFdlE0L7se65Rl4EGQKMySabpCThWcU7GI1H/yyLUgNmgZcTPcNqGN+SONmK59qO17Kuvvop9Dn0BZg6YwZbKYpqqgKGKCSceRiD3d/c56QIMWh0zZWC852Km4tSRhNLwZGJGLho7xBUELKj8SSCMwo9BHMxhAZQxWCgfYGyXYumM/AxKusl3XFdDpo/jOcjoK1Tssv/85z8/vH/v+eeff/XZU+sMpidZqOJuB1ya79pxxcKgAyW1nCy1R609cBrFacBY8h60m3hyHNkplejsgHkwfEAZvcjQxqLG/f0dMvIKoo/FQ5G81sCMESZgfsbIZZUiqkcw1tH90a9xLgh4xlNAkpaypMvauuYVDgza2gdeW14jnD0OKOEMM45AS8XvEVEkUwWZcz9JoApHvuRIDJj9qYJE1YOXF42U3AHgqdRHT3Pf+ta3qALowQia0eON27fff//9L+7cIXaYK8C3/qANzZNRyXKuAfPf/tt/Qwz+/LvPkeTW9avc78zaMlOazQubSMhCSUtC50+fo1w7e7u06GuffPjTn/40e9jG+vipcpnKrRVaTgwE0+nAHNBCFUMD8qI4+LlUBJlcyCuEEgDDXfKAlRu6AQYCPv/888yKdrYZeBhtEbvo4nft0p5aQy2RooHB2YLV5B//8R+//VkHPHwtigVKjMgZ7RbzyGRONwWNx5hJwv9T5845zuQTqniF8mTgnJ8cHcyfXwqfTK5Sm/0mgTCB4qMV8VB8jD1oJhxgc4TkQqGAzxSkb/EEqqLBkOtPCDaNpufv5dRwzOTcZ9fgCOX6vfAdSUQp2IgiCI+e8TiOLBBCRpKvvPQiBg0QQO482dJADVB3I336pMIHCQlH4cIiqgUAFix4gg1uwEkcydAGrAk7iygOyREJWj1gNLfANwgA3m3QSAJmwHhKbSa4B34gPRee7gjBkYW78IoHgOQrpHob98D5ujN9G7IDm00LBEugOwUaqVDr4WRBlIASDjAP5JmkSvSMJ4y0v/jkPcAj/uu2Rp9mUQsSJ3QbscfRJnJ4Iq9TS8Nn2FyvdxgUuNkN9FIOK8pMNv0VGxXqC3LKpTwtkRtRdekzX7zPY9OizTmrdCYN+iZNha1uOV3ZT7cuHTvSCQn86Blyp7HCGjwoNPQ5h1eIYkOBtK4zyQic8+yLC+f0Bz6c6PEq9ij0LG22UtZSVLvdQoTS1ldS6dRXhm8NlMu0a8Ye373yTWQ4zsdYTZqj9eivToM/HZ5wr/1kcgKdEgLx45Ke4Kd2QyoPFJHWZOAD69OOhOwCBvlPavhGki+YRIMZQsAhWTra4ydzxO/ILXkUAzwuEggL81gFHnNOoefikQAjTccAZwHgSWZHBHQiA3OBswSxDzAtqpojzDNVCzH+2K8eRNmPtt5xSCNNmURKzjzXrpZyMACVNRZqTLtsfQl5JglLdCDHUbNCJZwW4sWK+xxS4Yhl2VzPuHW7x8mwp+gBkjKCU5ARkQqHVQGeWMBIknTE4kjLpwiV1uLkOVYdBOI8rVDFfk/OpP7Klfq7737k4mrLhMx8hY8Q78JoBX6aodmqkwpiiAIVDn6D3NbYoyySRD65HyRgNoRKhD+ZNtCPB0fuVIfDQwwdCMBQ4bGekFhQEAJWvHhArkDrl2ngAT8hxIZX95IYD9zw5aXt7V1dSWJpvYMwUo3zR0kNeADGT0XO4SfQaJb2dpI899A6eEUb0wmun9LHCSmp40SHtnP6nplfIMOgEsw4w6ZNcUynOevmwE/+jPrOXNo2i+PjHpZeTInsoI/yKGAne/NrlSQWQgWocDhbTgFGPLVFeL8v2E1lGQhANJYdjCoIh7vcg+PpeHIvDsNj4bCGAH4WBOUSVWXvJisWBYDosaeljdqMk2i1YULgeMAsxmsQrju3VVJVlTAw/7EQXw3SYRm6scUq4IedFgCt0YiBTqskav0+6G5RCrqf0wLTgk0y27reod3JN9EdureiWOT+aOB7trGr23A5FGmtWvZfNDbrzrGMJhdOYog0c36XJWg9hCdfoq+tLQ45LJvFyLr77/7d9xkpbawtYKGhHnSvpU9Ysmc/4IY0roLmijQNT9F29O0HD++QGRtKsn+2FVz0K/nIdFJ5RAItnsiCA2saabr8RPe9nluvrC1wW34rPc5w0TGp2KGZ0WnDYkPCBqhperpV21xVDapmKIKG+3Ac1M5r6sOLybOkkV5cZbbPBjDhWC7r6Std1mbwE7XQQ1+wfdiEhnHfvuEz0NBteag+w+FzIzJN821Rw2R0CBlCJKRQz5M8gPe9VvoR/Ga8oq0rgyRAapqny16VT4lyDXcBW6RhO8c8ZtLBFFydCXKt66wFz+Ygsl1ZKNSWOnbkystClDhg+Qp77LiyES9c4qndP9hi0d6vIdEqsMWyaEuMh2DNpASG05fLwQEYa/PA6DIoxrJc6c3awUTKoj6C4Gy7pAXobrqbKufvZ/qD4uSd1gNOqG1VT0Nz9kB3s4z1CXj1lEzyYTY1g3ARwo4NY5Exe7pihrSw2ikttzQtZbIl7eHnLi2fJdzLyNYAPau+u80IJiUNw1Yu+FvtPkM9xoIGOeo2D5oHO3dv30RjWHcoHq5satrJMrQs4C5dYPXzzOLyjSn2+aIMSQGGottTzGJ/myeKRA9rui5M8otN7iIBI5W0FFrFaooEYqrctN2iSlsrttHiaXhSxpluCqHmQQSJ1yFYdDHpuV+70/voo0+eful5VtD/4e1325z/LfBBsyoXqtEUoRJIZQjNJv4RTYSri/Jw/UiWCMGD+Lmw4le8lTEi2FMbIkVFzsfLXqQIMI4id7zGOw9S23dpZ7MT2cMJRrM9SMhiCsohW5Urn5kMuozxJeAR+y13RvTGHcMs5J6FY5+nzEPtKVCArTyAeUnI1sOJIvskKvxWcZbsaC5R0JP9gIcyAptE/uikgRyVOZnKkwckiCR+sU3Oy62neke94/eyRrVP9oSH5PiDSxZTgd4VJYBPTBWSP97jyF3jRL2qJZjPNMaiMlnBCXC5SqaKoeZ/GS2TEANn0vYLZaZ8xa1zddYIaJDT9C6TMA1h6P/p/mVuZIt0EjnJH8uUY0Z4pltSWhNsPbz38Ycf5idt5pdT+jZykwoCXlLjvJ75FaFqcLL9OU+fxc6xkVfp7VxhbW1jZ7d+cNh2gBhPEodpG88YIHNE85sEshABOcdk00e+uo2UDyWwMIQ+FO5iSYtHLCWhG1HaaMsMZoxWfKJxx9HO5fIHeOV76xm+sz6Cmwgox3gKudT62ko3XfzZWx98/fwKd50Wy0uYQU9SbdTRUIfX6GnQrtkWZ2LqrWotv7G5RFkwzXF6bFQhrSaz6mlmOB4hPyoPkyv1eWbQhMZWCeFdVIhyNp8dT1l+bewdcEjk9Mam23pgaUEj0qmqLF+iq7J3OWA5ST0dZtjYZ+s0fSlVRSIwZMwOs19cvXn3zvbV9/M/+MEPLp1ZyHCfNbY4ygs7Gy684BqHofbRuObi/l6m3j2VqfJdjkl2kOmPsuVcukAnPipUy1wQzIi81+7p+k1Ja2aJr8Fks5dL2YV8qs6KBn2r7JmmEwyOJyPMFGiwEs2JtrEpVt4+7l4aa/FroSNLzEu5/Fpt4fnywqQzqIy1RMghqV6rVcxWGKrBJo6IYSdFiep7GkOyKc5q90TXO0bOWyLYSQvjwOnsgxgg8Kvn9Q0PG8AgV5IiSsATEHUu0oMMNzShwvAqzYekU83+oDNWL9AdD7qt7hvP/cnzX3ltlC/f23749j+/c+3atWa9LT6vLHP/wvLZc6eKRbpurNHPbzzFUmB7oG2Dq/e2X1p5aeWpM/Xp4LPPb7373qedYZdUlYUyq9CnNzf43N+ZU2eKpaWnzq1ceur5P/rKV0n12fvv3//JT3anrQdYPbcPKEWv2/ns5t0Sy6q4aXu13z13eqOaHY/qmKBqFb436r29u0Or4RrnSbXQH2Z7DMKH/dLi1tOn15oPbjfut5m7Nhd1SdQBY+FMerfKQhWzRvF8mbFMMbORrx5QN1k68elybvLi5XP/77/6X5hu7Y40gvrgs97PfvazrnT7lJM+sHav39v/7LPL3SmbGaWqFvdBFdWKWB2pHa+OP8jTkYMXbFbbjIB0K06X2xnZ+7cFaCbBGVbJuShddarBG/BqVSxJI7IU3VYZqOsTSDqmtTS6lzsJ+Gh6cseuBdFihIa8HY088S1SDkfjVLSkAyGlIIRBLsUPUZJhlUdt8bDd+ec333xqe/v82TP0L2gj6ppdIr6h2mxI4ZS4g4Z9A92dwjCYyXykSEHorQZsmWyRNgqnBqMJM7LeYHh40GLMyUV08I9WYpDWUqYjhqxUN+0RR6+UK+oCaBwIA3Jooy3yH+TCH3uA4ZWCWHDkIRbRBoMCbeIBpiBOUanjBTteQcqCN9Bgw1HFJm7iT0CLP4q19E4AIQAoibngCcBu0ZNnY7jRfvtX/0SdYg5rsD67gUPKgRA8Crc5lKcysLmHdD9B/sTDAkqEbxboZGeR5Gu37u4fNutdsQDxVUrTnNJyUX6eq7YuieRafDazy5Uii8jZMYMMDCq5w25inUBkVEctSDtyRWQmXSzlz57byu3sdbtoE+0cUECcDUOEmYtPmeuyY3Hj7l2+Mfr85Yv0Vq5SXVTI9DHO2QuAMzaUGk8QAE/OsrjmX31OheVK+ro9wqMWUclratMaZehrPrhT576Zr7/76be//e1yihkkvIiUPKKCDpBcqnHjVDvwzPzRgxALnIU5PU4JoUny/DXQySs8Bz94ETZN+Zi5VUpT7k9nTZvP30hAMY1UuUQEK93oRkCtcXlnDjWEQ5bQqnXpbDF/rEugiJwsjQSO0k0qQoJ2sCLMygU6YTQOg9aRzJ4+Mbb3UHbA7GsKNGFbREskAsaQKDeQWcvAT6D0nt14DNVajYnFzilR2wyZEoubIwZ9y7hWAyNbWSYWEO21W+70vthptbodDiexomxDJcOMyX2OwxYaf9rggbwnLEzpzAcfh8gN2FlkDAA2FIatS0IJjUpo4TBOLZGDGrAXCySaCH9oHnBRMbE2p0Yh2KYpWC97/Yq82EXS6wVU2cxHnjGA1RmIFcGAQQ5DZQBl5GjVHZ6eGgDhMX2LlFgs+YkjRJUndFnTN1688NnbHxnDJAC4AcfkYSF7e5k0W8dIGiuhjBzu3b+DSQQdAl8+IDltwVa2WAVg/x9+eGpD8cQPaAOVg48QbF8CP5YcMMKoHWQJfaVFkngnmBAoxBmACquGY/CGmZaEgEmuUEwayADASqA2xZ3giGwDizJ2vyJMOijb9s4uqxUXL5ynb6KP05jNV4qkDSw7SQIyMZNPS60qkwYFV0L+tXYkuZcuxUZR2Ykoen1pfd38P5jsPbjfbdRX1jbo6fjKF0Vutlh0K5Sr3DeeR1+CgWVExHjIwg/LfsuL6fVVvhMHnvxQtoq+ssrrY5yUtTFL1EVbnEfAjQEWorZxslNa43UUHaWJC2xRYiYBZAfR7mxMrIQE8hSbzCnEGK8wS2WxHjn/9IoXmLmE50iiJJjBiEZaFk/LQXmTIAKzcFZZCPEKhfv4qQmeyBDWmntp3eaTGnMKTeafYBlYldCJGc7oQVSnrYFLqlYhuYfCcGE2l/TEHIiiZkLs8PEVMExWuegPkqCWC2JW1y4wyoJ04DHf+NGPfrS385kqy+rL8SPuXHHFHIwkGr6p84goBMBheHe/Xi0WD4IOWmyf19erpMLqQk2U5cS4J/C0OophIRFSQ0VCXAinmYRY90AkOF2ueDowUfDBkgqeNutRPMlV9PgtD211BpzA4KGZqRqPyA9IDINUqs8OyEjRnnHi6fAGrAfYQghQSYSeSNkZGXhwDgxYhF83hOba/Q63Iq6dPw8k7MIp6xMyd5TRE1QOA9o4wnebiWG44YFHeEg4DCFr71qk6uCYPWMM+kUIgXEDLmqJJB3uRcznO53u8vIYSzJsSS6ee47wcs3arnGYVyYVvTawLXbnwUO9gMqKAiNtH1t1okoxfSp2uVEY2THIs9GyFC6OzgJIapN8iyW+FLQMKkpKjdGCUAW8WscPx8TMPiv8Y1bCR9zdttdpwsNCaRGzlGF6CXi6AEsl4QQtzpQJcqtapo82IkWP1j7MBUjeCCed+GybLhZAQZSIlRG11vSAPs8TPsmThA6GB+LfeeedlBZqp9yhiRU8+0/Qj5UOIazJixIDN50/Qy8kjsaxOYwV0PEnizBL9sS+JBL8EcWJZuv4oR8Ztq/ypPu9IdyAcnahKQUALpyMYkK2QjVDpuC51wB53GNJxbGQKqSVx0gMIQHG8ZjoKUyvoTDHXhR5lLwTs1O6k90R1AFkDmcIdw9ZJFrxXKReH5X8eBULMkH/oxKekMfRoOOYQ/xjohyGTL8UJmDDA3wgGVawLoAmQQepyadzMtxja4zWZxP1oOxI6FVDFB6vfjxYMu7dvs5m2FJJ4heGd8kcj/ufhGBgZoTa+JJXdvK5X/jDDz91nI4nVjOzfID0OSAU4kJ5A0QSs/sDSe4ZcK1EOrW8vLS4VJE9rwa1YgZl5JZiNEZA9T/HI6rYcWI9hTus7Fo39C0WYajWF1d0b/Iy22jVaq9xaGWX1QNjYmLv3bsP/KWLF2Cdkw0qXCi1/Nan02bx03PztCkH5VXb55UnbMRDRvgxwYAnm0vr5OjyQKAcs5HNze9///ucMrn2sIPd3I7s5tp0QCTP2uICJmxkjcEx2OjafvGLX+S+/oJMGk3b057pWcDkmTI4w56AEF5JhKLTmfpamY58Mmihm7B5BEllUCCKW6Ehhvk3xWTNC9q+qE/J3ehCBmTdxpYtsZIO0aBygQTJx+NPxgZMIyCStSTaAlDAMypmfMh5LvA3W02EAV6hh62z6+Xj3QiAJW3ayDwyVoF+n4iSiztGiXDScxQ2s3WKIyVjMEsUmsNCmigCAwBJiMH4ndL98Cf/xHcLmtstmPynf/Jn3/jGNwb53F//9V9T9bSU3Qe3YcV//s//GXvzW/f3/+Zv/mavfveNN96gA9XtsVatznDKAjOH/R5DEb4qQSnonBknLFZ0sTLUMjYocDMVx25SBYipVtaMKt2Uyr0hXhbBsNNvlQUehgcqi+2cAkBGjEDgLavh0PbJxx9BJLdMEw5+CCjkdQoBtoMS02fxLZMhyXB5mbw8/NlnnyX8kNk7mEutjz76aLfdIrZvowsIYPiBsQ8j6ouXNrgnHUYR645+HjKcvDjsiX5JciJcCA+1QwiFol+GWjweTtHwc2U4Za9y70ClwiuVyFKI6BFjNGMkCwrOk2kYAFHPwjAsm2VFDyfbmlaLtTnS0kZFEoRB2qyICks6sqFaCWFQB3OSUb+3P5QaeQ/sdQ81T6zXNYe0OVR359ZNLOkunDuDzBNOubjZB7b8/6n77yfLjuzOE3xav3ihM1IraKCAAkprxSK7WNsku5pjM2Mza7Nj/cva/tY/7v8wbTa22/1T/7C2a7bT7CZnrDlNLkWRBAsogSoURAFIiERqEREZ8kU8rffzPefe+26IBFBk9a61Z+R9ft2PHz9+/PhxddzvaFwCkrEMzkbKvmYStCABGE8cLa+eKQKPnT5INAoKxznEcr8QbKFREU5TVWu1u4MpIwlBJSENK9Fx8oQecneeA0kIrxFbCMfBNAIZeuKoCpLgQOjYUCXACMDmnvg9OSTJhScCI5yRJ6LBQ4TdyPNnFOhgNEOqvr52D11HhYpIm89CO5C8xFMR4qlC9jiy6TOInQY81Ee+dDEIM1fjBs0obAci1sc+YQhYKD5al+EE1XR/9S5UnVmeN54EussZBSQeYtXkbV+E5k+jgDBuMUJvcI6BOQdg6DzyhdloYJQ3p5dp2nPlIjNeqW8RIcxesUcbwaGSGrzkgfBDUSAxPCoMtFF1EEZFs8qGw5iekGvXr6NtduodvrlLm4rq2uEdIU/PxV6VkZ+/idMGAM7hSWs5q8o8xD2H/B7okIee0ImIxu+TBznUsl4U5RIlEZ7oxZqGSAkFz6RaFqNQhT8CNAClI7UDR1Ef7XHgeI7AQxus+4gSAWMJeWhkcjQLRYQ0E+tUgfYoZBTi5aaZemVFycEuv418aEWAIbqoSmWAs/VgAqNqcoTk5RqAKBQOjhVHwCmXR/H0IQbooyTkhEQRhfNAJyYECQri+fJE57mMAUZvR15WaBGMPyrasR6SkBbZitfjsZCWke3Gh63SF3gcmAv9v/nNT732wQZbL6yGQoDzEAooBRfSMRRE8ZIdfTrLr+yC00ORlnxFtjk8gawdS0Gkr46LjQobFTkKcXBe8ThLYS9dAwVHC9FFol5IJQBt7uNk3qc7KCJcYY4EGJF6x0P3QdqghxXpUkSodsuKagqSedslin6eUUq5WGAhngEkPAECklDPU/K8vw6S6ifKUS8sIYVSwZunsvAgM2JZeFVxLBby5AYjRnfs+QLJiJ0RYH3QhQDr6yQkZAFPXIoYxY22uQVP9gFE+ZhfWTzcBWNQo4Zdl7DcUBB49RP0eKEiO4QtUG+HQnmdIgsY6uvu0CW+sGkB8bQMjEo034yNM8KVYRWBfQPfOQkWi6fZhCtLamksPsJRz1CLTr5BN4WlPJIJgxRrWHnSq9W28RDGW0eEINHgrWVTauExpKIUK10bDLEBiP9SuTRIZ0/I+CLT7OlO5DZ2x/qwksbCzYJsAdoZDXbbNmJgKWc83nVVwtQAj261YxGQWRGpjIkDM+bXwN92M4JVHhdJhCmZ291pcNIOk9JJR0vD3D1289rN5z5zns/lYj41Uy1yu+zG7i7jzWK+mMAqRMeRUvRkm6urjOsRe6FmlwxtYCKiWb4KaJ2i2hEblQrhMkhCM8MBl82cqBVWquUqo95ul1tjMI6moKqvFF8isXVJkBpPZPwMWtOH6ZHORWbNIgPRRUBVLOa3phgZqYHAbkhkBKUEZbYNxXtRw8ET4bGGndruUIocZqkaaymWVQujk1TaqFAmVmtp7OmRAZnIcomJdZAawmH84IMnG336EUXbA3Fx8WEfRkfCD1dQ8Ua/ZaXNSNjPyqgqwRWN7SLu5Nj2Gj/I99kR7rCLp/u7x5hrce12g+3NuaXlCxdyszWkguElNEdO6MwFPzE/vCGSshHmfEAfoDGoKQpJKBRKzuUkzFq9kOyqQ3KbX6kIhsIGYpeNa3uaWtcxmWSi3u4MkqlOhpskE5zPfNDaS86XdxP9djn9YLSfW7/F9OP8TIlZzSwXTmaTWS4Am0zKZQ4BjwadFuEIO8pXn5/BnMouz/F7kDWvN8Yiz2zTavA87u23m4mmeI65PRODamUOnUsNegdJGUZDmftJkIAppri8koE9fopD+HyOWxllywMkO2yCn+SqtflUAdlAtMRzwBi5tls6ijscacrKsVk6bhaz+eFMOak6dgGIcYw3Oa8CiMQPBy1Mzc/DFZhMsmk0GTcWFqsW+1EPqzWupbDO23eYMQ/Mp3/60k9ZOIOeyuJcdWkeglJjDTHtkhbP1Mqm/ITf5Y1Gbn5XVpIFM31QLIxwyQjo9NYRW8P2BQhrXkiRcHr35gcSfIfTQ/WUE6xPzOjGqLtCKUcHD9sJ397YRHqHHfUluepMbQajEzv8NdIdUpG8wSuHBwxne7BC6q88fe/XSxdx2GNJawDBkx/VvrVlD0cyHJKn56JaBb0Fe68EpwRscEEHELxZkD3iFAo4RBt5pqAf5/vYJCENAcGOT+UyTRv0pA/J5RB/4lAfm28c2Gs2CvkItMB8dGyEJO4JZNWCgpoNKkqS4/rf+7JuOq2FtpWT/VJpi+OxmdQOdz+jQbiHn0mt9YyeFImVXNk6HfN+fdSED6d3u3evvct0MTNqz2P62O9KGUKzskZSgubhOcoG5Ij72NIBAG8dDOlCB6IYbt2+F2IKJDkUmSCYWubPJ5CQTduhZYdJpr9g9jEVHnpAvzN95E16NMKK98TKAlpx9R5jy33MCdTeudKvP8QMjcFcQh901Y3CU4z/2XxoCTW3NKahI2xOGevPpIt31tY2bt5nyDsqze2Ns1WObBQqo4b20dECPOs7DWZTXMIE/NJ8pVahD4RarGu0RitixThBuhaigIxbhmZbBJyq0HRFIN66ZZe7bHU5yf7WDksFi4vc8oxRhm7RUr+m8QPXAaYunz916dzJL2eKTFpuPdi6evXqjTurdAXdXiPBzZpiZAIjIwbnpVKBiz7Z8DAzLeU3GmthGvnLcQ1hN7m/vtnfrM8mc9yCTd+xP9mfm5/v0mHyXSg2C8eT+kYjOeBr31gHMtTQGIMvmVHEWezuUpMvFMvb7e6tZkfjsZJ68LaM90fcQg1kYdSnfvOJVHcwqtqlK5eWZmq1mQv5IgKT72HrM+I7zrjO0MZI7QljuSSGAtQEhx3Z8R0MaUGoxUa3wZ2WGsSZQfhAbWWCIQJ9u8uYMVLWLJQdXuXLJb6LwbyF1iGjNa595upAs7rCkI1RIDbXEmPdlMn6Y7HXHtYb7UmS0QwxTHMzjPfu3ly79v6ttQ+uFwrlbpI7N5Jf+/p3uBJ6s77zvw7/ZNjp7m5s5seT//6f/+Hvf/cbTMl++urP+Tzo0onyFz73zOeeeowl5eaJ07XZ8uuv/5LlrWxizO7FqKiPFlZqM1Da2m/cunN77f49WvogOayeXB4W5pGoVHKJzr2Z1mRvkNQSUi7VYP1ojJBVKmkzBGdFaW003rYzVeOuys33PiRd4/0Ptm9/+WS5fPnkpZ0NVqhnNxAfGd0zc2zkNTbQYJ8lRYZ6mdTWBNP4xFa3OZtbSegzh6Pcfp0B+rxOySdP1vKnZgv1TgPMLNn3290JHzbRuCGNLdv1Dzfu391/5tnLTNLGEwrR9WUCSf4RZzVzJPQTBFA/DPEABANaBEpYOWYrBA+Bmh9RxRoDMvThE9EDpIXlM3gM/Pb2+rA39ntXwcOg27UncwDQBZmjAO3yMV6pRB+ri1ofXbj+ATZQ8kGi+I+vQZOElUGUYTzq1/UrX6kNZSadaeNnqgzHWJ5Yiwm6V92mmkx1++P3r96o1/fZOdAnSjFmH8Aes0wf25YSCoUxvyYuavviA47r99WcpVZoVGhnDBL73NGrTxdq+kBt+igXncGkmmscYEi5VAVZtqgLOKDQqAqqBj8hRqHavjsSAo/fS0S2wPAkxD0izdaXpQ7lpmnxkUqkmk0AfncGpuTuyBcAAnkFAI8LhgM7jMfypJMZcle2rd2QLzA6ysJqYGP7zvvvNLY2MPvnrmQ6HoxzhQdmAYPgaLdI8kZ68Hi/f1QiyN9zJJV7QI4nHGl7mJ5O3sbO3vZes8WYALZIPUvKeIqVnkYDwqmoQhINn/5Sa1LjyenTSyvLs+US8zFWhWgJXKgLAc5tjQ2xkjayU5q/DEeNlr5gzPe/Qc+t3cYzKhEW6IlDAfLJ13vrmzPzS0ycrOwKP7L8oMBDDrbgDgXyeiiQ+rIc6V2M0lG3Vp29+2Cfa09u3Frd3++tnMjvNXtb7eF2Z1QNEcLGiKXgjPujHOMZOUCUild3Dozf6wXJUuG8sjT/VWVFMEDhz5QKxVq1f68FWzkGQog4q4LqXIVVuFZMFM7/0Hlt8gZVfNqAP6ZxdDZhvH7VAam+5QcPFY4YQAyCoKDQiVRbPgOVS7sTzGtAeQgZ+6VQiLdqMAjUkgMFdF0YAzziVfXAFE7M2CdtLBcBiVq1zynzeTVHFjAqoNkZ6xGkgldqLpqQQ40M7dEyKBi4QipPYwNejhPr5LlpAKFDo6OIUA+Qoj+6Pa058wqIa0NpCIpFXuI2d7th/TzAMpJTdAUWciA0I2IJ9vpRKZwhtF/aNmWRKtAKymEn+i0MxEKhGqbkHiwZsMBkDn0SFlxAobjG/fDTX/FoVMht4DqSwjiHRRSGJY3f+9bnv/NtGX4h8GxH/fCVa2wed2ERF16nsnRfO/XWyy+98uhjl9jkPnf2EhvbbZYXjOmOPCyWLXuI5weKQ+4H3o97EeUG5cBx+gF3kQCGToF5MSqIcQiO161NLblyaFBjd6oQNUQ1gc00J9VNcrC5IxwPEiIruzz3QbO6BaRypmKBRfR84GypRKis5+0+6Fard39tNZ3NLM4vsIGxu1e31qBeg9oHgxCZUxbu9bmGh8IuU8WUxYvjwRaGl04Evql1pGTDD8vsXofm3hqjtB19CXxheYlZzhZr39j513TBUQ9xZmSFBDJgTI8yszODuVp9c5OGotVGM/xHaj7CaQH6+OggeBo79R1M4N3EwTB7M8zgF9M9IKgIDSzUWgTgwo1XIP6MPM4vxx9FGSZ7hFUreMPsCZn7CJ0hlByEzkMibLyC3wMFYvAhrAcIwCuSBWEDATxwSCEhCCITjGpqlnHyDt9GxhKHpXUGwbYn4KDg4BUAIbMdqg7fqeHb6MOuyONzKRzmMssX4MUrpwSRirVn8gItg/iTp2qEM8gDJ8B8bRxm0iDfeuftz3zmM//pT/+Wbmxx9iSxaBiG7Lv1TRqzE18sFKRuXIuM7A6XI3smAIAQeHcSu4UiObLOR3YZ/+gd13AwZDEDfgfzhSdd6Y2jHdjiIHgIoGiS7JgBDrHW0mh6Vl4bd7HyYO3C9v3MfoFX4Eqc97UFaBBj6AvOXCb4Jq8YNa1eZarMzYkMc6RVzYXhDEpJxeo6z0BJ2e2NdI2k86biIhMg9heTTAEoywAbjDKvHl6zAKBVmVyVTpygglxClMQS+TNK4srOX0Ok08jIB2bot7SSRtQEUREqPDjCNctkoGDHYbSGh7Niwy6owpoO8UNXEsyrYs1FUwUCsQhgmThRK+lppgEOyRP8qFqWJsmdQnETkxYdBtre8IVIbZcYWiAZxNMo0H/wh0GhkidkF9btbEKqmY6hmqzd2cSGQGgJFIRRJbExAwEwMCcAgIyIoTviyfjYhuyypIYqzKx0Qz6QXHaIfT7HL/t9TqbSmbW7OqPAngk04JRLrOze4tQFh4FOCcwlBD8IkjsD7f6Zi8D8Nf4EOOKpI2G+zICLUtAumACzOkMRAAqyiCeO+eN4LLsAK2h80ysGe6As8fCP9SuXkBv4cUwayxWdVuXTgkS5ARTjewSmVpItVbWiSReMNXAbAtqsxV5RqKoXd7x8NKNCwBA+EH29go0nhXV/5FFc6JANvHFpQfzDyDA8Nob2qDhMBBxGeYPW28Mod8KOwh8b4mS7XvKE8eSe5Nd6KvkRLj2M1F8LM8CfHA9kfHLgiAyUCZanw0rFtSUiRJNU34SoWV1LP1tj9yeVQRWz3U5ee/U9jvZnBy0gmUmoA43w/kY9FM1LB6msv1z98NbeXgN6ji2vAzN/gARUIv0jhHkPeyxRwFsPaFJtGo/pP5jPrKywsMtd6vos3laDFslMDr1InYCH7yJpaFG27+6h+kwBHov/NxsIYeiB8+dPU/wPP/iQ8Qa3CLOBjTbQQmo+T++2eVtfxG7a15s6XS2woiSxfcYyg8ply9lZFBEWaG971/6gvat3CFuemG+xnruWOrjUYm8PShhnOx5y5HoL/MCol3Xbq0yC64kXzpxnFLTb6FALq2u74ucG16RpdMSzUC08++yz3JaghH2tO+DAz8IungwfPTBIQniFNPqrxv7+iM+KaVSmL0xQasQ4xw1r4dTCtRDweJABYjvdBrzC3s4KhyUdZ+N0Boh1QHoorhGjN5lJTpCWJU1IGZqpz+qZRtUamAkJ2JhRIE703VDFoVwWTbhBgN7WF33gi2iwbU7rQgNpgRKVLuguVBAQIskwzf1gIzvv8ng6PM/IkSnpyQ4PHSvApOWs6Ysvvggfzs8tgZAQykJnAQxdA8+17S2q6Qe/+1s/+MEPzq0s/eQnr3LiB3bRL2vTSGPaBGcfvvrVrz7++KOsGW3vbMqwnXu/8jI9BidPOAaj4BiBtIg769do9Xz+Hdoq87rfOZnX9ay9gSTQiqkHIRQKnoOW15TWGVRw+IP9K4HcEczNsFhXgX99dx9uEGsDHoHm87rNGSRkjYdUYrIJv5hmY3sS4ufLFHTf7z8IhtAgYfudJ0gAwNFd/uIXvyCvM2eXgfeNFicGsE/ilGPYxqEhSjJFYkWLosgUdlEFcCACJlayYSa3fL2TBkuR4d7MTAlIlqZJ5agFowJOe21H0mo2+YsQTj3i68NdSC8DLzIFOW3WZezhaR4aQylEpznYQu2ACkfDQfbY6aNmuYaeinMUZAesyWaK5v/zn//8ycce9UVwWDQa6pI3EDKGgTngYTDIqz4DZnyGPxEe9yDVUXUoF7u6ZMA3YTsdhAcaSkW+QppJ+eflY5UVFckx8yQjHNJFFLnzhFqejt/hCeEVhwcfwJ6Qp0IsibIzCXHMjtD9CrfaMVihJNyd449CHMCeqjASQhLOiaQd3Xr/V1xnwZ2CirXEZAQ8gLwxBKEu8ALfGei4yX6rCWEFPtZoh3UiArz/MgR6OOXRa9wDq8kCg2taEN+zPNrFUf6jokfpkAGIQUyWF2d0/mCsz0LOVNTAIckpUdEsM+YJhNy7z8Lufqs3JLuIBq89ambo0mSSTKVDMwxhTr2i7c/gfnkqL0r4STzQ6ZTAsTgTCKfUVDhP7rmisXD7B1b8b79/e21tPZGiweahgU0cugyA43nFmBwP/ig/STwVNODhCT0k8Fc8ig2LJlBzhzDCVRguUxQTMI8FEPLApmX/g87JDmkX3whx535SRb3/waSiB0ieh8I/4StpPQuHt9I8FJUDR5g96+j1EKoo/CM8RrbnGTAHPuNIEi8UpVd/Z2e7fbQDgDVH2/LxSiFBqAzBSCyjQ57s3jukazbxn8VMYxcSBRDhINdmrekuMHusExD5A+TGbfwORiyYPIoQNsH1NEcEBQlOuochosrqlpXGCL97gmT2A5GkDZ9BXsR4voSjCuiSoTk/V0V7/+K9TekEbeQnm3ZEjK+KsKr+wQc30FEcfeNgVrfbfuedd4bcRRsKDHhFvbnw198+5hmlcjin32kjBA8Uugc/hOGnO6B7ZZzD2OD8OS2ab+5uEkhthsUMzgcAHMcf8YGeBe6hXmjsDIKoAeM8DZMc5fdXS84DzUbNMtjooZcYLjOwRHUwmkKJEEumlkvAWy+Ckh3noIF1OS8U8YgNaZ2weI0TRTgDB6IYHaE/+cgXQ336QQSMW6yprziXACMJEwQg+Xp3FHUcCdMwNkjEXF9gnQZbkL1OW+/UdwBOnRMBx5VZXRfzKaIpCF5tQEn3qXGyyETPTAlxAkPaDTISIy+PRQcABmnU2hKDh7q9KspVGcFHOKndSiJNaBy1ZeReIVSOmuxhXISHw30WJXxOCzhBCB4fpLlIKdb+3MN2FHgw+OSVGyDyE0b8lUS5wtE1aquJyc2w20tJaTNyoSzNtOwfufmJkH2uV+SKaD7Alsvtd/eZa6fLmvCMykU408JAZTJpsCsVKmI8wG9t7nfbo2yGDacOxHGUkH0XpsoPNhZe/eW1v//79/6X//eP761em1s8y+cTGIsO2mP6sO0Hq9QN34mmHTUbexqI64qCzLCL4MIyqxehD8pW0tohtppi5jwKYbZa4RJg1vFY+KPFsOysAaKuIxqlR8iirzkHtSCp0z4nabFUomun2eDBLApu81V3HHcoMxsqaZyTKGDGQxltCTjF3buhwsUGh2AkApiq9Xt5S4tlFLstxvgk1y1SWdQQ9Z2WgRSLoOIbZ7zhFh4xT9eLq62Bhx+CXFTNylvVjVNgEBvsm8W7RrcmRlCFz/b/2zk13S0ubsyO9+wecDSNJjb2udt+Jn9i6VRufpkhVVYnSvUZFEptWR14xBfRPEeELoLwEJVKzocIeglm6sZuegcoMfKhXGmRZyTNFziQYLPJ04AsO8qgzQe6I2rSZ5TLVit3QKXGvQQDnUJfZvqTysrs++2dcm0eDZIb6jjzUrIk3WTNilE8fr7AnilWFquzC+wEDruEmOobY29i0yHNCXtdfWAhzUYF/SWmVPCW25ySBfbI8HN9CjQjnMCYzXSCg6NWCk2lIANquQkdh3E5xac1U/isV0NKN/drYwWjn3Q+ky3k2IxAJAqSNyobtdjtaaKVWuQ7ToX+UAsBOzmdymy3uowp6eeIHfTtkg2uSrcs9Yw5CY0RAg2d9mh70lhcmlHlP8RJtlRG7jsCSHSwTgZ8qTbz1HPPvvsO9pt81kYTYC4nJ9YrzGs/qHerZPwxMVHRQesMiZ6WlZ32oNuwgYKHuK4L/f7rdLnki3jweynsqeY0O1OmC8lqmxMHTB9DbjRGfWub5YP5ajWv1YY5YNgfp5vk5AMKw+XZ+hpZ0ziZ06zVwqw8EB/8Bq1vChP6yBWaGD95H+HwucBiXdywcxqCFmTMhZitvXiM9QhB+AFYwXjyoO0EeKZtbYo4ZNE0xEoRzz3uFxj5xkQDmTFOqslarD3tcTjhNOb/bz7J1T86c7/ZLdBXttnn/OjYRlTp5CkWJPb4eFdiVOdugV6vjV7IFbGqN4ZIX5n0sjCgds/eMcqBuwLv37u3fe8WC06ZiToA9Dp7OWGdIVFQridWBlEJvH7DIlms4iAnTBeBHvRACaoMOT977jKLX/furRJCX6TEscr1RGglmnChWOQDazRqhoAfsfpMEg1HWCXgcwWM+rjauNdjyZJl03w6xVh248HO5mYDMC1QBDKjiyIIoSVyfIhRAd2psj46QVfob8zRHWD9OTNX5sogzD3ff/9Kf7ujaeewm58t1/uJwijdo/kXSxj77u3t8NVyDV/Kk3K1dPL03Mwsu8KUlGUemyw5z3WPcdA+RmOt9WD7TG9vw4SwOZqthvdfjDpQ6TP5MmtMXKvBqhOdUdim2CJGBDAO1uIR53N4TkZ73AySHPcxkq4sl84sFZ9/8tJ4/Hyz06Ve3r12HT325FOPP/vsk6l8Bi3MN10GQxkzq37tFBTHgna2trNdhSRHbK8na4x45mZ7s1k6i8mYG9aGbMfm2N5E1QVHqbBcUgXRf2SymfNdLVjUZiutbPL9PQkD5yrplWbzBZarVqqVfDafgeZylQuMGD1VRxzc6RcxM9eyqZY+0eQ0H/QIjLFSIWLcBzkZd3SNb89tUqTD6DQZL7EXaRxGzzL9o93gMTH1NkgXj7zQVXOLiDEM1hIdLGwxBmCkAOegX7/u5VrtXK7TlRV2T/eZYajGBXNY9CQvXbjEVGfz3hojjjErTZVKKp/tDPuNvX1YyV70c088+X/6b/7w9EKt1dj7xSt/X6wmnjl3uduv15sPxt0G459fvfE69ztzfgaZf/apy6wj0hLokdNZ9csfvP1+s7m/1871GiNuna7fuX7znTvXrq1lZ9Nabm52uIsrV0zSZGaXF/O55b3yHF+e1Pctclwgn75f3r1X3KEs7CfQhCkvdTFM5lg5vNfZu761+oWlhdpjK5d+xccL+jM9Tfa2FrXohpU6qeqcxp8MeumCRgUUmdszeh0ksCh7hD73g8PzhVLh6UsXfnXjBjLJ/eiwjKspCZ/YMjRcJC3Gl++9e4N1MXSC1FfMOatjAUe9kQZzj6oGp/mH9E/wamEWzvoVl5iYG/W1Wkp+0MPusTgw0Qkrpex1RwwQNR0qU4NMSWgLLNprqp/KpnOcJonyjXA/zKORGyOOh0UH4WymhWvQ1mwVfJj6j0GhaGvvSWv7c7QO34KigLilpRN8vZI1RK4pgO04x6f7KOkdZOySfOe991mUXF6YZ9GM4R+OpVJWCuZmpElAiAMMSSArFjWYicFkGgL4CfEuEb8xE1NgjWmpYSSZQkGVuij1DtOS8QpOntBzKNyL40QCgIcnZLjHY728FmuEOLRBes0SQITTA5jnFWFwtNErnogt+N0RQqrgxQgnFSVCVSDM23evv/Xmm8NWXdNUM7XmYnEBS5L1nWm4t7S8wFCQT4CSEEqQItZcXn311d3uNtwGOYGkJZGP1JyqMEQFPyAKNutkIwrjDAz291pskFBGzW4ADViL39Iprbmgw7DA0bC7cmL+uad1VU6/O56ZqfBhAIOirNpyQDfzdSXqrt/qYlvDdBzNvMcKNG3YAbXyom5Xkxib6HuNjlAkE13QPLpzn7PGy8sVatuod2qmrSas60ASKLKXOiA3/IEJUTgem+Jo4R/9lq/WGANcubXGlX3dNpbyTMu0/0fPg669ub7dQJvbBNsSKiNDFeQY5uBzW9EIpfE4z9pz5+kyMJUEaY5g0GHjWMyYqHQ6WdUAvRN/vj5Aj5DMp3lSZCoKVACq+MaSeI4gJJxgqsA5xbyf8lMIhk3+JMYFw4GjJx53oPdy0BGGTNaAT3gPZRYmCX+pWrLHLsqAxfjARXg8a3+NCxhwEQzY3M/Thyd0LBSXlQcr9MOIcMHyMa0oom9mKGdU0E3IIImBoPAkUruNdrlYogbYsJTygZVMl9Ocm2LWLk1LQoxLWUVCZ9EoWUZJpuiQZACtyR9n4+j66b6QYDeVIIHSwHUpN5iMFLH8wJ3xTLktHLLhoCpH+M1PFspch8rEOtGsJUcBUHYqm3BPQ8gRxyxS6+bkhXoiNmxY0ZxILKV4kkyBSEVAPgh1OzDDHis8QGRiS2JJbnxkEHZmrrJxs1Oy29KLfLaKL5MOSCv62ON8+eVXnn/+mUcfffTzn/8i1gYoIrp/SbXIDypOg6J47ZLpwdcjZfmoAMgOxZklBfoCVSnjAUrE1U/zc4sYQKASNXdYv0eUiz5CLCooKmstVm1OHgAwBRS8QjaDaOpLa9BwW/SLEnvIH1FN145BP1fO7u937qfWucp0YW62Vp2p7+9BBuJJ41TvZcijp9W7GrWQHnSqWctGGSOCbhBiF0YBaALJiR+WWtCsiCBj88nO2ipfV5iZreWX8lyjyOEkBhLCitEtg3pqgJErH+7u9zu9e+hejMLJhXNvuLD7OUgEahoIwuwGhYOVJP7ggh9YRqk8iKeJVfQWJIwA4rEeqPKEuPA7NrEgUFNTVA/zRciFxyixpkHZxUXaqIcT5SUCKE5zlDxMKxkKUHlt82JO4S4FYYgjjDCEwWJCJCjuB4Z+hansjAwl+vVel2lkw8xPhoXAEgpI6gOcPBkejfUhX/XfNK1ur0PaNN8rC8voOXjWSLz1lrKO0TzEkPzrf/2vzzxymi+J373L+LKfK6jKAQADPRk3RKsbgVNJaaWeffatNjsPQFAoUxrWx0mhAeYObYFdz9ycbJ9pWvTHtBNyBK1KauUmKvQG8hB1fw4BPM5L6iXykhJIkyHEb0pgARqXNoskAuUO8t8ChBIumf+Y5mSUHKzI46Q0JsLKB+elcP+xT2gLG4GKiaaADAIpF2uoPDnKC3NgETXI5XFM4PdTWYaqGOYyZm2HX2A/hBy1HzmfOvoCNPQQ7iEOYGfKFEburjK0VyPK9aRrUlO3qmBzgOQZbTZIqZgTr5AEnkpuDr+/8iQA9YKnXChTBFZp2b96pLzM2kqmbzYjdGpWW858pTQHMA5+8MYnPhik0sXiH+saNciRJfKIY73Iqn1x3g3O6WOBIRq24THK1WBRcCQiBKdeKRQAKwJ0q7H4UhfQ+Jl/0XaUUEN/V00pAlGY1AVN0LCl2GDEIo8nBycxbxQSPiW622bgy+c6KREEEGhm1qHeMBqcGIDZ1dzeHnAwn+ws5vCD5JARwEOjzUN4kpZpM/Jw69ZtelDaNSkFFhbtECLHEMBEAme1JrJNyzlaTxjAB2p5KkyuFYHxwuKBEijkiR92UVN8wksiQW9GIL2EnGKZQjMgRrWwdLIwM8Pcz5EARV0jKGTqkkn5gA9rTOndxSmMQsLI4NcpARJHkD2n9HtglOT41wDckzusBU3RTBEE0YcQxTgcgUYeYCHSU8TbaQQQ98Db6NWSiDPuHA9+R+XPMFK/R0Pisb8Rv3P7N4Lqo5FEGeGBJzRDpqzF2Vn8SBGdiHSBxtO6wMEDvRKI9RACsaYB5oMr7yKHunE8bCwuh66pHD5OTMRGRZlQRQnjYA/zA0xzYGkD7ceIVrmq9o9vqUSBh16eJU7GvrxSNCgnxUfgZ+8WGOylyQX1REO7fZMlrX3SkJGRHKR2/LxgaIlCIyNKh5tKVSwbAzbVHwt0L0mOhH1UAA1cVhVLVfT/rQ/vN/b2iiktzHHnKUtXVAf6nM/IuRYFOaWmjyhXStiA1GakvSEGNjLnIRsUIa848+vpfgI8OIij+NYKnFp0FwqKKEZNyA86E9UNYTDPSQesvrcHVZVZDdwjxqHJuQgachjuA4nigs6zlx8heTCF7rZJSxWAjakbzj+FMRw0qfRF6xewfyVrqga9zbgMPIzQGVntb26Ch/JSNnCAxzpMsBA2YNUGlGzkMV46P1vDhhdbHuViG7EFnbgVSaSiGO6njNz7iV/XPWgVhIsywCPtKtBQ50Mk/mxOfTcRQBIi8kNuODZ7CpvAQsVFFtBM3xyDsR4hJkcghCGkosXRV4OcPg54siAKVOWZGb6CxQLTu2+8hW1po9FkbGNMSCIPCCeDnK997WvMvsDDxalYJBUX5mH+g809LOJFakKmOlza+M6br4MQbFzfwQc2qTt6cyjElAmDpt2mbtl+9+61X/3qVxBDvZ++cBLj5f1hm0Hv7j6PjZ1mmxbXHS3C4cVl3QzOp5yoOw167VuL0I9w8coSOfQzNIGkSwldqjP7eJVzgXADGMiW5JhzCklCqXmSNcHAQCqO5V2e7IBRalbPyb3X0BjAU9UW5mAX53HJi2NXNOIHD7gWAFPoi8ie43/Yk+xwHqsszO91oVKEUfHkxHoUT6QFUsk3wuBpve0Q6KqIkuL4liajUESacEbxhOzs7KFZEJY4/n+4HzQUJRBczXpCO+gZFeTXzARuOCXw8OTJ02DwEAoLt2mbiB8eqgA+4Ah3GGOalBJridR7t9Vk6IXyBAZmaWCzt4+4InJwCQdaxAy2YNQQ0YkHxhKLB5YCQ3HQRVyZyIjIeRi2UWtNwIWQpCIvkuAognui2Ii9XlPEOn48lMWTwEIPdxjSirVGEuF4cHhwERLPKEKOB5joCaS/OkCA1lo9UeTL3IEZ4pXXXqOYaBmA4SeOfD0tIc889TQafqZWoVob9T3C3dHq4Sqmu/AHAfM2BQd8RhAn6agfDGAmU7amJsF4gHVtEev1j8dWR4OkVib5iXUu0fz5cDoh5IgfD2SrgJYeRIRz2wajdz43Ie1Rl+kXkQx7wYZm5UwLd3KQkES80phYAuMp45lk0mWYJk/DR0GCjYGFM5AkONEa85DEX91DLJ7IEYXfn/RliAraCfazJo5eevvGbeCpDAfAz9kb5BDGQj5f1XXnGPBHWXv4oVcoJUT0xhxpId7piTB4AY5FazhVR14qYFxKo7wUFRY5RBhwgFenIchfwxs5DyRh6FSQaRorF2CeCg+xAYZP8CNUYVoHPza5YA5iBexgQJCZY4NmOmoxM+xzD9Gi5EJwgGxYTRVTg0R5QQgBKAC1LRyUWITKMIhLgOFs/VmvhJO1cjf5IYpAdwYoS0GcZTJlF6lQg4qQIKhwAOP37EjuBVac8EvRAWqxhtvKQhQhBjAVJM/aoxybP8HgkDzNH48MJN+bJwBh7hIS5qO8g5YE0IwOwY+KZlWXA0xd2zIhITCIn3voogH/5S/fYWDGdyYYTjPuWuNrw/v7ToZjO0CBvUQZ/bpR0AlmUjnp5A53XdNgjo0OvHzpUQ7Y0TExklk6uYRO29zYJhWUkASn5NJsesXv3PYngTjSYjfIAIRcjBkSUhiFn1j51KZISzPkClPWoBsUn0VbeknYhaRxHB3kwXJzWEKFMBawpudFCGOCX6IMJiiahzphEc0eiK42j4xm+PwWqunsmWWEPJ/WCiF3OgFPTQLDK0O+3fUNxww2LXA/3GFTYZF6Bq3IgT0RImqvdAdg9hj5yC96l8VOwKMgJwb3HqtwsxH2RsJmAAmpTOyh+MIl20qJiUxhtTNmNYQ/y8jUNv9ZVlI4UmiEgdGy1/RGljA4eimRYYrbiQdee0wGiWWzUQW8EBmw/I5HVSMLW3szso0PilWmZttnVnWWqaVyCkklSO3mydlebtaGgr40UzD9wW3RZL7IJ1VqfGlbgG9trfMFm2FGQ6JaQqvO+gRoIrGb12h4V7cHTlrZTGK328ntIVWpoo7f9tPYcvLVdl2h0K43E70R5iInlk/z7RZYym14jcbuW7/YQp2NK7nyTD6XZDCNEUdqf6/e3Fljq2aSbICfdcJBrzXghGUnkez1GTEUZms06f5Yg+zEiC3ocbHbgYZFm6qdHyarHK5qtIjdmYHV3EXUhe9ej1ljBV0KVeA3dBfsM4BYK4OnYHzze1By7Oth7kooH5S3xe6ZkQZqfBKXJwZHwsjHR5kwmL5WRWEUaVZvkC1nNdS3fUTuhgZdVlducomQHCoMEG0A0bYTOrLB2qZarP8ZAq+oaElX5Q0dqfASooOTCifAwaPGQD5amhcLDfHdeQap4z2zZsXaCwuCcSmLqRIXki0+cn7uzNlWFgsivq6eKKeLnN3jS8dkwRCGJ2W1p7KI972SYJXCCLP8TcYslEcwaYQ+FU+hVhiJGJ0NtNEcjWzZ8hsDAWESLKMYsxfjlsNkMddnUo3le3ZvwKXK3ATd7yVmCn3GW0OdbL2XHnAnJt+gR9e8f+M2Gv/ZhScYxJ/M5qmpxF6bdfau1Sx1RoMvDFUulJvIsa/gjmX6nOlrGwzYVi6TT2Zk0Y/hLAqrO24J3jrCwpj7nRKp4QDR91IPrD35GXvWg+F+oyirjaztbcJj+nS+fGlVLi7RdBjJGTO4BVPV1h8lCrnxoNNh9YIzCNDMjgbPBUzQWDjIaRmaKRiYx4v5ycKpqzfusti0OzkJw9Am4KSj4ynJkZNOo154dprj+najWtO0VpYCctPnKD3AVAQzf7jSaNsEdWTf0ZIBTvf85Utb9V36JB23R8wYnptVuCEJHlbrVLJ0JhVLjpGEEoLthsEBZYAiTzw3/UPrdiSiB9kAPqBN+w+IrcZAGNqxJr/Il86wZab41Oa4jykmjdrkScoLQWW0vddoAD5TqcxUC5x1GI90Zws8B0z5WF0Hkun9glWARxoBSKg1I16mLiDRA6zNmhy7NIfcJjawkTcVEwDzE/ZAeEWtw0cMcjjFiSdq+4edhdOHWK167odBgvejaWOALvkRAVH7PVA4wWMTME1GD0f3FA7+puGSn4CpQX2ZpCm1nGiOO+/uPCRgWKAT4lDH+4N+yiKde1F9BajIz7STM8f9jivSmbwq7bSwx/NqaJcydVE8WqpcriwuNnNlhkc740R3OG7rKvdCN20XKMETFptt35udEEhihx2Lq/2tXea0vU6LOzbZ70cE0RPqHcz6uZDLo5HQIdg0beysEl7Egl/DVnHTzs1oDw5qI0olkVb32uUPXSBFVgeE0XsuL5+iIu7evc2Uz2Mj5risqfQ2FETT0iB67dZGV98xUy0SFy4GxTls1tzacaPRc3k+lJ8/f55F+Tu3br/52ptg83VMCFBeAZkBkZ4pl8w29ltcj4OC0ekDcxHx9s7gi5RRcR1Ez4h+/GJO6PBRxTJEEV+kw5lcAXDx4mU4efP6TVbV6b9SuTS2lJRv0tFlR/d3e2cem0/OnyrwAahzlxt37pSyg5MXLszNaZSSK2SKfPisoN6Q1RTwK3cUK195UPuVtAwY2dkCh7p3K7AXxA5BjTlApD69Oy4WysXOpLu5n2l0Ll5arFTLmSzdWpoZVbaXuHvrxurqOlOORPo+JjD5lcUENsQDrOMn85l8oscYo4nC4iJcjlNlqkXs5odlLf2P4F9+lO4q6+S4BInprkYdqbVudntcGkvGMD6Cl5geX3vvg14lQ31hf7a1vkNkH03IMSKcDTuyusF5XOmJb8M0l66la019S7Cq7x4Ou00t4+ZNDzjnfQuZ0z/wAXWAnOuDF7iyxn4AZ/KlcVcjYT/dBV78+YH083DMREytHVGDPKs7zW+5OkRHCLFd4rMK0hjqQZF2lmFIPcYmvJjr0v2TFDOPcB4CUSKJU2qaFyCaGZogp3q7qXFz3KsP210OmCUKGGMlKMlwmC325pdzX/rcp9bufjjYay7mM7l8ikMzHLPqt5qPnF7+1qeeoMwcuvrl1av7icSZWXrJ/dPztZOz1VFapTt1/tIXcsX7W5scHb1X3/pUelLODJm5/fKt12h3xUKGpeq5UydOPz2bP/+ptf2773yQ4AzRb/3Ob3/nO9/BHpkl5la3xfL0ex++SR0xBNn8sLV9vUNvzlU57733HtSwzpirTlgm28tzHRfDnHafC7BmOaGV/v9s3nl6ufr1C2eemq+svfVei9FqXhopW9AgsqNLUPo6t5wdtbgkNsPtrwx0WXrGejg9YfhkxkszC/NPP/2pBw82c6eqtJFssYIEgoSKgySeFIQVK0jl+cEHV5kc0tJZuiI2nLnBicABH3qnv6riw+HeCxAT6XrakSpFi2rNNkNoUki9c3BJkyTJDK+qWZMupAA52bm/sX5rrTqrUxccb2MkxqY8pO7vNxmChzSFlExpCHuxKCS04TJQm/B4okOlGQXcgGxWbBE1oKAnKuCh/ohwAQhKw1l+CKFyS9ynzk2TmpnbydEJu19Eagf95MrpudkFbvKFzxRBE0CpHKygNVAHAOLura1v7uyeOrWCvvV+jePDW7s7xLIhcerUSSbJYKN2Oj0b1gu3+grYS4vAHoLeqdXtsfdGXaMbkTEOfUNYVBZrQfoKDto9n8pTLqxQwEI4OMhUYmkXvLiQKAcrHVM5SspgDBgKy5OSEjVixGXnsvFr2qwZkipI1pc2qVRBMJ/saYgtXaSducMLY9K8qPeYzicLqXFZgRiHx3yuxSp3PFm/9f77b7456TboQb0aTQwlY1BYKRWfeeaZlZMYo5SYOQ67WmUwktgGw2RqMD8/e+sW9FN9UA6lUmX6iTmv3yiAV3esWdy6s5rNlx9s7VtukgGTBR1qwmN/Iks4Y1iZr5RLhTOn54sFDiG04bCbjzDHZD5gi/mT/Safq2Fbid3K+rXb6+TOxIhhtGFTPeM6XPrBD52Teic9dY2AK1jqBYtTtpS2dxZ368uzM2k6l7EuoBNo6HiFNmwz0STkQHUQQ13DIsKpGgsX/ThPBAy1AQxQq/fuX3mX41brUMXyCmqXQQTLPMwSmN2InuyJnXZheHoBhLtr64hTNZvkY7H5VJ9vgie6DeeXcGsNHW7IIgdsQqgQmob6PuiU5JCHreKIEifHRkp4VQxmkFoloSlRcCKYS4jvznpmvulCjvEAw1m2c4TQZivBhEM02/gbQg6OdUFMb8d4sERXxk04dY5/5bZq9FH0ZhIX2E8WdhLdOASNNtJW1ZAYGGOy0YycCZjCQLPpONWnkS9OmF/3HrszQeXmZKa3XHdyVLs6FHXPbJ2qVkZCYXLMEEEcU69qTCHcHGlU6UDzlNrhxyhVuVkzEhgwpKX2EUDg8BCIHw/4yYSsuNWAc0/bvdZcaa6i7l3jZkkBokyDtpk1ayvwMok+G2fYaaf+zLKb0muthoLxqsPA6Ehm31A45CvE1L4WYlgVRb8xYpImEBOtYqxqNAw2pazZIdH6nC/M0R/5K0vxX1d6Sn4AtiILgbihwuL4nJF+TAaUASN1kyWFgQgEFiu/VZ8xUwVnyOVjTpaR+dC1FJIYIqWtT0Dx1Sa+fjEZferJsz97OdvoMTjhowasXUGNygqzYTDf5oABq6u7f/mXf/25zz3PRjW32bGRs7n1gOzEZ6iFcKTLis4r4aLIagdP5Dw8enVPHNilyENYw+ArrT7FDBjB6f3h+L2rH/D3pc9/QVvjs4sLtYUP8x/I9JM6gqGjMXZ84ZQeUwOGkcYgkxZIgjmMH4xX9D5ITCAkEOP5Ag3htGPaOPnCMRQF3Xciea82PzNXmWHxXespOCs7yoguBsEXBiGj7owhggicrzZQIWThBMAx112EoFS5DyWd00eb6SSRq1xBy9x9SaaWDVqN/fev7DByG509yVriBAklCymcZJcF3GS6ev5cc22Na9oUbmtQJkqHySBWShMHEc5rlxVCrLABC1QQnD0cmJxw+L1S3U9a9wjWYuVRSw4chVS4ySWwapw2c5O4hEkY+TkNQAbhFhvimEJGpEZRJFGqWI4e5YiIiiAPeaKoyBMBHA0JcDq2GM6YNyy+BXk4hiRUZ72rjU0+VQK3UQ7eRYEQNjonHTkKqzXWhGeUsxaVTjPY0sVtqRRjXKwYmHYjEBjgQh7iAp6WZEB44GqXfQosGY3PjpDcgEmXskywtYnB7a6JCaKDSgEP9/kSC7x1kEJYKWjZju4VbFG5yOuhHPRsTJBssBS+I0hyekW+QcD2kV7sNGuwAM1YK6YaPBcP4Rl4LGeXSdNb2j2gonEiyiQEhSZg/5GX5q+83MvTyY88RBHCq+gJncPwFpRUOKx/CgEoBV5ji0DwMBjEUy1X2I/CaguSykXtC/ltTY6QQPAczc6xhjREeR4giRIA5nhCyIByXonT0+h0D8B4ECEx3u4b7TLhNQe1xEKM5wsWxwy8RzmFiAfu/c33sSV54dwF2cMWtJQ80Ic0GS0YT6TWTNPJDjrAFxEAl8Qiy4hODokaZ8rKhe0PDFj6TeXb1YebxnZbkJPnqnliCrpUlj1ajqUDXEpjX7pZPVn1p9bsOBhjAZziWQQhiJoqaZA9sM8nMpgnljvQkfBxX1PETJqj88VCpcDU6OJFDovU/v6NDc+atAgOfmGMOeU1Zstxv1iukUtU0ghEDLEuhPbCbIeOf9jqsIhTLGoASk/AxO/erdvQwBK4FLo+LPaJHAUBzhumyDMnam3E6LGHEBmIZB6x5klzlpnPbInBK2sRcWDHQ4jLCQvQpIU5JMGGDaMSXvELVaiHnT/Ocw+ncVN2Ryu6Yi72Ou0UonhifUEQT1QQPJ7KPXqaJLuUkjYOYKhURhMBvcVytMiHPyJIEMahonAC41EWHrQUh49iPUlEWBxb5HeYOPIo6hC2Q+FRLGk/OotjEx4NjKg9FOWVqObzj3bQCTY0Btd2ggyxJ4RJC5I/YVPOqkll8dq03oHlKyb2dDOsKdy5riVgNu1Mzart99gnS+i6MdrR2bOnQHj7xk1uIjt5dommMRxJU21uPgCnffdCxqS/biE0cFxcpIdlR0pqIHSRRLuY+JNAZ5c/yVdZH3SEwOqIq3ADcy2UA2rk5Zdf3trYcXDHT+p4Ro5LfY5MLaS3C3yMzlRcVEGeY5jrgXZ3kJDj3yCMJQMwu+7CMBAO4L927RqbcmTMwTp4ywCaQJcZmMwxDshAkzCSgbD5atrWUCZYvM4UtSBCjweAdLM6u6BXgoKQWu81ApIUGBLuWVCzC1zkkcu1t3cZokAVlQIqYnkyiN9dX+WTg3yFBhquvH+FXb1T8zModDCSdqilBCbK6QE6ls9EcHlIIM9abMV5xp4XuQNMInbdiLLqChQRgcTCEBaguWuCFSgmEJwa6bc6yHC30QY+nZV1vsmyaDMKvTBBvSs7qx4D40aZ8MBZVNMBG4If4TRFGszjrY9mjhuHAhUoaRfM+4DHGalxkKnfBUZJzLmSh04udfEQQPHwpCo9BIQA8CQLPLlckUpnAMBaajmVp/XdeP8ufKCPAIZBLLXP+hTGSpBB30rjVXs0d/b8WVoonAEzZYCT7Baw/kzFgdmzpsmTdnNj9bXXXtt6aZ+8JvkKbRAYF0gtHU5mSLu5swm2L33tBZK02rtMNf/u7/4OwtqdIaKYL5Up3TCtOwo7YdskXwJ5cscCG1oXS1qennn8cWpwPaWzWWsTxLnFKpSzjBIB7H5x2PyQCjFsmyNpCCRynivVYBeyHgGTxMGAdMNn1tahhB0ykriBkqONP0kex+BRzhmYZqpvCm6QgSTghwB47AR7EkB5pcanaWI+EPL1YAqLuWW2wAaRHPWoVexIVdrgQomonZAPU49j8/CHCLDacsA/ZHTStYs7ScdZq4hIPNDvT0fJ8ygfCIRCah/Bg4cMt3kiWiSk4F5MhBCNijxwFweiaIYgsulx5MrFzq4hk3BGnYs56p2BH5KMYgGSjNASfLM0cgSqfu34DofkkGrgWbCOMiUWgfaMAAM5ackO562GVwKJ8kAPh2xTp8FAiNJ57p6vIF2VmKJTFjbAFkLFyQEPThyxKlGOcynB3cSBZBguIInl6ZijJwQQSHLwsInLK8np7K699St6WxC5yPFkBO60wfzHHrkMnwmk1fTb+niDT63gHOGwjn4NJUBxwExeR/ONCHAPmN0BSTMhFStlFMiFjid/4qitn3oSIKPSGIBMccndawQaQEKxAHIJgRDkAa3OE3ubzc3tMGsT55AxgAX9gepLIBENNgCQJoQz9fGQD4mXnnyc9gIMucS5bam0WQIwaFRfZsxIHXmNW7iQexkg1PjM2+RdtNiVK3v7NGcRUCgkKjW1ytm5GQpYSsngjN1c6uhGWieq5yoVjck7DbBREeApcC+eNSivXMLBr8zMkbUYYyuhBIgS/psLICxUpMBf0S/W+KtCQpojYALBFuUVhbuHWHB7QkKAjMsh4aAkLWZylMUXnSJg4ONpPdwryv2gUi5h3fkrWSgwdECCJHwLfo+GHALg1RJOg50SkHtaYsmOV6fE4Xkei9lp8CieohDjOjtVE8E7HrhB/Xo9RktwTgSQOHEvrBEwsMBDrFPlAFFZIwpNtByHWA0GQnBGvnhFQoBxnpZXAnkFxqPI0JArIyw/PDbAGP4YQCAtIYDwOIvAFgLq19Hy9EBI8hBeoUukGHwczIHpTD/1qU/dfuOalUDkOQYUPIzhiB3pKBJy8corbzASeP7557GGXl27xwkqkgAs5uPChGQUIXFU/jw23KmKg4ETllJlojegJR4vP9+BQB8yOqUV80EIRixraw84exHIj62WAAZh4EF92fYFNaK0eg3dfpMRhwpoLvhROQKvysQbC9ncac/Y5snLT6APuzq9pp4M7UH189SSfdQkQz440qNPiORWXsZIdLsoNF7p+/ZbOjWIjsXt7dMRUxGSQzywiHvHWOXoDLt8YYXCOdNICACpGCwxouvvc/uDhr48H8a4YIlBNWp1FtWTmyGHtKo6Xfs6QASGB4Z49vFAT0gIbVlpjaOuBQkRt20Hht0xRdmNbCw3E2xJaPFGj1lPx7nnS9IYBFleXloNvKKsvZzTVxvR++s00ImLPaOoyOPcAMRzDGC9FCaCriAO0WbJXV1quce3YIwBiYW2vuWNnXI/l2mNdLR/q9HBXKbW1bmDE21VxH5OVbupoUVqPqmDZoNck/CdVGphffUM1stUfytRa7eGGKJwyEMfbtbNizTLKjYrA13nyw1xne01dlWw9uh3++mi6GEYyFJWe9ylD9wftPf2u/oia7+dqvClHWSWm4GHCxhbtjqf7qcrqeIc5gTYh2RZl9RlwdDT0clpGdDoqebMqj5z1NRsV/iLNuv3m52xZcN5A9PWD9Meb2a2IcXHcIlll5eni7JvtrLNQtmd20Dgd7lhTiF4U45sfUlawKMBCHY/BHN0FQFQlsSRl/0qC7oOAYey5ziFypqlQQZiSeBRBwNIDhHkJg/26Tmpob5ISYx8F6uY7fPFmpEm4XPnzzIY4XgCY1aoc71zBC10Qoj1BE6Y7/cGmsIYJ8UqDzkGyaNZ6RF0IstiHThKQlLuxQMP21UkYl+t3+N6alvWYXcXhmkdUVN22DFOYsObaPYbkM3J1Uwtl+4XRvv9HUx/R/vrb95nCP7Eyml6BZaQ4Bs7RNRTOa1rLlKJCpmm+zr+luKDrmyXaH8AvHgwV/eOXDUIKdRItljOlqq5RJVUfDWe8Hmsj9RNyiW4k5Gn2e2yAyLlhfk0ZAqtdDTw0tmyu9cgnikJ/63UZIqFQnA8gWs3gMSYjcfC3JJqra8lsF5r2K9vd3caW3Tqs1zBNXriXJXp5d54BkZIfrx6ApzKWTli1zYar63VOe+TzuooCnZW0Ak0uWcnOb7/milqyPilL3zpu9/9Lhcz/cmf/MmLL/4Icrh/pHZi+cHW9oN6nbk06pw0YBDzDztJKPKv4ljNI/n4tbsieVe5ocWajFJSjTw5RCDGmagbHxjLaj7Ag3tIOEShrgg6uYDMhsjaUqfWVFbyspk2wpBOFbP5+RmGuHY1h8keTAMtqGQENl1oFjHKPdjmdj0cDNcUcdDBHyuplSeMMjVgBhYKoe8KI1STMlxXnVqYziWELuIXJKme5BRrWaiRmt+grE0Fu5cGYKwz3WVfVLB0VrNKFLlgzcjeXWfL682fVmLh6mt+s85r7SBO0RaXEIfh6YU9CPxRb0pyMP647ARBXccBnZ/xkNDvtIVv/LpJgDycW6FyUumFpdzyiUapzEiqntO9q43xuE8z59ZcNRnpRpQ4fpaeIQ91wqfXOM126+b1zGRQKWIjtK864ubNVJbbdFjHWZybJ4dBp8kgb+aZJ+hJQcPAC7sflnte/9WY5RVw0gO6fAb0RbQF7wd+nJlwaHFx+d799atXrzNzlKi4LB2ADV4QZ1JRRp6UAoUJP1UcWpO1GpI6WgBpazTOlZMnGdgxPWat5Ma1G+A3JEQKZ8RnF1EFucir06FSND3odQdsDAUNThDBuEKUhprKE1lk8HAy4iHul0LQsW4wsyedQqeVajWGm1xgxxoeuZZrNdJSNK5yIgmb4IA/2Ni5fuPOQqV09uwktXzmZGl22LhXqNXKWBxn09WyrucqJNku5/OAGsnAIULwiD8qCF2DuOSOcJFnfOZGCQJJQWGLuSJ9x9bmFl8VGM+Xi0tzNFoScldyr995/533OOo4szDHuuDczNygN0i2+UpYanPtPsvEZ554hCNbWNr3duu5UjHBdRxldkAnObtXGYVCJrLP0lNtC0vn5GDU39nP6/SNNG3arwVLjYuVcvXkUpIr8guzT60siPNQy9pfv7+5von1aKo31jZJS7MoUrFFqQM5CEMq06dvGrHHjo5V2SkpDjWqp3EjUKAH9T8SZWTpZn+c+O79nDNKYYGDkfYFOSiiHWFLZTo8jOWXFiVi6euzfDNhxBCTM1LtQZ/TdwghVgqQQtdFrYAKCkvpQn8E1T1GBZgAd7P9ekETxWyqPOoM71//sPf8c8v52Qtz1ZvzlfMn5krs9FIf3b1qIfnc5QtLnGQf9R/cvXVr9WamlB7lNEZdXKrWZgs6dqfWMeIUwIkTS+IB1nX8pfrJ9GChnHzi0sqjF/IvPHvyyv07L7/88q29emOyM0hmk/lCKZtnMJDq95nGvPniL2jj3/u93zm/dJ72nmlmf956lesyP/fNr8oKvqQ7Ird6W6+88srbWzfJnZXIcqKyxr0ciclmtrc3zL28vXe2NPPUwgq6KNPmjjDWXrfr3e4ae935HJfPk6o7yg4SheyYXZZUERtYqCzVgHv96jUWodY6k1G+inkScxVGOLRskhzrUFa0Kda/WINmhsbIkBAaHf3psfAuF9SzxUpOImfy4+EKs7UCzJ+1EYJdFbVoSkQdHWOjoMtkyjG9K9+6POs1sJLo7jWuNxrg4TpkLhWhQeH4ABrfzRB2iYUpHb3gzJbwqP5EpBEvk1DBHOuQemw5O90WbW2iL7XqABJuWpQgWaSjrKRTXLxSU/PzS6dPn0Oxs2iFGFBqCCQcc3vXwKwjs/TwwQcfqEkmM91uhx7Bsdj0IskUmCXISkVXvlBYZtcXL19aXjnhpqBssGmQA2P5o9XoUy55knTazb36TqmImaluHGJQZOMRGkVQOzQcGjsZRWQTQr/HxBx17RVNjVM6HARHBRMPTGPjwZGKKEJ0ukWqgqF4sHIHIv1ZShACDJg3WDxefPcgFXgANJR6WCK9WjhtXOoXvyTELvSAG7361s133+L+IeaaaBqdRtZokCXvJJfvf+lLX/r+97+fy+jj9lxWp5GzrW5ACVkzEWCRolIttTtNhA+94ZnyJDbM16kInsrdSuoAWB9t79a36o0WVRoKHR74gSWfrc0ooVAZAqSIWMbAcLU2X1xeZouqhD0VOlZrUkBK4tB0fBZV38vtj5PbO41mS5xXxuaUUeDVz4GWFgLwa+xSviDsJCbrG/WFuc1LlzgSmFdyndhWz47XyiS9j8eG5XxWgPkW4/JkMV9ghYaZqWCSCQzExCKxh6PJbb7UevMmG+2yRTl1qsY+HGImtYd9/VD21PZNI27hb925t/HnO3B9WCukWdv6ymOnQQiPuad6LHtncqfVMjzXmJl6UAVltDnKK47s3MNTEm4hUO6CpOZOS6aN8xeMb4mUE7XusSe9Ep0I51OZRaABYQ7Sz1NswnabJ1oXdeQhptFA7hoJVGIOb9DMRQGDYZHF88GEfQ/DPX1otmU4hMeDJRIIhSBhn7KzCHASTOmmiQ/6BGDaiXJPoSijpEHzGh+1Oga4YqlVpzjxKio+HsknTyPEIaAiAqABhGoBbafU1rMjmGgkDTdIKrbDMGvCAlAxtADdHegLE9j+hnKuT3MPuYuXPRnPVqyDSPIGCVj0pyJonIDBP+XgjSaosvaGfJaMclFiuk7+RkUCAtwQozVlIxt9YLWn3p8JtiUAJ8UQHyQplgV+L2b0aqUnrSe3Nz145UM+NqKgdkz2FOxtxOewhlCtWH2WqpASgZynKVIhZJBHy8FojKJzbOv8I5dS797p97r5LFNWbtXSORiuFLb8SEpakcd23Ppm/aWf/Ji7vC6eP/vNb36TuzvY3AJYlYvohByT8BznQEIB6TZVTJM3lwowRODAoMapMoUch0iUJCYP+Pjvj3/M6vOlyxfma/qA0tmzp7HqgB5ap9AmtbTnfQcHbfCzQkJGoFS8LUOjBFzZRrkf9YiyZIKTMGvrO7nsLeYXpxZPkkuj14RIK4hqk9qmWgSLXPjMN0a+FTyoYgh54YXPsulI7gxdiIJICGPr98HaOjiL8xk2C/fQWWjOZEFLdthX8l26+6uv7jeeeu5T9PKJSpERcsrO7XWyieKJxebONt0ui2JYtzNmVNojQ4ZgM1ZEm4s8SLqH2DOoeI+NP1UMNbE4cCydGDV1DumYzUKc8sfUgbVVUFF463JMIkhtksqvKsxh7Aluy1c5RAQc8tBi47GGTFXyCZ2wGXiEloTxssb9H4ETLkliGDja4ruMi9jntG+Lbze6dJkAUN+oG0NiGicjPYjc8xzlclhrFgYTTiOSkNEYkza4xJCWVDRMRIQbDxTVaCAoo3YbVjHFgGz6JPFTRy38ikbmSvpWO6N2hKOyPM9eDQBOAAO4mYIO1bI2DCXKWp6gDo/lxkeX2jQeICqL+8kaH90enkNpDf+B2jG2B/1BiAAl5QnVkuG/ujdzABt8gNUDecFzKCMPjAPH/fEkTi0Y8MBJexUyCsEwDgsKQjQqqtVgHUiKBdmQEggMbNSAwCTQEooiQy6S8PGgyoXNGOyxBhAQHIQcHBwAEDkA4gvQUXjcI5AUY/FuaaHERB0icRJH3EHxhVo6NnQQ2gTBG7dEPMAYg7y9aR+eTqi8jMsp+0JZksPY3tAIG8DkFekNIGl7JHf5YVzlkIDR64lFVkolMQdk2teNqWHhsYHLUJa56pJCUnmN+xUbOoVbsWy0onPBxLpGJhE0cxMH+fYHOgTX5JNLw2G1Ooe5xH5bMia6zDmdPB03aD2cg8AXL67gJ4RapgvB4xnSmj7zmc8wIwIz888f/OAHV668x/4nOAGBUUyhMb0hbVDskOaH/QJJ/lGsElrDsfAgWPXFErdRSVuAJJ5UDhWUt++VJyaycrKemo5dlABAYrQo2HiFJ3wMzZxqCuetyTMgHA9JohVfXoE59ASfB3qq6BknlcDodVoqAyVtFOV+Xh0h/eaxmKMspmiNKscTiECEBI+TTX9sJYon/wg/LDoE7/gjauNpg6ytLHGaFW69FZ6gUEc8cTz/Wf3HUk6OlPQfnG9YWMkn4sWCS94WXBwh4ofm6WuaP3UkgSuCt16MVWZayuqtO0BYlM2jMEPLZOihnnj8UeQZdcR4bmX5BDVCdwla+lBUCp/uBY+vOwBDCFHTnD6BD1tX1jiuXr1Ka7KWpDSSpkNiaoG0ZppDWOSpSMfzgckYQUBnscQX7WcwrkbrMgxtNtumDwVLRi6l8YTuD2iwaPy0R5gANu7Z58krYBBAqY8QeBTZMSEUE0UBBvS81nNnZu6srXFgEO5VajUCY2mUAywl30Q7wULP+q2rdA0XT8k2+fxyBRpyhRzKLTlsUOTixL9GEDRncclIFKmSPHuE2P3FOckTxU0uQDL8ZUeQTB9//PFytTqwEStMZ3gNbVoMYtjTapGcRMDfuX37+rWr6LHlS+cYLm/evA0G5mZAPvLlFxyGPJWFKTeSMHUjhDETOhnlL0rCJunUURbyunn9Osu4KO1iufhgbQ3ylk6cOHXhwqnz5zkz2sC+5VcfkJAk4ETq8Af9uOUWtXrPXU/HHlSwv0yfYjIwxjE1D6N5Gm0+ERrmQgD5SrkcccB4X0Djww9tiBBPAh3WlbwvohKO6ww7dGHIgPCb3oMDfEMQ0YVLmZOLGhnOzLBQQriWo3o9ZIC5FpxnMx7xptYQp2RBy3zUHTXoV47AdjIFOWhx4McRCx5W0jt9zXZAi8VTda+n2drNe9YSRQZ0UkF8oYsx7Re++jk2wrn9AwmkUh577LGvfPnb1PWN21ep9/mV+e9973uL99778Y9/vNPYpqRDvudkWfNEBdF3F+ttqJ10tKi9NeS+Zs749iV1WIXaQI4cAcZZZyXGYmH0zjtrMKGbrTo9FusNP6hPZyml8Cg448WkUNBPWlaiaSMs2wGpwj9EAByPw0T+MInqGmeM50Nq0wugHZIo79n91ZuSp/XC+DzI8/erQh578lFKvbuzR9UwwBv6ASnKDnkU3Qvnr440ejr9xtsoTB7lFAZYa/cXysupVih0nMcWH9ZFXAEAkaPSZ2cXqGWUP7IHGwlnxYa6xv4XxoKQVHQ3MBYjOGxFmcxHOWrbwxQ1ks/IHWGDJyy7I6KEI/CBWrMEYOOmQLwwFrQAE8tOJ6N6CZIsEGW5T+sgLYMJ4CEGyKgs+FljI9yrBjAnz+nhSRSOcBAS5bHgxEEMT2DAhgOMWPe4TnBgQyAM1BpPkgCjKFbtQt7xiuPVE3ruvAKsEDYqjABQIdiwlFIprWUKStLSoOD5H/7hH2LX1mk1aXGvvfYG5xJARfM/dXqFJwukQK4/WKXXiCjxXDxHf0Y0eOl4ugcWfXhNN0fTDEW48mdgpqcmdlojEAJe49JHRsDQ9k+cWKDSuZYJ/G4moCLYST768f2mTjpu7radBoTOBrz+9omeniksAppbAqAZzUaPcG5liQy1myrNrHDzBBUnvtMbZmRM5tlQ0RBGIB5CkNJWs8MJnld/AT936WYfe+zsY088yvADMNogU2KlNTHjO6oEFu0cGIwieb3b+9GPftS4Mc9nTi+eqHkuwDiTbWlRhEm6gkF+MOIC0l3Qop04FUGZhW+Hf8Gs+NDhV9lDF48ChDIaYDCnc5I8KZCeCKmk3hma8YxQheipaxGPC1FpRhzHEwEA436eDgyY00PIIcIMpZWUCCixtNNSWXbIFQktryAmwuMecgGGJzAuFYQ75gh/9GpJ9AYwdYHZkyOx/AMoQnC8oJrUWRRk1EUIT09IWrIzKG0Tu8coDDBE2DyKdkMIqIJomcfqnEQQG1LruRDoFRu9RmAWJRoM/5QbHu6BMJtXz8g9IXDwSyCOl4gY/IRESSBMWZjOYZnewx0e9vLJREqKZqDfRw+TEFUMjGPg24vyB7glkM1Okyfl/eUvf3n39k2WobmRA/3AeF7YQpbG6fEc408wTMmNCSEwXhcILcqfcyDxVHE/+GkfhGQSGVTr1vYGQ5SLj1xEW77wwgu0+g8/RN/u+/c8yCFK683Bhx8QjDOx4cMSyORhHRilcg+Fg5d3765C/+VzFxhj9LaC8RshDhN5juVAFAurIZVXCMBPHXlnh95D4yCoGxvrwtBoUTUc5wPAGw6tkB6ZD4TQX6RlZKBFOQpF7sBjcsfMbtDQ0clDxEevwQI08B4UEeopfEjtsVr8NbD4UwU1seDXyqNhQYRdkfZCILHSr4pWGP/xQi9PZ1fcrEBQBmCpJQdB7qaYqBoDEI209DikhU9pcFocm6M69hkBkI8DeAjPiP4oYRAUvAc1HcUGlWrJfHEQHqIj+Kci04HxAXTfnRvrhuVqPpvI1+qDIaOu3U6bql3qa1Gvm7OOx45MtvqqpqW5AhdUbu41Z27dRfsyxerkNTBKZgqYu+5s7ZN21NwosnSYtRtbbMDENReM/4ZYuZJ9SpOQGtYlycLW5j6Hm3t17v7rnymUELineomTfLd9q0lzK1ixUtz7hPLtq3Vh44y06Q5dSmEdc1oGQKNzPdVCzsw7/H5Ya4kJbnUkXBfahCyUfKimxQYGWaFIunpK8nFuYbYZmGGYMhYMjhNOChu8xGkQbgY1SqXtDJm/hg0PMPzQB87Ahcu4osBiY5YiIUzsV1kgsWbFww2J+HbY1R5POlmVq1fKMO/iKBT8qa0s5uZnWIUrcyc3+5nWV5EFW0QuToTEEB/wAhDFqmjWiJw8+RUAufZ0b6xA09CDPm+ztBD+0GMk13pvMlFbqiTz42RnQ+CqQqYEdqU/zIVXJvgt7LnZXp3J80X49rhHgWvZIiu1I7ZqiRoVWcYcddq5Ue7OoJVtrS/MzjBMLw20BrRcKQtzZ4Bkjicl1j651wkeUg6kTndoI0bszTJWV+cIXeKVXwJESRlgDu1+ZJY7cQzftPhhYsLuLSHc1+O8wu9HhBAHQpy5WaPfbsO0xmacJFI7syJLMsO+CpOUEqcHcBhC5VML6dbluflfXNOXsrupBTjNB2vFcbu+IxAT9l1FkawY7t7dQqtybTt9HoVC1fpyVCFfymULXD/59lvv5XNl2vKZs+fXH2wytO4ORrOLS7uNZm8k2HTSuzHXMw8VDCPYe1CHtDLYY9hnd5upjibPWD3QYXBIH/5zBSEhWCRQU8wmWJ/RjjKjeruxC9aTGkWgslMjWW5m05QYCqh8jf2NTVBIIE8Sul95mgSGURZrISzHKdbg3eNPq3QF81+Sd9AxcIgC4pHW9IO8wKDsJKIS3wgeD3WqEkB22KLxuzek0PCzhWHJFGi6lyV4L6PgJZhTMgzwwENiZfvhHgrXDLkEly6IQKaqPJFTPY0qPGCENlhnUeQrEnjhz1WYb545/SB8CA0fJRVBUcF7vPvItMcnORAar5EDEVbqeIhrJ8wqCBxQFI7jLC0Xlpf7s3Ncv7qX083y21ycOeyPkpI01/COgVaJyvbOv7G3v766luBeeAyiR7qZNE91ThL5dOncpfPLC7NMO2cqVZjGmrOUvl0nxUUxoGL0w/CIKTE1wDCOXuyQtHh2VmvWa5hMDHp9jW7tCHappIP/GA+hJ4IJrcuNpwyfEkYRrCaM82AJCZVrzkNkEMM2ZCmPVsQwgUDWMRmEjbrcE5oa6+IpjRrJSAZLaAepXiVVeCxfDyTcRqJag0bb2DBOE+NP4iDqWDB0KthYXsHwCll+++0r27s7cHZpfo4krGfAYU+ISiDH9qiXS+c6eS3nDUeZ7frevfpOpbL12NkZPrpyfnmOgyeFEUouqa0sTGzVQOwucLBgWayJkErnjeIQVbx626boZW6dG+l2b1ZGWLwrzc5gjxcUYTDM8d2m/nB/e7fClQut1t7GFkUYNTv3Prwx7PYev/RIbr+3dvv2sN994ZlPYTDD7cB333r/0aeeGqV7UM7VA9bmKYIMn6Eq05307m5k2gMasKyhIdoy4/MF6XwuM0iMmr1hvTFIF4vlfuPmKgthiCTXyLIisHzxfJ8VbBgkQyTpVUzVVRZdT2U9h1CpZGaJieHY1J/TnJTsFIKHJ+ThGWMajH625us3Wbq2kkGTOWAYTZHA6YzLgY2OggJwXg3wVDHH8QGOd7OU0evDIboMkoIKCskOHGqC/PHZDhpvMptjtYWzUXQWfLYOdZfojbG672Cwt1/PnMrOzcxj88gJ8VZHFqmN5u7JU0v5So4FH76jfe3Wta379/km4IlTuYtf+MLJag2rV2hFfsad/sbm5rvvXG03+VogdotFHXwYJDCLQWYqM7MsGA05bzVuN4f9RCE9TPV7kw6VlOYywUEvibU5J8+bW5zsAyNNCIuoxy9f+uqXvthqtv7kj//4F+++g/TWVqpsAH/90vMnPlP6X3/yQ+poUCmwqCrtlCr2inlOIL3/3nu0+nN2Z3SbxZ1+GXml6KNkboTl9iCBPSZLjOyHtZI6kXplo/GzD+5stphWlgaZHFfG0oNoN1YMRLUjRBqWeCXCc/f4hNlfIQwpRSmxNMa6PItQwFtlqtKjJB4SewY1HgcAOaujHLztd3sMAMja5CEQBktromM+pFJ9nkRFIiqHrCJdJkuMZlBH7755hYk6H03K1qpsbrHBw5KgEk3RkMIl0VSVsMScRNfpPJDAslXGkGeLjMxohgyZKrVKx65Bc7OYENG0pM6YUB9wkULvJz/5CQtzl85fRkIydgidV3hIxr5FBKsZgDHVR12w/sI8X8rfhgLGLsY6Gnn2UVu5xKOPPvLk00/BdvQb9QIAjletaPO5Tlt67vRb8IFYu1d0BSIxwQcsWiNWjdupPhIC5pXoT6LAzK39pGK67T2FS4h6JVscBBUE8+TVZ+mm0jVmIMTDoZ9RHWIVqwnEzEHwUCBkE0FEAckmJmvnVDw5GHBOjyfnlSjklt4NHEo7GfKx0NsfvMOdJsgyNNOhgh1ILyZl54sFr/z053Rbsqpr7AFC78BXB5948jFkBp5Qs+9eeRs5JCFF8FycxDB3ssVRLjUWC5TOQQ1dvXa90elv7e6jkah09YZoOk2LJTaE8GdU66k0jLdsrxHz0NnZylytLKNbG3eB3/Zi1BK59Bs676zK+JFkrIGA9tC6R4TZifuIp3VZIrA/HGzs7hXWN04sLeShgBvi4RtfNFAta/QLS8Vzth9sUk8IfFDlUo+qVuqIfXEs67v379xlE/fevd2LF5ef/fTzbOA19uoMRTg+n+JjTeSm4QHoNRdCmLgjB2z5chH8KKhMvvrKrb3361e/+8IjLEP363cG/cE8X8fVVNraYNJFyyi0NVMXQtiHEIAWetALwm+G9iYh2jRlNEIl6I9w1Jp0h/oa1Yc59WJs7VMWaklX9aoHESbVrOb4QPVod7K6xKs5Gxyxap3Qk3InzyDLQ1s3g24v39WsuR7udIt7ZK1a0xw00iaEaHwEIpF/wCkJGYeOV7yEROEKMQAC3YWwUMZUVHkZsANqDI8D0j2HngIiARTGHK/OXo8lxhAGGEBlsWpeh9AavBCxZ63LVOgTyFmijoeqkEEQsYxHeRLE2g4ay5EQxazYdCytBTD9ieeMH3p8q0KnMGA+3WuijzTBvmRfZ6To54RTp9D4cR2NTrDVZHjpRDr9ZISzA3djjXBi5SKchRBjhrC4i1JRXtQbhLgOB1gApo0pV1RqPDhdvqrOUyDQD11kSXg3U0Db//DdD9544431PUZukK2lJc6WCVS16onwogQwui0RTHOjOja2dn74ty9++StfXD65gprixCG6XVLEwElN9fjKVdaoF+eJcUcZmVO4Je8NB/zZ0X1TZyFA/NdYxUhvmMql9pqN+lXK0UBnViu10ye4kWyJ41CsTbdbAw4mi1ciR+wlC55e6eZnIsPCgtp+5AAJKFR1SCoix4LKzdtrvGL3NleeRWmP7doBqT/1+FKGpPGykEvUmMgLEMsxxYpNqVqh4VNM8cq2zeAbPQIfEmMb8tTKCtuNWS7e1vZ1E4XB4A4yIEYYBsOr717J5p6lox/ySRI2BqA/m65wDxvfrtutk3tZcoNTYf2beaYrOMselGZapjBE4GFRg1iPOgigKGg1YPx6PQDAuzkCTawlcDiepOIyer2YvV6EnHBAIiRBuOCCQELEysCpG4ii3OP0WLAXW16vYws85uG5EHHIY/QdgAd5BOMRsewOQDoYPMQDzFEwCxGFrB8xrK8lM9Tv2n6DcJQC4fR3USom2PR2Q2QK+2XbF232mP2hsmQY0U3p89C0f4pJJZMqzN0wGA08wEk4Qy48GJySCzhJSCokOGIRWXi+POnjEcp2XwviOsQKBmMqHRGxo2QZhIFytsbsbRm1ByQPJwO/XsxNQ2KvlMNzjGDinHd/KI0CcQpRPWBjZKVmE44IiQVVHBshninPeLhBiZ/CeMjZ4QjyPQQDBvJi7Es4fiYY88yWufrZljUjHB6rurDhHU8ngHBgRAZ9tIgxsQyL6mCe1p8AB1yLUB/0OMJ4EXxJiGElUTjhzOjrpadPPMncntRQFZzuixgq7qOqVAfc31cp6fjhKN/HoJ61VIQTJKSCYFwhr+UkOI/wrK21ZODDNzq4KCbHvKAyV9BSER+id2BgVDXmCDFyxqzhQxglsygxn1f1IialrEIrLVioV1vQwd4BGA5rOx6eKGQcx8LATwdNco7PK8g4SWqHBIMOqx5xADKTQeyFajzGkoti9jrTQQYIIySRB2BG3vQr2TldVcHEBvqtIELCEJPsKMXf/M3f8AoACb3sTOihk76Ba/sZT8XImfodD8/IAxIgmfE6kbzSHyjQ2iAVRCsu5Tl3zm08QimZMgcGsnM/BJjps01B0Le2qMRFf+JMWEYVMORYhMFLHZX90KuDfezTi3AILMJ5KJxXld0aiPu9Nj0kqkWSO1oYGacqKlFUrqP4oxBlFLpjiQQggohy9BQe49XoGyEhJv1GnCcVJBHiRKpsMNw0MB4XjHjC/6L9lJEt9PziImLJze5eFhY6+Y77oXJRdkKcJ2gSlmbu3LhB22EzCnaN/QsBNq9jDZdGBDBLDKwr0FqZyLEGgZJhfMlKAtPj7qBFt4hpG/3mxsbWobyOfaW6YT7tdHF5iTXier2J9RzLAg4sFXgkmbcPNaVw4BGBqFJDSSEW/QZ5NHP4wHATO46GqVxfWNT034B5orOU19HMItShB0ULZpgJTjwmrvxqAcxB8IewH/+LDqGjZ/YLJ9fXN1BoaEKSEc6TKSi0udw6oyg1Ie4IIa/BcIBF6ofdB6Sd6V/E3JUqUNqwj4uIIJVTG4VEHtFvPaCHUNGUbtIZoyQJYUBMp8PCDce6HABLsaX8DGtMV955h7RUH/CQQRacHGQFanuDFbQ2hxExEaSRsWnBHQucIMOQDDzYjpIqx3qanInfZILhJyf29R46i9XNv3ggiQqHfsBgAuJ348aNNndUmJU94c4x75MiFDFsHnZMBUcwkYf83e+0uJYI0h+DICQ3/CUtfPA399ApEMgZIAIpPg6PZwHZAbDZcmokjijaGSm6NqSXO11pjwQiBuAxPqRZP6WWeQIAKkSI6y8wuiF2r9vlLuZvfvObMKrT193HTg+dDEiYSbIZwMqLY4OT6EhgqGgQLp7QJwQJxI9kAUMUkuldM+G2sar7EwgBGw7kzHPQNn/zN3/5s5/9LFktY/fUHO2TUWtrg9sDdnLDP/uzPwMVOOlBwPbO+x9KezT1+SbWsxH+5IJ6cEonhCMpavQ2fuch0oW7fv0ORE5SSwSSEBhnoAMR4sDHPkEFPE8cA2z4xgDD2UumJHFUPH1LMkLiaEnlIYYAv+SQiqMsPOOkRgkjD/lCL8RGIXgChBZGpjCW1od22trdnV+osbxLVc7NjdGig65ZdjDGCEgwNMhlSFIc7TF+srCEmpDjSSUYc1J9qMSllRPUdQND0I4+/mEcmI6y4qiIgmAS0uIqxSpRXCHnRYADrJtQO1QooqhOx6y3CHnxxReRWyg1zKoybk6jlklOdlF9wUCvApIASdNATjhgTXYb23VUCq+uEokiIfwkCUioR/w4J4+nO6yK8fC1FdEZzg4A5pVMIYnkMJxMxXab1RNLFMBeKEdLCK8EkdDD8USOWJKTEEdCCKNMeBwAeABwEbx7ABCMzcK8Ia/du0dj5CQgr6wHksRLBDdAogFqMvnqq6/CDaIsRykBmiENU58RrddhA/e2//SnPwUSgAg/OYKBkDgNhADAk1KBcHNrk9qnV0QlGzyTEYE74VZ3xwgaqSn13By1PYMflkIP9FNBiAEnGHnuco6RL53LDl0ID1KhkKPO1l+OBgch1AbNDuJBzkjj+vUcy75sZMIo+heV2qobD0Vm6diKqRokHJbaTtWEjgzu8eFcbri6e/Muh8G+9KVn2fpNZbJoAyQN5HzAkrToIBLCB8dgAQrh1UPIBbFE/n/0ox+R6rlLi2CGFYcYTkJSQRrhQus8NZIUGsQayMMfyBayFo+H2xQKSlzGYIDEwwXXN1Ot2buSCJ+BJIONxWuwkdzVV0SzF81xOoDSABk8/TfgiYV9zMMRUkz3kDKeQMU/ECCZBBYYoigNryQM0jq7DJ6QiHWRJ475qN9kQGdtPSqOwUOIYsQyrs4CKa0pUpWLO2AI0XTIZnPAEEIUT/lDtA5GOIxVjfBuDQ3kJCQELUHdEWipDuQi9LFclNJcQIH9KHt3B6IcT/B0qjxVgOLgjwMA7cHCZ3PYCIoA/EgF0wHMaX/y/h3MjZN2yefDBJbikIoxBglpR7TKYlqK+qWXfkZXc/nCRTakrdlet06BHTtBHuuUuzeag9FOFZwEOS6gJCjEQdDwDUww3N/u3VtbXV07e+YceoOlC8ZIDGIxdrl75z6Ki90/dBqloI7C1MEvfGBgwoIG7yA0nXBIbANIEW6UkxHaYGlWp3ZagzbUOvERZl6DWrAgz5SnpEIbfNrYRFl5KtZmvdQoHADgKkDMwkCB5ml29enmIGPDRsshnAu4WXC/ePIMYWGhkox8sjPb9B0RJYc8LHBKrK0kYSXE6pxVvSgBx/OjMhjdQQzkxkZRhsSX1TzeAsK1EfE6zEbY2HBL8ik9P4BpH12IxDTK1zP1VFAKdXx3kzNTkHMIRshFfIzXVpY4tVGSuCcq11GPL4rFgeEVWYQh5jFCwgURKQH2moQKKaOCjW/ciWYJg3TEOv18cxuYAnuqXJc2Ga7MlS8t1KiwB0MdnMxiQMZca6AJXpm9FSyV0ifYa91vD5Kd/UZOtTNKaA92B0Odfr+dYaY07CQ0lOczxii/rN0SOEppdSzDVZwyjoA6bKpFybPFWn6QfLTeYTpxdrchGH0Ujk/+Sn9hecpz0JUOtW80s8km3vruFTbOwFfY9tQNQwCKFSqVSZRvM3gIT5w4ovqzOgr2hxVuFxXxXXZj65SdijrksDAKQpSbd2k2uOFD7LoKTYvpwaKVwRkyZYcjgKdi7Qmxpp9lwOSUAxBPi2EQIXyTlSEB7Y/M2mw9TpLdsrQ51nzYkOZnq/PnzvDlCMNAM8bEhI5bZno49ogIwgO8Z8HTPNY7akgPp5xGlStQCshBEKXsLO10SEfNEEJBDA8AWo0UUnvGM+qGG/JAYwbF95RyhdI+x8Axf6cSxG1QCZskiPRqhWxm4B+2M20UWTlb7O92uWW4nei3JYCj9FArthSTJ59FtnxV8e2xPlSy1tid7E8K3RSjw0WswVknKulbJXPFKmxge5ZcciVNjOkqUWp8Zp607EeDLW8r4hlufMaJdRg0UnptyaOOBzZCG/puaiBdUpmUA8blTHLgnnhu/a48rGhbCMpAOCFbRqg2K9Agn9BhMZtp2q3TleT9rz27/NLbWwzvmuMlKJQpAGyKLXkzoKMjYp1kb49VtQ67gqmkDrwkkuIDl2jOn1h59smnWQ154913mRhTWvWL1kGyusXtZ5vbD85dODPqxgd2xv+gXCYJsrgXtVwfqzs0sSwwe2eywKWTI606FzJ0DHxMBgoBJHMloEasPdJ0kQVaKPB8s9ouyxaefJab+wqwC+bo1jC6HYMf2P378EPltUYrD+UKhFYey8jJCyTG7UzZ+rQoJ9mGOECL1YFDppRT4BC66ZubwWNbRaSaAa3Rk9nFJrZvq8Zh4calMK3RIZICyqRx7DW439lKYjk6PnuCW/9wAcFWQErhjc7LG4uS/HipEDGPNZSiR1mLWi+L6XmLczBHQoAQmMozk0SQSYipFQegFiJIx2xPb4+xgF/D+49Je3w2YdNRbMB2/zFwzi3zy/EqntnZhTyHSefn69g+66bZxPZost8bdOw7ARxyJySwxOfeWjbY01rg47js5upac3eblpLXGKbHFiv9HWtHGCYP2nx1ebAws7i/jQUcx+TzNAVuJqbf6fKV+0GbVj5TqTEwunXnNivX8B/nlU52xzq2owDgI0FopyeeeIblBoZNLOlQWdZFHJtIUVQWnR3Oaw0s5BX1cSRjzFapVD/72c8CwDLGB++9t7O1TbiuYLe2GaKW8JCdKS1xFeTx3P01BFYUjr05PqeGIoRN4PdAdTcSp1iVRMke7mHD1JduWVwDLeqElgi4NwTVJVLq0xXbZdQAlbN3Ntfk7LU4kGDZNrE7maxd29QZi7mzz6+g4VneVcHSmK+KPk6tSsaltMQACFUuQaMyyfEGacD6yCTyMNrYTbR72WKhOjebymZQhaGuFAo08ApzixPL5DXCGi2b5XOUuv/usQv0EaMbHdaX52dqIpflynojnU8PGu1cq//O669jhs8dmplzsmrkNjoJ7nYr2xxSILDJiov6sxrmzFOv0ynOVU+kz21fv9FvNVNcydgdTPLJXDYz5M4XbDBLmf12vZFRb8WNzzzp+FnDZYsUZLolEwrtPFbXGIu5NHwbcSCIvjitLzGwK4jr2d2dBd3sPy7oMBKECAa54on2gDm+jEkWYROEt655LOzIgwIRlirmGbTwqQL8bb7jyV6pQ+q2a8Y5khluq2Zro5fGAG/Eul2TjiCTZfkRG4ccve6Qb4Yy4kktzC+V5+f45M1nv/iF0+fPcQszlH/+K1+izeZmKgwISqnCd7/7ne8kkkyuXvzbP3505dyFk6cxHaQ03C/8yLnTG/dun5xfJEe+/wBNSV37VKzX2UC6xrcnxJmSlktmc8vtMS2+POby1n4mzWFQjUc6tXxlJscniVMIBKWopLK1bCbZaa/dvHmiVvvv/tnvc33HTm+PPvevfvHjarn0lWc+fe/E+7uD++TYT2fX13aT+oBJYbmg6dkMY18+8b3Tp0JZUxEfMuN0nv/cOU1/xu5FdpzKcu7++oNWJ10bZGWBmKX3YXfZNb/1mN6bkPyQUyWq+bBKqG1gDVPRAFytXq4ybGCFl0U0RIhSA+bAEQZvC9Fr5AEPWhHnq5m8IlaWj/LCDyYkBZ+1XQsLH+AkF8+IQTLBNrSh91KPBJZOfR+ZoQHminz8NZsqacLJ6T2yE2JJilIF5Q17YYUEzqTRJC0ICInSOJGPnPexAR1eu3oNhcNOP+MW/to9TGjaAJoqc52mnKyHxCOCkf/V++u5dJ5LIZipQwnTKTgAb7GGGWR7naaQIDkM+9mT+MIXvvDWW28BAH80qhGHmcdwrE6puBsUXfeVr3+N+SbjMfgvbhtzqJc2N8oPBqzCs8aaQM3mMMrRFW2AoRhFjTny0ixKq1RoQvyBdYUav8zEOVgmiwEWFlj98++OIGykosbRVwBY2mAVj/IS4lngwYkDEB3u5OFxGPeAxx2BhJAQosRic1MMJhkeaAASQHKHwF67dfWd1xP9JrfRMWYnMQjBgHxRJnCeOXUWGdna05ZbWp/91nCRzTz6u+367vbOJgsKN299yAo1H3ikUBQUNe6WlfASkjzfuI7ycoFhbX3t2o07cJvBMCSp9zRTOpXS/BAu2sWGAw6uLi7OnjnHRUC6Ygv7H6JhbK/LLkWLi0YRg96Qq2+sJYDh4Q78xuaHQ3iMREPdtnE+1Wi2b9y5v3zq7Mr8jNY61AqUDewFIKg4kS4t6zxg5w6xZNZ/5S32TN/hSMbCwsxnP/fC7PwyJez1dZfmMKVTmFaDzIetHs0KiA4AMtU4WfFJ5hAu7ZLRIjK5fDm/00j95WvXOLWArTrnWTvdXjGliz78/LHAJNWiRAa1iKYRBP+JUmDoKJ9xGgKchjCC8IM80spPli+4Y5HU2efQCPhlYZ4o2J0InrGh5yNlQun5+OgoR1FMXMkdBTrkyFGrX9SkyYnRU5NsBhuxiqeBEnLUHSoCAN4QjkLGQ5Blq6ZYGP213oK2g880Nt1QMGEnJMjLeHSIIcQSggPGwdxPOM5CDkTRiMDssTwdmCdtgXEdHSnngkmlNRlqDvUhhd8XzbrinNUNphKMpJUXzrMgOfkj89QxnEv2JukB34Xg5JEMm8UXUgrAnH+zCtkgh5AOUBkSXf1OBswKhTHE7x6nlnCvVAIRJy63hjSbqysfCwyowg+s4CMGqn715jMpzxGCDadIQTJxqWKZ8f9P3vnwp7+61s0VhykOP4s8ooRfzRbaeFUIziiC1uCVc2uZBLf2adeZz+f96u0rj/UG586fqdRm2PthJbqSrXjC+FPEmKQFPPFtpIhB6vkm9CC+52s0OxviOB7ut4MJd1bv3H9wn0/mcvsTZsaPP/YkSwdYE+83tS8rC1NjOGyEB/YnmUR70FmzjGyVGGRhRZ5mxyux2LkRxDjj/toDFDKWAWdzujltffsBGpIxLfhFCFDW+9CHqspMCBF6W0HhOK+WzlgfZ6TIEIWLZFGziVEXieB7WozC9FnV4YiLrRPnWRi7xj5BX02KqpfMQAlu0O7+6pevZz+fphulcyKkRRdeypcvnNu5eze9UyfEBp5Be/fGF5TfuSAIq2Q8Rx1RUQcTwQvMOtFD8AcAQOsFPgQEv20c4AoCGBVJNRE4R8ITx/IKBXXqgCcEgdDz13fHpooCIw+II3/A46AUQSs9lHME7AkhkhETgSYDupSQcFWWV1fIE30kxiTDscEBTkQiSQv5BYZNWVMk23w+raX7OpAS3/Nh7BHhEcbQoReA4ToAGJvMZxAj7qVCEIkHM52SatC+wcog9tSpxexAoyJWn3mSECmjAZOKOiBEEjxCDDUAHebEaq2mx9iCHxiShGVSKroinlEIMLg4czyEJyTxNF0kET6cJoKLog4iNZyBAFha7a8GPYunNYn1rHma84hAbcVyOOwFIfCE4nE6HYIsWLzgcAqlPnnuHAMyJqBwlWGwBqxivNoeqTDJJcR5QX5GoZ6GD1UXhACsnsJc5A84E3TSinZiBBs6AvEyRJymDduOQkwzOCydHJWLCLHrntACdMA0fKDWH87TIiIsKDebSCALlgw9+3PJTqNBtwwIo1hDKBWGi/DggRtY/xLLQIjtE0z0sVbjPDZosNIld3bnmACUCiUgy/71asuWgbDTr6fpJ7Cp7VhJCUMC0TtgdqpBIr+nskUnD4ftJJSCMHUE533lg7k9ISThiXOaebKLR3FwtDJYQAiGVNwklQiWcw0e7oaOescLLjLCj5peXFSLZkULtHi4iPAPv/fP6GaYk2MhUi7mKanboEEZGXF3IwlVko9zjhAoPPTK5AgqOFnMyyIMc2qiNGZQvgGF0A8YjmrETxtUDeZkJ6Jz1Rw2TmmhHG5KefrShrVTqgZs1KbS2nAlog40loUyEk5ntEvLwRZN2ijVIU+QykKhymNBaKRa8w8rJSoLsRHfTc4sURQU9hqUMwaoUkdZEx57OxzlYGB25kVI3HPo1fdWI+TEuosG9LxG+R71EOul1lBCTHsoo46m/S8iBM4gk9X5ebRiwwSJItMpcIGBpN2ueRETYvWBHwcMs32GMogoADTYqLyoHYQXq2E6JmIJB0CSYO203WoTwpUMIMEUGgDwYGBIPH77NkaESR7SRk88CB4rVixYcEIRq8n9/SbxMeqU5KjzIQc5utwewgk8ayvY4aJjGexy7QYhiCPAtu4aiXZAzFH88RCIYXmap1MFeRQN/qAKMlldHwQZhv+A/McxHPLDFpJQQfRWaBJWgtQRmE2f1JpdbE2JgAnKhVozhY9yIGdx3qSXJ7mDzQL6LOdhokhNnfnK0ywEl5IlFgJs54v7saCXCtVWRNxFWcQD8VPjPHfM7I6ysUoOnU4MSbY2NlD79z+4Sc9SmaliQZafKcMdX8vDkBW2Kr/hkJH3oomTtDpXkw0Gt25y+3aztrQAtRiWIJQMOcmL+yKQQPoSSuTEkBG1TNGQpRp7IfRTZhLCtR6wrrxQ5ZDa0K/Mn5+rzc1dXjw7YjlsoAtS+q0eubTruiPPsZEcVKx2kS8GAchbvqYPPrObCpGcvIaNubLOvfGtcG4oHu+3VQXWm/sBJcgCA0VzhPGnOOPCEQ8N/WRNpZDWGQh51CZbPs58f6rUtnpC7UMVN7X7CJNY0BBLcuStWl2GJ7wi2ydOnlb7GvUIuXLlCku0fEiJKIoAfq5jBwPt8dOf/nSJ2oRyLvOAkuXl7/3e7312A9PJ+uyJWcfvT7JghxJub27sgjO7fEF0Wn9EVUI8AFEIVenFIUf80EbtYFv0zDPP4GGX+1vf+ta17TWOHaycPMvSzJ+//iGEATbgxNIkxazvf/jt77OUWZkM+ajgT974Bf0103/1p2bX07cvqfgJADKiRjBXhI09s5r3SiAc4FD+1Qsf6wADHsqJBR6VQem4NgMpYrkeLlEuOIMDzvsXCmWovAGqCnCGR0wABU9brdAF0zgH+Nin6AgVYATsaLU4FUYxRoKqZqcNJyEVkrVHzYJ7XzyPry0HUufsiDAe5/EVMI8BnD8sVTkWXZ2t0WyzBdlboautWg/oMWOYuIEOpIVSWTy57Qfd1e52YJquClHT0F32KFsI9kokEC0BcCQq0C5BSkjC0fMI7ckzugbauc0TAJKwDclWEFSVqrO8YmGuWouUoQ1BCccBT3Y8HQN+r2W9WoF5RTxwJMcPMWQNeQiSvxJFCLFUIng8F57OKH9G9esAVlitMIKE5J4jWce3zwn0VHFU+P2VtFDI3ee0WY4pSPDGwZEIsvDOyTzKH5oBxpGWQDIiU9QaTamYz6Lw37nyJr023AAYMJ5QD5+BU/qHOI5B0O/0mDhJemkT6uRcMEQCq2hSSIcTEwIBVBRtGQdJXhaG0tDZbPTRKvv7MtJiqUjJj2A4jPHgO5kebUzgccIglQLyBD/ZQf98RVtHIa+UmVhMApsBwSWoBdwD4dJ771y5evUmPeHp0/Nck4oW2trh7pERR3ZBi4SDDfFAYuEIRes0tH0Lj8DML7GOyqkGgLojAadO2VDhJMeJYgAAThaK9HTLMk9gwxX3fvQzLNFDoTxrI81mtTZYs0s4pR8gkignVU9HY1qKQvAGYWDAo1KbO5STCLAZcdDbhfVIeBASJvC8wjf9go+nWkToolSgcarCGP3CVZ6eysOjTAAm1qMcDHDFWukAjnL3JMpI1RUoBGELKcfr1YfHoJTW/Tzdg1BR9XAGioTHyhpPBTrCJTBQFRRFBXIM8Sc0Awm8E++v/hRmk2SHN7B4UiePYDlHzpMc6QTIGtBYlOd+qE4CbCb8XogD+P2FlI6HJzMvPY2f0SyMPhcVTWXS3JwJDqNU4TzXQzwh/ohaQmgJ0hUZWelCNpYlO7tbnM9jY5LrL1BcTkaEIUTiwcc8BanLfNT25RXbjy94lNjAorfAAzHXrl1nx+7ypUcZR6HHGLfs7u2wDF3f1QVQyMChNGRNODvOIIy7Q/ihSEpEI3kdkQQhyOcvXmA4zVVsdI5QHk+O30UiXg7KBQEkZHSEZQ8dbq+tG//ooakFrwsfT5KYvgAMjHWRSlIdVZ7UILbnlSyKisuhNMtbmZ9ndNGxBeioyiKqUFfWbmMizRYK0eHal/zuLDRoRfECEBSC8DvVAhSWd1/zlkcVGEAeEgIiKCoQPP2wKu3RwC3EUiFepKJIjoonITxxgjzgnGbXf6DUkmvcxSs1SmtVKzxRrC/uAyAUoi5wzhleoIduzx214s4HImyd0HKdApda4EHOk5XKABE/lCFXJMJjnciiWRz0uFWiVClg2TUeL+cnfS6Y5VhCv3+/t0PIbE+5943ZI5vlrY3KhDTyWhTbtsHlJjv9Kb65kslk09x9CBMYFmFE3GvsJrqDZ8qV8jD9XD/FfsXFXR1wy42arAQkO9zUxZYbA5oRZiwo46qZFpYG3tp5isyQYJnPBDcXUvewKZSciI1h8S2JemqlZ/mGkberFWegcPI/5hzS71X0YIf3cBiJB0fdBJy3EU3YJOCsZWTvwLAw7ZSDihko7Za5HQm50gRn108FMu9Le4CwetizyeqmmJpYr0g2uBWb82PDWrl65mRpYZ6TvULIfkAwoKdZUu3iA3uElolLozB4SbXgSmXwT323j2bUedguVaBQoEhQJrjmEbBQhI7CeADzIzzW+wAgYgwymZNOS3ZsW1xGt5XC1mA7t5Tv9TdJO070sISRjAJkLMIY1HBSmymMivc6OwgOp8NmHz2V2s8PuUiSoXxH1lvDlt0cza3iSiwMez11Gl37rvFgUp5k85OxBqNsrLW4B7aDLHWvb9+AMPQXE49KKc9MlaVpFGU5rcExdz8Cn7cdSC6JpCMp2LYH5tiEMysnBCMl1TXb8eaoOyyUxUfrvM3AmgBZVXMFH0xh8wYi0CeiPLijU7F9jdXH2dQiTyqJHRosARHGudHGVx9b+qu39/DzLUZIxYIMeLOroC2TOepYHXylVEQEdnYbLJRAPVbUM+WZm9du/tlf/OWZ8xdKhfK5M+ffeusNZrDJbB6OFfl2VoaJU317u748twBOWG3YNJ90OcbSHP9A4+o+ogcMljv0AkuzfEObNZZg3oJJtCSLYV0gDqo1GhMsyXBhCZwsySAIM1H8aYzWEW8bMfsd0DQ/tVKJpF+5z1q0s1PNQC0Dxpkthr16VLBH7S8aRZI4bEuRx2MPPG3oGeJRlvzn1e2gyRYeYH0JBvFYCl9N1OFpFcCzPashhftjFgTa8zfnuSuVN0MPNPLcGgY4W8dTIYM0ntLfZJEcKDSzalGc81XhRi2jYgsNc1SvphA7GkER4v2dAQb8lN+dl8gXIlnid+4TZVnTyVgvYaBhXQQJD//YsYWoDz0c+49+99y9dI4MmY+wqqV5SWjXVlMtm+oM57l5bLa/MItaaWVyk3R2Z5JqdFESzLJyfsW7LpZVHQkZu/Fg4mZYBjHba/dpoamJlrF0I6zs/WnpuWplEfjLp87WSgvceoqf2RkVoj5J5ofa5es09mgXIGRRA0er0UZ9H0vPAxUgmjVKQ7mKz3NzS6wUs9bAWI2R1uaDLd8RQMSQIBO0sMyGhjRqMTRROhBd8qSWJkYZN0gA5mqlyIDy9MlTTO9ZCr9/f5W2qVkzlGj9UNIiLamS2o/7rb+wpiyKgXIBd8iIGGBNtFUEBgBQYqstAR44I1I+zsEcFkCZr8IxCg4escUIU72EyeGqIZTqkJ7XGRLNBIiHh4D109gw6SCManFU7A+Sxfzyzz/cmK9Wv3f66Um3n80tDjqcSKFZdQDC3lf6Wbfek7ri+Xhxwjz1XWyG2nnTFcN6M90ZVFbmkzk+4KO1G1gN/Nq9+xxgfPTpJxnI7u/sQn+210c5snaplWvORY1G5595IsdBxPtrd+/eh3ik67nzL/T32lur67N8CvLpR0azJToKFA5XqALQ2dzVtb5W225rw3U6yM+4kChVKsn+aG9nB/yIynCkbfj2A75mxsCrhIlJigvQIKuYSedLMKKULPPV19lMprm5xdZjdij1fvH0I3B46eQCJd3YXptdKbX29zguU+91507OJUvz9+7ezc3kk5VEfrZ2bi711vvvMoUopor0Vr2Wlozz+nhbakHmynAP2bGKkpTqZl50I8YsJoCScBdd8kLi0rJW4+5NWqD0W6vT5PZgzE6JUhsgvc1m/QgGdlY0Kz4E0tEHI+g5M5MWH1/kDq3MUnXx+7/9z86dfWRnv7u+fmtvd/PrX//6bDW7vbP14t/+hJkGnTitKZce0JQwpOAmnD/4wXefeuTJhL70wGVPvZ3tjcb9DZbgWWs/uVSrzFfpj0fJYXWuyMIxCzrd/Z3JMLPb6O482BnvrSMI2HDlMCWfDNm6yWTH1Zn8CubT92Yy6T7XhCRGnVyaG6Jb/H3m80+z4pMrwO7WpNU+szDz3GOP/vVf//VOvX7m3LnKm9XsKCvL735pLl3+zue++8+/8wOVftw/sXDx8Ueee+WVV/7qF3/b2+rNna0SjhaCkl6xzCWn9za237yz28gtEs4IWC3F1TweNTj+q41Qy4wcGIb4HAowHIoCZQEQPReVCCSah1stqCy8AOSLthbJJYkpLdBztJBATKUFaRWtDBihaeGbSjcjXLFTlzkgG2hAa6QRvDxg4BZyPDQ5oQmcw6gqcNDrwYwt8EA2LZuc5NE7Y+BJe68xOz+3uLzIIIGlvc2tHSyDKTJpKa/kH1zOBOGS0a9+5YRD0hc63p2WOEWsXe1ubDZ2dheXl9FInUkPE9regLElxDsGMdr7d4Yt4g9b+7elsj7/+c+jBGDpTl0fxmSvS0n0GW9MdlKoCz791A6/SSXda9wUb0GX1BkLFgR/9fqvMOM6ceokzETY4CTFBOdMbQb7r3qj7etBpAKhFYX9Nvb8tLxFoHFeVQ9/qAte5VITpnSMOpEDNUl6MZlTD5jM97oyb0SP8WrYNBYFFTqcEpFUFWfOEB14kAsJHYbn7Oy8Y/AnCelVsD/2yuXVQgIQpdKFl8FSmmc6Gvbv3L6J6TN9rcQBpij3wIi72dwnO6485g9rQjCQCnTFkrbxZiqlxmT03ntX+Exop93ENoUoYDw/GMCHEd1PIAX0cRE7bRhqMLLibARbngYvqYNTUIusuChJMDW/VoVNhcvkRwZXycTKmcWFEzWGvKhoJEU7sd3RLpdu7Np1Lq4YTeCEw6opejpV8ScIp2Ibjwj9Uay1Sh3BREI4RPLe1Q9zqQkaL5m2r8ikxB+6VPgDv1mnglq6TTbdKOndm3fQkFwLxs7fc889aV9cmKDx0lkMUzJdbrRHuTNOZ0eBUzSoC7Us9irKMCrD3fxkSoNkG0PsRPbEanQKc69UupAq1X52fXuydO8HXzg3KWT8UwOYPHFQTIBhvXgF+cxU6cXg4Bm1Wws4/FBCaYVg95xOnyHfwlOX3t2+3x1xrxEGzHS5o4pkf5hHVQc5MpKToghssS2PnnFpQNhkjJE6sdmtdoGLseZE0cDODWBhRM1jKUSOWGxZ7kocohV5cfHW+xHnNR4FI2H4jQSFgSrJvNCGeQrXrAjNrjauk0amLgTDnbSMJ2xM5CNt1CJF8iQCsJI6MXCAJuNRNGKqCb/PIzTDVhsjX9HFf9O3wuMYBGj57vc7iUK2yAcXALVYKRdUq05SkUwDILoIzoOk7L5cPrPhs1L6B1kWkQnfdep0Wpv1vBEu/GDmfCIz40miR28vtUyZfIAPUtUsywaCNPoghk6Oy6bV2mLNBwAcxYRXASeVQhIpDkTMJb1RrqKKPwcjQG7CJ9R0OHb8zOXZKWTIjyq+utV55b07q/s9BiGM+Fn90tqaRtBimq+NqFRyChFW5SXUIibMlWKWCjnONELkvftrO7t7n/vsC3Qcs9UZ9sDQHFSEMdUqRhgOOKGyQJDjp5ugo8ECWkC8k49nfiCRXiLLokOFZ2FIsXwbq917590rrBhcuKQrqk+eOHViaeX6tZv6Ag0VJReUSGya6GOA8/NFcmfvD10N2YwmgmGC5e60exTKg7bYbHevvPcBNLMBf/78xfn5xd3tLWYl4qFwTlA9E760QbsbcZhbsyec6/m//Iu/5qzbmTPnGGJQXmLJkIuBExhXtFolVqDLZZYh+GVwhQS8+dbbTIkQPETG61eVZdYbr7/+Oov+fHGxhw1mKr/bZ0i/0t7WRRy26AAhYrs+wqNVC2td8sacqjaoGv0oxZGqioEf7/UkARoDCZA6NlMBaB0REFpTksTrVwyjWuCusOiBj5D49JL26sQL5Bh3bOBxcLHkB1GJBsYKEg2zA6KqcFSeOzoevz7CxxCOuhP7VqY3Hm+p/gSG4khrWNF4pUY94fRpyms/P0ZMOWjjzAcMh7/VlkBwthG/NRatiAmt6QXkBg+iRRx85Uk7Ii+OJghmOESgUShMq4odma4sVGpC29XteIQDz2FSnjRSXh3zlLCYzzCLwzBBWlgTbmtpZG/8JA6YWIrA6wmjqMhzCNKReCB+TxVgDoc4Hg6MZx2iUqbx5I7EMeDHE7gjijKCFIDF6hdVx1EY9S5qBYTAGUarXJfGAIRX73jwQAZPGALX8ahWzOHnlycOlqLh8AQhFuxgh55R6Y6W5RCkv8bhwe+vnpYn6gzbB+q6lDEbHCl2a2pWWZAlJR8OIh2hyDU6eUWuKF2yr1Epm1s0CtQU1yGrv4YD7HEUizPj3MaDB3CD7PhEI09bVdZqqnFGC6MkR3p3tzeYDGBaAczJGmI+e6JSZprKYjQhmA0rR9sxcGnU4pM0gzgKA726eSKlznwR7N2yaRLrW1Uw6OdHkNZGjIxg6TZrnzWg04UkUWomV5TrySdPcUceOakebejJmShyRxyAoTMCp99Mzclmcp/n8oF8nisBCP+Lv/gLOhgsxbhVlh1XUlHNPElI2aGBzoAz5CYhkiKoFv1GA4dSAeMDL8AXclktzWPxnM/zqUvlbmUHXmVXs9PYwgqlAR+6kQryqU6WIRyvZh8aaEsll9oxblhiq1bSOVuELt5kDN4D/RkPMGKCyLg/Dh/6LV9rMso/dN4LQAwBEQ0RKjxOpwMA41GCPuIiSI+NahlAD4mnise6P8riCOLjAwJ462ih/HggkzqivCAGo7I7qXjImrTRqwH8l/EIKiLUXRRRNn2zs/QjshCwMlIuuhjGeaxkxUsl1rkcgoUhy2RCA2GSj599KyQfjvDKypczlpaI8NNSGDuRS6Faxk6NKZpaDYfxMxkaGkuBd9aucRyYYRDhWBWBTevbRxw4C8UCC2QrJ84wK/7FL37BsA866S0ZqsbF+1BSojw2qm6qT2WxwjJKxpj64oWz0M8iOKvP7XaH1ujfunhI93IoB+F33jjd0eshODIlaxgLWyKlp/LGWtahJNEr5snwkPEfVhjgIZUXAQB5wrUGbxQEUhxFmRolkOyYV3rBecq0NcQA2/EzsqdD/CefOu86zZGbkIPbJnMRKUc8ACM/7EqyqETXwCuDE15FgPEZDBAPSVjYzSwsVApF7Fl2WrpAk+IgCcvsvNlKDSIxywKiDWMgc2Z2ll4GRYoVTHpxEWyOEACqHgdmd4eI0ohoxJ0T+4SjVxmqQ1KG4yfcSzGjC4tZb0VWBx0dTFyemwVskE+TO4dNsHHjvg6QU03gYTWBTBFmuhn8Ci9zyXImOVTfQW1C4Vy5Wq3V+Fo6a12JdInlq0Snq43Iehuh4ntxEIlQkYvTz6t7CIk7Aj1KvYaveZlNHBMSh1fVWRfPq0Js8q9AU0qUxbmRy2Whs5bPf/e7312aW/rxj398+4O7LJ189oVnASYhljUYK0HgN77xjd/6rd/qd3b/9E//tLlXx/IFS1UGBxJo3GTCKaj7H17j2e5PsGD9zJc/y0JMspyhQqkU1ctksF+v31q7x2frX7lyA8zgd+LxYEdPWVh5ZKzFIVbhtEEp5LGwBcdgYLLdQNIAIzvWNKkFNlrgLWVBQrYaTUSQSuR4k5eOGSkCQNUwR2rl9KU7UEFPp9+RtknIIhvl4MC8gtwlCj+BPN3BCtQONtS80uIhAEeO2vU1Inm60nAO+3gSDNBG0fAQDg+BIQQkxtugigHwdg0YeJBomgYOX0SBaHGVYQSRghDqx0XFiYw/NfB0F6Kg5mnjcRj8qEc4MLeoEwMzNV1dzVVC3U6P+QAjMAZ6RqeUkRKSpdf1QSwxPgURFIRCeVr4zD4dw1EWgsklV8iLjxjHHkTlSUgPYxG/l156iUrkjqMvfvkrcIyNNDZ79vfrACAPYJYwmCrjCQAhtBvHiZ9JMn5MR7GRxwobGYOZPJEETHCyJZ0YIATaUD6WSpzxGsRDCA7MjlzmPDFHFMmjAKqVEGhA8LjUhCd+QiIA1aPxiFS0O1FnUuqBUIUNCEkoeJQk8jiMv4ITBzbPnajIIVVREkpBLutra4g6pACvRUIotEph9d+5QQjVja6DYBwL6CzcPPnUoyy53r9784//+I/ReygThNxziWhwD4FkBBLy4g5/+muqGIazCk9IRMxRDyMpW5E4IEqwBw7wpNdeXtatNVoiU6Pj4q7G7g6PJrcm4QyM3bMgC0KgIf48mmM8hNxjVTeNIWtqjKbni28UnIwYwMMTjnFSUo4ZU9EIDHlRzZRRLOWsQ7/PMv2ND6/S4dZqVUYIi0vzwEA8vOXTgvgBw9FDEIgjRF9MRfy6PWVkHwGjUqwg1vPavgVgZMcICYndG3cwgv7y+RyjIBoAdAOsWPssmxcDIkkyFbtp4X5tH3hwqNb2Pc1TWB0nOzwK1QqtnJDaXC/QNPbDNBgQDn4KPq2xRGtri75ACeWcOj31Yg6P08cbHn96SPT0QE9CYJQkAog8EAZ/o1f3OLyPauC/YGyco8UZk+EY/DRtlEtEEh7cEfTT1GCjffNOUYCcRpiPTNFdKBwogDMRABl5XiFt1sadzdbK2Cg2nModGJc9/D63IopAxVmuxGozyjoG0Ho4pVJs6DxQIaYnPdioUPGFzZWhMcPDIwwBwiCUnIIlqRC3fomMXqX9pHuCEPfQstDMZOSvwhkmMKoE7GGkAwZtbfGC4hXHD36vPpiJh514WhO31dNmH7l4CTMR+hE0EuMKIGlfkmQuGjrOkRfE0KCMtwYREXQc/CcMQyvu1B+srCw9+diz6DSMjhkx3r2/ylAWUr0gZI2jO6MTM11tpdPq0zFjfRSUM8QJYJDAUIpd1cuPXEQzsJJAD8uwCsUOQpzfxQJz4gTDLgai//7f//vPfvbzjNBqlSp48vkKFI6HDZgABuBRW3RJEMxdz7t7+5whgzy6AxNtcUc853Nx7TaqiSvvuIiVQNhIOHeTMm4fd3RiiZqMcre1Fb05QUEn6iU2IAOVUE77V0+sKg/cgcIQ5t1JPJsQUr8kFIPtEgnZCeHsXhJfWfGvgaMaCEbqnenhU0uzzjuVSupOZ9mE4RgXkXcgjpxjlCtKxIQOaxC8wbKyFePu5rak0HjcNzvKCJ0S0QeGUuFo+aC44+TpC9AR/AGPsROse62O4GNV4oweNfuterdQZrM5z5eOGRAwLKHNnC7ksDvinjMGbQ3WwORUxwXbsW9z5RK75UVN/quaCEyuV2d4jsxSdTziIGBypZcuJPOP9iaL1coj3AOGPVFXxz8xfKQ8eCIX7ICyh6NtnMMC4MVhxxK2cJ8eqtstInz/MyisdUKQ5PqDbQTfAAtiQ2Hw+ChQ221TEVWwAwiP9K+EFufhUBsljCBZnZNfVWP7M/IF+h14PomgLUVpVZUVyFBi9Q4oQtBheTM5aXCLBIv+sifVniKQzczk3Lmzc6dPDBmEWOdqAiWdS2tUFyBnOJ0w5RC8AmOxxz+CZmF9obcOV6lC56gOpvNis+7qwXEYmJPWbgKLjpKEUaKdzqUa/S3aDuKEclESNSrbOEN8YBeTUza6lTHVrVuhRtg4sxtZkO1zz2ya0qMsFkW1ZIVZPTV5p9scjSV8XGa2MFt7avnyz7fqe6s9RvGJcZXTYtVyjYUJblZEu7U6G0zzuskWktzL5tC1bP9DZ6vVTbTWizZwmS/qqzJLpSKQyzVNEjgNAEzWBpd+zzirVdCe9r1rOhK1MXGVuqHAJuYqHE7GSOhxXZLBBoLYQZfFkyLyQxcGTI6KHfKFmyGWOIQReX6F1fPqBx/sky8rzezQcQUN1goitdtp7itVNq1Bw6A/4QQN0qhJVDJfrJRA/e/+3X9ocnUkLpeGS0x7pHAnKbLA9KS51+JKKVbKkCZxlX8sOg+0QMA5CO5gLNXKKP08Hy02xWhjJ82K3bnVrbbOaQVQp4tQpRloePCKj1QDT2bEUn3ULhVJQh+JuKVtoMZ9YsbSPH2B8cHxI59qVTYEseYV5DudyhAg1gGktuCN1EkNQMkuFPJAh+uVNijGeypWRUCgj2ITIREN2rpTyGhW5Hsu1ulQj0DGtx6p0Cg7YfC6FDnC6flDFS9B24ig8di4JZAWCw/yMimSXyDC5BYDkCKcgQ2rEiBGwmxbp9Bu3DBE9vATGwoM8w5/KS3EilquKud5iG9TFEd9sfIejfzPFxJQbnv4zgHWfqlfhF7snZtl7j6cn2dLvck1jYlEI5NFt+wPsUOGbZgxcjeh1YgN+JjJAYP9BmXf295s13cqKfW0qSGXThHKPTPMxzKY3LUbHb49lUnnZqqzrCfRw61vb3U6A+wiuMWHdaml5ZXvffcbLPj+/Y//bntrT6c2aFP2KQxxXoOEgCtIUzaTQ6sw1KOlMPrkmh0ujqS58AVtxnzMGgGd1lGQbvpDQVVY0NJq0UU6lCcZ4oKb73/ve6jT1Xv32bLiI3gME+gT1NUYOjcaMfRTbP9gH7oF2aGH4aYHNqe8mB+BDcKYo6NIWbljhZfxPYNIb5topFDmhYDSoYVASKAj9FifWJJE4VqtMJ7q6+NY2lCzmkgnkkWwrbYnP3z16snZJfLKJTt2cWEWbcq3mUBo2kNaN8LvufiTS+0hL8+Zq2aX41ozuWKtOoM2tgto1VgAm5mbZUuPeSTE3Lp9myfW0F4WZvuMd5GQSafLALfTaDLSPXH5fIFlUK4jy+dWnnsys7joNlYqIyPk8ajbaPX3O5jry6hdZ3+8bYrSxScv8BFJ9veWTq1kRrp1l0CkutPYgf5q7lRhplzlPnJZXmtxcNzUwmWz3RrniuWLZy7PVhOtHtwejrpr65v0oqTqd1tcc16soNjTbMBu7e9zdo1FhEI204KYdqtaraSKtCAUzz5br4lSJjuYZCvlUr2Yvi4M9F38p66cIcqXb0BLbmW5Sh9InEaiDF2477WQp+fiAjA0J2d7WPKQslW3Tg2qEodmSYQGAif3HUP/zn6zOxxnimhZTJnHrBmdPXu5ud3/q1d+xPbw3OzC5sa2qokVsWSm1+eGrXEqXb586cmVE+eS/fIj5y/evfPhC889zRUHIoq6Fl3DR86cemR2nv2Jn73+Hg0EMUPYuKpxa3293e7TKst8mKK88KmnT58788S9+n/iukZuHSxky1qTo064cSafP4E5M8xanqUdJrhKKp/f3m/e+vmrXyydZHEq1++SIYeGWs39T196bO738r3T52jmjIgbjdFsona3Pjm5NHs6Nz9q60qEv/vrl4i9+Jimo5/59BeHveRPX//7ZJ8d8wIXlbUm1Rtrm7fXG/1kBcGAV0GHYb7wAWksHHRZqoVXjAQY+TLgYlzA30y1ymSv1axTUkmx6SLqi9J4coLprAnBwRAKSFllImIDP2CsoiX5eLxvRAhpy8ATQEVamwBM1S0XILYQdX+uaC0q7A2DF/tRJ+RyoH7tiONT0KnE2p2NrezGuQvnFmdr1XJFy9D1XaxeuMGGWAprRHhqQxfwKsAcUhQgd6HVQCh0yCyb7ffv3F1N3ptdmOcef+ZxFBM7UxVakmwWe2KrFt24/LbV6f7oRz+i7hCb3//933/qqSdY5WSiyzIf66qaYLf45sB0rVPc0+BAWcI1ZnDZQpEsWEVlZs6ShNhOU+MAC3c7WO7c2MtRv8LCHFMtGwioH7G9DMiRHTV4vCxgVX3BbGt0QUVQFcaMsJSWteigRoIVLjy8krU/EQCvVg+3BBq3JNPc+JrmKT5ASpRBHLX5QYXDCwxIeDrBPvr1fAEgl821W+N+A2sKScxIOx/KJ81R6tGgN8xx6bo+lL3O1ygXF+fh+dx89fv/h99hCZV15P/4n/7jG2+9Mer1dUu4NwvLHdo8a9HJaRYm5Oba7U104J3b95w2YKRj1S8qcVgcT8urItypoCbYZvWBact4BhOpoixu+z2mqsMH69T2LjZ6Kq4ajfgJ5XDLiYnwey7+jKKiXIzwIFNK5IPG4D38gRjCQY7o0ufzCYqNnb13r17/7LPP5AucfemqMQIs6iFEjbpe30Pdsc6FATyXvXCHGOKK2lGh+IcgZNQKuByPqt+qN9ib5FOazJJOLp2UDHfbpL197UNOCSwvLlARnFjGYYZPEdAzrAGMUOyTyeyJk9TL//bSO7/7u+cuF0rig86komfszISVJxRO0YjA8Yw7YgnyNnmEPwekjjk70/xeJpGbn9l80MKItIs1MSebhHPCpdBUPnY0sFoNjrOnphiYdZEdNwYCA3FIAtNCAumNeu1Byb7nRAOkKXC0VXJici4uHecCCg9GRmRHHpLG/SIpdI6VN/cITH0i5YBQel6m+lqCigBCHalSHOOk8VQo4HnQ8kCI35/uoVIkOcECNHZIrL+rkYLNieTZ6XV15iCHRpVJFNTBE2DAgIMp4ocYbGdiNJNCJWXp/NNiudY5ku1BotmX5tFZFtUncxBmyQDgYWzA54QonroShm2SQbIV9Tiy0ItRjoTwB6QyBpYZuupOnROCTQqrTy0mesKDVWHoECeNLU0SQj6DR0yOHNkTz5/0p74pNsoV72/vvHbj7n1u4k9pc4s8yF8bns4o5SSFyS+x5K9hSyrFvJsIFluNcKa/CkxnhUErGHTG6Ac+hjEc375z78HGFlMAbsCgPTLMoE1x9kuLp0e6QOcGT1PO6BaIiKj/tT2mIJQK1RiMKEaZzQeNne2fqhs6czpfSF++eGlxfuH+2ipt36SOKtB0hqMS3i02Gh1sZaDDjNZUl85dfyIs4Fepre20OsN7dJOrq+B/9jkuKKtcKFfZV15fX+VpCy0cvkhT2ehyyugl5YpOVml++MMf/ot/8S++80++v7yy8upPf8YSc313n4mYThlxU1mX9Ua+hHyTMQ9sJFPESRJlkqFCWn0BiWpiNF554kn0G+La7feLs/OsJNX3msBUWDWyo9w8owVolcELoB8vmQXFH9Aaf/1YP3gcr0O6cjmQU1i3Euvw85Hyh/mQI14ephm0OEFjAJvCrdjMKo+SYYlQDoeFS4msMwYJrKeXNSHTBjJOOM2puWrIDxWBfNoQX0QQTnYUK/DIGy9iQIsHRsrUgSM6hfigiyAJdnQSkQmzTTmpnWSSaSfjcLQKjnaOEGDWRUF0jB/ibRm6b1cQOG6x0SysaWyUhcIibox3mezxmMOkh5OvjPjMKYmxi/KRr6E8zD1He/QJArYNIClgxOHCTZkWpT2WaYe45MDHBhIVhUeeCHncQyyF8RDPlABONCr8oDD7qwVrQKPBa+gIJESD4UTi4sUL1AIVwKiCr8E5SJQ2rL0gpcINI++wlKdCDgrMlBUB+6yLMr8nCXCFP55X+BYTxVAmoyg8AIMfIcePPEgSOPIVdzEmxDHjd4fYBOAmTuRHOAj59ATTv063wZYeRrc0Q2aqmKj8bO3DL3/1q6lEjbnB7s4WKyDjYUeKj0Nsw2FhRnfYsXIKMfAXVMgO+OENhQUMt2WfoVusVtFxi+UC3cxsSdZkiKvK0rfNErrDsMFKDcJS/tNlZrXgbggxvDZu+7u1WWcw/CVfb2byWBRP90Mk9lY7O3nMoKw31eyBQI40stLR4atqUjjSOWo+XDBiRnbkTAjwlDeb06FFDrCLqrClOxuBp79EKXNkE2xdW3pmqA2q2bIGkTkmHerCpZFIztNnbfhJawhlhYTjYm06J4LJF/2g3O04mOZtDqcIOS+X+6OngYgfESxgrjHoKaPACD7uMTDpFndx4AiJoqYNKICM4I8lKQI6NjYeGEfs8ushwBgxeoN1XpwIrXtc98axEX7oNQwJlHyEAZzmD8rOKz2C5+JMMDzK/ShCAGKBAYUR5v9SPCqyyQmbK8WlJaS97mrBimyGbDJDQ4Zhf1QoWERCHB6SswrA7BTp9VANe3GJCdga+1rvo42wD48+waFmtYHflsWroOxzzM8//zx+jtuzmow+YeuMVxopWXtegJEdT0ac585dADMmBthcMypCF5Iz8FALgJOv7IX7gFPzDfSKWopjRl9BCTbFLJrQ9rF1Wru/KiMyVAAm2NLuApY7itHDP+4JgiO0BGgpFJoEBkoZ6lqSo4ABdlasmNZSfKjFXtULC21T8kIyohB0LIuM/koucJ6MUDXicFh8T+RV6X5oAAD2cqcBlk0LOqEr9xG0hTm7zpa0kB12siShXCDhOU5qkOp4EAM2G1jlh6RcXh8lm11aouPBhJbege/rMnA/vbDIrQ5b6w9IBTbxZ5KA/uryLGhxhPMEIR56GQZ/2rQz54GIDiEaWNO5N+rApHQ5wBADB8Za8zOL6N3EUk0LEsYQZ2nZ7F63tpuOhKyVPQQkJ0gISycsK3RaWmTvj3Q1YbGQJ8SnFiCXKk9n2bLt7YOvw1yU+hr0ZGY4P7cETlgdkMePTckgGToJ9UJ5EeJP+hfKCXK6KTjm1AJgHvVczgeeOPADSRQehgeE9Meyu6T7hue/+4V/orbWHVC/VBPhtAJ6fBhLEtblSYLtMT3m9WtXNFK1XBxyb2MDAF28bTfzEhW44RCBvH79NmlzfOoekcvmmLFwTyIc8MVZyKC0KBk4yUl2ZozPP/ck5tX5tD55D6kc89zryzj60ysL3jBditgC2aho7AExQObSRbpFsAEDZr4GxrXvbGa8d+19rMs//1tfpHSbjfuY02aLWeghFX7I9iKEFB/zCwwUMhTEIOiJJ57CNnBjY5tAWidIKAj8KZZse9hUDMBggRKeqsjQsJoCkhcgEUAEY7lqvgNaZ3icDmmJw2PJICTopuLQn9gPJST3SdXt23dmZ2vsAWJXXpvTeYLtzS31AIzQrFDR0Poj0Hu5KG8EQ/V5GVkWJZbGi4a/cOmi5FZb4gjtdIwKsNcFT77lR93923/7b7nfc+XUSVQcaUlFG2JRErGEmWCWMGNEBHc0xgyVjAkA68skYXJO2yQVsQinxN7mgzRMskPqiCI5DrKdcujEOTbC3c/rsQ65o93Z/JKNteDSfCBB5ZjJBeftzsMpXBwnfoDJyPHjAR5/BOZRHs7T4YklFYqBJzNWT0IqAMgLduGHMBqCAEIp4ZVYmiFiRjOk7fCEqyvLJzhdREdKk+HMEDCc+CFhMN6OCTOdpOjRcq26J5LD5Hq9Ab+8BMY5L8qBZxQegeHRFm7o2CdAw0M+VbO/1+ZR322BQgtNVn1e44SIgIc78H00gIv9IQSQxx98Qg0TBeuyyQz64eTCHHpmMlAvTDUSBXPgDF0YjKLtw70vfelLdGSIIv0IAJJts1DGFANIJASZL1Rq//Sf/tNnn/20zs2oKbDIyPpO68qbb/yH//Afrl1Vf7d0cpZqdcM4dL4qDkS4xATls3rzXT68eelzj8KNcUdDL6sgSRqZSiwiFvP+iZ3Q21pHlNyy1NwHzO53ZOaXF+7jD6YjzJ5xtj4zMGMLkwSuVVJfhlECZU8/NueoHA9+hMcxB8RbhRLilMSyU0Ur3Jq2+yM6HSx6iqRPwAHP14GBxxl+tXdcFO6v/jQAvII8BB+BGSYhAcbBCPHSeSBPqhj9U+S2OoofpgTGIdXskT/peURQ42c8QOFBKriNhlgw0DtEqpiMIuTuIQQ5JCEReho2NIqjshCRBzDYkE4P5xUX+QWhlApRGQBj9hPyVlGhg7D4a4TEA1UezQ2CsQQYCGcUzZAgJIyAALMnAYN7PGdFYyh2/jzSyJCVlggAEoXaoRkyjomDQQy7bFALixhAzlbKjB4xzgX4wdYm2gMLaKcBJO4oCfghEmymXsIIceOYkfk0+uN8Krg1TDBzp+n771+9f/cO12UsL51By81wFdu5c2+++RbyQLmgATAvCxMWwz1lsvHqQH7AQx7wWORKYwyHt2/f3a1vwx+uB2QGxCCNI2J0lGgkjHgghlUuUDhVSBGp+v3On//5n3NXNfSwmEMUX55FrXENHZhN3RE2ZoIW5Q2LKBSB0VwYSoilk52fnWONCNs/AAb9ATixBYHzUVo80wVoK4DE4qDzKbcKZgBBmzwIo9h4SPjy0OoCPoAxxrr0MpcHibcSX+p1rrP5QjgL7QiG7tkK2zOjC4p9/fYtQuLO69iam4Ihm+bHE8crUssrVcsKkaei1lj2cb8/lRcdT8hWAlmyDICtjvHzayFii2P2JxkSbtTyG/od1hJ87CPAa/hDwrRf0d/rre2tTsaDUpEjq/RojLyUO9cIILIVtre4gsrK0bJt2426LD7YayV2hzs5WX3G/jmZfHJYrKVrZzqDRL/LzdLw0D96w1fs8Uf7TmFxAnqpl5Aw3/YNwikhf+xt+sjEQgNe0Ufa3oyFkRiWktQXyKweHQU0RXlRp/jFNZzt0mmDRQEuKKKB2jPxUVkiWIHzL5Q47RMaCkGaAKgOw3rxVC5vASEaYWj3j1vG+lgopid1+1jfnl1Us1mU4miWdI7gxGOX4ZKrBm18mkIxeiw/oz96JegAhWKCtERISUilS74QyFn5xQOQe/KQIz4YUnLCwWSxegUSquL5DqwX4A5r2MWX7okaskM/HrcaTSY7Qkh/F+yDWn3h16tYzWYsgy66N177gxZyleBiazZwubKLQ0ONrd0HnWqxiEarlUrLhcLW9bXRzsbJR/Onzl7YnEn+wZd/8PjTX8UQ4O9/8jKD1wf3dj/7pe8uLmb+8i/+opSfYezFldG0uX5KS9vJgp2FHGstgB02npylR273tweJ7b1cupFO77AAzRJAjY/IVqunTtRowvmMnV1NaLkqaVswsr6AJ5ip0zVi90WB2VpnaMgvcxhbDtMIR0LiPb0khARccMYOPHcrIqwo03yKu/J6n35mvpzfvnqjDbUTUw6bdb6VzqieM245dgSTmQEzBnLk4qzbt+5yoxMUeqVUyzPgafc73KKKTrdArvnKdWRinlzfXS/3y96hFPLMzjnPJzsvXeTGXXpJBrI0YpNVmN8fFFO6IZcd8jz2c2bvnPerBqAewy+3jLM9c9eNCoYPEh85csdsTD4bQ2P0Jr/hp4PDF9p5CdKiHBoN5i3Iw6bPUMYCYHsNMwMq1lp9pz3IFwE0Ynj69p4sBrjpU6N7VUOUAfvjUBJv9WgMYjUK4xnOmvAzo1QqqwVpJ+tSDJGN5cGLZtN1okHHHA6YxFvPjxhhCJxyQfJ5QhoRqA2eZr4QQFizCSjxIPJAZZAKZ80HS4Xp5DlI5q3MhA4iKbU9AyLCniKAPfxjtXY48D/ne0iP8cEy8tpleEK9dNg7xy3Msfo8qs3siUuaOfdSBUZ4TYypZPyYoYx20oX0rpTAwcyLJpPJJScPttaHze0iUs3wm5sTc7LBpPWyCpfWfcHsRI8KLBxlEdbRg63VdrfNAZvOoHn50cfROY29/RdffvHKa6+yhNQfadiERbmk10TK9LGEgSHX4uKyHfZMv/vu+xyJ3dujObNHpbE83EeilMqc2ov/SQYUJDUto+cErc2tlji0gDIt5AosWrG2y5D3lZ/9nI4AKgtsIiEFnjJAafLq2P9BTxNnSNSf+SVX+GkCsFqLCBRSwj/FLlaYGiSIb8ExQGA2y5q7Lz1EcFQgYDjPAmVLFCtoTIPBDE5UGbkwbCCKVUj0LWtqYIMThHOXMI6vSSH1jCF0n1imAidfu707+Ok7//W3HyVVRmPODLdT4jIjLKWM454fpXAhS2ppKc/X6wqFTH2wd289w6UuhSK7jTCTEyTg5HZmtZNiOjtTbrTaWBGfPnV2bmVld2uTNY4zp05jFJ/VQRX0ZoKLXM/OVDvY4G/v7txfRwBYWZybDC898QTzNcoL8eLXKNnebdCxkworITb3JTr0j3y2mesDM9nO9s4u5zS5cAnB4xubaX3MYK9RZ8KfX56lH8xM0sVMYTTWBka3p+UGDqnNztSSncHe2gZMKWeKyycXsfGUgfgwQ2eAXS+mxShwrIxT/eTs2aUadtYID4u5m3UMiutdbbFg9jhTKe93O+x5TvjkGjcfz2gxC27CK4ybXd4oiNQo2VvtS1vSauzsyCg1zBSznKmhSRBPWhoXHs4aZNNcuO2JaN3oKd0yyTpZu8td2c1WvzvCdC2ZWKwusOK/eaVfyhY+8+wLX/vy1zbW18idUjLwwb6NrVOO7YDz1rUPv/z5z1VqKe53LpcSmxu3RuNnqbV0hm2MBGbmbJVMKggBHbAuxKBGmBtxF2i306Jvo3HymQQmJNt7TSY5qWShVq7tZrmzujFkdMpaYp+h7XDtzuorL/+Mo0FPP/kMksPif7E0x47PL1977+69nfF3PsfGQ4uPT6OWKgsI/N0HWz/95RvdOusx3Wv3b5Jvt9lavXP3UwuzZ5YWPv3kU9w0/+DB5jvrb/CNh29961uff/Kz+/frqVG3Ve/fG7Qf7A5S2TLFGzAIeohDKuinOAl38eJlpBcb2mIuPz8zP1vRpXmXzl+uN7Zfeuml+6s30VcwBNmjs8JDrwVK8/ArqdPygm4S030dvBKK1omy1Wf60HF99ZNhqihSoGA0dTUNjBRC2NqmUXGfNB4LmvZ0veWaE4zRoB2r4RZK0z5JvXz61NKirjeh3W1vbqtFK2+NsoTIVGqUtULMOQ06PBF20JRCasR6eWVEV6uoyc3rN0C+sLRIOvYNEFqVWTyyKaqmDbpAhmpmZsuVL1zcAVcZOIHQWYcHlSWKaDsHjmGKdbpGe6CP5khC7q5evvwYQo5s0O2g33TNpy30kyPNHCQkwa9G55UShhAuZ5VI2VH+Uh+82wg8GO2gV8yBmRV1MMQdaGm/xJMjT7JAmfMkHIdC07m4nrYlOPRKCKUDntpxJAalB2kdD+HAAA+MwNKyAWTmIC1hyZEhLgNt7++y8KwTcixnWgScEhLmbiKS5X102mB/b7tvH3tcX73/5puvs/Qg2BHfU0E5UU516KSiPsiE/+onyVlnGTusbnBUQtt77H6hXCQgSs3/uJPs8Rc6FxtBWiDCQilm5wrIw1xtlk+nDwfcIs25kSYprKCUNdDnhIgEyDKCQpQf/0uio+KqZDSGWITAhFxNBH4yW0arv8tl0BzcmC1xUgeui4DB4OaNm+xvtVtdLJdZu/HpgD46a3d0UNXsRIAefBzJunOX61D2/q//l3/51W98o99jNpGFh8TSM9MZfvEb35xbXPqf/9X/xKpcqZhi1mN2seIOjNdxFo4F6CxPurB88ZV3b5+d0Q1OlVwG+c+ntEAPmOXFgxfnv48fFG6WpwIR1FTZGDA06HImt30J4hgHU53c48D8jy/99Mbp7kBZNFWgRM4QZ2yGZcdyEqyMwisiEUtECvbYXImWIj3A2GB7c6f6wTrjjUlNlx8yKhNVijQYiVZYC9IPFJjeyktkUxu8tDvkiiIEmkexcRcJP4FCZ87Z4mAEUHhvXM4ii1VLAsCTx+HjyB0nTwCAdOR4SOxghHhaQvA7jMPH0+KnD2r1ezNFjbgk9yoZbRjK+JaE5M5R4UEbE6eqoXHTRjD1HXPZ/CjRonm3mJIqJSyRRkrRsnUvKkdvxaNxZoSX8RboxQ2RLTXPP6lvNCg+5o+0If2p1Yqfytscfvgvr8mVR4FMaka6Tw6cITAKGQZ68PQJIKn17QM7lg3wIJnDzvL6Tv+1Gxub7R4fr+CsAXxAH4HLRzWWKiAGYPpTjCpQ++BF/kEChxnfIkvsKbLaQPvgNkpyJ8qIop1QuGQhpc+H7jaav7ry7lNPPn75sUeXV3TZZn17B2wcGgPeq0xc0XeJ+dCxOoWwxFIMRP26znEaMUqqWjan28aTif1G940337lwcZ9lcb7SkctWnn/uGUzfVjk8ZxlDM70DmtlG5lpBIDjOWl6hEdpRRCTROjDCgzLR2hHHwjpX3nmPSzk4inHuzFk09czMLBXVaOxh9ABy6MlluT2hT19DcswvuAjl3/zf/u8YYPz2b/82egzznTfffPNXb7zOFGCg3fARIxz1zqGjj1P7JHfV3LQJEH/jzl1unDs5P08494mgncrnzpFp98E6POAWV57TBWheIjYZi/zxMSy3JEGrCxKo4qfO+RhEWQUS7YGkdVhPQFMFDIFTqHHfwyN4Yj2hPykVHpfFaX6hj7E+AHAKloHQhRUPSfALg670ptXCukDPOtoQwYFfJxTeEep+PIfgw3CTAyfdul4aEpQ4ukNJDuRx3Av0eqZE+s2SzIjxs8sqxV1iBKXrqDSF05BjgqE8RfYmlOMacG4wNOtXhBhILjqg7DXuANDIRsYCzkMQut89+PXKP1M6BH6Eo9Q4CSYnlcKBFyFgiFLx6n4C3WueaSCxhOCoUvcL3hD4XDV4MTBFqRYCrRS+Btj8NXqCE3/0jK2IKzj4b9CwghV4EeEZWyoviJfu3LnH2KHCZMJrRIwVO02WTLNYMQMy8NPzCHE4r8BrY3YFyR+jVyPYkF0iQIuGoi1eO7wq3LuoGDCS5WBEAeBgPHGEQwZOGM1BLYEeS3TgsR/i9WsK3ZfQCKHgfIeQYGaffPWD/Bksz85WHnD9y35zM7t50m43Y2sLFQYYQ8aLF1MszZyo19l5+69OYRz0BJfHYTD42c8+wm2SmDBgTcNSNvq0n+GU8LDZh42TfqoAnaO2bAoqFe6flG0+pDraUaeJecsGDTWdvl3luulirZJiNlspJpF8LmQjnFoBg92kwQRYNsuwX4VnnOJcE0O8ZCpo5MQ3zv2qpehT410pjBGLYdC/s79Fvm2ZwWE/ohvoIIzhYJFzZd7V2xiLPLDo5MALLQy0zM/BBmHOUtojwKRqbu9hZlKplNUSWYmgSdrVXCg7ipkr2GjOatnbHWiBrLB0jb0cJ2SYljF1o5hWCkdOdUUFCcVWAR7rnlCerX6n0Ad8Dj+VjYPyGQdVwR/uwnyVF+s9PMMQVwj2NMn3cLDFMz2KOEyOZCryYbmrmkMX94dhh39DtNNUEYRH+dNom7ZNYAgP+RmlgPFqKcQpaxsQT+OURm+mroJgVWKM4CD0N/3jRfjNYoVy5u3FEyeYX+1b3cEiyoIAI/YjjkWEqolA9/uTFsoUGz/wTFB5gkpdFXJi9qQ8GaAgW8DQNGh3W8UZQgBAV+z12tg/fvvb3yaXP/pf/t2LL744aOyBQbMTc2qzNugEJ8lR1NDJFB5d9NZb7zB3pY9Hcx/ihmohJPhQFK/Ukg1xlQcFpO0z/L1w7gJLGFw/yuTQvvTJsrdoRpKiOnWSjiL85CGg8qHkUQJBjp0HAwB9Ky8mRRYuU1YGkQzHURcs7VF8qIVRUdaAmVRr+O49AOoU3YVaJglVg44CHp6jwfBQ0dQFxSeVqZ/pSTVweklBRb5khPXc08s9bohLcJiSTA/0MBEJYhQJeQLDDgEZbWzcoGYXixUs3yHAxYMEDsMFKxQqn8hQrvLMHIICPRSNZV8tizPcTqUYN5MWyw7s3CeDIV2SdQFJCoLBMIgiagedDoPpihlziUg5UUL3Ajbs2H29nr4sV9D1bv3mHvDIF8wpbgR34EIARaDU4A3KguWJmdXnuPmDLy5sbBB74tJFbpXqtffBTxK6LEoES3Hqf+3C2XRbxuZkzROR40nlUl7oh6XVQgE/Uk34oK9WwxQUCe931OEe68i9bWNjYknl5Kn52ZAADNCA3+lPs+Bl33l3VIDBOtpO9lSGGR0bLXADoxiMX4Enlqe3SlJhFg2p5VNzgJEESGKhjRVTeMXWBRzgtmZqihyt/9JCFw4aHBt+eJIplKn9dEKzSh+y8orf9YN4ZdSSatjXkhkFJNW923tUenWyh1nAhF3bdJrRxcsvv/yfrn1I8+QOFvIlEDq5yI4oUmEB9L3vfY+j8W+/fQUrWuafFPb5zz3O9Oz+zbfJdKOlEYKTR574j3XQQHmfffbZp5/+FPivX7uthlOs8ez1O+BEWTFP/tkrP8J8m4Ij0iRxVHhw1gbFCg/0Jql3nIk9MJAhBvoNM2FyhwfoYMAxZDpAmMMxAIeCDo4HLVIzFzKStuE0LiuzmENT1xAGbzWEPUjEJ6EqypSCRj2p0CQT6KutnW3qaGZunvrVFgwfp7Ehm3PMy4KAwS4cGARgdKtFmKhH+CMPaWEjr8CjSXhltEZWZIGwEU4IT4SNYqKLqFlejzqrGTUoUa7NTul9B/OoKAnZQRVcIiRnp/3wKJUZtfF0mgkEBsohw+uacMggOcD4iXKcvBIQ+fE4Ep4WpektGMhQLdSCgOGXIOQTifL2C7yArM/1JBElBNJAEkmZZ7EkwSvN3AkgFa+RC5NIdEHiNMs4fnNbl2P0tWTDH5VlfxJnS3sASYTNITWEIt5SFYs6iHPp0ilgqBEkrb7bphSOx6uMV6eKMgLmfIhwHusB8lBBDoGhF336GYd0kRNtJiG+VIABIL3/wuzjQsgq0v7+26+/DgdQgJ/73Ocunj8LD41g1S/NHzA81AtIOMiJvSf3iPzLf/kv2cl2GkQ/lcKNtChA+pGkZh9/8Ad/8G/+zb+h84UbzGumkIaHW7BIRRT5vvTSS/RWz59fUn2NW0A6W3iaZ1pwexWff10H/XCe6gYDubAA5RiUgYp1vEM2cMR5hj4G5qZrsPl5pvnnLtMMdwcdZ5FL/lFcxJo1ETHBnIKQAKuUkET+aKoohFhyBHkU8tEeh3fK8R/Fb4HCYWToac5eiQslzbzKGlQAGPw0idMAVTS0AEGsIKTy5A5mfNQyC+GE8MRRHkLQSDh0oucbZiSRI8QylcJxCj3EcUbPKNDJiMLdE48lxGjwld+AM54KMDw8cdJCtpaCP8LmYN7EokCwcV+6nX6bQkaxeCKcFGdhni5Iu+OOFjlkbMCT1VvaIJYWjADv3r8HPxn5k9AVDx7xyhhgojvANo7e+bFHn2CBdfXuPTQMd0tBs4832PfD4441gYiYWFGisE/q8VLEoSGJSnGct27dYdH57Okz9K1LiydoEafOnGf4RD9FQk9LkVWK8JYIUJlACaV7iJK1ty13wFWu7EU0SEU4NxddufLeg7V1rK1ZiCCXUkkjol5HugJN5WqcjBhw0guQ9b/6V//qj/7oj771rW9973vf//rXvz4/W+MqbTpNIBE2KNzc3iIhGdFj+roW5Ima0IGN8SHaj0tPCOOKe4jhldHaTlNfQ3HAAwvQBHnVhkgUwH9wRVHhhkcIohiJPo0iCGKFPiZLUOzhYjfT8BAVIPh9QVDbLvA3qGvlEGhrZ61h9512ogyDMmA0wQ9XMQb5Hv7hyhV2ghQLAshA+PAoUxsHIGFw0JlIyCH2kUrr+qHQeYkAIxxJVqw3NmNHBCYA228MOSCeuJ9qw3/IOUICI48BiBF+zbz3O8gVIdQvPPROmgUpDVqw4+8xMVbH0MzKXjXPdcyZRNUW0aqYcACP2SY05GVnWp3QHabLfFFk3E2nNLfPsujK/bMcFENEJhqcjRByqxlC3B3ljHckoslKRwHZrEvpe8oybXCR8Vr3suOPWEQScvGKcD/vQiTW4aYLUsFNrx55ZC7rVEGDY3acoHGq2I7B77um0ABq5aCn4tlCjOhBWkmLFTnPvtUmVsPA7BcEuaeRWHK4OKv7ep59mlR8BAOtl07lx1kNFolWSkpkfbMrPrFdNXjYBQvQZhoaSpZgYIfrZSGiHcEF+ZBJDUDVcIJeVhwiZEq8XoHx3gV/MM5WYuqClU3sbtIpPgEhPGlmxlrNlCgipbo1QjLAXqgjhS2Ek1KyoD1ncTfRb3DqOc0eaipZSif5ztNj1RW+4755by3Z6gzKutRlpTTTHDcL7VahXfry05/93//0f3/l72995StfefLTn/7is9/Yvduk6V088dT/8F/9n9miZCaTaE44NpnJD1Fk8yvzmt688x5Tz0fOPod5y4frjQsXLz7YukcVUzO0UO4pp8ilsib26wOs/7q5bQ7AbqVSHYrDd6GYos9VSzxn7RNP7BOyXTzqyUJNVnhJPukiuaJHhmEYKlJet7SVQqKZy4oBRkjwKSPXbfRGfRTrk0/Mv/XWvVF3Zqe5z+ILaoNDtZUqJ4ZJZs2KqYK1FkZl9BcspjD5xEK5027xJUFaHEdByZdeLJXN1KCRyz6rSsgdAOw4Ul8Qw0V/lHQ4EbWUmNrlmj5yr5YYrOaLBd2+RBRVKkTWvlS98aGYT06UVKoUYId3j+wODjvR4Eul3lxhkYQ2vpx9OEnwbmycIjR5jIEGlFjnGmyrSKsY+Xpatjba9kRUikKNHgXbPrNqh/LypnBLLin1FDxD+TdsHmoqRVH2h8dqFEiDccTqLsArPCFLLV/HYE/Y5xkrV+MGSld6I6bVw3U1o8dGXLZrRnq1b1pthI82JjTWeEUHVHnW4rS0UliQKMU/xjPNFyzO8zg6Z2M8xP1UqIsM7AfGLeL9vnsHEPOYCpqVVmFpEbvifrlaZ3MozW0L48Ewg0FVCyMrNSDpK22V4pf+CGyCES5CMe+kQXT2N4edeppY2/BD8jlBiH4ocKlAom+aGS3Wv79+Z2xLOXxggCkWXctzuWdX1+//2Z/92d/85V9BcqGkpUBufuJpmSbYYkUh16o1FkCL5Yq0yvUbWEkTSxfp2zb44bmXSNJwWHwdk55M5ejO6FihkC9kMeo9e/o0DfzWjVuMC5FPbg+gfsk90J62fU7Cj8A5xf5xPuSG6nSZCciNJYFqjCJZJGb+GQWrXLa4yVIy4cxyGeShggiMYNwDhTiKRiz6k91BejdUE7EUlnBULgA+NKrUuJIixfIoelu3CTJmMCwyhsNeTp3VpJ/GDmeUKs50MvnX3l0rzF26fKoCihy1TCm0dsKH3NVXUsdGj0jyNRNMBDvN1trNOwgTU1yIx4JPOegT7wijHPV35qnHOcyCzHAWh0FKkUuTMZzHaioPOUnO77/0p+++v7Hb6fcoO3dAs3hKRtwAmJjhMughhtr0amrJnFjYa6U7A0xWKaCQiyqNN7lFJVMuwAKkCKN6zKhpDBS8Us3NF+e5gpR1h87mLrKaY5TS6RWzhWQuCf3CY9zodbraDOXa5VYDfUVZpGOzqfnFBWCG4yp7BliOV0qYfWd7e7u9ZhtucwkrK8CjzPjEyhIyjMEpF6oXS1nCk6lRciF3igVWTg+wPMRthrdv13e7TB5mdWeltCJVopZrT6lxFB9zD+oG2UxMun0ZQ3sRYQG9g5SVrVYx0uBrPNhKcVW3egGtwHPB6JiFpL29Rqlz4vEnH+eCwld/+bO//eFfwPlqhZ5tnBv3k73WY6dmoGTz9nur1966uPR4atSsFibpcatVX4elH167yVxl1JA1/c4Ak3QMlJMZLiqlYFKq3HgAq/a2tvt8MYXRVKfdb7P6JwMuWhHVLLtf7jzM2VW8mJejcruN/VG3g8giOohFt9Ud9RGq1O5es6eLmIuVcvXvXnvlj/78zz5s97i0mtMWu8ltrtLl04inJ5Srt8+tVwPMQOvsTp09f+GxJx7/05f+5oMPr52/uLi8uLR+q7hf77b5FliqKD2u1if7d50gVwdwwBFOB8289xtf/xY1+OPiz5mzoenZX97YXGVU0221n3j0sSqf+u703nv/baz6WZknFS5CFPlpQQw90KIWr6kKbGB8BCQVw53vLLPSVEURboogCIgQ/oM9Pp637pceS2jcb5pNudgpn/H26oPdjU0WiJfn5wqZNIbzLDhqkB3TopTPCfSyeP8bJ4xw4KloNKf73apLqoGsJsnN1Y319Q30EndDo3XhBo47SNh/0bIHyoShGOYCthbDq2SGI6w2pMdv5mCQHCyQkRcwToBiKVkmzX4Siw46P1HQxQg0TbQSTxQm2oNBl3HbNZXWWEmuXsa6eV0jQBG1woxWC7qeeAHx88EPnX7LqCojbA6jVCqmuKyCpalobpPwLMQZYvgWgpuTRZR7Wp6E4Dx5HA9qjD/jQ7DH4xnRcJBgnqw1EAIHsGQlV7etRrbxOzazYxnltB7ORxm4dJhb6DV+5S5qm9OpspwMI0EPXjkagnBy1nt9/YE4JGUEGQGdDmMVq/BjXYgVnqgIfG/4xDKXUC9yFFdLzzsN6sKWgdQfQhWvFMQxRwj1GmNLFP4b8YAb2px8iQpfvJiM17e3uVWWrnN9bZMxRmN7Cz329a8/D4WtjtYTbX0YlaipmS7lp7dkXoH0JjOrD7b++X/733//n/7zXHnGLlUv2OkpbCKloJV2ksgXqhceeWLhxOnNu1d1CIDTihCBpTQfh+QybHhh5zKZBZxeObl+f+PPfvruQvZ5+GbT1rDcGkSoQfNH1mCO6sIZSAiibX9hkvBXmK/l/wABAABJREFUohI6STy2BbkMf9hBc1Z0MLZBggYMybaBZaSYWU+QbGf1icJUhlOzWnjSXNbvffS7oelbOdmTafZ2btynKrlHixsoJJzqp9KcPyYVmkekQXm4mmQhWicKNSBYHYK8BAgGhFlpQhcAhK/8qtSBmhNt7iipM8dfnTNxv+OJhwcpRZ7yFvExdhESB8YPAIE0LZF/0EFOm8998pWUdIrDtoZQQCDEkY4Q3nmqVxIDNDS0b8ybiRsRbMpixUX3AQ2yoze2hDTQqpAurvBmaKDeFwpsFkwWiJrsopUALKzgiGovi5YCTM1Bg+bF5oDhV6CmVIHGYUPFgUUNK9TwxX8CeaLZpD4OzHEojt/vqFZAEXvpCmPpdziyNOArDNLusp3E/FlGHaoo5QeB0msalyLejKnQqIztYSZkMLQgFSuq9OmVmSobz48UH2ENgY6JlkLuIhUamH1ktXKasO+H8aEBGm+38xafV2U9FMx3V++jWEhChsZybh1DizOGJUCOMuElLxB6yK/7dKZFqaxyERufMXHsOnH9xu2791YvXjjHdvgsX8e4cPbkyROMsljGhTCM0Hzj3zEYY+U9RA5oIZISMUGAXo5OEUIVsl+1u7P3s5++evbcSYoMQhZLEthG9Hq7W9uMeNFX2CJg5DGSSko067uwixHvBx98yK3Q1AWD2Nt374CZIfHm1g5UkdYZEm4OiR7lZo4oLqDb2t64fk+3UbtE7aPHOBl5/sIDvunCV2RpFGECDQg8ZRjib5JIcAUv/MRKHA8/kCoGHsEIwFEZhqgiAZCI2U+UiyOgtIQonoKZMEVECjKc98t/xPGdmCjMMfhrVIsghySXY6IieqJUavcIrm1gooJ9UAIBCKAD6+kLLlEaPLQedTMepMpwf5yGCDzKNPJYlBjugz//wU8Iwk8siEW5DTPUBM0gBXniTAzhxAInMBuZ4SmXyviHWdVvqi3TISZehKOpDmZKmByBKmOslj3c0bqfZyyXoKgIsatFaR3wH1zMAqEncY/jP/RU5Yatm6jQqzwjf0SGiqMFSsXEMQs6rCAxy/wRssCjIQX0BLHUrKvgaUJLBVqHx/CKyQZ7ZJQReQBMpNLdmoE5aKBEzJfTk1Q4FxjlYY4QcuTpi1lRmRRiKgMPaKX2rUS8kgJ/VEzeHZXnjl8Q5vAQyBPnMKT0KF4lHmYYyKyJEbY3AdHpoEEC5RU4IzTwB3wOYukASlnm0ZUtCyA7WhBfc4ItDM4oMn0AdkDvXn+dbcbn7Pv1d27dQuUxPvvGN77BaVn2+tb2G1Dbzw0YwHExJtYEN69eP3/hwv/4f/wf2cb8f/6//h/siJI7ncrXvvYlqF29f4Ob17Z31uygboceqFoeoge5DVImMN0B/c09u3O5WiiS0Wy5CoUzOtutDQQYqF4b/XNwwqAKsvYb8kwlpjjwCt7xLJXKWEu9/u4WObJW4ahgphYNbP5DpY96KbrDdDKPn5UylgYyKHH63qzWd1DBsIXBIuVlbRrkCBHhUTWB019HLOih7mytp5aXZR+mM9QRQzh7ijbavcPzVE2HtQ+1+D2Qanc/r3F3MNBrXk/TZwI0gKlEIKbx5HE/BPMaURWP0rkSXMBQUOKzRwikAM/L5EeVErYmMCrqSL4S1ABhiCX8jWhwD0/3OFr3x59huuDXo/wlyiIKhFDwUHEWEtCAP4I8hO3o67SwsTgLVGslzOmMRf5jvRFtRqewRSGHUDsBhwI/4hXRZTmgZMff4lupCANuMNb5AIah4pXVLB6czJKNBvxQwpPGQh/EIWHCwUkgjQvJZxmaENdUYGIt4F77HuY/G7vbcIkF6D/5kz9J/G//Ed0CEkJ4AibpsQ6RkShX2TCZWVlcosVhX4P+aTVavo4DDPmRxMCnD4WIKoVEkS73ZAFV/kRrQWSr0cDCt7HXJBUOypkKGy69OlLntj8JiTwee+gZNb1D4f4q0o6NsEDKDhvREnEQKogxOlxl+U9MthV5NKT3WXFICAYDBQQeYxAqBQ/WJfCfI/Y+zaZaoZ+KIJdqqcywnhMqQhIW1hE6KrHAdObq6gb3887mn6CnIORhHDBwPbyuIQBI8mXIi6E6nRRRMB+pwkM7QfeRMca3sr/K52cKi2wzlCg+sTp1mGT3kZKShL5m6exZaIMDS8vLQz7ITHL+wi4SFU3RsOgiFc4pdL8YxfqtFhF2QMUeMr1PfU+amQs0CClVSqTlGisxP18Ang/BAiPgJAcbG3iq5TIF2Wu0KAX5djk5xNU0ttZPYRms031SHFJ5Qqad5FjgBLUNLUDi/KcDctqUivv62m2+2EePWa30fFsl4r+0ZijekAo2T+gsdb8D46ekEMmTEF6hh7LAOviK/SVp+dYc7uT8Y3CSpTrdj8yqh/EWknZaTRZbMTUiI24wxC6mVGhrab7T4bSTG0Fz8R8WfIWxvofOajUYNgcPXLqo42RGQgVm9jw+94UvI34//NFPKDLL/ZDkpYYkJxI6yZ1wCACGWiQchx6gl6bSIRX5ZHpGQnXTwyErpAjz1voqJUJ0oOrzp84DRixV/+KLL4L8uedfYIvi29/+NsdLEWwmogwYZL/ZldFQMDDy0ZqrM2dfrEWDjTKSETG/+7u/y0T6xy+/QrujOIRAM2WEvN/5nd/BgAFL7XJl1gtCrKQgVDu84vduEj8OpQIJbMnBYV4pl8PjP9jyBPz/G0e+/EEktDB8q8w0KKa+H1kuczyAkpqAuiXGYaVFZX2UIjtYAGOLzZ87Xa5qRq4YgCEn1eosdcf9BoBTxS4keGh9hJCKEJjmfnLED0n80O7UeGPjB3+lZ0ECUZgkEXvt+ygICVuMEbc9Fa/AgNNfw2fQrxHl+eKJHJkCRjuTBIZURbF4PBBKmDQAEyEnHD+iZYeFgkZtqB7aGXhhLRMRCQaSIWlOg+rMCsirEwA29zsN/iSQtACoidmk+P/L3H8HWb5c953g9ba86epq3/28hX3AAwhPEkOApLSjXaxW4ipWEzMTMZpYjWbN/1r9o4mJ2BUVsTEMze4GVwqK2hElDkmRAAiQIMyDdw94eP51v/bdVdXlr/f7+Z7z++X91a3qfv1gqMmuzpu/zJMnT5486U9mymHPEvJMK6HwmFCHcSJhC1WSlQjM1taedFo0j9EiqrWFRkmC7Z560oZYqwcSeLAT69Sp47SZXBRE0ayv3VHrYUpRypTtmEIVjiTljtAoFFfdkUzl5+ImX54VChaZQk2Bmn77tu4K//Y3vwXNqBbS6PFQGAREnGePHN3AnhR0JArIj61sMSxhS+wzn/kM5c7tv2BA+DxHjDhGPa3psGorXpskAKay4K4oxJhHZW3yTqZISL3VQOe9aMo4Zf/SUuFjH/vYRH7FEBDexXi6dwkce4MEYiAVG18owYkA0Io5BqUSG/MZf7o3ACEtHHzCFuSHrgF7/ukHrcdXqxijOfBrSUVBd4MhwuEgInr9CqkHeg7DH46OD5kFMkTHB5NAIrd7xt4eGEmjStEMlCRiRTDBh5YH9gZfknNDVEW0BJArPLHxBNIBnDMmFZGnB+HvAHwSm1jwYYJCDwLMowCGG5u4JOH+HmTuca4dEk/JZFSyER88FnZwGABoRIbywrVPRJGHBlecV2MpE/LUcBiM587d2OARMdYLI+fevdKDexCZgm8gZPwPGAN1KgJUUfVAzrNIgLkhaeoL74iC0CmhQ+FB2sW5eQYMKydWWXlg6Cu67WCNNTVx5F/Ab6jIjls8YeO93+clDMg4f+4BVtVRo2EIAXUof3A/IYb1A1ZZkv2pV25sMckEBfqNz7SrSG8kFeAnFtaVK7dRlTt79hSrK6wxwBOOWIN5e2cTSrT/jshZ2wIx9L+s53DLPE/RgoEGGd7SGcPttXWVGsDqef0aIsuJCw9O4NH5xQYJpTZb1LlGaMMHDIwGB/UG7qiDpKDB5byYsPH3xVD8lTHPcQyUiGVTBfOPQaKGT6mi6ychUnXS/gbEGbMc3JHFi7b6wg1mDXsU14yxz7XS+MbfbQGMK9a41uFSNO3MgCDCgUceVSvv95gc6w4vehOjyjsJxYmMU69LOjQX1d4JAZ6W2VGDgqdDyiFiwleMxxbKuVguEVd4IIN1KiNwTF4UR7tcNtQeapaeLWpZk1IgluvqmkqZJveSK+6j5V48CQNJqxqbyeZtO7Xa2PNv4rJAGOWd/p6tcVIxnHBb/iweqaMFRivUQgw2+i/PlxWPWZ5BIwg8kGlcVBSB6i5C3NLvjDljsTSQEYwCzRFKTYniYxrwjlsx8dWHoeFXxr+96O3TapeIdnmwltog3SJdMHPxG+jb1hahEUxOe/Y6uWtvoRcM8ABFJsrYqu6wIGX53Yqa19zCLAOL+dXT3PsgLW8UV10/ywoAmgXjkuZJ6h5p4xif2g7wrPFrHYnlLh4JegQaemJEuaQIrYZFIm2irSDBqHeI/A3sQH/Gh4QhsEsTmDQKal1uMt7aqs/2uXC5006fOnmqtDD74ovsb+1pxBcl65SIh2oFSY4CJT29NKPGqFtrFIcDLklKtevH56ZG2+0Lp05ef/UN7k/maXvWJrr1QSFVPjk9/cYLL/zkO12Opj702FnSffO1y6+9+HpvVF89sVwq527eulpKz55cPjNV0tC83m+sX16/cbX+wx9eLKXPtnaml5dXuYmUO5T/6I/+6NLr146vPvGud36C5vg9T38UPjd6+5TdjZtXuGqtfmcDOzet96O35l+jKezltqE822+n+u3SznZqJ8XEnub1WKrENKlYKTJNZU2EHM20lW0aXdbO83YP8kCrzdzQ2UO7r9VrDnmhudNn+lAdDB48Vl2Zn+F9rZu9dH+Xy6LZJqTm6DbMPNeA0nFmuQCHpxHVzuhZYrmaEjhJVKozaPIuIgoM8FbnwBEwvXsA63l6nK64z87mdE4KhsV8DptDodCcKzDObqCTQwHluJo0LndkF8oRZNJC901iw7+4HsUOU0VwfWErRzJDrHgbW/DB4B9aVNxQZ7bJgGn28zlhIMZ8bNKgKHyOOw6XHG97RaNItcbEItHkTWDzuB7fVUlpv8iQVX2EfjwM8ojiXJToOOPmE7V7Tl5MpJOnqBHVckU0wIQk5Um39/BGCXsHYfrK6E+ovNJE/QuUqpW0HFjTJohg4k04SyvyDbSRl+AOMX46R8DvCL18PUfqSGIJceTIDfUonK7A07hNn6vO3UvNmpNUm+vTGI2hP4xYonqwsNCpTqNR2UIfgTWpFErLwzd6kq5BusDZUedbxkpcnQLFnTWJsk2RUcv64vYgJ30MK0drtEiRQZU2aeh77IBzn2F2v81jo6g30s1IAa/f3drWEQEZZImmCPVH9IL6WhykQq2ePMHDzSzPXb98jWXitY0tyoUqKJWR2HjsYLvU0Xobu0icLVwF4s/8iki4Wf5g957FRTQRLr9+ER/1iPSb1hvT33pcESm5De3pYQfhBwylEEkS3LMQF9Eg/yCLhj4qwQNx4SnA6DigrLq4OE/pwALGqayeaOq+vo4diKE9SUbGn7kfccG5uMgK7So7AvhwL6qOkUzP7e3Vu50G2maoZSInzDKWFle5ZXlpaeWWvbfhiojep6vvVY+vFBoFrQHdblfra435azeeWVnNDWskx81ciFveHyUQYDTUzAyzvOw338+uXbpRavTK6Wx5p33lm88zTGFhqDzL4t5S4cyx1Ny0GIQvDxOyZoGqPSvDw9T0wiK6VBIFBg65XKOcGRSKDz/yNHcBQhsLlK9//0dcmXLy3U+Kz2JxutBP847scL023y9k2sXCqMDLBnASabRx7XBvd3vu7MKxM6dTp1eZtF2//GZda8q0zfka96AOBqcXF+lEZuxSAmbOLNVuNPd7mcHyzHxt1CmmC9yU0e406HFqM5nNYm9lOr9TH6A7DYeLOgvZkSYbQ/ApLs4bFnp6hDDThYZ0d7/ZbHQqszPZHmrdHBAqjiqlM0883N2sDziTI13gUa2xTzbKM7ml1enaFSoia5sM25BxBjFcvyYhHBbSHORq9cGiITT9rDopE04lbMZkXGKvJgjFvD4KylnePxyUlEfGLUzwzp5lZ6LS20KF5c0K7zVkuzdeeWG5lP3uV7+CiD7+znMnF059a3Dzxz/6wsVXvkgv/Csf+eD73vF4ar66cenSa69c5Ary6am56bnZVLpz7OyJd31iTk03tYphKa1CuowezMrq6RNnzzMF/ewXvkLoybPL9PX0ftRILgFQs8pVAOgl0Q/3s21aj2E+XWC9ZpQrzJ4599hTxx+gBJZzrVFnWGRHa5RaKFRy7f7e7c1Us4cW5eh45vr127fXt5aefoZSG1AuxWx1qvjSd3/MLIzz7w8+/sAvf/jZNy+/kqb9YZsYYdcQGnnROhdcQv6dYybgYqI7mBMwIbx583qukGU1lBH6ufNnpqozjElu3LwMz6fQcteFBv3VJ1dYZPzd3/3dvU0UtRbsTk4aPC2kjuxcF4VnY2Kv4Wp2SJRSsHE4V35pIo0P8GQwIsPKT6Ufvi0UmKRRC0MqBuPuZGhUfxNe8URLXkm3g0i23IxS9f06au1M8rWAO+xzAlPPBiJQFs9YF8GKZiPDm1N8PdRz5G5xWpVTG0LqmTAswOHJIyStzmsvv8JUefn4CqrJDKgYqDFwYx0SlTEMmoPEoqdBfmASCK05pyPS5JHhGBrG4BaMdIHBSj2CGdrr0oG3ITpiHA7mK8Ob0DS0czOaaWNArrG9rQ4rJYxO8OhoAb4kTrmwsUTr6j1pr6MtE2ig7vAWbYE2wy7QYH6nA6zKjujzygiklp7ZHZVGgrQTEDTmlzCAlpzDOj4Oj5OVEGo0Se02Q3TcoCJf1vtJbHBgA0mBwyMA3I2NPAmeLR7JGny2rjkSdhJVasQzVI6EVXuQYYNFrbvqri6kUrpAkjS3i7JzQ+3i5VEAwJAr0Awj5Me1HYWWrspCNLuxJMRV6In99Iu//lKp6ekqiVK8zZ69utmku6fENCc1OJoNncuyL8MjnsYGX/tyDsS+9/oNVFjdcsgEWk80RuBEEoU/qWPrUZz+Vr19/fs/Qpf34UcfW+CaINVcz53GHAiAVeE+G2fFii4RQn42ULzc2v8//P1fXT1+liEUh6x08lZp6b9WAyS27LlJea5Unh4M1YDjVo9vUIgCNdeyme5w+syEp1TIzCwufO77L4+Wz37qvadoFXlvA/DsSGdQeFEGO+JdlO0E64DDaOynAbiHI2AIvmRNtVrASDXumcXF6qKUbCATjUmukaUycbiTNpCTAYDZBjVjB5U719pjcys0aKro2tJu2xs/ZQYEREescvlCt1+7vQUfFs6eXZydAqYxjdhohICMERHJZqzVQXadMisXpQS55vac6Hwk7bftqSPdyvVACkCq8jCRUwVcy8jhHXJhDZkji+IqFQTc0AkzhhRIULWGfIDNHAessWRarfQw4mi87eCqbOKnUGtexGCZem3JGzTU0u6UctwKlO622pnqFPXT2iQpKmeU9Shdq9Y0V1TBFJoeFA0jAm76SvXz7U6mXWul1Riq8cGGcm7Z5v2Ggb3XAhKIQmzgJsfmJT2eV5sQGg+jfkXNFEKoVpNniG0FAC7HNBjJ1EBoVCstf5NDGg1xKp5e4iYIgy6Vck/StGzUF84NcBkaCI2pnVyRLF66U3/x2p2dLvr0HLA2zrA2CNvUHI1FFEZx2d10tUJavtzMmBwANn0pYrYuGM1av8DNG9nzZy/cunG7me9K8dvW60WEKhl1jTNVtjNn+CmpPS4SZcC8eeehRx7lUNT84hJbxTXGYX1E2wj1ElBJygjPmC7zii3TvYnKK/bTbyQMSa/IbVIcu8XOSDbS+7XGT156ceH2woVz5xkDnztzlsM6169f5SnmFuNHDnRyOkybhZAjScUgGWbG5WXvoUvwzAhGI0Wloo7o0qUbTBMuXDiHkkF5YYYF5enp87TnnV5bCyH2WCgHw3u8RWBXQt3ZvE2V5CQH9ubONmwHVa5YYImL0hH2sRl/etparV5bGx5fYX1D64JmsstL/c6A8Z5aN0jHGmM44JK/hxGR5A8Exh+GIf44+DsRBAYolqfGT4bcfvCfKFqHOYgs+iLIDd/BESDxcX/5mDsEkYUQNI5IbVF/K1QEe/QQhU9Cw7IFxINkEsag3VMimkjFmCYfkLg/hefI8VGubXk6JBcctGOE0nSClkRxWoOsMwwBBgfY/FQDVYvPkDSx4ILSMtFMRnEYsmHwJkBOg1VLoiiPJqtAiISYh8LpopBEZ27HGVKfCHd/lyPcYIl9DgCSFkkHryPBAgUAB8gjHSRhWRwHgjygJ7qNsdK82KNGDVUKpvG2n5ZGnwa192GD3afy6jEmk33TiuUuC0lCbIwSrDFHQnqRZyJ5TzgAxDiiX4fHBiAe5Kl0ArkWUfkNGDz7fHp5EWQCNoapt6RIlc1KlQwwh9TQYTqNKu7uzqYSPVgQTkZEk/1IfpAK9BY5/UrrzzVMi4u91j6ELS/rwR9GnbTg5UFFQ3jTInn22Q/83b/7d4vLT9K43NndZdPsDtrJpqfPm9q3Lm+BZ2NTul0sQKNHkEsv0YXQo9Ag0tjhZmaI/S/+37+HRhLAnCVnYw6fTGmE4hXnU9B96+7tMtn74y98EW2s2QVVEMusqlgyFww49lodFiPo/PEn4+A5kdMLnkvM7XR9jw7mMIhURC9bTty3WgCTNIMA2MB+3WOPPXbzhVe5y4h1G4gsFXXxKFGcV2ScaTME0KZBCY0yo8aeLTpre0uVV4AOzSdcZXwEDbNcaDo/f3xhGqo4lQZCXStA7Uu3sFmAFqTamyghDZNiwzgidk7IiXtrAMFoA+N9pgYvCYkKcQNyUgyeOFzA3AckySDc7nHYfwLMIMeVPQkfoxRmmKmIcTZFSZSgfpKUCCw2SWzu5z5JO+kfx4t+AYsTTPrErLaqIVT8N1phFBLh7FLpxBkw8ryA1XwdpHZcRqQxERowTBB2789QXkmwQGEIDY4kmLvF3rtIQiCJ6LgxtIpFqs3sbGZmhmLqeOeFkhLdRFrX2h7Gf6QPqOAMsTRvB7kBOZExqVGRe/RxkBfSgcAoBXDSULPQwwY7y69o4n3lK1+5ceU6d4FSObhsiilVkhjg/TOZOpmCKhKhO2DUTf5gDx3R8eMrXOQKPCc29nf3aJ2AdNbhGdMsfKANn8ExASO4uxjl72DuINOlUI5IHpUKCAL+4E9jRT914aEHYQXjPI4tUyjeGN4lwcgbhPANldWlY8vEpXQ4icKKEq063QStHKG0Y6whMgiulotwwBegAxtBlHQ7XqKAAcVSms0PPLQIAOvYSUpCBfGpNasVGK4rQtIcIVtwCMk+78SRl9uXUXspPXiMPHJfA6ZoWi2BD8YgrQp86EMfwhNuYeneTd2YH3cHeNliDfhhF/PzJD3uBrjVadCX7VakoFEoF6bndbUx6Var04RyhgzIwvwUuxNIV3FxMSobpvqsEdQaHbLd2yd3zOVxZmb1MgcMgv7U7EJ9d7e7u0eb3+5LZw1/KORZcXAywkKuvMjQiZOMZXRQIJuXhmlequFRvviFS6Clu3z5yhuB+TgMRFlBkxca+DScWvKWrxmHEbB9ujDj6RqsEECrBn69/mYzeXQbr//gdUJR9UUR5vvf/z73Jl9YPY4GHwxhJPDss89SdpVCCkahaMz8PlPXgIROmYnc0swKGwBlLdZxHclZloDL1QqKyqVynh589dRJDpaROipLyBucQcDgP9WZbST2sLWAa62NpwXZFByjYvwhEq5+/NO/Tk4LDb02ac+DSdmNuBt3diRSKV2fAgM5DHHpwkNQS6aI+5GPfAR6nn/pla997WtXb19hoZz1TUYaIFeuD/aqxrGDldNYh0UFQTPoXe96DeVHBuUUHF35+9///sdqD5LK3OJ8H86ndd3Bo088Ck/+xf/w7zwqqcQFIVmlaM2O6jlugImFwybY+iQK5kBbFnfBMTl/Tb/egkIrTSZ7OfzpEVAzd6NAlJtABnHFB2BFiuPgQhcLwYarnl8cPHSpldNM+uqVKzfXblMrKSkaKBa7kUm0ocHjwD7uipEd/UtqRomd1evo7Ii3P4g9DpYnGIsSk7bOcqPJF/4KikcCPVsFI5QoLMTg71MIYkVkWK9KKkwmiQgG3IBhgFEs9CTsJCISAgBijE0QAKCl0IPbowiVzVJwEIShbQeS6Nh8koSwm7HwyCKUtPyDQD6x+bTMqiP2z2QUfEgFWTPMlpwnOhxRLlBBLOAJpbaqo9nY2t1lzJ+anc2WihWEnzPajBPofGgrDAn5EnwylcNuaBFi4xXVHzIQrfsfVxxG6Fl7y3QPR3xLHxd+wGgp0QSj66SZeeDUCVpyjwvPLXVlOeYAHuI/QQTSztAiMYWhWLXrrv0GX/cTAsBIQuxAvHo9kCMtCIlxPipNTyjYXii9To/WtdFufOtb33rPmY/SZwWAGO0RjZgzKgmZdBsxGogoA75CkqK/0mvwdgotEjzJZyROiSQSIkeOVBeYx1hdwE1lo3zZ0SA5MJNHxInkVk4eR4pAaLxSo6cGH2N3vhsVWGOJIooItgBc9mlib7UG/ErKQNjQcpyKL0DzTeY2divMSsER445D9DvxOREELDRDr2EQWQFebo8etULjthwYuMGAgR7KkzAilS1jxRiJVU9pTVoSqo9AkC/kMEmbR6d943IKSVdsiI4hNFFOcVji12kGEvx4C96G7t4X4wOAG0fkANh4OhocUZQEWo8ItXazmjBT7kztKXoeJgMeH0flkNiHjfY7rREjCTJIX8/wBpwMlhBLeEgoIvq+973vi1/+EqXvuaDRc/KwnQM+3o7yyH5qvc7VoCgaaqDLmZ6Wbq47nPqRPk75kUE/iycE0B9tbtxhjPTggw/TrnJIi9pxnY7w9u3d3T1HDtuOLE2VhYlxolDw0NQmiphK8ajVCy+8wRGEk6dW0bPhJDoDfsQUcarv7UMAe6MOjBT59ieeMI2FSffHfeTyesTYKCnt1FKRyQ2jxGKuhDdMo1mbsiGZ+leHNEdMYByZ1Uec5Majxd5H/AY8hEF4yDn0CjrBJ26NkY/lLnhLZuMlaeLqLk7tT4tWAZsRU2NK8Pcgt2MQ/apUQBY03eIwX8w1jbCoMNjNEUZvpRRTq7WkG+MUN6iHdI5OhAizWmkox3U7TiH61fqtjLbawWk0swMossDAtjq2Co+08RHQEcbUZ1GvVHpsIlKL2LkgCsMgfLx1wIGPNtjotrS5zwWJgoF+DFHa1MypGULp0TwNhbrLpoVc3CFsMY8VS/fR8dLupCQQ5PGwk26xRiFGp7IYDeElqQk6cTsNwEIDn/dhaC5VJgHSc01kCJC+HpxR0soSPuDHrhXlj44dtt993DMO76IJzQKK3bebnaKdqZa4NXB6OsdLdsRlZkNPwIzBYuEzNyWFMraYNeKTXGBACb/sRw4ZfMfZiVnkFJosQZPAyANgLjHJy0m0UnLQ6K4cA3bvMfJ4zUg+CI3xEJu1E9oFHBhU9/RjnfT81DRNfL3ERYnc2tjVjdWp2uUbO9XZCruoqICAwnjo+SAn0qllWZ502bsWmlGe7XuyxzptoYmuVrrSGj1Umd/KpxhIPXX+oe9+97u56WKGze8O+nKZ6czo1PzMRj21MlPtlGZPn5pePcWOZXaU3wXbYLD7yV/55ZtvbHAGLdVvcOBle2ev3WxlS5ucTq4Mp5krz5bnbt+6zRHmpx55x//1/7jwl3/5l9Plwq9+/CPdZoNm9/bmree/+939mrTJHjx1/slH33nmgad++7d/e29nb25ufq+yrTKxp4LrLGDS+5q9byxhfxUayo3tUX1rDZUNFl8yKvdpzpFWKvOLC7jZhqVZrFalJ1KqqmFkARrecq0nW5EfKy/wiCKvhHR77Wp+irZcOjWqblRRRgM9JmIIHR6khABmB5rY8Lo16Y56aKuh4aJFlkquQ0O8ulShU0F5nFS4ApRRPfeKqhwHOs1CBIQA9c28138TBTC4frFLFJ946/ZOsj3UNMZ9sM0RNTL4M6mWj27dcsHhQwLjNVRbmWphNOPCgbHo2P419om+rXEjo1DuIOP20yKHaOAVV61uRnXG6o7TH7eeiWTi6pMcNoHN1EpiMO/8YEUMHCcnPqi1iG1V1ABjscafcRz1MMn6SyJGKJeQGRNUdrS9Bk4DQV1Wi0Qk8co788QhL6mQMJtwrjidUSPh6RFJzUhIHMfEZzIockd47Cvu0Q6DeZEF//AZhgKhNZbDSPBux9pPb0UpcUmu9MRoA23w0ZmaLs3OFuYXuBOhbuKB2j+hg6Gkq96jLqS60UaLEneZcX7Zlq1QkUfXhQAxf06JAUciFhetMGDAnPQJ/biHBpsWBczoLGCY6/PY16uvv45GDv0mSpZwGjSkC4zTc5jVkcYHgyhWnwsmjzZ/4UADg9FHH36EYRPraCyDwiui0x4AaYo+gYoDjoOEHwg68sOFKEgEyDFeOwK85E08VAj/I96GYBvHq53fQ4G2ubW7Aw2Ss7guJwAjJ9kABpvebW+3xlLm2bMX8GH4zjh+7db27jaq59kTq6ss9xCHAz5cUsqdQKwyoGmLDwfGhEuqME6yvoJpcXAml75Zqzz347WzK1NaxR7WCNWqLWVLdsRGYaigWUy/u9nI7rTZ2WBmwlvtrMlwrzIyNVOo0s6neah1p9P89sWbXL4xlWWpsfLgGfYWUpUSoF4YgxLqr7TVOtJFCggYqkvl6bn5C+c5zc6uIMxDzgnhJs5Wozlod42YsWwA4acIabB3bq7vpNb7mQHXucwcX+IO/nSxrIvYuMCSBeUCqwO07AhKl0fLyUWaA1Zwu8xL4p31bp3R9nReV3NgcS2xqGE+zJqpXSrCGlu7XQcPnUez3y3atWlcoi7lkuoUuejs1Vmj4nprJqWVoZ4lpFOgPaTswZMr5CUEPMYwhcLdYVkgLJWfnepI541nHIaM5aSKeLCdoawFp8KQo82DB6kMG56sldLrMGsv5yoM/77xjT+/evXF1VyJxeLFwky3vToa9lj2fezBh+FSp7VDHucq2crC9HxlimlSId/f2rzeuK6Hy5554iFkcmuz9vpLP9hfv85W9E6KhmR25dhplqTf/e53nzx1anlmUQv3/exjZx763/2nn2E88OLrlwZt3aZ97vQZFIp5bF2Xd9NTnzrx3l/6AKeFsjwsWJqWcGcL2zv7Fy+/wRS0PFds5/ulobJ5+tTK3/vf/+2n1ussav/5pdeR57mVJQYOf/7lL528cG5upUDHt7w088uf+NDZhx/gVNOrF1+t7e+++52PsdSe6uiZC69hXPENc2gpYRDKY2LWIYPCx/e+812GkX/jb/wN6guT2I31bdbm2u0mC3bvec8zzBiz2QLLL7Mzy5/53/zWd7/zAhpMmaydmoqxWRGoPqpQrWLi9u6P1Fnn4u5ja5YnafB229vwGNnRv/cDc3TMo3wdmw8UXP4QSwekWMSxg9Kl/lKSZhMWq/VUf6uQPsP3UOWO7MO3fKXkGJB8ig8pYgNe46L+YHdre+fOJu39qbNnzp8/Qyhn7Gq1pq1BiAQwYJgjyx3pe4lSNVJQoBZACYG5zB0+MzMFbual4qO4wyJab8i13agRJJ8UoiC8IQUt2aQ2gWHAVdRadONMAkWmUSLkEYrtDpKQhJrRrM2Mc8ltYpNQXhtkkKaRGA/3AYUDbDiwjZkanLg/NqjgBq000fgEgFDqmgMkIfEhId0gbzj5JJSRJ5HYfStOlWEnY0uPYtEt0SH329AM4vZhFHxTEiCDTrahSXFrc4ftWAb/4Eex9/TpRaozq0K2jsr7jloqbXX3kpNN4TfOj5PDC09LhoYTfwzUwD1dA+/Ew4qYGwb+ti1whjhe7nwqoVg+Q+jbdThiGLxfbzBGb3ZptLjUWIcjVTBiniQwqhYaMmh/Ah8WoGkljq1eQH7pVzlLyVwBPWgimGHrhYionxKDlwOynLuH1QslTmNFhQVjVXfUy3FgQ57cfYhaEE+k4189cZoVvT/7+k9+8zd/c3qwaeLkJej1VbwNJgiYM4qZTkLlFjLoQj0W4aIPSSMJVpKPn1ze2d+s73dRwNVShsgZ2Olwbbc4fh7mIHW0o/ns2eyD0zk8Z5PqlGpsrA476XK60NX4rWiLj5wcZSGSMQzL6LknzkxJUxJk6al0CezDHqvxTMyVlM2OudFY6fRs2YJDUKLQlmhsNo202XXbGsJDgejP0nnzXgT3xIEoXlZzUt0W5RmpF2g7QPk1LkflqEyBKgmPG7DDns4B/A2JYrjDbeHHpdHPeHkXTy8L2qVMEamgTogzzJt9/ErCLNFSM+1EGppLehgirzVVrmPI1m9taXKvdsAybMM/BgBosXJbN6+xQAuyyhiFLFrFFE8gTO0wlrXJIhSOI7ow0qQLqkSorQYgz+IpsZUdybgb2AwMMWRH3KDcwcAcViCeLwcWZxigcagljf52ppmdunrj6svX1/ZHub5a2QwnjkFCqsZVi28xNQOj9WPwwUAonX74wQfp3KlQdI60hAgVZcpAETfCQyqwkZHtE48+xjlIKFC6sQnl4sNWW8tjEZ+pnPb12TX3HoeH1bipQxUNM1nsUYHGKH9Rv7AC1Iz3rt+8zeCEsShDL0ZQ5PfUiVUermBUw1afkqcArAQmSNFqjo50ip+UHDa1EdmCHeCmQSJn8GFnr9dsXrt65foHn30f+JnyLJYXWBZjk8kX9L0VoN6JkxE3dNmCVgjHpTSRePSJ7gkgHDVCQz3VZIu3kzmmm3y44UOrErO5cknn3RyN7EO1LEYUwejT5CPy5yeOPvZxV/D3qBPlaKFK0dM+FIVIUYjjCYkm0Fr0BGHCZp9uwyN84No4ykHixcRYLyYQkIwbsOEIdOJ0YhJ+kzzxIYLoscFrXAcCDqUGTMhUDOBURDZ1GH/vulgZcN+QHYjH7RiCjcPhnQDcVM6AFHiPwsgaSG9oompm3PasEUQf6LFIRZ4xYwMqHJb9iConwKObfxLw5+D2onRESfy4o4KJWeuUAKlckE3fLrDx3JkTZ2inekVdYZOTymklV5IqTc9aVKmt0qUZPEsquLnW1/FgW0J8yh/McapjeQvpApA0iuiNccIXr/DFPQ/B7Q5WUB1bwBk+g8ywRQBi/LEpZRcGoruPVx0UvYAXAfE4ddgdkvd6s8aQfSJR/wQyKlH7VrrqDXlmpFuw7o3GHZzMPElRHGRSbQ+2QAPcJhKTrtd/soum84VnH2SxgJPheDJ0xjAgYbHjsbNPsz/J60Os7Kxt77ItdukaD5isXXrjEppd4GFS8d1vf5sr0jgb8tGPfhSSUIxihA7mhx5/EAI4EoyGBWvBTFc+/b/+zCc+8Yk//MG/YDqxX5mB4Ize4ObJISwWdGUz7hZtNoVQ+6sGgWxJeRO7NRiR4s3bt3BnS3nryfS+wVSuSO6OzS7iJgqEsWqMWtZ3XroITq9WYCOW7+SzPKlUdOkSM+WozYHPra56R/iFfXx5non6VLmnZRTkTvq0YjSsIa4f+fElZhcQlRxlRwqxUU9CdmI51IeFMi7DCWHgwTa3hiNQZ58WinaXsUI+NiUQ7ci2NQGOU1kwPBYrGtC4D7YbSDKH21YFxq0jIfI/IOGWC4/LohWOuM2J/MY/lm1LOqoCHgTGOFEij7kxjnjQ5RiI4g4LVE1JfI4j3M3zSH+imb+mo44Ch7NxjDHhCmQ7tvAJSNKdiPGzOknIMTth0BZIBXXS7STF9T0qzZA8SJB/9qipWcNy9NIdUagIBOGg+nR6abUk3ADvuELk2AFkEA1zRbTJbSvRE2WJv2PCHseMsSV/CWW0JJ2RTIaDYxdff31vt1s0Mn0MwxxbGtBCg0lGnXQ7BHWRNorecn6+wnX/DGEZj2IYhMFDXdVD3jmnz6TAm9dJNEd8e8Keo4lgMcBYTxW0xin6nAB7y0/woEdFC4naAs0ys1pP1GXyyKTBiT8A9AU880ZOkQpaJAwLhW9eukIohe6ftH4slNB20WK5eprhj4T/SPLARq/KYU+wff/7ux/84AfnpdmsRJPw4AEKH4bXBDnZDuCf2Agb2YMGsgbazd1tcL5y8zKUzxw/xsZD5dgCvZG3xmyJeH/HMSbNrIrFBx5/HO0L9lqFyios3ZRyYUqISWJwI8mINBnPsOBcLHZ4vhc83S4aIgz0afzbpfTqyZOpgm6dZlgUqhJtLkWIiE3PzSE54Gls7dKnZOeUO3SQyAYP6EEknTGbBFy9RM1CRxuqQKKM02FhqlVuYiY6mWXYTigCyXJPtj2SprDJMMDiCbedmLIPMC7aODw7AMAcd2OTKAidt26HIHcQkZTdDfd8ksknNBRGeZaWnr7wMJJQ6Oc4DVAu6fgqK8XkotvehYa8DgJlS+ks2wz7Z84QKV8sA4+SuGOGmGFjiiQKgzzZQacP/rOqwgbP0+987/Lx481mjXL8xNmHEOD5r3+LLWcKFO6has1oocRRbjTjVlY+/OEPs4kLtowd69Y6RS73b/7Nv8Hn/InSL//yLz98agmy8URZO3OywF2Qne9/5/Of/zxbynCbKSvayo9+4l3A1PebUKJ3fubmqt+qcpv2N77xDdaRKSbng9uBpUnPpBsAWMpuOjSsrp4GA6HMYJF0eP6Vr3yFUQ3K0dL6seu/GMBwZoutAcdMXHfcLSGkERgv7jhKMv23dt+73Xvr+PcHgQx7awO11AtINeORx1UeMBtQqw8CIJrKuvgarDxNfw1bwHbampJitxN+wg082YDgzrfd2j6bggwjUbpnNEqbcPnNq4wkXdQhw/FNcJjaho+3FadP6DlZBpBAIrTYe9t7zL2PH1u0uFHv7BgcIeRRFpi+jRtZa6WVoAXDHyKxo3zZsovXKSKC3CknIp/4Y1h9trqmgTGeFlH1PSSN2z+xCbU1TC1i+vqIowIzxmkjYgRpIzqnxDHgJpRE6SvXeKme04p+u5SG8NydYEsXxjFQOTb9SLeSOwt8/d0XyFLIM/cyU+upOLSTS0vHnGBL2sb/7EvncuzBOAGEWtumjDkxDo8dPkOKgBqeqGcRDffdzzpaYgS0IaG3DJqAvM9PX/FB3uAhbRonh5546DxxRTbGKE8SQ5EBaYI/onlEbJhRAMjAQgUsfmD0xgBIkApaPdB+7Wtfo7xOzE07Uqct8BYH3MaTmIiiNxfsGbMTT+v60XecESEmUwDI/VaG1O8Ghj+hdDagQpBokBu1G/IxoXWBtL4uWtOI9+yUKCBAFvIFqJ0rT9N05zs8ONtqbmwTkcz6MIDs0AWoCs9mEDDpPBUKNN1g6PTUF9dazbuRd2TOnFGa6sTGa2vYHYm9D/ySIwwJhbTcxz0dlCA+k9Ec2GFIl/wGSByEile23OSSgB9gBAnSHPiT/XzFjm2ZjyeRJMPTBRkRPS2aQR0e8vK1dQOP5c2LJ61VZZMEgjBiy90LmihuHBibuJ4dTz2EWjY1o8QAEM1//TsiLwoia+6NQ7EslHLn/mXGfmJIPlqEFJ13MVCChJMOssExaE410ZLzSRQyS9ONoUlHTgDDzcYYIzd6Cucz0WFZyEIyIQDAgA+UvPTS6xx6b9s4PyE4d6HpF+wNzRo0ovHW7epSwbU1lkRmZ+fhAIMxquGlSzCwgb6GZ8dsiaWXWpI6ZV/+kR9848Q1jQYLEBREhzeguyMebsGHoSatB20UbgSSZWju5AAzcuNIQMGnuHrXslIqznYgMSRBRuhmsXd3O1pGz7LDJLEhFe6WUwSimB1hJVrkHycT5wHo2MsiqI0zXQyi4BGoFEYD9LromfekHDLeS1HGkCPFNdsneEgLPmE9jBDt38RUxRgsAeDIjKH2b7cj+j3JKHcgAJYEySypHZBIQpTraEtWOPky2/FF5UrkKPWYceYj+JiGqFriY4p0muxZeYgUCAjwsUAc3e6rzKmuOuyj5+RB4roY3GyrCkMpxB2AEoIqbT2lmAyIPLW76RY3qXFO2vIMV8U8tMbYtzLFeTXEAOEmwPkCIu22IVxGKoFqvezDghwFdjRMsMUgFkOZvbsMQAL4aIABdw1lL32LHeGJuGleXjgQz5eStYzItlASsl/bEbZbabp2oygbYfhnS5rCoeaDQd8Z4/J2bZZuPccVXHCpODOFuOfnp7EzU+rTUnkN/tgNRbHE43rqxJUjKhJRHS1e28JrNF42cqDKCTM7yg1ux2MEi9c4Itu4gXTgwz6eA2BToNie0+BJXtCmDZ8CiKUk6RnXOPmxyQBMZCwX2kDA6OE7zeCzvGxd0NS3M2yWMhzrzXV7dcoQ8hAY0JtGm9KFsTzbLKQWkulnHn/06e6NKygwnWvypEZvvjx9rjO43Ozube1l5qozmVzd9kUZiTN04FpjdMo/+MF3Myv798/9Nv3B8VM6+nriZJ4jHovzC8Bw72G5lD1+/NipUyc6Qx7oGzRHuojj3/z//gNztvr+MJeZ+cs/fy6fnvrUb/za00++s9na7rb6r7366vPP/yRTzqPxdPbs+V//jb+994zGZ3/wb/9C6Q7ms6mlYfUyhHPNHbYyBSet6uR04n+UR5NmOJzJ6LjxanVaA+g0c8YqFy/TyN7cx9q7w1vE9VomvY+Y8Fw6ZroyjcwUC7O4F+dPzh5beCrzBB1bfaSh53RGSwy6josWTPLLwLHBKGemOqAFr9nCSrnEpSWzy8dm8eH2SRp2XZc1QM2tO+pGWgxFKje31er4v+40xWizmNKJJCeSGRWpSSPVlhTV3phsmD0WP5c6kyxHhq1Qb9BtW8vjWUqxlHqrqyXxGKcYKKkWmNtRBPuJW28LjSpBFC4CPGWT+eQQMIKIx6d8RisoFsD+uOg0d7zIfhC1cSZCEv/E+Y2/4xxFJJg8IO2ehQTwgVoWpWk/4oDqYIwgosg+reWMAsQWw256K0T1swteahN0ky7yZ+ijdANAkgMGcA/rMM1jYLGO8nLRsJ7TT7GwpQGQMhUb9Ptwcn4Ym+MR2B4qDSg0ItGpoX9ZnOfODYZ7KDR2M9qD6WXYCqF5l4TUBmnuIqvRvXBF+bi7i/vrkDfhmzQiMi7lZJj7R3RGLb/CSRp68PcgPjmzgDoAFY1JGkqF21sN1NC5md+1zThy4eunAPCoNsvTzN2VowQH4q7b8JN7lnG5WrjPSt8CUziqKrO47fU7pMg4TQnrxmQVlHKWyB2eQnFPQ74Ivx/Ie6JR4OHU8AE9e1cMFn3UKDUsuyDlbilSc8kKLQ7NCf0+r9KisXJnbXNnZw9+dmzDbH5+mfuguRpve2sXLjLwZVt07c4mz1IFImN2BI/IweOrKNL63ZRff63Znd3/tXfPE1YwpiNt9IEsaGqSiVoT26TXNlgxLXAz6Sg9hcYOWh1cfM+FjWl67HKG2zBpiluNfKc726NHK/Yb3HDRat25+mbvIr05PfuJ86cKKysMfXQ5Bs930fuzQNxqZCol1GSQVBWBVjK5erff3K7NMmCxSiGK3WGb/dXK9Pzc4nBlihH/sFkrVFiU5Ep/Xv/r7N/ZbhSGcxVOS0l3oF/r0GeRDDzn6UuOfXLdOCWRXZzhxqvyqWPziByzKupLp7tXr+2sbYgD9o55capSZn1nMGz1B6w3I5lFFI3IrVS5srki08lRt0ed43nDEtfkpTrc+tRP53WVM2dlqIM0ZvX4DKayYIY8kj7/OcLDjIEFD7jBo5GswHuLpPU3HajTmoXfN2sSktkf9jYYG6BYlaO6DLnPlwlDoVp46ikeiD9NFdpu3Khv7bAojKKfFtCpKd1uHtVeeqC9FrVsJVeuzGT6g4VipopEcTlsp6/+cWl6fuX8QvoEWqWlfknL0KXSFIJ688qtTu3acGu53t587vs/Rpdn4eTDLNQ29zcLmT6n0uDtmzev7bbqi5U56vvrF69z1mp5cemZZ2YXKtOQvXD8xFPveebLP3zxlTcube9Vn3r/ex4srODfbnaY1u5nuMp8upSfadb6d27qXq/q3uhb33zxAw+dY/xQr6+x6Dx/YhX3x37p2RPLC9/51je4azjMNcgXvNIxQPWbxtyjLFhNvlj9ZOb2sY/NVatltpsZYHOHCsq7XMX94guXtu60mStynQvLdnu77WZjUJ22XiDuwQ0xmPQL/bZRwvBZZYTuc72u91q99TMgo8aALeIBy5Ec8Lo78YAl28BkLB9FJ33c7fxRrY+NYyA/BIk8kuMnQYdxLyJCmYuDcKrZsixjG0e07kZmC1VNTVlywqa14QSY4ulRDN3OwUWuyBs++zu73/7GN9nvefCRh0+dojdYZS2Ga/qRJSSUuBilEtvm4VMhbS+xCsboVNMBGWkrb26sd9sthBApReTIBdFxuJtBnm4ZHehuGmqhxwKGOq9M89+GNARBYTGvxV1PnbTYA9IERdqjvp4r3SNDLG7RcxnHIi4ZZq01myfJ2hCWvtqGgtiQR3QHA8YhPRRPEjLbyPYxsHVbANDezi0u0KTv7zUlURW7/UPXs7LOLHzAgJDtRhzkCxg+MbSQLBeyVFSzV1VZnvAgz6CmuIqsbpGHv4naapHfaPakpVXEIh6UKJmjDORq7BKLR3AcBfu/LD+YwBGT7b1dLhtkWZZHd6Avb5flWKFr0E12kGQgvSgoMi81ChUuA0+bAd8oOqYG3U6rXKkMuh1OV9CUsTlH/4wcgAZE8FMDY+utkAPF8lEluIq8bdXnIE1/9vjnvv6D40tTaE0OG7fB78NxNkITvJscSYrI2Dj/JdiUiISbv7HIMR44fnxpd0f7iN2O1V2dz5Ehhypx+yFVH3+iSooHF+pWS3qtnkxUSuXisbmZE8fZy2xsbrMaX0Jsc9kyKkOMSt/crOV2W1ntDNWtsiP0tLQrdkqViw1JyEeqrjrW1CRs1LFtlZ5WMrVqgWG9SISYWw7Kg3cRlJejjXPSIeGEA/Fp2Y8aE+fM0fHNF3gvXBwObBhUudT9xiYK1Rxcq1p0NcNRrznoTPfYfs5p0SZuQIgIHrTRqUO0KZBf7dNmZKZbevF478paqTViczfPmSxyTFXNIlPpdj7LH7FMRYqteaYoNCkaS4OQEkEHWSmTN1/jimooSZFxtTkq13glHV9MTPv413PB6gEl7KoenllsjJoE0cvMmJmtUuJKebjQzWpT4dUb269c3+KSELQVGN8LqZRzLd2DSXmPQxAtMJpnbDDTEQBOZ0GXwQCMWTy9MD0sgxPWnWemucaf92hKiwvL27v7XJtEpqDHy/FgRpRL2mBsAGBOqVLs9Absdsqf/2/HgDmITTKe14Kkz326A6kifTRaW9tgXMSgBQ7QCVZKWgzJZm/v79dBSE6AsZySo7GQy/9gejQYmiJkueiOBTHN6fKLx2jSb928vr3O22AXb964zd4/qZyYPkH7QyWF4XDb8KtT8GySL3wO4rYWI+GFYCO+LFjqUjktZaaanSle0krP69UZbsqjskSbD4lYRztJ2Cg4qlwS0jnmWkzchI8+rVEmGcMZEe1gHupBsg/Qoq/gM4Y3mAl/45HEKykT3sQcQOlxjX4QAix2mSHEkwjwfB7kQMR99x/Dx7kLEXGAmdQPRo/wW0sVyB9H0oRGhGnq27eBiONhiIMDT2wAGEBQiiDHTSoYgjDhc4wxzhFBADiSiYTBg0lG8VSCPRHkn4YtRjjJpSNj3Jen5yKA8uk+2METB9lxAUT/Bm6srs6zAFGe0fuhGXvlk3v+qbE8P0hE9KFgF/d84qZRcwMSvoUzwizu8QUzYQZuwpRIxCz9+EfwAk9wh1DHkKQVhHjezTjnQQVAIMyB3fNuESf8weP0kJzG9FJY7tBdnXpIz1NsbW3fDSfwcCZgAwmN+0NPPHH2bP2BdopLl6a6OohH2wEL4SOD+I2tNWBcAkmLpJn70WBxpxJLw5nCN9nEnpnt/NZv/db/6m/+Ev1HuqfljFJFDVk/rYd62BunseNayS9+7nPMIhgn05H82Z/92fzS3Cc/+UkQsnh9YnVFi02X3+Bq0eef/zGaL8889d5f//Vf//1//6fPPfdcakEkB/4neYUndZpQnmXALnGqa2rqXHGFVMpN6ZFBKhSOtPayu9ntcC8L56opAWRJSE3XrFFHB7xd39defXFK96X64TJnspeaHxqgvUYXbLEq9fB86rwAUl1SZCEFVKjywCIeTiOKUxvJQzRVUKkpSixnlv44X3QSrHEKj3XT5FgABg/BxCKuRzE0HpiUPg8c28kowdc9sUkuIPQ8Bhh3hNDgTyynyvPlXTslEACi8aB9O84woXVsgSQ1xMpaNJELGCzP+rK0gvcRPg4QUvTPRKyA6QCSmKljfkZ4EoyMKI/raQLnQVQHvwA7zLGDID/llzFK2Qn4A4X3SNSBQ1xPG3iEnNqRXpinvrcOtldCa++ws8hCxWGRLZmoMBiXJhjCJ3IREgK/fY+p9aSRNzXMZpw2gcUF7T5UPdYIFpf1WBmrSFzKA4EcoaYopqd1l7+m0yndAIzD3JGaLW5U1xz5hO2YUWLjUlCGdGiUoKjI0Ja7hkWSaUNQchOx+PSIwX/iM/i7g7zcDYAsMDLzSn9UOhGmI4M8FmvuAGndMFpS0RoHnxM0BEooR2YpfEIV7R63qcJ5buCgxS5Vylou7Pdp1R9emOdIL4ca8eFQCyoYtNpH4kx6ersBBhoBVi7YjHxq5SzLFmTfC9SBCa1WK5QjxODPI5eiR6fGNfOkvHj3D8PuPQDExRBkEqjcZYs5+g6e2qO8dl9+GU3U0bKeC586voQmMo9EcdIPaKVoNDsSehZImkmwEgDHDMoZXghYWuoWhuDEk/Tr+7tQwlXQ9BQwAQJgiCDn56C8wwW4zKm5EKNSWTl3emp6mg1mpWhPdalPRNU6pR6nbIuwltQIRdAcil3pIZKcKTTZ+OQlNRGW1rYlyHEjeuCEVEyxpzM6S8dXCIIq8HOjHgttcAt6nPjgIBTuORjwVFKMt78i7O7GQyGA6Jl0Htq0njIYXLp6lXMALGQr9ay0ZlinpnBZiAMZG89wgMLVBvPiIumiq4o9v8jQYL6U1pH8Yln1sZkRVTQpsHFhepF+kEuz0RxkN5eRyfVNFIdTWwMBAMzIjbkW1fB973sPMgAMmtEnV0+Qynx5CkjYQlzYAtm48aF8oQoleNSZv/fGBhl5fZgBSa3DfSclyEaXHD1oemcMHP7hl77EkOPhBx8gLQyahhy7BVtCNMB6L0PWSBTykIQvfvGL3Cty9swDELO315SwDTj9laVOMX87cfoEeYdgouBIFgSfoRxxw3BCPS8QKTw2Mb4XHRamsj2iiXrLeL9AgCQnLdfjtCzX4093kXdGWaV+VdWeg4mmV0EQDOGTm4AoRLbJkRDnCfCoyb/6xutsQVUq04RiWFnGn1jAOAYHxgcBQVYJYYAKGIVun5FmMc0gNQ5P0oIMpchAy1ABZnNt8RdsFCI+8rSicZuSgk5K3A0wxMUHAwDAOPDEGCXjmSkl7EGAYQh14whJUCis5oIZSYYwgsDDJ5C4cSQiRkQ6Eve3+DoYweoMdXlvfRN6htyaQJ8ofDS0WuRih05JlLX9rIV1UznEru03PBVGBaQrbJYXksDh0uttLJ/A06BhY5I0kE3y4j5J2wfnHpEoONwNjOvLJ4Hf0h3iHoYMQcFxGObt+jjB8ITegaYG/pR044FWskhlornFE9khFGBYNDUv/XEUgHmJ0KPwSRz1MqMRCp5/+Id/SPv21FNP5fONw4S5IBERnnpK2CSBP2LMNIoWiYZuoagrcbvtaEtmAg/ARMETNElyI3ecAX0eLDpyyj491YSXookOHqTdaInWZ7xYfSGBDjikCyoMDTX28YVj7Bt1tna4aaR2+w4+KMaBp9ZoIJlTVYkm3QRo2dADAw0pNgpKknwb3/lbTWy1hioQCZ0l6PR41pSq974HcxoIwwEkBgLIDsbKS9nG08HcAZ5krOA25qssHImINKOEY4MHoUCyzI4DTCSEp5eCT5/59NRRQMRBm4Idj4sNo8H3ekN6NLaFVFtjJO4ACAZiWxJKG7fb+LiJEMU/eLoz6XDImPYID54BFXgFbxBQanSOB/BafDaTxIkHn4yvXn31Ev3+0PbkiGM4I3iPddiGS9SLb37zm+9973uRQFaKwUPjb5zXxQ4MXxESLotjGs7wEcnHx1PHJgl3u8NSFOchOxmEABxO+j+iT6AWhQbK+urVa7Th0EPtrkxNc6zqhz/8EZ/wLkB6Np1m3OFT9VjHEVQE5JqBlk8cPCKVcXlZejgYtHZoQx688ADjQJgJG2m1sKnlcAwjpEfVA3nH/iRB42I+pCDxgCTYC57dXd14nrMLrJiwjZecgHO6ZbszqIrEQuzal2MwAQo1hsQih/1EVdD8vFSlZyeJDVRGus+Ak7Rr4YX2yrCNaTsQKh1crwmoc0WpK038PQvjfCQweLMiGNYeIwjSjcl2yLG/U+X51Q6U4MaVxBvWOK5iRdzTciVcE1oVOfHsj3UYCg5pN3tMM8Upyg8ZFJ71/ECuCECj16LVpsAwebRnVEniFKVQaSdZMsxsIIF0pT8pfP2Bll6N9axccYSGlSt4r+uEbB0WSL8r2Zlh9+zoeQ1CNW8jCtg8pUPkTXrYjpd7Ok+0iwfDo8UyC1GpqdhCXPJobudqtNik6AZpiafuTCuUF9mwB3ZkrKdpaaaVbtPEjCpad8hM6eBnZW4Oe2pOujaoTzAD5p42LpsgX7Th2v3UUj6WrUoZ/8il1yQliolIU5NEGVIykZiASpw1Cp014g3QUTkm8iQ0brhdyDPGJ4IeJRGHSkYwlqLLQERJQsYCLGmxB2Gfot9v9fDdXSowU129FQgySk77kaTHzW1pmotGt5Uv5bmkud7de+LBp5iDDaV3C8QhqYPnTi81UYmksr30my++/sEPvJP3eX+Jo9g0WLVMvrlfnZmdO3vmhxs3HzqxemV7u99EOhnipNv9dGeQ+ZPPfY6pY37qyer81PLiWWK99toPX37pzd/41McvX1r/ztefY7KXK6/QgJ468xSt29JDS0wqbt/ZZsz54xd/wqNA5y+cpmf97Gf/pFTKvfPdj9Mg3rh+i9HzB3/pox//xCdffukiq9vf++rvcor2Pe/4UKpf/vwrv5caVvJSRNV+stggTX/6eWWjYG9XVe2WvalOeml69uTMLHokaK719mrbOzVisYvI2nK5Wkb9KtO+USwUV+eXSReNKiosjTbPrKB930/11vfW+hXJBpjbAx2S5RJczEyxTaf41Fl0M2dyaNinBrrmkyyNWKzX+g5FrMaAd3gLaGlDIPuBKDq7Do60fiQKajHMmMZBJFtRuRsIFUN4VL5+HzHXfFlRRdKFZMAFFa+kl37CQhVVXoKUQyD26ZisgUi084IW5NHmqCBRCt2KEEm1fBwSEuTvRqIZjbe87hyFLYYVwmRrIX9pgyQqW9JtrU3EM/cX8sgjJi/CHf14jfMP9T9Os+luBML8wltaVHwcLdVaFTZeHB9DGqK4DkkaOVDgyBO2/M0Yu+KPn+UXqgINIiwYyZk3MMFr7PB1KVppvPyuN5ZyNZY/tcIx0VG5yryna0tpuj2cvQ+T+c4w3e4OW4N0b5Bu3yUH3mZ66wRyios/Cp6LBfgrVWfzpb1Be4cgYEjQFDOl7JUszUAoAFQfLvxldYBGADeXt66vbzBxRs2RQqeFYXZNXO8HkRDaf6E1o9yZCQgnHNAgJFmuFxiyvMi0CiGdQvsVrWflGXB1hIplNVEeLifmuH8LckIxEUs8cVz3geJukEl/Ry4BsMfW+qmjF6A9NScG7TwWe/uDIkN5FEwoONRKFhamGG6yxHl85cTi7OLC4gzDeh6gY0VsZkqLsCIdK+oaHZ97y58DKTBMRzkgI1dd22n+xfeuf7h07snpaEUGGHSH+cuPsjvrW9PcjZ9Ku9YYwwWFMsihOZ6ZYuUG/ve6vMXNQUGyo+fsiQj2tOk5TosWqXaRnebt1ua1NzZKl+lNjj14bm5hIbVQ4TqOFErETMtNS6u2Uc9ttUptFlLtzkgwkp6yo3SZESFjncGotV/nHjxEk2thy4UiF2AXBv1CudLer7fWthiULwzzN7bqC1muoCjyfn2228rS0Ta2Nmq7GoccW4CBrfU7iGVpcZY1hSEHYVB7HHC5c0HKtek0nUt+tsqUotne58JMZZlLYOz2BkR35qxWrPev3GA1IdMk44wDNc/UUIfHEvdriDp8guakgVU8+NFDzYg+ImdalnYGjpG/iptcIsZWdvT05Fjb8KlRkxNG1EsdIOgPMlkGmscXT544t7rd2b76+tXU7h5pLa0uMm3g4vfWsMmqQw/d9YEeS5zvZVcWVx595ALHgNAsZjyGfje7OKsrixC2e3sNm+so00M9PYN8cgcqExyGZUWuBxn0lyut9z6+jM7AKxs7rOD0NWfMzx6f426P1y9d/A/f/MMb7Vtw47lvfe+1ravbo+6Lt68unjnL1shLr7753E9eXjr/ELw9vZjKlxdawzJb4hwl7WUrt+7Uam/eai8unzh2av/KeiFbfe+z76N33tjprG+3HzpVefKd7/jR6y9evPTqyrGFGzevNWstRIh2EA65asIgM9CxsNCEJbgcqrAvowztdas7d9a/9a1vUOg8Vlytdlg56rSsv0jlGRm98OPXWd/kaXvd9xqlM14SIhlwYqxMVUI46o0GCzFs8dmY3a4dgYGqXlF77r2M02VFmiDxP4YT+pExTxkVK4bY7h73P3TF2p45QFyir4o0oNnUybMLw42T6UzP9HmjCBZR97m7KOv9v0y5kIcPXB27u6ULNFj9py8QQDzd9cPdVBx8vGekBrGzwqgTyQljcubb1H0m88gfYOSFRImFGxu3FY18CMoXdSMQXSWerHETChhuMONW62RxAcbgj40M4+lFHJDjI0+aAyWnFDHQ6Q66QyKatIwp8SQA8BYSVI7Zoxhmi65qTfX2V16Elf/EnVtYWl5Z3bp5h/nioMcbU7luWze2kw54psoVJTiy4ay1i4gkWcOA2VmqLBlD+CQF/DkZBXImWMJmF9ZTr11Dwua5MEdZM2B+Jw2hEEdR2mh23D8GPkxGeKtvIyxipsMe9on9RdXPYsAs/mey3f6A92zWNzYvnD9LL4lM0lhbg0vmVLxhLwRm7+xu/eTFH88uHaOnsFstdPewuKqFIVqc0asvv/yH/+7fvvSTHyOonIIJEzWVr9pvK2IVqthFjWDpj6MCNEcjfrVLwesy8xc3b3/xu6/8bz/yCM/K+hiPrVBhiExU86ww5UUJu7DFAPpVWoznKWgbv3mQCwCjgsWlWdYKqHS6KpgN7+jMiEMpLSARHr5xIyco7LY6beSEqjdTLezlBr3CqHp65eHTK40728xMN9bWqYDlbVubaHBwoDVd5Y4nLeoPay3md9ADb7FJ18lD+PKcxOWauJkyLK/r0d9UWxdTp3i+EJumE0ZRCgzq4BOovc8naMJQVcFs8i6LUMjGePWZAE5+Gkzk4W5sMMATMTA27naAaC4vNWQ0loGQinKnz9pcjwkoR9qkrIxA8MOwgccxTA2i0lO+Kr2MRmvrtd3Lt0t9VrLpVSQ6GnWlM21dzJ3KzE+1i4qOcJjIwELjhga0yiRFEygTSf4RteFqBdQspUbsjZAilAPP+htf3iVKWEyfWvJvxmHItWcQv9gBtgzPgqgUuDWcDZhM9eL6jddurTXSHJWTsRu9JfpHGj/BOaTMKfRM+q++9lXeV3jve9934tQprtVi95ca12BUouJDOjLtbufNK5cRCUiCHmynxG3/xE6mRVBfCvPyVBRzkN0kjLsV8ZD3BLbDsX5GH6eZtaghTbqtI6H2wBPdo407jHVBDtetPmt1kbwYPcqLClY5jSgGhjK1Blm3hFFzWR1R+88gGU1WzpByIIc9Tjq1gaT3yrWr27s7DJzo72bmZonSrBNJ+/34+CklS2XMESfVEiXEhDgDZobhOdbiKELeHmPo2uiUB9v9pQXaTiYCBwtD8czE/geKSiGH4B0iznkkeXH0A59Amr/EIkolkZa1FO4d2V55+BBysz3AoyftOEKEFjFCsiPPxA+eLNYposkln0ASbqjGfPQYjj8R+36dimhlD9khDp4kF+zgfySdhFKZME4eeDC0V3RaQx1xUj1XoVpLR91zbPgjGYJkBmieDhMy4qk7MG46rURDpLTw8dC72QHVkQBgAADCkwkdCRk8oTa4JxweFLXjWc2+0txZzlRtlnpXTvl9SRW501U6tRKvzgO8zrorbLFRI4N7cNK0wQ2bYqg/TRpLIpKZSX8nzARVCJETq9tOrkrY+OyxDE8SgTigKLFhd3cS0khBrA2Pt5Iizlu/ON74V4w1knAI7Ci+uSd2MEg7/HFhYOMd/qA+TGcf8AIZ3BMOguzkXQ3ONxlxo82U0RMxNO5MAutzVfau1RLZjdLAEERnwEFIni06/4G/zSb5yvJ5qP2DP/j/EhfDcz1PPPwQ1zd/47uvAvbcN15kij5zdgZtqbVb+9lqlVVpFqCXZv4hpP7ohefRnqtxoebu7g++/zyjsdljS2gtnT71AFPcF7/7AlpRL79xky1Q5i5BX8YzA+chxkbI+uXTq0g+X2b5e7u/TbqzhQqqW3PzfWheG/SYkyxMadc9Y+tqS7OL5J0FaHz0hIOdRGCE3We5RBVZi2IkCj858o0POjVQdeKEdMS6du0mieJP6oK3zRjKDTcKK+KzNqIQTF+ANltjI5oNLSsgN4SaxZloh/ES11BPOTsoJySCjzxN8s2llo3PhFGKfLoN/VEsk0AqhkM6Emwj4GgxIxdKIjbQFDuhYFzBHAOZBHMAGLvNLyZc4Y42tqNPj4jwuMOXiWN3REagxwcrHpq0A0DwjMlQrmNPYQMSH54CwY44YAP3gMHhoYcho3keYEWM6ujfgOTo4J/WN5GFo1EA4EkHRxIOT401c7nK9DRXgG1WpbvH0DDAwAfnPw4WAxF7rgQgFJwmRQe2BPA/MhUjQKym6aDRaHPkXVQJCclRa1jsCykmHXR5wHOeBRi25VkjpkFAqdGHmJ4vKCIKbhkbTUOqciE1bRl5xwUtd2w879gM4bB9dAUG2jS/iwgu4O/gHi18xjj0e6RnEsDdnnQADkTBbO8r5HM42lv5eAFhs4yiRU+7TP8ekZwA2j1eyvO4+OCg95duq5HCAdvlpRUuWu31W9ylCzYKglbwHmgngpAZbniD+ajqsGX45EceSwIgb912l/nDdKLie8mIAhM5/dgaCHyTBDIni8USH88FtnOMIDLeyY5YiqLjYOm2tDKLsC0vLSA/aU6k2BIzoVyeSXTKO0kPosKmIP3Oxk4Dmk/Mz6M8Bm7crI2Cgb0O3CRBb4UDH1aiAEDMcOMg7ubOJikCA/e4mJuFyMxWWef9l7XJquQ03lIUhFlx7TG0ylSmQChqie32dodTOFUuv6RTozSRwwrvMNqyGoniA0vmpnimt1rf6STpdzepeK6cP8qUssnXWOYdEjw+7SQInnQztvCU1r1UBFHLKk2lxUKdekATCYQDemptzUBKOakhz6R1CTQLr8xS2BBiaMGyhKgd9WAaAqN0WzVsnmTE5uVjbK+hIAHz0tIMjsIuh3Gl3Mri/szKMcjmJXq2QP7iL/4C9/ZWE/wg/OxnP1vNVoBBHx/p/c/+s/+cXnsmt6cFiKG05OA8vfDS0m14xYWRyBjEEMrjDahNDTffxI0M4wnZjARQ6mfx0VhE7LeuxUlI3KSCDeXMo1gU4KJG9HqeevJ9ZOTm9S1OaxGEHO7yqvzeHsMJ/P2QvhKLkwMDBg5jwxocFAfYcGAcUjy7i7l7yF0i/CK9ycIk+uBjhIYvwA5TTn4pFIJK1YpxQ7rPMDnwYQI5bIEvWIzaCAKYEncMRAGDlGnivo/VYzypd0gIVRgZcFqRXrgNEkaYSAUSLrRGHE2KO0CO20dIXAcCEtY6IAxDkDWbfXkiCVapSBT8HgROj+ptqyN08nADCRhGca1xix3jog8wgDkryCZuUifKBBIHBgzUwmsGGvilayDjOIAx2rnpwVORPZqbB4x7UEQAazDql7FkPJUQEYeZMCSLKGd4DCcxhBr7oVDkHC5oj+8Emq37f/AkrWA7zE9hO5JkRHygw+2A33xgBSUbdb7JKBNuJzV4kjttksSGsqaZoo7T4NMCDBt7SsvG/0QhIS8JPJmt8MYt2s3zyyfe/+yzaglNHdhAROHF19/4gz/4g7/60pdo2RBUZIbG1NMlFIcw29ou3jgoaKhwmxUDfLS1aYfYuEbs5aUBczFExPAH8o92OAy2J6SUFNEK5eAYnhRp1iCvyXW5tLQdnXZCj8Crg+MREvHWr2SBQM2GWKkn1Ps1VqwRyMzULNTS6bzrXe/q7O4xHsjmYORep9mBpcN+FxjO2RCXWYZoM9qF3FiBg/qLGdRLatsL0y7qhI4zEkfxWPalUHdAWOS2SoHU8+nUOsAEfNITt6fiUfgkddxuQijpYmAFNkFwCQehIS/6NO7R+BDqsykgPZZqJRujcdHjTwexeeMGFW3eVA49IWj2ZoFP2iJsN44kZNNos+x78jEYAEh0iAJXcOPpZGO7A08w+CAVzI6NIKTEARTqMIYfDJIK7bxGhn6QdQOywLcixoaI4sM9jcP863/9r//xP/7HuiPixAnuv0LOy2U1O2BD+EHieBzY6QGr+ydTCalDJD0tQRi5ojacKGNqHOf42xAmP//a3FACSylrloAZBbn8wnQVi3ioBg2D20kKjiSFZJP6xX4/0hXYBUNgAJhNt0o3ywPDbIv2jZEVth02qiCl+Cex3cNNQodZ59G3t7sgtIb/Htw0XdQjE0iUn6YlSCA29ctqk/IfMcCk1RtHI0W7MoEmX2b1IakzLm5vbevQxBq0yoYTYfS4NjTLOuAxhU8PQ3CiBWW+GctFvrg9GSsVNp3gMk7iwm8mBJqKeJsukbM/dcDE9ipxr+ZbBISMWnq28QQW+YfQ8CnCzATa7HLCiFXRzlgiv9CGtE3zJqqtrqLU3EtxJZoW+0DDtJlMTBXLuJ0n0K+5NNSzzj5kGJTrDXUDpk+n6U1Iif0OUtdcgO0wF1Yb+JKWqqEoExtoUWA6v/hgO8FxMfiX7JB9YIxnLEGJsoGu3UeNRAlwrVqIQBK4XamDCzFIiqsd8WmVNVnyef8gL4WCflUqMdWVeeycFJ3zqZIGc6jRMdvgcRlGATwWjJIqRzla6T46eeAZpXgpXhqwsi11WlFyoD1QUWtSav6+WBb3abYUFS+fKSteC41XQJI15T3ig6QQGJIAp0zEHv+QrSALdI4FLsnhNBg9DkNhOgpC3XuMKHax++hOR0WNOFwWAHgh4Bjoce9ce29vaWq6U99aPTldnOnP5wrHZ1e/8bWv8CgxCNg+VXIo43JQd6aKdFFyapJqbRb2c+g5DbLbl6+h11Os7eQH3cpokdRzBY3R3332oanO6CcX1/cae6leq9dtDbqpXpsrQEf/7X/zXy89/jFooE+kUt9837McnOkNC+WppXd9+MNPvP/9v3px7c///M//8j98HQ3k7ML08WPzTzz+wJ2NPda/mFEeW3gc/L/00Xczl7hx7Toz+ZWlE2+++ebFN67+5EcvLy3MfPSjH33nu9751NOP/MGf/MlXv/rZ1OxsejjPDc/KdVa3aizoUanu3I7eo39iXhduHM9MITlzozI9X6md6aNU195ncDM3W149vjBYyDG/7XbqXNySn9FTBsu5KYCKO51SodRpD/YbnXRRE/7S/DRUtXKDDLeWlqpAzpY1y71wrsTjTzyz1KkPyj2NANI5dd7sWpKXHFe/qN5ZCQ60bwQmCaVpOvv2JXd2A0PbhE2hUP3ooAnv2FlETj/QN+IlCTE0LnEuvYxtiGXGqqtcyHPUNBEFg9RR4u7GlttetmXFyTwFjM6nwSgVVDI0dLAUgy3EBw25kx5GMFENgtJotzyqBf4TwMijqxabD0j4pfX2cPjmOcI/CnKAqPaFgUsEr1hxzXUM2FFtCd9jh9GWaJHI7DhQfDMjCtXMWhFxikIg+jRbjpgY8xxT4gQDZrR7tHGosPxcjZPkKCk7d+AZyEBCRIQJCOvGUI16JB6cBQGGS9npRdvTU7rbcXZ6k9Uo6Uigum29tHFjMNL1Xd1UhkvXuPeZ18tbfn80IsqRFJvmxx2s0udhc2wnxXPuUkqbj2IsPUN1bqbf3W33uI6G6zMk8qx7dtmjE1NNEkxrjKaDT5aekQbmGAxbGbyCmTkebVTIo0fhE6N0yS9I1PWkuiO6S2nmUA31iLuVnS9qxuCp3BQ7TJZlvKQsq1TjSuasoydQbsRJSwJ3MId9QtDdHGJvjAdKcYp7sXbJ4Vgx7IGQpCfzfB87AcHoEJskKL8DEeIPumYC0NvttTvTs1pLnZuf63RbXPGJm+VTVHjL5Sot2PzCLCeyv/rFz29u3XIORN3cwbrm/HebRHJqM2TQ6mmg3ZJNd3OL3726vfTCrWeeeaY82CHo+HwuXUzt39gucwLZ9JW8efATTkPU0acqXL8sLFKlpl2AO4M0unVM3uw+TZcuRj3WVJB4erqnkW61T6czWsqXC618/3KdmdqbqTeZxi8/eFbH8+90p3uVVC/DbSNZ9q0xJhL8MOpGdmoo/BZ1DrrMm3Kt1uz8InUqW9cdnY0CCtGN+UfP5RYWajfWWenmJA1cau/VKE20uRs7NcSYZU/ae0ZOx8pzaj/3Bv39zVp7VD1zpltOa6Fa7ZyGYMzMGLPNzcxrxgAFWal8Dssc6ipJehFxlKBbrUp5plgucYwaSlqNNn1NNl+k4FrXdUc5HQb86WqTjNceUmi0oGbU7+k0kPTiTCtTDKQ9pdSoGwVdcYsPRosBaCQN2vyhZEVeeIqAjUjeZSAUTWem9LPzC/SnzUYN5eh6rVZv1NG3Il+89csLCMuz808/+fhcvjRbLX3w3Y8W2KLgbuh2LdXvLM+XaQI4JZtl0wIdwI4uvKYnhX5uTwFnNYfGd7812KR0+qWt/Hzz2APzp0+Xt7l0GlX0B7IzD5za2WmzWpHZ1QJE58rw+Y0fbf2JlrPXb9dZs9getdqNrYVsj8cbeJASzDQmT378I3PveC9b7N957RqXqC4fm6fpunT98rFTxx+c4w2LhWZvLVOsnDxzmn2RW7duoAlEZuEPJ+QwquP6kcTxI6+4VxK3rQK4Q72rgFWeKtR0FlL/9E//dGZ6ifHJk4+/G9Wi5775ne6wu1dD/3kH9W8iSr3KmhfDHLUn+FiZMKHRGVfYiwEYQ/tp1dgp8Ui/WDvZkidT8tFmcnSqdgTuWPOlVte4INtJTkbGrbyrN9A+khmBu8tt5i099vdReFTzxciKo/fci0LgATBLQJ7WvjlV1B+VvhkGe0g4MsVILwx3AGafiBGzHTReZtjHyNe3mamDrXpjdpbXPkhTOqTEZSGD3QJiYcCqVCAprZMZ9CuUjgpfONFe1TUsGKeLFJEl3KEdJjqfgGETxUNxu8HfPQEDLZ8Vu+qHT8jABh7jSKg1wJAWUYDEDQw0YMAGGDYw2ADYAJLBi3bTC3Rs3CbX6pw/deby8WVG1B27lmFo5xi4vpyaPmXHiTiUjaGT9hSxI5NcB7D2HymOw8geF9OyqcNVdpt0osouTKNuWFEbC1WLKHEvr6hixfGB5sJaeB9wQn1c+2Kgn8cvaGGOY3I36eDhvae3i06tQ8Ww47TFGMlnJA9olBazvClZYIi0s7+XX89yh1UxU+iT3X7PTrhLO5XZJ6XDQATh3Njef+WVlz7/2T9qNXYef/Kd7NJJ0tj5qNc4ZvRH//O/Z6azunKMEum1NH9B8VfJ283GTM6EDf7S/No6JSdiKAeEQTBaSR8WKmUymavocc4//sGV4cmnn1ipIo2csyEmL6QLmddWxZERTyzbjifJJRRlnF/B08nhRHKpWjh5ajmT7e9stZA6xAYYMGDr4AYCyf/RqERnOExRj3HTgRMKnSycXe10lvpLnWh1QgoQ1anS0vwD6fP9YqOxdVU3LLUa3fWdOwsFbsZaKg9Vp3hwB05OsQSguZKonrFmiKu1RuvNzs0thi+FpSosbZRUKdAIxnSYlGJgla6YtKZVGrUcNmEICq1Wv5BYVVJqFb2pzYKBgGWS/EhmyJPQiYFWKOYGMb/CrxwTYv+tygcwHOIbBwqj2ZZgLLZQwTpGxaROs8O2brpUzNg9q1CTRYOdcxx2fne+qU3f3SvXd7j+a317lnjcTMSAy7LC+AXhYC2F20dHPJyB4jshWW5vVRISP1oM6RlDpahlMgnd3toTBDHYojOeSwIDTZQobaKo5ZiCXhCTgeV4kCWqg9oZsUUhnkoksEARhfQHcBztsmovox7wtfWdK4z9oVUrObaC59xT3XQ0knaweWK29MKHxB2qSW9tY+P//s/+2e7+/qc//en3v+8D73vm2f/wZ3/Kdi9zBOik/Ch9YjG/EJJEleczacCv/Gog1uv06Gp0q7QVtvIyLmBDEiKKFW/HwJO3A34A1ssFppmvSSmJGwEcOAAzQQTyZw12FJexA5n2DxJH4g4gVUejMxnUOPoTuln6EwCoF9geL1pLFM95thdNO55q0a3N9JIcANKwh9LvRtebEMvIUCo4PGmQS96snnIbSrlUbVq9Y9UEaedKeDRLMt1yhud7Jhj6U/ArjiIRd2xGh9ogaArGg7CDIwA4PJBRmOXE0eJDKO5oYGTch0FHI7E6EKcoIcYY5BGUWKAagpCQ+YwtTyJ8+6fTPBEEDD7uSdF4FD5DvoIjYEs6HDLpo/wKX7pndyky5/ASZfELrQ6GIADDbUcLGJ+IjtPmeAjFUBtZZsLfiwKseDLOBYZVAny1UU6Yu4FHSky6HMlb2oZXvY7zUAihytcuvEWLGY+/NytRB+aoI1aN0yEvbK5WF6vaG1nkWsXpnV5DZPtEy3buvYmksVaiQ6n32639Wg0BkVckTwV6nDkhgYhXxjHniWWBcFEedzQOLtLjymvZsAqPJ0isCT5EvceLRC76iBkQhx31K4RGzQS1MW3jOId9CFMsS8ajOzbqOcOX/hZnY+tk7dlnn3218SZrN7PV2TG64KIh7nbnlpbmFufgf6WXYvi+e/UGHd7FizdZnOIFMtyLVSkVEonhES8TcwnGqTdvsl9NWkgaxUFZkCijfG75Ap4TuWSLvgeJ5Qa/Vou4QGUeeOD83/t7f+/8ymO///u/f2V9Haqmqgsu4RRCu9mUhKd16zTvyPPUTLeTIhfX1tbZjnv15Re44uP2tTXuR/u1X/s1MvvFH3/VM45IDPPck9Fr2k1G5+bm2KV/x6kLTE54tAHW5WqssFQr3RwD90JfOWKqQVzWJjD7V3UAP1eWkDFSY9BzuiptLJ5tgSXXGjUUtIcLC1xE5aVARqCTWQC7rwyNwMBRI4JcPxrVMTDTw8IuDUJVNWTzadRGJe4+Xg6Ct9C4+/DphwJZMYc/DARE20ExJTSB1pHjp3TBR5DbdPiG3mq9VVgqpAojiq5WImt6oDhUizX2kOET46nwGZLDERmrTeHDHJ66+5GspxyDxL+WuD482Jfj3U39jTwNOZABRXCIr2bkY8Nd/zzS9iw4OG5P2rOTQAhzxulqZm0ZJ5Z3yTjch4gs0Lvb7IgSc4MvYj7wThsOUvTkcCdNMvWk/2G3J3fY/7BPwInjyFjytDYfWUWjsHDyBJBsK4FKo+FQyvKQAR6BQPZcpZhJrTyjRscgDlqGP458MIjKyKSL45XULNR1wMnKFDazC2JpuGpMk7SbkhFDH/wVZApEjoxPHNDpDrc9yPPuGadBADP+AAAbO1ykVdbuox+vLNYLwAH3EdoYxhGO/d3109pOZJJsdWWW1luidLIPgyUpPBx6pA8N16VLl84/8CDtOc8KUTSoAdo1AtO06n7zxre+/WVKitEnGCDYKT8S2908PQr6oSB/8twC7OVWfFCxDhKiACPDbS9mnDOSQ+2N6g0U91FHY/n3T8ngXYziWhtCy4mbvEjXlfs1bIpJZaT2EtWTRbYdDaHICS05n1w93NjbR1ZXK7Ms5U5PV0kd/Wdg6DgAYBZNf8GLafQpdEz40/EtLC/zeByCB7BWFnQRtC4XRq91wPtCLmYalamNrW9tg4GhDr0j1YtQ3KQIWVzWQecFTOPWHfo+Ht6EMQCQFjb+5EBouTJNYzktCsM+ikmb8GYIpU4BCU/x4DMKsB+ohT/EYtpmdS3iBv50jsQq2d27pOKxEBVfESM7+DCloDd8z0Pv5FTTw+Uptm/RWubuCTGdXEyV6YW7+3XRjOSwEFCvwy6eiQSJ1KF1BkLLfLx4Su6I8uSTT84+/TCLDsyuYS8tNHHpgsnRbn2NRM+fPy//Zo47EHksk1i/93u/B1WrFT1Ksbo6y+71iaXTjFWqsyvvf//7c0unJWCZPji/8pWvcGX8U6tLjCXe9dgyHGZcwW4WazSwiG3e0EpQ4Z0znutgy9OMO7CRGWxM8Hf3H//xH8ONBy88xaXPGDx392poxHtnlSyFZFyQGKvVxkISXPJQLzSV1YHS8zSP9ozC/mP8IDYkG/I1ScLdsmFwBBIRQ94xVB94xeckkrf6JhZxXW4ZRcBPpwpP9H99AZpK5AlhIwNUgfOPPwIAAomkEdeNo1IN8kUZBihgtj6C09DEZZwWCjSQioNE8QeJeSoLQmJFiCdujOfDk1A1YRtJCyBRuXsUtwkFDMKQfyfM/UHujOITwydglqLcSBKepCkyjLW0LcynwEZ9QTGEMe1UVVrbxmlsLQJS3YXHEBJkv27dtSDAbyyPtruIbkUJHv0FHPcoSYO8K/4EDfdyOrVK+6BxnrhNSHAchHp7XyGRgJz4tOQc2oDJF07qrR11JMAlxrEwinaPB74oR15V5QKBCw8++qEPfehXfuVXgPzC5z771a9+dWtzAyll5RrMbIggJ95PxT3dOHcEKfW4YQc5UgYZoMKQEIvd67evPvfcc+c//UH6d26YAh5yCAVMcUM2LPeKFhvziCzDrHqk2EQ0YeYTNyIkje8+O2y7viIGjOTBFqARTmA8OtQ5BkcC8STl1U0ybIvLvPyLZ8me0qXvoyVn+ZlDKu39Ji35bC5Pf4roKuPW9ILc6cWBATN1hJa209vXwv0cV3Iyma3izyCCtABGwnX1oWqfZlIxbx2BbEcIgHIRZHccLgD8sd3PUEXBSkgH7xQq5NZ14kmjYf7qZUjYQ6EkgVVcIpQuEtmolCyPxkOAic5JKToFQuFDd3NTK60W2ZNIUktymjtbA0hJiE4jOKQlH6MzatNCQOwAACR84SAyyJks8Ymn/C3jComNwCyKA2CHQMhjSQkMfTaorJWhZ7xy5Ro7teFuQkWOUb3lrwgwiWKT5l/+y3/JoO4Dz/7SO97xDgYhnHDi3nO2cCh0hOTqdSmuAezoPeIEfvc0gpVNTAw8ATj+VJT7J3cc7+fsEtPMgBf2BQ4GR0hPqwVHGfJKvaO+IC2UjAtSEnCCYzAHyWy1GsDwgCG2Fa5xzZYZkV4nifJ3bGBAcGkiaHwofUKx4R6J6lVNtvxZWtnfl751MuF78NcX9QCeIE43GYkRWhj0jpWmkfSowdgAY4dYHhcbnRr3FyTNmuqFqU4Z/jDhF22qR2rKzegHaBNFnDiM/jghS06NoxuSASb+0q8zDm8lH2PBn4gy0v+KvhwYGzZji4ZE3i1b7oM3hgbDEPiSk21Lim4zCk5IB3AWxaKZC58kACmysIoPhUS4lZdaKJohiEZoACAUm+z4+EJ6JuKM1o+VmCWHkJWyBTbtoxQ1x4U+S125J0SI2AekcLspLvTSVAzqaMHkiGnjN5S++R1tiQKopcFXedo03vaytHNGD2QF1c2Jmd2Mdu+bGS0iF2bQN6oWZtBpruTmJa/9KYl4M9Pf62nxFJR+CMW7WCghO9mSeghOzKiVowZpI04ePnriKgMVNFhghGfX5MQnSMnM6HJoGbM9A/btscwZgVNCMZwIEOJEOZKcA2M7Gsfgy2oJ7ql3FqRZZEDOWDaCQwBmgg/RjBXyNU8VM252QfmFHAOPLAjjmaZ2v1OezrZ6e6urlfRwf9Csd3OZO+wLcm+6gUty1B9TWjpOgFYwaiScqp1J56bzhYcff5p9xWGjeevSdu7J8zQWu62mxq/57vGVuWGtu7299/GHntx+4TXGS2wdDuudldUzx6qVN370/IX3LHP58c1LLzIHe+6bL6MdgcrFj1+8OH9umrFFu1Ovnir/p3/nY+cfW/q//ff/r0uvvp4fse2b63e6nWbvs3/yP8PMs+dXmS7ms7wehlJXeW6qWDx3PJ8//cw7nmYwce3KRYYmt3dvPfDQyeqrM7trO5mVHIxAbWaULS3s1zie83fOPUVvNNii0jSmM7PiXlOjKFS8WN4uop2d5rmtbmaIapqm3E8sT1+6tLFfQfaznX6LWpYb8gpZ53ShnDs2fXKoxehWtZo5dep6r8+QCzlv9duN3pABwXdfvP2xpeMDlLqK2mYnkZ4dOmboQZEUB2jfoGSm2UVG6vuYqLxMhCSguvZQ2qkKUp8f9/oDG6jQlre5e3PEDfAy1B3hiGuoHLEoUhUsxOdM5nTLNKBxigaTt6jHMLnSrWqcHZAGtgzDS/tFyJW8udXaSNySxjQC4pri7XCUOlDCKSOfGMY8YstpDhUHGYxDdK2J3IbfwZI2Ox0BkpKLIONKpHhRpcfp9MhPYB6kyjtOK+EUQ4lLNs2OuCpsMXsNhVobYhlDHEZdlJWdoXXaoT6+4SrK6ZgYoXHPRMsgz4QJlMd+EW3xp/2q2GIziUqNMH/qOu3ODJUIifaszanpDuXphXOnUT1q5+ykM7my8yUoO3ByhdxpTUsvwxXYmKwPUu1+qiWdDO7jF22enL/BwNZFTAV7gnJ6a0kmxT0bTANvf2hkFcuzy5Vhrlvfpu5QnenaUMgllrMdh23Oq4Vn/Qobg2cQFdzOfBwThiQ08rBWsQ1qdnCB4EpM45PwR2xx4dJEhAol6i0VUavqJ7ITmL3GyOegfwLkp3VC64GkDuJRkInTQe/JL+fPpO+9vrWQy8GqqzeuIMlMI1PpfqXCHQhL8/OzaGw+//wPuIeBNpazd9yBzBZYxDrVhXvhvVvY1l7m29+/tLI0xdnJdH6wu7OzP+jcae7P6Vi8XrMjIjeTMhkd5PujfJaHHuia8rSFOn5Ak8peucY86ujVthxhCPUy81+3ecSPXZY8IxtemdMKErcpC4rdZg1QVUUig0QhYGDnr2ebImiTMai9s79baBfyRVuWlQwiwLSUmVpbGmrtdpObwXqcXCwW5k+spCrF9F6H4WsJXW1d2sg6QpG12Vt33ujPFck7VQFxZ3zD8K5zixcUmqN8KZXjUD9v+hUKq8uiZq/JVg+63izynpxZpHYgyAz/SY5MMQFo61VAjZH8tAGsQrlpxLISN43YsIe8kFXPC8KtgwqUmtk4mByr8eeqv9SoPmy3mLnnR4VqQaLPgE0akP1ymYfd8vUmDwuPmq0OF2gOO/skWckXeUZ49dgcCKazw6nMgKtDcr1Gr73L9JKLPVmC2bjVZl1vZUb3nIKSDphFPvg2tXSM19fbafVfbXrqYnans7fV3J5/cGH1wWNv9K5e37venR30S51mQ0uB2dxst9IdLrT2WcDN1YszxSovpM6X2tc52bV5vcdV3eU7Jg8Xr73Ru9TjWB+pz8yf4s6Nnfrwys7rlcJiYXa0cWtj/9rWYGvh6s1br/xEi91nzy6zzXDrzatDDf+oCQiCHGo9rG1x4YkkI/450lOlYMYduzt3/uxP/yiX/wvGD5u7e3CAxnOYbajXNdyGbNxa8klhIYlIHodAyLWt7TBwYBVeF3sGEw3xEj4h6D+iw2rMOH1jkQboUaPmNdKbjHs2HMgqba6Ler5LpVHjfzeGj9MzF6WG8TEtUYgIVbQqOJA0zoDCYU4OcOyYfTU2Hih65hrMSQC4ef3a4jzjxDnqF7GIThSWunBTg7gVEw/VHfU+3kWovKTBRRVTOhGdoBJQwgBgX9aq2AITMA5GLIJwe3RsgPkkRWvr1Cgx0oYbOAjCsKZJPaIF8LjYABv+qCvkEzBDa8MV6cGhgyi1S+Aoh0a7QXsOUxcX5/M5zSIZCjsGco32jtGGRU7U0nqQqw1Fbv+xOZ2KGGMjNHLD2jXcYopARKgIsQVC9QKjx42x+he2JUokyhooozwOs6D44/5+j4yC5zg7UYpQpbTMjqi1MoGZSkmkJEjFndwe9qzTLXFUhq0Iio1TkFTddqt+Z2tnYXqWnbPaVkdHDCPJtDlxmgXQzOrxRYppd79OTb988cXa7tqLP/oelNy+eZMyXV6apaBNJZEjRNoLse4IiiL+hQz6momaLeu2vOgB5v56yOcT+R9WF7736uVHLpzmidSZ3Aye5UGdKKhnK4/RYF41z/6YQUtN21KMMu/ChhfSxflIHCYasE3pwr+5OVSQmfa09nZor6hSrOtIkQs6iSsYW3tyNzWUlKwb17JDq9HcsxpEiki7y3MjhdJd/w6TEZ79QBF8aapfa7KsXN+vZ4bN2cGAfFU0U0jTs0NOyQZ21a4KtGRTqx4XJfJ25truaGp/sDLHekJ+qsC+Z42OnxM/5H+U7jIG5tEAOwsITfiLn4wroq0jFwz5YIzniovB7bYHYfOJTU7Jo4IA8WyiFseUFADQaKhhWTWeAIZRTNI1fTqGOjCfwSq3G5AhUKUHymRRr5/k53LDm+u7nSt3uhube3v7DAZQ8aUBsIG6JIPJO4cJhzkOdw0L02Ve0E4hQ6iLk4aN1a3PV3E42UqYdJUXK2+nSpWUlWHl2rNpMzgoVNOEoYAYLQuJVQDNJiYnhB5RiSBdpsOEk7UeWJxrZKdeufna5fVbLPz3dPN5RI3LlRTjg7FWRUMRjPHcQ2AhZPis5JWLr9/cWPvSX32F/Zvf+I3fkP9AzxJCFQ6OSzIaIZcB5ZEOYnkxEAVyjoQHhrhu44Dvjiom/0jEv0BPIxIayBpOyY25lCKE8gG94qx1wt5SOTVefd2fjoUsU9Ew2nZUtSWLIBDPvdUSQmVdBsm2Xwk/3WhggnkaHaJFbYJWVNTZabm5Oq372StVrmbnnZMCBadZB7JqmzTNfpN+djyTB1fgcsDrjvu3nTKqU0AFWe5O2o4QH0JxOwzUEz1EDI7AhjGquxDkCAPaI6GC3Dj+MU6+xW/RgyPJ4ihTce2N0UaNkX9a5Li4YogQFFI5GHKvL6IgIkDQAKFt58zRQm0B3RR1MAAQKo6ZbHjaHkRrjoNdUA3CrJvhc4IGfJQvyxTYSMsRJmkCZuJzAkYA1lw62AR8iJtMGjcDqX5OV8wcX15gv7eyoDsNu6wIcq+N3cymU2SM+4aa9ILTbcM2FhhnSMi1kuY/xhsI20eFPcSi2RrbqqRWZU3qcJMoqHDIWMMXOS2Wu2ngI4fqdeyXYE5SWuJw/UKOCVTS7224yXiADkQmPT3UZGDcevJJaYYCBZ7xB6sJ+UKedrmnye8ho4WeIkNw1ot5jGVodxpmR8z25rJNHnbfo88DoLOn238KpTbLr422aGNGDfy1K2/S3JAiw3ouceYg6vGLOwyktnc2GDq8fGnr4x//OLLMFuWd7u2PfexjH/rws8Qlc08//eQ/+Af/4J/8k3/CliZJ4Mmk95FHHnn1VZ7ieBnurSwf482xSgnFsuXzDz9IEpQYlKAPhRLTm2s3AWtsbU2dPUtcMu78Apg7xZayc+y1ljJaaOvv6X7PVFNNJ6Nulr1Ozh8nL5w1ByeeRK/XeIqQo5R4MHbQeSg0tHQQrJ0lX42spjTOfMCMfr50oxl5BwOaWU+encVn0JXWOWf/8dzb3IRUTT3jhg4nccl9sIMM40MUyYz1AHK4CAlcu/c6zQweGwrgF0QC91sasEEnYE5JiEsNw9OGGgkirS8B0kPNIb64w22laHrHPj3zuuY1iKQINPL59doncDeer/hr/Bu6vbGXuURE3Ai4OxA/ARk+HT58TjhAAnWORHk5GCwP8zsUIjjDbDZZHY9F5EN+iaIfM1FRWpTg6UFuH+mZBHhbbrJDisEmrn+SSjIjolAilkYTh9rdtNszieXEiCdxpjx14GEHwxROF1AfWMt6W1QpuYMEIMZ4sh6Ag7sDHRvpqrJZPYJsgtTr0e0ZgNPmbuAdYTJ3ySTwd3hSASd2VL5RqSpB93LbhkpORWQrShzpQMDP+yOQbZJzX9iPpCuWOHHGsQTHvZF6m48iCUZnVIrF9bU7tKg7O3voCxPXC8uQRFy8T8yH06VwmRKg+cUGZLqQQfZW3/lO+pHqpS2u9qZBZm2FVoNBS5m1IYzfemNijDBERUn+oyxOpkCMwE9aKpKjXXJhwGbqRATWomjz83YwMEyrlFbc3CGHyAPRSZE2n2YcekYdTa3pHMEvUyyyvsBCZ67WQVJ79Sbrxq2c3j+AgaRC0kDBOicRVD1upzIDnv3aPro59ND45/b0bBodJfC9XLpi9y/zOgJXfEAJ7SH+M+mCjfp0ewcGMjotnhHfraSnQQkAntgY0oUAduw9LXJBxkO6DgxW98EmIjacxwEZYHB4R8jMAc/iqEBaxMKTtVRW7i6cPktJpQbcYqLBANS22TZA0aFfI8WiXQnN1gG5g3t0jrwRRyqMJFA+zZTL4GQNmkS5NxnT3muznJ15cAWf7eY2mGcWZli8Rh+fUNDCVTiJ2Ws0yJ2K1TTNKYJuWyeQZuyNhEazBjGtWocp6PrmG1DV6ut1ClQ8PVOMAZ55zzPYLPh85zvf+cEPdLsoWQYbXAhs4csza96BVUc7iE5AgEw6KFNKCmqhgbkeNNhY/mg8RHRUJE0uXNRxg94pDNH0+bMMKAOin6sDqiCVXLhBKn9q9GCwvobzdWhGvW08ToCoiQ2cd7UmHMgYjRsjOm5oQV9+5fgJBopwGwlEkJBeL3ogSR1U4Ai24xSAroqKGsM4kcliSgIgUHyKQ1a/3AYPqZifxiFkmdTBFtJ1MGAIIjpShAEAH6cKALHoYN9KED5QjeDZqoImh0TklUbq1O0b12hp46SVNUCBtyiUma1AGOuS9IPz3sYxQAyc3NrahTeH2HOETxInGEhR7VWkgWa5mORxMsZP4/ZUQsyJz+B/Pw4yCBspOXLNVgQ2D7SrJR9JMQUm045hgKEsAkJSRMz4pOXhyXO1V9anMHFQqxgNzjUQKla0/2HNEp2AWmy/cMNRgQfjwfjAurhMI7FX8Vl9JCGCvvzlL9ODvOfcErEcw4Rt+GSFcjdHBOyx8AmyF6J7RLpypLTXkaYPMwTAtIhqExZSR7oNg/gQUDk2csr0EE+rcBry4S7Y5W/Sx2GZvtvC5rVQ6M/nCsCP2n2YVswUYS9bVIRyJ47VHVWfYDwhep/mSPfp549BI2+pcecAD6xFUGK4luqi6hlkFNrcEwd4JtpbPEMqEw6CQigOqGL/x3KnvaI+c0prAbymCzIuONykCDZs5dGMIyci/ETzFKHqr6+TFwAIwhPjsciF8st/jQGkZU/9JxRCweSlSBBuj+LrIcTl87BAKEpCTvh0SBJVAmZgmZEnm9DYW12tkRSxiLhkFhswbMqa5QVEBRgKgVgePbnybGgnLeI6cAjgE3GhPaclZ1PtC1/4ArmGMxpJtnUhDABOWIiCwymZQIU/kAQZ5WNw58Fh4ABBZj1u8Plrc0BtwpCvAymbZB3w8Q8HI1/uoGi838GhruSIGJNeJIoXNhGJ4p/4wCVnlPPEo+GmkrLEjAwD6QAe5G7EhbqsjU/3lX0wJwi1/JxehN5SEq5YeqKIJtyOFJHDk7GDFadVKhWTGx7wGn94E0P9VioxCSBxHWQIoYJG60kW29wwTzjJcYRRxMQtink5f0WM75+Y53gBiFrnObKc4RQmW3b0dK1hj+YtngTyiYNcI28IauzJr6o0CSmIDBBiE54EgJwOcA/PkEfYnMQOWgwNCfpH7B+oOtliNKhwIwFgpgUlqGeHowFGJsDGtVB4eyVECNgmI8g4MC5KIeG/1rJEP+ozGgExVBIztKAdMdjYHLkn8hB/2jSJRUHEDMUW3ZaI0qRW+XTp1KiTkwY32rEkxDUaUNgps/Q8VVzVhCS3NEVeNkYMraTlxGlsby9RLySuUhAB0lrWpioukSaaSUub+yg4eIFy96+Yr5cK/Ka5eNHZ4xPJdutUjnK7LrbKzY2INlfwEXwcqpCx+8BOnUWCA6E9NQ9ZEIlt5OqTjkg/bqzZdwC63tj3Hr8qK8u6it7h4I/zygYx7FBp9zWgwL1b3yWJUX6flnpmPrOwXEhnmOEPX371usuZ4MkvNicispn2oMPRWF6sf/3apczKWerw1s4+bxEfr55gu77ZKjIISKfq8IU3k2rrWwVuP84WG/3Rh558x7+/fI22Jp+d7bSyDz/65NXrt69cfIMp+iPn5h/5T579w899q7V/o9lO3dna+8bVVy/e2fnsC1/4yEc+8tF3fJgR0kc+9Ng/+of/5X/33/2PdCG5zEwxn3rPe7ni+UO94Y5yPWifOXNq4+baKy9eKZS4AGPhK1/+AZiXlvXo3wPnzs9Nz3z+r77QqG3NpWfb/XYlrWNcv3R+5V3HlyuvdSqpamWY37ixsX1zC9ZRXZkZLi6vDDu9mxu36bGW5ufUPu43mV0/nJpOLUxfmqqBITvMMZyaqc6mS5n1Gxu73R1u3yxzo1V2mgs82qiFcXXpUNeMTM3OMNBpFfIv3bjeLG8zimrU0Z/r8Joh+Ndu3ei++eIDyyfQMTw9NbuwuLC0rw44ljc1IC4Zbc0mdNEghoEsDTdTcPCw483k4X0XHmH6nW7pJlDpqJkSNGIbSjw4iG7uhLx52Fg6Aqw3CGQ3GrAip3InjDUP+OjPUqNfxw1y4feuADlULBdHCT7fsqM7Hz2aLUknEEdOAYPHiE0mrcbukDHMyjLoLdA1DkSwVNcOmrHGq/sLyoyygkM4VJlEbQgTgFJR80PmJrmhaAJWWoCZWz0dicttaMyTUHb41QxGGbQk3K00DpgjCvFA+N0/SDCQ7sKQtD0UKqDMz8fUi9qSLB07hjS2UeFkWcT6U9SfPBeWlOhhyQTe9O0W7HpvxBpTTUN3roXXkJrmGXi+lVykpzCm0lsqY1IK7UvYaQrGxmY+87lBV2IseS6XqUFdNI5HvHFXAZsadiKYUUJx0eBWiggoPDVP/xynqt5BX4ov2ZMiDbpvapm9XBK8gjZweEvKgEVK3VYhiQhyNwfGRVEyXlIHxhtJAn5GN/1XGGDE+b5flJZpEe+Oe0ZTvn2c02rVWeen/zexYfLTvr12/dbta0LjcxPxbrxhCfIJzId9JgD8E8La+Up7kHrtSutbP7x5/lceoaCr3B186ljmsf4xnge8coMt0uFWYzjcm97vaHnXL92ychmhLsukxQrGZWks94n0FO5127RgGMkQOF2u0A6jtI+RFrXuSpZ/gXaMLytSZElRTc8D4b6zvllcnF09fvq1y683WbAu5WYX54fTur0u0x9tX7m+dfEa4ysucqbPWl5ZynONZyFTRIBRFeeY2tZeb6dG7QAtjC0Xip1Gl/M9+QKroVke5UDDu8xBnT66w5lBo1N/5eo++q7FDFeU5E4vQUlBw6WctyrE7dS7S6uLYjXdM0vYO/vDpsTdmC9p1PahRor5XLmIm4gu8+0mN0+ppQLGMsgOZtTa8MkWKaooOwy7qtU+b0IxDCzluRqePdhigVu4p+jLunWd/K3Vtqmt58+cRl2AixvrO7XioDVVqcxmRt2tjWpFd25m23nu+2ju75Hr0mi4kM/v1+qaqKQyrGVPHT89PTWdXT7GsW2OOaFUv763vrGxxqsUx9/14NXC7npjpzureeN2b2O9dXOrs0WP3Os21mvrO530IDdollv5wqA73N3o32ScAJWZXJn7RlpplrXLrYzWbXOzhelRtTzIMVHPdPrHeJZh1N25c6dd3i2dWvzgb34EnKNb57hc5cUXvsn6eN6qMg2ZD1CtnYBV92UoWXLqvA0RjMmIGuplJlEwHjBUudRI8ndECl4u2g9lEN4bcjmvSyP+E8g9Fe+cQopHOqzSHBnyc/X0blvChYEbLODywS7SEdl0oHvbqoJcKZPJ9jrdIZXFrqzxvAjxvYwKUq9owDKphImAiLGajbIgI1VoAhF2tta4KoGZCBecswBdnjq+vLxIaRJELI1CTRmTT+AZqwkVhclP1PzLB3iMa4PymSwpL7iQOmJJu6GmwwyQGJzeOJAKBgwYITRDEPDuxgYDo1Y82RJTp2kG/ETExuARgA05e7dcDsvmTePNN17nrvP9nQ2Aud6KUF4TFwKbRPoCVBIDblCBLukZkB90GB9YYmOQw/Qjm9rZ28VLRyFphRUIWw7GuMsX5JCcmXFjBZWBIUfGgyFJfwee8ATAWZ2E/NndCL62R01CGOmxpDbFERZyW9FpyBu3b7GCfGplhWcMABQNypYeL5B0Zvv7tU088SmX6HxpQ/Bv0wghopz4nCohK/JEom3d2Zmoc642blFxY6KTQLHwwD3HyQFpwFr9fqPZmykXCwuLV2+0P/ftl08ufYhF2HJm2Go2GWkFJjg2t5F8GEhLBNNIRixNFiHXtlJNjHJPSwASFa5knEJEuWOJ5wARV471W0FoOMdidEgCWOB9ds9BJDozEsEHVTMMGMgFwPQ7TCfx4awqjnZe96FTH4b5HGIN5j3uZOx1qind9zilF6YyZXvUumRplW3oX+xr3NLdH6X2m+xtdkrbmeWZk0tLm9SD4aiRE9momjMC6ERK3YBL8mE8/6gvYgJAxn4TzmiN0v2h03muaGYcRqWtPwknPs4i5wBur+ohIg6yQyjayjx8ThO1V28s7Xfop0p9bYPNd3OdWmfv4lrj8p3UVi3T66PZ7K0J2L0CDAqs8aTpaFnAH1Ty/TJcYqBB8lzAjGxFLYkRrOqgMjDaSDoIAv5WZKIZYzM4Fo7UwnB+3TVKPRcWmQxCcwxsCN1SXqyU08yQMf0ct0r3MpXb282f3L7z6uZuhze6pMWsVkgjEBugiB4bLCUw4SXZSK7jJUOZubCHTVK1ZmPvco0gZzVpgtmyZsWXjHOU21tRj04H5iAu9ZaXqEVyAIU6+xwuTtS/HB4C4sBf4K94qy7g7mnRD7JWFS8vBaIk0SbSHKKmqlp/J0njsK4C7oLP+1+1SRSIGZcW/wyc5zOXm8JGRKmb9FbCbKLlYy3T/bD1N8oIiWcQB/TPbiAJdiTxOE3ysdx6kGQ2Ng4fIuIgimUthkj8yj/6jH+RBEsx4AngEWwcIxB2oILF0UOs4BhTHryM6Xwlg8BGQiBXcjbKd/oBCyk6AD4RSQmE9+MkCV9HLtoRabbe8WFkQp3BULRueIbWU+STNl0E+GNELCVDns8kjVeBHk/dKcftCNXKWAE5tUkbGP/0iEnb/SFMadmoyPa3Uhme0wmJDqU+M7M4ywJicYWjncVaSXTWuKCRZYKUFqnDEoCYrJ5Jg0X5q16ouRJ+s3EQy2Hs7A4zQQUF/jt5+CimxEANmZaxhU1uoxQCPFTgcatsTZ48RJD/mu2xjLeyZMhy5LCNByfPfbBD3oPPhMPhQZIUKocxmpPgRrl5BGClblWBes4yJU7qvJOEDXJmfWhaFY9p4VhrqTMzW/v7uJnb30jixi0W8ZyBOiquBcJGIe7MmTNgZjgFMWBjFqrJm93jo+Kr1cqzc3xSEChBc98FBxtxQx4KJhRcdab6oQ99aKrSIl2uiv7BD36AUhJIWDV+4oknfnL16zxle/uVm5ydeWj63Mc+9rHPf/47XPeBihy3a87Ncnllhtf/QMiqL0vVjd0acbnZGWzQzz7q977/Haadz370wyh3c+Ej70qfKVRJlwNNkEcqVBw+mTDvXJEWNp7AU4G/9rWv1Rot9BROnToDN8CKZvSZU8uMftrtOpsihYJuYET5kuF+NV/FninPskx2zRTBuqaHQsMNk5l2A3/18jU4j7YlOjXvfvfDuGv7HfICEjDDPWolm7SUxW55SqrZszOkC6Pwp4uFh8wLrMgkjlYa6Uq5AjUbtRoRXZsAMEeFg1tHsUkIYIwXN7Z/Cq2MCYd7RfbYh+gQQJSQogZdElrRoCTits5h3CcgS3yqXvjmUDRcsLrmbl+AjmNFqSfiKsQ/4yoVw8b+429zCdhAo6zaKMlhPCNJeFqMkEH8Q76CGx9MMoq7A4VMpYLb25AIwHpj9e/ileuVR2NT4B1nqKcB/0RaE58B7Gd3GFVOm5ey8k7XSv2dP7ZEDeJyGYTHJ58uBvdIFDFGr4UGZMABPzPiScw2OGxzpHsgGAc5MyGGQQ8ql9QpaiieXhdYI0gyzYE9svPqbqR6KJAUZoiFJ5RjXGAINaEeEwOwZwJ/wPIM4k1IhC0GxR0QjmP+XF0hiaMkcTKlmK5J/yO/A+YjQ93TK4j3ifgQhbkfni7ttHLJEiEULiaxmU/S465uIEEFM1n1+/rXv/6+c1mOubiP28unTi2fOJHa47ncrdraNv0Ler7ISd70WzUdv4/MAxO1YzaB1Aa9jW2wKWKFxpMxfPiy7Ec9u6KaofOk76D5LU9P40nHcfr4cei/Wd9E6xYe+ZBM45lKFRlmd/BkPl8bKcoS91OUy1QWMLMqQVyyAFY+aZbzXN/se+cmnAg/i7POGUJbKZ2bWcodZxmam6XBXGJll8tqGwP146b8e3P9lvqyjB3DjxtBzwt4KDvAtLBsgwGyTH9qebqrBQ0YWgPvNEBClqEZPMRBAOhqselz6bnYLQbn/OwCofOcrEulVu11B/JLIa5yi0u/f6vdYvG3mMtis2gGqlxe2mpkxJETDWCioKwE2pNPnwWGJMDZ7+7DBy8Iul3w02WT6O095QIwghpNDUjgJ6VQzGmEwMI+bgwwFIFCtZs1xfQHH+7vImJ5aookWHem9VvktWRXP0dnc78BDMYTxWYMjO3GgyZkPvY84pekhSoO0WfUIsn/boa8eBCJkh0M5OG+G3zwv58WIwD/4hyea8d/gGzou49cTBAGN8g+QxMKFNkBObw7gHYiwqHPJD0eiA8ip9KQw17mMBFieIbAvPtj70A+SZdUEBXkEDDcTgmLZyDBTR0BBDfthZLgv8AIiaaBSUImCAYGAjBJf7JJHg1JNGZwVD7VB9INnhJuu4Wf6hC3z5qyISokGsaBTkBI6PKVy6h713Z3wMAxJ2qWtkHIiB1+B0wZSIygSE4eZnC74z5tx8ZkgXiI89uMrUScDx4xJuMtaCBRsjNBIZ74HPafAPs5fNpUF1IxFASlgMNbreaezvTMVnQH8aCjfickh5tiA9I9FdkMACzWYiNY3h5SXgi+M9ajJ/EEhBMOZdxXAKznAgPomXSz4/LVr371b/7Nv8mnMA8lOcFQKyh4gvDBZmxIWloLMDl3sJC6O5KE4UYsSQX55KZAWn7E1ZrraASuuqNHJjWAFOZ4XG2oIqkjLT6xMcg2XSGfUlHmOixTd+BRPRJyLR0c5BS2M31jlZ7UWU5QKAuvgpFUQINT7jY1fX+jTYc4miuhfuElxVUcGEoOGBwkjQPMICF1jPsk8SQBJvz59FgWMXI7DAiVsaghUk7JI3x2bPAHAHz4xCZTkEc54AlVlN3GlSvMWKuWIzwxYFByVl6glduopSmTKMII2Ojv1CXqOJ5RRPvxiMSdMEJlXg7vUfDg0w0JuyefONzEgfqN/ax91HrQADV/+l8KJSw5GoySwUEUMmVp3q9FNuEVDy1jE5dS80TdDUJH5A6C7obX2Y5wAtPXem4EGTB4xPsnj4j3SO5uZNy/v2VT4N4fvWXEmBOUH6yOwN3hlYj+AQc9yVuimgAI2STLFAeh+MCo4A9aOgXu7sFzIMGcTIKI0pm9t4FW7xS1V6FEzI7KV1Xd18W94Gi1LHn5uxQ7fi/OCNLTQ2kDRWltALLUR53nlxjasnHgkI0IPCJU+APNuKOqYkuWDjlhB6bjDzzGHdgxPZ4LlZT+bBUmlsMDyOLcyVPEM/gwZmDBYjWXsfT6RAg6SS1JgE/tiG5kGJcOpHDgQ4wgX75LZBgpQqpKKEglms1WiqWOsb2U1xidSk5dhLYCDxPHq6hOiaUbJRENdNiGtN1wZ4uFaYh1gI57fgyyXeSLFTuI1aOr6XQzr3ylFnQfX3apgr2wukTLmNYdh4VeZrDX69V6TdV8PbbAjonGf0QkL1Hp2DE3ihYYgkDG2jYAOLAhGDCtrCuODSgzBRjB5jJFx1BRMJYFNJ2tUAVJDy9/Y4jrCxNZdHpZJHSf3VNSGZusL6vZp3pmdyTK1TCLtjiGfn0zIOmTYLIBeF5ioQmQFHWQcINzDpjMWAocAyR/OEHIxjHZYNdbgmZFKa3G4eCT/8kneAX+UoF72fa7vf1iKdNZa7BM3K61qG3aeKS3Y98RLIaTK5GZ96R0e1S+0xpc27x1sjw7rGT77WqunL90Y23xxJl0fnd+aaab6jR69XS7yZhjC2nr9R55/N3f+MY31vZSj+aXvvCV53jYKl3Yyuaqn/7Vs1zr+cTDxy+/Pvrn/49/ju7J1HsujIpTZ595ion0N69e/PI/++//L5/4b9/1rkf+zt/+b3707f/Tl//qysz0lYcenDt+fI5NcK7jGOWZmhbKpalctlehgxgMfuPXP0Wut9e2OUj753/yJ7cuXz65eoIL6C7UeKRiptTcOTa1/FRmtr3Rmi8ujRrD2ubudIHLxasMB+nTj83O91tttMFHhfQPXnx+Rre/VK9dfhNuXFheXHv1tfz5MqeR7/T0DPp1W15cPrm8MH3mgcoMA/qXGnup+TJXag76La4H4XqNzhq7urv1dqM/7L26swlPMmdnX/3x3rl3PP36D251p+FzptKTLG1zgXvj+pXbOpmyPDUP5PL0DBMe6SCOUnfy6Y2N9Wsss/OIYsou/eSKvn7/gaHmRZlKcUCqNAXcfZYpe30BJyKHsbU71sxVkHxih3Zm7KNeIBJpr1OmlmVCJUnQpNcNoTiIaPa4FgirGSWRqC/URPcfw0eh8o/ri9zS1IvQvsUkwZt1b92dqiiiYXY8nguuOgw441hKxa8eCkE43ARsHh3PuA/x8IhOwNBDjOLEPxE91i55vhwb/gmmRbG8ILAjtuvKWZkY2U/5i4YYMb19IPWo9pILG3aDn8Ij74TwA8DeVJ7lmMryMd4x6xXy+4qc5pgfPBQwJcLoDaKyVH/1Yqj2YHM7HuLRHvKW9LDWG/XQYvBTTUo8Mt4D+mkSGlojJg6LKXQNaE4LYJgZ8TfqtmFWv13fuXO7sb1JLJ9Ui6KESTIqyG0i/GinYsFm3WHNS8uMJ9VO2owsGsMcEU1Hb7hAT0byQyXyoaz5gHAiX+b9C7FoBkgc/lvle9tJOMew3UzEx5OMYONvtoqEEsRzpJvZRr3+SH00odq3FgtodoBROLHUoxIYlZHjmUjibp9gIKiN5gsMHlXXdnp//sUXpmcfWVyWfsQgxZmVVL+k2Wl2ulg9NVN914PHaS0vr7NnuXdnn9LnFgx0YBkhdzrd+egSGBtjJAbNnorTgGyTFqejGVHoWQsIp0elikoHLT3gAlSGHQikMqcYCI1lVLkDCj4w8WPG/OjTT+kNvXy2vb7eQT5RyWSUN9A5WWAqczOcECpmyzzIzhUc4CxWqlrqbnW4iZm02oMeSkgddtjz0s1kTxUtt+rS/OmZ6mibd2s7qTv7GuFwhybLjgwyivkhq6rVUnqrvrW7Q7dHEbR7TdST9+t7Z86dy1+7w9AFIjUCVJ+i0iQPXvczuRH3QlMfqchIMgqJtRpLQtDFAEGQArZfG1Oq6jMt72W1bD3MS8syk+Z69gKH2Vj2ZZCww2hh2Jmen77w6AlRmEqxBH984RTtyTL3S45GS+36iOuo9xscZdiu73NP1onFYzQZrb1N1hFz1GzSGHLwqtjc59Lq7P5g/8aNly/vb1PuxaXiuQsXBtPFm9zuWWgPKqSdqTdqw+xwYRWNumntOHf08JSVCctwelOx2+kjtSUeXWSMEw0a0Zvr8jg7nx1eM0mnuxldKtLM2EUu3P4910sdH9zaeu3/+e/+B5qap/sL5K6Qk0qp9bzWdtsrSbAn4pKLhX+YLlvkjAGAtGGpMT8BTOoqDnysNTbmM9LiKucIc4xn/CtgK0QkQQUpoWScQ0YQGSvIGBbkONU4x2qPcchf969LkTPDO0l8aGy9pbWG4iiSnFGRJB4FID5ozJ8ZoM/eZvqCgQVWLomO5+io+Fr9tVF9GD8706xEVDRsp+Kj6qP+aNhq90+cPN7uNBEGJC2T1rXL3h8JHXVTRwS9gJA7coZqoShRZ2X+b9k3cT0D8FZ0YTqjmaPNbDTroS5ACw4M/hlOCEWaN5EsDXocS9BNjEBCjPgTL7iAOWkcCSSzgcdyD6N9dCI1KhxwvoGYajPBBRiqjYxLkGJlyYiLFJWiIhRWz7hTnkzFqcUHMsAkKYXdmdF+oymueMN8MMK9v0hIJEFgX0d+71FZJvDcjfnuDzMn4H/2z0jyDZHzh75mwMkFjfKYhebKpXRrf/fO5jZnKxmHVEu0S2IJYswcVeVrOtF6rVxI7ByzPSFrnT8CKnHnql308DWO8bEZHLfzNJ4i0XCAEON8UxFSTnQNILAunckATT4asFRPXljitdxvvXJj/vzljz9+Ml/iXaANkKBxKxJUZFo9cXfSNpwGcChQudVsQnWB8mcgUa2yO8Kp50x/0G5o9VgZ6vUHmvKz7EjnD7CP3pFgSXiEFECNuOKEXUdSnhCmNLiKE3faplDDpl4noddXydLfUWOoLWwBTKlLzE5LtSIzhcijXd7XYkJR6wfDWa2IjCo94Ed7td3eWiO3MM38qyC1zPR+TmsmPd1sionoIBZEWvsjbCrAuxiFivMiF2xEDPBgoM6CBAeexjNhA0bI1PNnIJNLv2hV1Ppbje/XO6mpwVy2xJNI7Ssb+1eudW/tVGwVz0ggPjMuDWd8SuFtb5fxQy6dm6t0C6kq6yqZlJZhyIWEiJZLFR2mmq3U9R9pMcH13spvlCYPanIjRqh8tUbHqJCFRavylkEJnyGA5phlfJuxXMAEct0fpJlFDd/Ybbx49eaba9u9DIrdwmmlqQEY0Q9jiBD5j8lMaBNIV9RYzkzC9doXoyuEgiEKZKuGqskzCvmkSKJci+DDxopC94FxGqzLor1VBZGkls3B4Ydovn8TcuRcuv+I9w8JZkpAhRD+oDYi2DwdlzItdXjyRajDyzY3Whnot/HHzaMoJFuuXS4sWpz/I6k6MmtgMMIknAySeXmaQU26JbWAXKnDODNT1lPePHgBTp85qkvzBALXjkzPPQNw7LC4EYJ7xDsiyGlNBuDjxj1xh9A4OXkIxsTLPeMg5dxDQ6zgIEjVNc5p8L+3IyYgwuzA7umoqJOgxR/H3VDFSFQfzH0A25GxAFObYdfyFjhzkjBqEC0Xag7iTFlJ14GirQ/J4VBJGw89SgLNAadnQY2OmYAhACV9cCex8emkEh03szIa3IVjeoShfFLX/2eWpUNa7zUJ5ZFDj8tYaro0DX4XwZBQRAkZpM1jkcJu1wInAEw3xmC23B8tKGu9gcZCgzMe/oGYTJEZC9RY3u1WKX8O0ZfJ1HQr2MtLdpSdQ01MWFYTvHW0EbAu5Y+I8bgiAAqdKgHHxgZ8SXYl3Q5ErCNNGEArik1mHIxPDE0qtvvkFjKocnCkJfjQENOfsuXIPvwTn3yC65Uvrb3Og34oVTGFY3U4oBonbiNymDe0u4DIEdiAd3nDn61jPMHM5HPr5hYixw2K2Nlyic6V65h/8pOfNLea4IcY7l9+/sUr6G2VSo9QfFPp0rPPPvvnz3+V3bATMzNsbu+l9sB57Ngx9nX/1b/6V5ubv744+zQTfm50+p3f+Z35uT7yc+psBZ3iBy+gGHfi2OysdtcLuuOSjJI7NrE//elPL5yY/9znPnfxuVvewSNpS1PHiIUwQCrVgSE4WSDu9pqUoOnwUI6+dPUaqevySt51aTeR0hx3C/R65JGpKSvj6HSvbawTt1zRcJ8zUGB49PGnH3vssSuv/ETa32V0qmRIAmFGD7o1VYY/L774Iskde+Q8i9pwj1xcvXIlV0UrTSz3AoIMYsEoZgi3hiNSXJyaAeHlhp7HQeEZyK6VQs/ewiYWOPEkU1zuRSpFG2AJY2yQB3JE10soBm9qfxyoX3kmJNwpYdHZYfjUQUprT/DBgXEHkp9wa87vUdSDBWMp8mVJO7xCPddOD4HgwR1sCE4gcJojnAKLnOMaSkQMuYAGBhhym/EFaHfHsRR6jwXokG6MRKnEWTO3pc5IJwZQDNzCqrZISTMlMB+3BAkGQzLOF2CefWwmt+4Onjh+EUap2JALkmEyeySSt5y0nl0nBFJJ9yAx8gklomxokDzs9rQsxdKP+wBA8VMjfITnUWiZHRW2Yz4yUx6Efev2LZqI5t4dWo/Qt1GJNCEyI/yxCSTFHnf9NRqifDk91GjIvmuEgwEkqrRiSU4GClssbJ6LZOjP7g58S6QjQuI07ysFkKh0EoMcZ0KSYPeZQCdPq01AagTvi5sxED65rBZQ7m2OxJyMAnJqDjaGsqad/9KXvvT4Zz5BmwbZRIf1kjFTPiIiYIvHj9PIp3aaO9wIfPk6zfKgr0VMgg4nh6e3MJ4XtxlEeMtJ4ZKC3FY1PZUQqrj8j9kNcmR+6/JlNm5PPXKeQ8VsEZaWlk7PldkTJRVa9WyjiwxDEjD5bObkyZMLCxrPyNiQAwx+9QSTIy0DTZXJKRmn2a/k5UD1EVjy7qpY4KlWSmzVQyqUUDVAjgP4laVlugxpS5kaJrGgEOO5cLKxQYXxIJGRStH7A4MDz2ArwIyC0izo62JZAMAAVdRkHBhS5NVRloDpvummaUDIY7apCvXaa6/xeaKgwd5MFUAdLaKza+5sEXGuWoE/rXwK/qBShg+JoyRe39IN0Zv5EhcCvLm3BTeWLywDme/zPFR/dEI1lwICc7vXhpLSlHRR93Zs/cD0xzsDaYjTqpPNvi3cSHCtOfKMQ5tnxHNEKGzk3hY+ycIe9741qrhz7BWYoedt1XbxweARHMHtPsE/+Xk3z8NxlZQtFji1h5EEVAB6K6QoxjeCcJitBgFDTrFVyvFwXb7/yzBGqlH5s9FDOYIKmwbc8/uz4DvESfETPmMzR7DQqNvCnfOnUG1tmE/BWNuQJAMRRWIBScY1yAjegT06/nwaJE6NMaxXktuNilJ1OWrTcONPRcC4w7Hhjw/RVVnieR9BDg+kg+GQD/cdtdt+eBEf4DEOQyjtiNy4MFaD+HQD2D3Kz5Eo0iGDJ+2V1FFFgJh2/0aJJnA6DWTnwCjq/tElIAMOHC5UicCfzRlnEmo1CbKiIRdknIRATevHSVDa9goKPWbwNwBiGHeMi0TUt/kgk7SKjKag1vaABSZstuHhSLAdWEG2SYwDtBgHUGjaTtajmcWq0lD3BUMhbWm3WePs0SNzH2Z2QxJeBTxWsJ34kAT+jplKgCPwE3/PTkiXjLBazicHXRBRrgChZ2l1Nd4klvNHbnv0L8IZyVqCciAsgyAXEyxTEI9nSIhtZD79Eg9bTkBLWpJCokq331E/i1ZlJlNltoRoccuUEQcMjMSnYteDIKvUEXSJmKlxRw+xbGwL1Fh2nYxQRaABtM4it4E+bAgiQSVtnSmfODAhbjJKYDVguOnbKBcfuDI7Y6Z558YNTsGioAeGBGmRmIEKf6KQIm6SoC/G9k98wOkEwM5AUtJBfEeCJ44JA3L3x3aHyDjKEOpByWyajKnVYgiEHjfdN+SF2EJ4NLIAMi73ZLkQ7MQom0Y1CeFDIeLw/AZ6HJIoTp5/4oZOTwZ4Dw0+ousuhAlJnNOA2fEcaQNP3Tky6OfiCakqt4MpyMfo91xYvqTgS5dFosnc4YZjGOpOqjg5zj+cwcCVw0HJ7Dh7gUGSxTGTvW6Nk7f5XHUaGcgWNXCkSyNWmAMmMcgt3ikrhw0FJr5OGIZPSixhtAcXjE2JfQ3E44JchFr7jFqHx8V2B/ESqZOimBp8OLMOblYpHAnAEiJqIL9jDEo9RLFw+7RiIExBRnBI8TC8x3KAJKoQxaXWj4okwbQlLP3uKFl+Am8Ai1H5Yg1fERxcMEjVD7a/uIAMyaAXYyeJhBAUbAdmqw9ILtNsNrXbyOP1jYYuX6cS4o8KBS0vmtGqjXRscYrifJQUXQqAGusgFq12nZ6P4V6gBP0hiLBNEcb8Gq4N/RpFKy8Wf4EcmX+3bJt7i2rK0yvTx0+erCzp+ZpedrhP45hpbPea3AqNoZBlW7raxxRO+Y+NpEHShQ87WgV0phBi62h9Qc25DQYIglX8qeNDjrVXjCzBNnzVAfB6NXwe+pGdrA5j+vFbqZDKKKekrtbHrkHgE19fWlLiY7egXbrkUgFpw5dYfEVtmAISulj2iTW0Sy2CqMjbE4/FgiBEAe8kHodHftwRbK8pQgIF0bxMnRYyQGVmKd+zQGguw9tBozuttc3W2vEsV26ncnrTJTdVKb/rHU//4LuXgCFFUh5mWlrMhioOLqVKeEnHNp1uFXgTKdWRPvugOK8e8dad/Cu3ms/MPZhKrxS6e6VMoTHqNOqdYuoU+nOV6aknTz369Ubreq+wXZj7h//V/3n4bz/33HPP/VetbrPdS09N/8VffHltf3vEZcuv1fqVwWbqNqu35ZNnzsyc/vLtr7/8O/+ffO5sqjrNVTP17e1ms7uz28umT/zVFz5HUbJAPDNV5UqQc2eOI2OnzixwSHl+urS8vPTL7/rIQmr6X//Ob5fro6XLe7nrjdJK5/zKfKkznM+U86lmbftGNc0+d39lcXamUmhl0ifOnqzpxfnMtfX1h1ZP8G5Rt9+7cHyF9fphu99JtU4PU5U3tz786JM8hHipo8Hl7kJr1Gm+cPF77Jm3p7LbDW68YbdDygsqi+lZur5MWVeW1nu7/W5/8+a1jTsb5xd4L5QHl1I5hoEm/+v2MuxL2phuzVLZKplyL9dr7A03Nxhm7ZyaLswV9mzC3LdBJ2typDDdS/F+BkWMrOa6GogwCCJd3ftle+HYesKccqfouDSb/XEjzG38I5PYxrBw3klWZXbZduEZ2L2ruPVp9YJE/dP6UV+mtppiA0GBqT64WFs6ql+SJz7oeRzAAiTPpEcVlWccI8JgeCSR0KMuXg4MObMfw0Z8vvWqNmg0ropTATyGt19IADLOGaId1VYHExKDxzYwpcN/Q8uvyFN6qr+0DxEfHJJNKYX6IrhukZMRJA2HjDC7wVNRDJO3e3y6EWpVOE9E71a7T7DjzacIQMB6uUBJUPj8gkc6UbT/1rW7zkjPtiqZxODPY2Es3vF0JlF20z1aeJpC/OmTsCISrUHBjY96U2xZMEKtbpc70Hud3TbXvaANjazBl75iq+qwkG0tj02WckNJUdStGDaulMdw1pkOi3iURn1f95g3ttcYUldGe4TCKTXKZqRDZHt1sUfUHjqLJrhDuQfWBfjgoBAZ35Df2n6DKZCLzwQGAZucREJIKcFJFEykD0mYfSOnUhGDv4oNx+EJdBLoKqji2c/PUKBKReUr2uwrwi4hequEILvdZkeQqz9VymCjtQcbvHLMsIWywIdQSjmBX7mAS1LgoR1H4Y8hvckBhCBrzGFRJRcSCbGIdDN2WXJGc+QXNm4hxtgZpTfMSi+V47S9VHctXfryq5env/fjT37yk6fb61wem0/pGrE8B/VKJfSGSbVfpU9nnXJu/tGF+fecn7p169Klyzdu3642G2Ce480+prsd8PULaLIyXhLFLExGo06wdbIZVi1LKOFkCrCCNnaQ0dSUzFKsbYa+6MCa5rJN+MmZTy+xRxVO527uvbn+Q/q+/Owcy69zp0/01mobm5tsiC6deHB775X67mCmwvrwTLFS2djaIv6x6jz28vkHu5cu7e3udFotpHE0PbXVbi7PzHAitt3qVzKD29evDzb2kNIsz8NzWTxSls0dP3dKd3RAHWfIsuml1ZVTS8ssIrf2GoVUrlBZGKWrxdJiNtfO9e2gW1HjrrYtyHZHA6bTC6fO3KlyobFG/DnS2mv6YIfSt9ZSReeH09CWwvTymbX9ndbssJPhUIK6/i47yKP2Tq5/s9tYaGlUywGzq3sbjdz8xfXtVqfNHLKy2YEDnaJOdj9ZXeXumtZeXdc6p4fbO5sbV9ss7M7luY15pp8tMM/f5pbOVGpjVNjc3fzu5TdYF6vltIj2YqHx4+b+RnGbZegzj5/ShGQ44pRSJaURXyPTyfF4ervZGPZme6Vuo1tJ5SvphQH3hQ6G9InUlU62BeYphLPbH2mwPGR9Gjlv2TPLowK6X8N6Rpdl0c0W50bLWQ2Vyz1eSixykLDT3iEqoQyR8VAbLq6o+gBmLuv7vDWKJV51Qe0oSrraRDBILMHrBxNXWJw2ZWGYJRT01daHGljUwlkilhzbmSh3Q7zjV4D3REaLpal0IZbSlAhbgxknZdDWtstlMd3xM9haFY16BBCC3EatagExxh5CoUSNlrLA3IWux5r+e6YqNDEzjwQUtj5JUzk5wkCVlbCy8CR+mtF9w8Zt/ww28YIb2uC2mJnkURysYhI20cEMi1zQYALcH+jSIfKNRhwXowkGPvMD0TSMQKBG05YOtaWlERIYDFVkewoGYAWtTQX5aXytQtfYjSYFH1oGZJ7k+KRPg42ccOOIKEVM6FDamkiMqjNgjCTZyuKhHm3UScQgXZv0Rgh5FTn+ReOWHfR21m51dtdZ7PEb81V8wYRJIoLvXTf5tY4DEG8xEowM+fJ+UHIMDYKncESDMEMGvW291lRHYCfxdFxiwowLJ5IfiyogZyBIzBmVC/74eNBBTG+v5/W8qB83vgWENJOkpCxE/pFWpg2BgZVwEBeH+Awk8NKMEi3KM5bUvnUoBOGhVuIFW9iKa/f6rL2ub25VZ2YXZqshdzT49EHwTpDgJ3e6SVpnDw2l+mtEG28msipNeAk36ZiRIYLENmQfUYkMcTGk7sUnybHL50tWZYYoOGsUw0mmEpczFdKpWqv1uW/9+NdmVx9bWOSZmnRvB+HnmlbAmHbQSqGehdJFpOwFCXRMNs5RfknTJBP1RcRTLTh8swJSqvzrqB6VyoVev9SVKjv6vbTGPZDo1he6Zi1Ma2SCMMMHrqhSvoQXxJrQei6Y1bCUbXklu5ZDWGCyIlDxKsVjEu7AzagU8W4X5U9W6NZ2+l3mQMVGnZWTJTTSufPNVL0LFqukVnZQ0nOJg2GPpfl0Zpr9Xa6sVbn0ijpR7YpxMC/D9aBFKwkb2/MWgVdPIN24Qht3BvEJv7ApWVuKV3uCiGT7jDOsLnOHnYkT7FbJAmYyltPAJVXgCDmPJXQ1Tqv0e6Vcs92qb9y4Ub+2VmhJAUTCZ4aTicq7LcBwZfggPWhynXZm1CilZk4scFh5emGGFWjwcG02gOxCaEjBSFDRRaFwmfj5p7yZp8T4FY6HpF1tuqKaNgoaN5SdeeCpglM+VDSSfPGU5SmbM2ZHZeavnCSR/sqosLG1+6M3bl7baqGKz11dXZp2pyUqXZBYewKWsYnqODTEwCTEvD2qrXKraSVF6zFFiPQnlDEzIRZfBLo/nu7vnxqpcnEZr3LoyjXmC1wPonDypqJ3RI7N50bOJa09Kj2wUojBEFc8OWy8ST3sD6OtQk2ESHc9Nt52aXguE/ypMNJ/UmoHE7RiNVj8VXTmxopyQxz5E+i4VCtZnbCboGECFdMjkC9YFDHLvcwO7E34RU5nLB++suepUEC0hz4j4KqNdCrf2+/1a7xxlWakV55dVL/mXPDKP4EXpEcmGTxDqhMR7//TMcAoHO72uNATiieuLWOsQDoNHifQM4Y4yhViERiiJBPFf+LzKDQH/CL4qD6P0TqQ58LTdTtETnhGiY5zZIJDm+iNNZC4/TNEd4dHYS3POyE+nWk+xAl5nIjln04AgxsmFeo/TJbliXRqwBIJLz7eZQKh7NiI3dcLWOYl+rQtPfens2ielk/Oq3fRfEetFgnR2AXbqmg0xPHlYN/dJYUkhYEPE56Aee6oNhiIcUhsGlwF2fAR1WdL3CbhVoPZ34F7GsypwrpcEUnDa2EQgVHBRR/mFbst1IHkjI21dBY19jn066FKIpYr94F893SbUPeHbDLlwOOguAnWWlhsKGYAMEShkuNwjQyhwjObOX369NSqNI9Y8WFq+vCjDyMSXOMYLoVUZCPMUcJbPicKQiw1gz8F/dJLL33i1z7FBNKJBC2eNPqMqxh9crPzV1/+DppcrCAwNbXLlNWcgRY8XCr96KPvRXmKGTXAw6m1zVTq+rr2qxmYAEYPQIl+7GMfozX8qz/+nx5997v/87//mX/+z/85G6ekuHbrJkpzpYJqyurJWSTtgbMnnnrqqfc89sDjjz/+mc985p/+03+KdhQr1URn2JFON5BDNJeBh0KQPPzw41CLQhRL2LAFPWh2ZcHzxrU3gYdCQjN2TpEoaHP0trdZ8v7Riy8jOX0WMNDMare1SJ3SLqsWDGyJx1nBNDtTVLfKqh9xG31NeuE8Y1NlzbcBJQNaOIBCPDtdE+KeHbe066oPFz1gGKJ4UPj0ckM+SdE9seU2AUl6Hgi1jyQqD4VUknDjtRU3nkr4oAHegXEQ4tHlaTUr+hx7AxRAcEQfjtIDHInSSiD02u0AnpewpCV4G+JENddIjGTUIgAv/MYVd+MtJJa4JxfS8nT59OlZ/DkmmlTkOfaIcgp3HIkjBIYklS7U+Q/BlqNx8ZhP5Jkotdj7vn6dG56voCPDp/I3GiHYLP2U9dg3Sy/anHAtSz7BzqfTJmj/tDSjzwT/ASNHSCkCzyQFaWeBzMVG3ND2h8YYHvEedBORWAx8qRF3bq+hEzRs6f5W+EkSyYh4TvgkQ4Nb6d7FkJCH4sB4TvFxz8lIIfHgUPaNqsAcURiVnshLCsEkup/bt/OB9SXvXq1Y3x5y8k4EOE+r5TGdFUksKoKjTNLf3aFQcNwl0lGIzM8pmQgGLXgoFBAiD9/+9rfppy48zJXH7cBheodcRg0yz2gJmjkqf+n08rFj8/OLmsRu1rjKqXNji9MzrZbuVuYQinD6kCDRKnrek3fogxBIZvMQRqvKZ+g3wcOnCQ8gAEgrxCcYdEA8kDi4comqxEoV9sLKsjCMRixMp6by9fX1m2s3cW/Y+2bveOc7T547lxqeVva5C2J3l7cBqZ68d0FcrlRXj2D65loH54SBPSI9bZWCpEmCxSk6SmLTmXIlBrxiXxYMRlhU9RiygY2pvlJJpdCepvvDQRbIS6erh3D9020x86CBDIDBGbFFijCCGOolCIy4lM0XyH671UCiuNOGLD/zxBPvfe97V1O6Utk5RrOj0Po+jQb5Il3m1Hg2WyIVaWTU8YNr6+SoVtST6KDFH8PIs5fp0R3f2L1G7pYXFsl4gc0yyqiQJkfF4gxgTiEjUnilWXtsCAKbVkbj9sRL3D/JGgZ6lFI0hFZML2jH4fCaLMlYax8jv/evoGMDcpzBxz8P9ziT3I+je1y45IZPKPRBggUJThlQKtFIALcvMSjsr8uoLU8MQZPJkvcDY4Bk2JHu0Lg6F4015M+ZCjZYQck6Q1iAJr+BvUfie1ueSVS4QU5aQSoQNhdRa4e15+jwBimJYtnKfMYCkEwdgPAJGMYbc8dJWiTk/iSk1syER3jVHEVnUMDgyxAhVD5mvC7wqaCESDkk/iBHeNCaxH2kccgYXrmI3UeCv4Uncd2w0ABths2iSFrfIi7FbbmPwKBc5zx+MQb2OnkJ9OrUAhNxU3TQMCHnojDBZzLlNJNrUFGC03bsmNE+M4hSVu/xwApKGQf91MJUkaUWIMFM86XiY/SUEJIEPUc4rbgP8AQ8wRABMvikpuAG2A1uksCfUHOoXCCDz1deeYV3cS584hlmbf1WtGAH5MFMAh4Zz6aj8rQIcM8YRJ+WinU9ab3BMzXFzVVsm6g7QShgBZNUUkQ7BlR0LdhESSKHciPjQLohieCIwBKThKiVMGnLCWvELpDD8J16C5+SJtaZstUhljagJMdTjczCcuqemL5RXihw4E8lU3ZMoYdVSIxX2GjnwRgLDYGe4BBoJP9jcQEVhhBsMOPAhhLHgI0byTAodXMYd9NXbu806Td5LQFPDYFoASxrIMEN2RCGA3hsqjzbvgwA9PQbF4j5CMcgHT5Ahk8c5h5TC0wwFiShdR8cJOS2U5gMCu1Y8IRmKOSTImD2zVwbGE4w4xn1tiGlezo8dwEk4HeH2aIqAEzA4y9qQ7BVQ/mYIUz8NxirsGwEGirviZLRrHLZblAC1y/GqeJOGOUukQM+zahoEvkeR4g8DxJP8ATaEAF/ikajvaKdGLCIAXVwBPif2kHRY3h1m1rG0BLZLnYGLJhIiDHQQWFQLv7pfnHy4wKWP0jiSu6zcyQTbw3WLfcBxYGlf9s3iIKinziWJZa0An7owh+qYkqSUNqmcH+o9qmCbchqijuOIqr0p0kkrZ72bMf1KokODNDFnzuUbjI4ypy8nEtJkpJ8c0qUNzPkBd4CIOTG3hCRT7ntIJjnVDonRgZRaUsjFNbq4fbdWqfLkfDAuhyD4X59F1VmnJ46PnmGqCMtyyoXUHOIh7bbw6uso7bu1RO9ffoHwBgh8WOsxAf6uWuJUJY1aVF6ukUxk61mmGDkF1H9qQymC+zbVxbKvG/eKHPXBtv0SlNa6oFp0SwJD5Uo2OWyfAW3+wTm+KeFSixN2SBmqpWgV1MWxAnVhaUSAulqca5UfUaWaw3zmZwS5l1gtd+oidIr2/4S2z54aItWoWM+++4NUi00tksECCRFuzpAB2N7SuOYsb9FjD/s1+lMenke2cnG090BhimeY9BGuGgIIdGoBU+ioIxHyWLA0BsSiR1kNffeTXInIrdMdKbLucVKi3KoFNe2tpjE3t5pf+9730OwRExUAnJqo1s8Ni80obT7wGgAnslzO9sqVorrM3zWNtOjZqt5KqVd5dncPIqRtd1BYarM42Z0ex94JsP9HnCP7Xbrx4WD1i1V7KCv/w//67//+c9//k8//40LDzywcPoYF3Rcv6arM55976c/8IEP/M7v/CkDt+UThU996levv/GNY8crjz557OwDixfXXhs029WFmWeeeebCydO/+7u/W2zOLOdnnn/t5ue+8v3HixX8H0m1zzQrJ3cbcwwpMvuLw/TyYoVBz8bajXKhvzy3mG+zSdzLpDtc4omwfvDjH2Uctt9pfu+F5yupzEJ5qo8WOUIsRTAUm/pMg7v728vTJx87der555/vdlYYzNSWpdvF2eByqQg3aS+39/e6VwcoUJPlPjrX8/PT8zP5QeH5Wy/lUoX6fuPCuQfgNuXiw1wW42B2y54QZLMWHu6j388ihG7pH3HLMyvTTbvCzYuCFssdxKJ04VV3IAn3C9K5exQhYO8F5DzWjj/q/kBqr1vRiMFXZBAVf1IPYnBT6014JD+824FtUULDqCbLJd/pB0DSdcA+LPvuYxIb7bjKJ96/dUkeTwNMwIQ2iRl4T5cBG243UQ01nKzb4Gn35EKPqrXjdSR8kpED/Y6jMDvA8BWUu8zT54FOvzUVCRSO39E4JY5HTIx5Qgnjxoi3HAocG2HWfXwiDH8VOoXD/zDM0PPek8bgvfickz70tAoqHU/pfciMClqrYryv4f4Ub5vrLn4YpBe1xV+SY8vQEBlKqro+DA81PCRrGh/0kAroobQxGO13Ry3ueieL0oKRobUU2fBbUzVDJ2QSUkKdYWOMJKLr5VLNtl4b2928yRCnyFWtqNR2klCG2ixh/KmME4PNIoiYCzFWECqPRB4P4IYEY0LwROpgl7NKP4QeTWaI8QtxQL9p2ilxaJcsvh2jFUkz4MFNVL6QSdzOJSGzftmxRp5RjyBJdwZ6aOz20RehY5Y5Y2MAB7+r7cDYwIOFPxo/tKKuXG/+5ZdfeGLmWU601PbWbOOwBcHFfJddZPggSfPDU+kOuvQMVXkpN31q8fzppcG7tay58aYOqOZ2d0l7flSibc+jS0gSyoqqARSjKk3SaBTSfqas/cyaQjQrpeSW2gpVjFj66no1Q0XX0sYvDBXVyYKGeyQ1oGKtezgoL0yVmEvOTZ977OF6fT81w/EiXtQszUwvLC+tNjrt/b36yz96kTupWKtmEkabXJldrFR18rS+vU5B5BZnzs7Moo1W295u3FzrobmrHjinSy45+ZJPoz53Z32z3x3sbe1Deb3XqU7N8Uh5rlBK5aQubfTxUO0MKx0d3e43bOQGy2eO0QJwNCGf1a2LA9aBe13pbarZsd7d+xIr/SF1hSuqe70Or1EhIFwJa9pSknrETjdIZvaKWoKvlJg0oicO70ep7oit5Q/+jU+h8XAyl766u10r5ZZOrhCL13JTFSp3vjos83Jip9nXcbpStVtovHHn4ivXX39zdz9byvaLapdo8eB5Oo/yUg/NOx6i3u+2W90hZ/gpo1xeu1+V2WlGFOX+LNtXM3XW7LKLTc4Xz5xA4WFx8dTcEpxEwY+854d9bXExAt7dvaOXINvbXBA9HO52eS4ys+ZXa6oY02iHkywLmuM1TbihoY5VdXMbAxA8/KLWgJgRAC43xsPgGRwEgj8GsjxaXFq20JY4cDIK8FBLxuE2Do8OQBJV5MlY1ErIg3xFgyiaM8TG2nc+nIyxfxz+0/9qPKHUkxjIGfwVzQNOBTkbk+Fv0w3zxHXbCXCeYB9mBSUGB8j2IeN9SMRDo9U71+BDjGQ02ledXjMVUsUlOZpEUlTDyKBYcy5AyDOMpzejKcF7jA1IjJAmDJ+YEOSfbgMFZqoJn54QYO6JTSTEgFDIAAAfR8IIjbJmjdxgRCQOGUvFUTk8FYuZQL3GyJShOn7UfEMV06wMKgsgx59RS8SNJFMU7SgjYoyqg4EQwywyjc6vadEqkIUEWOe33xwEjr88PStrrzHOB/ItzkMVvA8VM470c/k1tql0aPdBWLATh5Yz6DbpNsYRaAWrND2KOWC+sVXeGqm2aWNtsLG9u8OGWbXEO+e5bk9bg9liZb/Zub2xW51Zojsjr22W5OjfxX1FJ4m4tuqTjHuZYosBGnkraYosIk9Qk4Yg9WtmnBJsj+UCRogKGc39UaY0Pbe/O/+N51956OwJrhOczk3ZlqPgfYYyiX3ym7JWF+kkhXQVG/ptaRXlX96eogGHO5yzKRRzrC6QIYmD9i+VMbJnOaKW4VDirPci1B0kSN/ygupk4gZvtZthntioEvTIrF04AcDT/RHCjIgVgCYShNI3H0xIGaynBoVhl6H4DF3doFNRRc8U7DBDpkWnMCrV2fTqp4t63aFU0ZkDHzl0RZSIsR+x2hs6v/qPFPGhJ4LF/g4WqFDqTOd0KotKRodth5QyJbxTw86ww6GEDnrhHCRE1AepSkdLcPTZsIGTu/QCXHVFX5Zr90etjumJ64SCpYKVKkq5eMCLw8wWOQHEokYr0+GZosxSNVXJVuemeHzCTd7PJupI1NiQDMZzhC95iUJjz2S7agWrkhBbbcpI6UtrUPy0IlDRys2TF0rDkJlaf6bLg2Cj/PX95ovXeeylkypxNXemB+lWjAJO1Cz/TNgOJN66CdMQiI/99GtznNjH2jo/LZqEEVHEUjUyabNGGAC2EnEi0SyfNFoM8igIjcbIH6+2xl1xApPjj6ov/gcqzQF5TUR6u046UkUZaZ9SDisXz3UoNdpGEqcbpJUwGOD1e6Shtk76xwwjBSoTEoqYsY7BuChvs0sPd3aNE53EcuA7gCWKV2JjZkxAZtgbdHp+G36Kp64KcT4ncpiU0QhHnA0SGGfJdiw97ThTY7I8A+PvQy5SmYiFBzXTAXGMEzKvBLg1Oi5PHpSwPbrb0IAJPkl38MThlCRTxxPgkOKREQlNwgSEE8CeEc/X3XIEjUrLbaNHrUzMHs+AN3xJ5Bz3obVEkYRNvLm5BacHMpjtq6W4p3HKidgdSSXzHsZ7GgSVVY7qrF5mz03pNt5+hTXe/KBYYEKSKml51wXNyXYC4uZN6D3vXuLOjWSini9gQgbj7PtwUN1zBMMP/Ip5ZZjVCPq1GzARglkFUltHZ6B7tcyYGGh51QavYGD1TnjsQJ+n5Ys1Mc3iulchxyDgYBxb+LQiS3xFTtDSfznyA9EJt0GheyaDHBh8AtEqsAlGbEeQulhFsyxCsXH3eOkIeFpRuqXukO3uy7duMaVvNDbVGTeb3AH96hefo9RY5BSkGTWhcNXEDI84acIjgyeDCWSMVNAIZjOTsiY5cLLCSxEXCrNM+aqGECVogL/9/CbAqE05NmwIBZ57GNERY5n3b/2tv/WOD57jLsjf/5/+7Cff//6FCxdYRM7lPs/xnOvXry8spM6ePUvy3LdJLniM79T587/2q5/85V/+Zbqv5557bn1LN28WcllC63e2v/CFL9wqDBn8cXAZkohIHnu9fVKETj4h+JFHHmnxrsdgwB3N5Gjx9AM6Pvzkk+Rr7c0rwFODkGSxgmWJtuoUn+Tr6aefhs6LtTbYUCVX3NICdqhcKD4QxGfbtP77HV1OnV3XewhwABPxMf7BP9WHHyPGSkgQPR68Z6CJGyRQmCwCEvJ48NON58hXoAcdlReLfG5wa6khNofr14FNlHgNUeCWiKdFKjECSUhwBweJ4I8N4UcCBMik4zBa4lL/gHEkbvOpbE50CcAoKQ3IHSducj2OEleNEHoE3R6WTC6BjUCwedZiwHE3GvXxFuBuaHR6nAZsUWcDNHMrfXnFJi5WjTFdcoyBUb2Ooe76a6gMm9VT4iKxpZkZaneK22lLJc53EJn9NYcMGfFUjsQLpIMFRwAmd0g+NUmyatd6AIOnkiBT1kF73jzuYfwOTDGCgZYHAwfIOPub+CSE9HDUt+cjeg4azwXJ0QOSlsL5g5QJwEMyBgBk//XoOBwk+Ygvn8DbdBWeq4nwjKr8JzKSiA03aItogXGQd2ZQ7sBNa8BnAvaAE6aJb0rlaOwK/XkYx2/tldJiw4Brjr423/3Upz7FE9lQnmH92F5+h1oKEZgs65TSG5ChgGjhS8UKHR7TN5atVxZXn+JiDWnYbNeubrDJinIVNcI24/WklVKhU9dPlAGcMETYrA1UsJbPbNhJV6UuQ7XbesUo196W4kUoa52KuzhFj7PAARd66Fptenl5emURcffugNnj97///VRPm/fcUMG25ezSArH2e000vus7O+oxO+q+UVliOEUB01PwVAMdX6mywIaNVpbZQG1swAcu7qBYuU2DFEECbb7Z3G0OwLy0uEo7sJOu06Ptmz4XhQUMlc57wyPLjqTxJ1GBFeh4Mx1bC2E7NDRQANDOYNMVMh2ndI6vrr7//e/nblOeT1g6fwY+P/zwBa6YL/d6nPUp5Qp01um6NSw14Vxbr7PRu9bfwk0vrEStV2W/3KUROkFLptQR2mYJyVFzcdPFw+rKQHxYLCyzoP/+Ew9T4MWWLsUa1VoUIiwEvprLcNyKx22ItZfWwONybZvNiZ+sr2OPug1KzRd6NNdSxqeUmIkTBLjxT/M+YCk02qwd+9uAUQUx9jrK5XFDyD3goZDsYJRYjFbRkcO4d7NygXzeih63oGQf3jIVDan8vBwaoiSMPo9qA6DnCH+yENqsBBI5FTThdeAzxFOpDfQ4Z350RMMl1lg8OBDii12Jz+B/DwfwVC6PJZy2nIdNFBgr3sYIJXXmnwR2H8ePvwfxGTDgpt/D9iCxSz7SgsTwGTDgcEnDX/DGJhOKPstzeAYMHsU/4RKr58AHQ4WiARH3glfCoVQTn/fvDHR6FFKHR3hCp6P0Xt7yJxA84WVode+RkBeksxmEZu4B/tMHOZ1eBOJeJDgqa2XH6TD08Ehybd2u5zSkGtMpD6LQOonVbPzbUKdXnaY1ptkkFZo4AGjEaMqWF6bAw50YmmWwXH/QiBiVuMhwO4SLG+HDHO7hUdw9AeJBnll3W9NBK6Z5NIc+EY+vfe1rdD1PLZch22HAPYHnYLIHvojihkg4jG4JhLvJOFdCEiHP7RPsXHKPhS6ZkSavlt/NkJY7aMDkNilCYQxmqme24sB2qiYIs9CIHoKS/OEDD7TMyJd0zVTFRJUDEQT+dkcjQ4YA+HMDjkJladEfwyWE0Kw7S1jZKKie8qIfoXjipjcnd4KmNK3RJSqxVBlkDpAj0rTAx5xOYUHg5DYvF0WBkS7XgbAfXGNTuJfab9GLsUQuVXrtWUR8wAENdvWTxFWCh5J5v1+eKTOzrlcHdLKQDTZjtXUN0ZAGvyhRIoIH22FE10GDv3JtxmkL4Xy6j5MUYBwhnwEXSIAkFzdv3rLmSEdMrHDFo2T0pDsk5I57BE1A3ucnCN0ADxlMEax0lFk+uRWG92KZS5GL0I7dJ+afOxj0OFWO2T4jH9zebzhMlHRgfYIUcLjsOSr/jEQ1BsOTwoIPGCs1cSgOFFuC++foICGwgZzeSgvtJIntviZ8SE8kr+wwME9gZfHofmuguc0RlNlegUoykZkA5sNBiWsid0Ey/Jr5CDgGcHom2OGBjs25NuacxQ/wnjX8PKfuiJKgs7RBEUGeF0cVQiccITQ4AoB25sOHOeKhoW12xUEeMdBmZwrVersPNgDYlA2NHe2LPltdln05bmkiEjMlrvCA0f3HjaCS4bIwNf10OSoAwVMg8ND3wriRFpztfHp/f29/gG5RtmWdIio4gvYFWxvlco8zLVpuQUvMhamCljym1BYDqdXHalo3J1UKJMZNpvTCpKQ5vO9hJkbFaBDFuY8aKZpPUYX2tw1WcHutEgNjofd8avapxls/2q+ycJr2WOyUTHRjD0qehAIDNDdWS+fT90vtWQBPmWiSbUMkJ8QqllildMR8JzxajMZX/qLW6TEPeY2NxfWCG3vGLt9p15cVa+yt31Di7hnVC8s+NzImAaAKEh2MWIiBm9jN1MU2aq1iN/rtX/vIp7Z2f8Dh1p1Gk+lZZdQpzy71NSRQ/xRMrJ0qjqA2pVSSxmholFv0yu1K/9Enz1x8rTGTzZ7KrbCekWqnmIV3MlPNdnP7zh7L0ywxP/TQQ4Xp1we5TJN7GQHJlFk95HrGUa6wNFOe4lxtbvTR975j9fjMoyunH5g5849e+kdzucHp+dRUqdmqbb786pf6w99aXc1vbt5hIf2hh2b+y//iN9GPfvrR85BWu9Wcz+1yd/PV3V1uW+EajeOlWW5Snrlz8fj8/FT+apMHjme7ZdMB1XIsr1Gk86+8/kPkdvncOV6YeffTj77wwgs/+v63GRceXz23unLy1DvnWTXYufQ6E3g4QFs34NrqRvp0ZjndqM+N+p9+9plXakPWFF7vbNOhbl6/xviSd4YL5epUscLKNecF2NPjCtNGsz7sDavHph89+eAbb7zBgaPp/NR73/GeH/7whz5Mocgk5NYosBeP6WhqEd3gPFfKc0MoChmUv0uCWl1VSCQ0ap+ZulPimrhwUFQyyD/mRYgKmgemOWLFJxk+2IV6qx7KNpI0+05CukC6pIf6pWGUtmoEDUAsIl5LfNHTESfkxxojr0HKgFRLInwwwKCpaDb0tErhggdyGhXyhcPvVovwapKrNsXJA9i1I6n3AOhMY1w1I5psJd6BHYPb+AiP2z5pt8bCIX0n33GC1aLYDqjRe2BZ3JmAP4zRjrG5QkquuUO2YVlopVT75UM375mlRDzdmj18FLfeMdeFzdw2OK5Oz9LIF0q6NTJXkqYzkkH0YZZLYDm5YpgFrmE0/natHLQJC9fFeEL6CEatHLRre0z0ozEtbQJtgex2U1xy3qBF4RlrZVu6ZpIxazJJFYEBMzSY7gDFIqQhiaQ4WUlxqqCZQ+2ZSH1bbIoFKNDiHAyfP7WDozqsPFqbP9RBaXbjyNWh5O6GX7XTWjzKwurLJCDZCdmcDPt5f6sTlXjB2IMaLHdPiLKja0ZMmBU7ndgYSoQgHNCvLMCgxNZ+5IcweEEexG+e981BRJYJEjyn/iSmMY7S2xyvR33TSmbBvN1sf/7Hr49WL/z6O5/stDuoCBWLPFa/x1x9uliCZg7rSkpTXI80HBRH7REqu60sJ2XbWmdnsCIdxbPzU2fmyo+dyfMQ1s07LDsONnegfCanO9D1HIaWPiWi0R2FXspIu5pj0QwmQJAVGeqsUI8lh7ux9UUNYB1/lOns12//4OXb+NgCRKvTYjJf0lvLlTvbm7bSoMFTvsRNUERK9/d6d2obuEuLs8VBef3WDZbL0VOiLnMjc5d/aXR+qW/UPl1ivTB/bLjX5ZzhyvIxNllz+ZkmK8XdztTC8t7aPpgzhYwG7ouzDzz1RFd3xtIZTBG/y5WiNKpplEy6O/X99oBhmlhFzlRZLU/ermEjEJuN/a1mrTPQkq6vV4nXqFvyj/FhWZPGLtU2nWqOupli5sS5M8dOnfjhF7/JovNuu1Gen8lWZ/PT8yh0lSpzYh5yVuxBG00Xl25956XXdqB7uZyfLc+kdBtgbXunPexO67KQmb00JI/mstrPHrS66Xafm5rBwduB8JlssNJa3BucPnbiv3j6V1i4P3Zpd+fqjZ0fvXr7zp16Wy+qc2cyFBZtwwDFa3g1d3zl/Pz843Mr2dkTtRU9//h8g+ckL7+0s0bqlYGu3CWzSsWaLC9n3IcNMB5qzYi3+BGU33pyOK5HIQky5EnQFRzGfNiHAiQKNsmoNBN9NyXFDTMPP3iBWLAUVRMtkrAYalnwIRuNPsSwaHYIs9fc+6LhUNxDHt6dmLfX4og/kZbwwVRM2MYokp8AQtdBcMuNwAMgyCUYTBOQQDo75CIukdA7j/HHLmIFVFYEkzwxHKStztIN3J6AhP34UI5uSxjN8MkvvkR0H2yLK3jHhg8OPjFExI2FJ0Xmxer+HuRRsMmrxRjPQOkumfp1e212m6jFwBAdblC5cCD2Bk9aZNBSoe+iqem0qYP7WxtZTnwYds8/xyT4So76nE4DiayQl6TnPd0RbvLCjc8cYWCtTMNXsUKswyCYEwV9AKHzzAmNAyADFopXnPQ4cm0hhrzHL8liaAvtR/x3maH9d5TCHwUKRCNoDn6w4Z5NFeendnbqFL6GOXGxAuMxiQg29wc9D/BgmB1jmAts7uwxT8+Wys1aDX3WShnHztrOXpljkyjxMD8e5sqRmpbSDYMTY5Y8XLqcfn2bpLkjaQs+IYceBGFOpAclbeHhmAu55umZ2dlru5tf+t6Lcx9+5/Ly6qi1Dh2avWMzN6GjMHSRQEetivtFJChtE2/fYcAt/PxDBE34y0Uus+CQEzddcAbOziz2tTVILYBCBqSDnp6qGTfFdqI3y/k95jpaKFe/GaGFyUJgKXhJyq0CA0bOhHFtaO75IJR1E8DsfCgJqWb57U1Ntc/Zki1Al3qq2hylYZRU8PaNVocOdFuaTD07Co7CEHSSH62E2EvCnKLFwUgLGPpKbCZheKAVBs30uOzbjorauGUiQo5Z7EbqqLEm2AXOJXVZUYtHZbNSzxg0tndbe3sZzqJDdK9PY9FLpbXyzRFGmgAfs2XSLOTbDH4E4flSsV7Qvb1nnjjbzg9GKAUuVRkQgU3jDu3qap7E2CVIrEgVGd76ef3Fzd+B8oU22KU6Eos9sfwTHwyf9qt1QuoUX1YnogUw3MRrZco3d2+9dnNrvTHoF0pIHjotJBXHNQR3twxMVKldU3IHKLx7vHuFgAfjdYRqSwNLyRZ4WoJSy7Ku5dMnaGdtOlXQOS2Nwt1YMy4nwMH2oNiOQePvn/sv5Ribt4ebWCGCCpt2yMvcvBGqdJ9hGyI84IQhPAnAP6PD13JdzpwG5551idzP0qxzNp00CPMA3CZ8Sjci2iXtIIyCfzZDcslEQRYImEDsNeFuoQ58OBTkwXjN4RNgt0MSHhEbQ0LYBLkdYNxxpKcHgZNQ2RNxDn46TKjYycBA1RiVUQIMxCMXEIfDWpbJRPAnFkLjcYmCA0gG6EcmARm0idjtdpO41EPgNVBQWsoIsxVS5EUmcJZLWXrW0rESY/tap6ZUaElpe8tFOtR02W4hzIsklMcwWgGwCU9IGty4QUpawTM4vC0gImDOGdES551oePqTIziE32zr5uhhNGTHz5IQBkeLg+yHCy7kH/NMCYW0zYEPBqfj1G9sIv+xRyJMTI7hjvr1uIdDgr9T+/8n7j+DLTuy/F7seHu9K+9RqELBo9Hd6EY3pn3PcIZjGDRivAgqOO+9CDGkT/og6asiFKGvUoT05cWLkB4lSiRj5pFDcsixnDYzjWl0Aw00XMEVUChf1997vNXvv9beefY5996qgulR1q08uVeuXGvlypU+d24eCbgfkGOcMQZCG+Mf0KVYWlJlgAmg9RwsQHDGiiLjlsbyUpmTU7u1DZZQWYnmHufAOiKhApdiRxT3VAFYMK5iCsfpJ0k7taK2Kjvk0olheZmjW8z0mFKmqychwmEop4ZUSAJmo90gFYZBZ4kwxDYaTeyT3XgWkQk0mzIqcJjDIx6mhfxAvvvd72aLpUqllBtoSQUgx6W3tossGe90mlzisVAsIgOyEYt4cAdH3G2e6VUAC2cKunjyJKftcnPL3Bx99rHiyy+//Pob77FAvzAzjcDISSqIuJUS4DAdCvSzJJcuPYVFrQx2OaDNV5tQAmfr4EUqBlUzpVlSNeq6R4w1AM47LB1d4sZqtEj46KlDXELKiyLQ73Z01Q31IGGSgmsC5KY+blUGxv4mLcFLioRCsOm0h9GwJ8F3HPfJzkQgGUtaHj3jnlDiGQVPaGG8yFaTac14x3jFsQKaSRIw+tY6QhAhiQoiOd84VQQnFn0ENCQEwlDH0UJap+yielQsyihHe5IoawkKHr+PPyFYwPC08vkf/4TYZMAYjQDgJlk7fbLG4T5FGT2zDVEFk6JlCJvN6+QFa8E8+7sa3BFLWu8RRtQTIUsr6QiIVlz6CRQFPdaBjsMCLCZNNcLn2qK9OEZZOnabIRUQHqPkcQsfc+RXJzGjLuYAMUJyAu4gvq/yD4LH6fTrglFbaXwkRmwQCnglGKtMlhRIjBZLHj+blpL0/w7CXi74dNO+q5hkir4PUqQLTxvrAZTgCXl0iBeWq8j9QNmjnHUA3jsQkgS0CZoB7gGXgVIQmrUwLLIQ5hWTH/7wh4/NT7Os2evWKDimcli+t+QFP59o8x9u0LCcWP8eysyo06pkCxk+jpddOHyh2+3c3uBqjs2PV7G9rk01taCPuSZSRfL4zCp+k084ZiFJVWjvRFVJAxiWp1Es9kHn0uPIdkZ3kQGnI8DUvYlyzcCZQCaneuS8QCZAtwKFPHuNslJNy5n4y+Bz+i4ZC0wMrfg6L6d9me36/Xycks4WC9tbW/RKll15vGqDT1fIa0ncBEI/WJjVRR/MJKBM1yNShbILY/7I9BEDPdORwZ1rmxGMmzuEY2M0dElfxsaWCRwtA7FBS075igP9LNu3l7c3gXAUGj037uotK/JFxz3c0csHhV4BUckOe9K9w0O2peu5NAX9+ptvIdj5i7ySdOFWcwcB1gc1Eexsob3V1iaC1RvysRAkhAJvRx3KH/rpT386+JvLaOlQSw2XGzni4bwUyCzjgevat061dAaxmD28CPfTp09jV8UPL3OVVqbuPa1qNyyC27e1AQdHlCwCFxei65MogxlSwgPo1JTkoIqawHc6ZglaeoA4qQIFcgeEkqWs0djTTz9NLtjbFmVreCNh9ljsOIdfyZPlbtROekY+L04qfzNRTJGSpbhZzQrEjXXUaBMOJeIIeyEhYTIQ0OjsoOCqhp1MKt6lIEASYikRfHfBdDwqAhppLziHG5koeSSw4YBPFCXuzjlajE5G4zB7PfJFUHal4rU3KLMMBQQHNRyYmAC+C+kUgLMMSq3yxwf3SQgyPuI9eCqkYoWNJOwdwtez76QggmBMxR6sEoinJ4SIJUz03Q8uUIwZ5yJq8exRNSuOl2zOiLIFSJHgrxxZ5B2UdjrFTGF9rebIYO49yB0URSHS3FEKtI0UDY0YYQ3bOJ1VreqdlX6bFm9rq8RkJ3A/KCA5RzJGCkl0WaN0YArZlEZGPGsOQeARnoUcTlCm1evRSuesJX9tJv3CCy/MqKlnk3tMPxMUko+BmgMDa+A4Lzs3SyoxEC7GBJOeDh+L9lSgMSUizC84ZNqNxyGKTLikSYqedaMWPyazSxKKJn40q7YulvKHlw+LiKXsOCct7vHcBJro1BMG/uQFxyPao6yxc99ewbJJ6wdlilO8kTiTL9m1CV1bAmZZG+q2T+R9K9ezILk7E0OWg2vd0VeaWtu7aIDPJOKz3COpTAKIEIajCaxWyMXTZHOoCTgHv9o5hky5mcUFuss2V3LaOByZ2f8JWvXshOQ8Igm+U0vCHTP4UHBMR0Zp8ALfkgQsBUTKqhgBYlEU30zSS0jx2k48gYtSgTOWPvGQjDooDHoyKpF6/2ASmVy4VABdkwTIprIgU0zNzJSHXA2G8s1mIoomcJLO/pw+M9SkmLRDZHNnsrum78UpaJdU4DlNT0CUW5c/EkUpY4rk10obwziwaO7F8gHiXBh06GrEZ99G6VSVgsg2+Xdq2kRMCiuojVI9OulbU54E6DB7nFYba3H3ZnsaWOMok85a+zUoI8k9MRe1riImZ2xIFWi6oZA+KcC9w6P8JvD2BSbi9w9y85ZlRhb8IBTQqhsE+JQ+RBUwUPD5OidblvR+2v7nGy+8xmIffHPjcDSyy2Fn8l4qcDVQpFBmNoS1bmHCRncx26Kzn31mytFsNq73dzsMaaoaQLS4apFmhcUOBjQrw6np6dwM1/hmB+UsjRr3vG0NW+UlfbMyyxfZ7VILFA3zJs24dSHa6bP1rwnjiLShq2rHlhWszY+U6XmxZhORVYIQISGbZrSTvNCjZjRrCzGyGjtziJ7ZrLFeBF2yuQu+09GpE8bwulIRJuSOK36Yg+o6RBG1hRXf2Ioa+aQokUSjH99E9zoi4bzYpGoVuMdGNcJOTsFAiQ3Nw552RJGQ6hqew1xsS2XeGKY9RLfOheopFZHY/yKDj/gaPqfR/1//9v9z9ZTK9NjU4hvvX8ktDt589Zfl6sr2Jh+Kr4Dld7kGXiT3uoMy+Eu+8tDXaYrOdjpXy6W3d7vv3b3y3W8+t63Xl1LtHl8MEj5Hsm5+/G5myIb/Wppl2H6rnmpt92o//eAay7uNbTaRZ7uNVqfeLPUGlUFqMV3u1vtcpvnkmfNL5UKp3z3MhY8sJeebpUGnUmhnhzvVTD2bb5XK5VZze5ipkZdTJ6b+D/+7/77eyjGVffPd63/0R39U+2ht/c76uYeO0NulSy0GAZ3K7ke1d86ulLGCarZbLqVO/NrTdITzC+VL5bP/6g/++ObN2qNPPFbO5U4vLHEP2vsfvHv36tVBu8mgsa0dXN2g1W01S6luttvYvb0D5eLh7iE+gTXz8GMrjx956MJPfvKTK9c+YoK9fnd1oTTF8K3EK2fdGYabPaSvdaYGM+VudXVrc/3W1sqRFW7S5WAIQwvXto7zad9YT7QaVO8+u+g0xJgXhmqOa8IoEW4ut7GIzu8zpKWX8DqNhGaJGKMs34ZHdkqIA6t0ora96HSSPsa1v325XY2P+UioW1gTzix/RICeyiItG9GOuo9pyEbkolPMdmLbaifIUR8fZJG4cbXSIZ7YOXnt0ypWcK960lxcBx0o040hUmEkWDTa8Sin62cBRDF2IWFIZaxCNbfc2dsPHBEhESfR8T1V7GuZzyV0qpQlSGQFhOgMTqQfnRdA2jjvUWb7S8ue0H2gUasy5ExKbDSWGzQOTZOQTQvQeUdZibwsXHLnKwoRefDVJbm0IUAqYULQKPetz611c7VWqtblmm1Gt0qvrsUYgagWQvIYtfhLTU5W8FjtAVLMdrUqt7Xab+7kh8ioM01gIrb5UXUI+AaMvLjvSsImw+AwjnSoaYNzKDoYDiObWtgCNGowPUQGohzscZGiBHc0f99AX0th5J9At3IRcF+ZE4ifLJgsFFLSTSKzf6UAw0E6mjL5MVViY3uPQfbrUjH75YnJsCvH7O1AgT02SWVCmGTUfcNo/r4F5xzZrkbaIVdGYICV5Teub/5/f/jS784fvTCnzbzMcJvD0Lwug8GAJcvhP/us1ES+d28nXlt2333OloY52cQWKqXPYd4eL8gW04VLR5cuHT200WKp9PbVa2w0DmpaZirZkg3fIybsR7W5+1n5Yg2Wk78C0ze7lYzMQovXtg6JDBnd64FRIA24YA46LR0doYUm73kNouk+jYLdiqhVImywzNCJVbR8ptmdnV6enVnhnmUuShu02qxZbK1vNBuNsw+fy3D1025r5+baTLWSK1aG6zuX33579ba+60tNhzLdASvyvKoL1Xwh89HVD7KzVa7L6dS7BQ4LLFYYJXIOkVeOdnmbJp1iyqvccfxZZ8p8BV0AqHHb5N3aNl0ejYjiyDddTH/AMSDafa7e4KQYLlvKY0iLxekTh482s0NWnOcHvfevf7hW77DcXzhyhNhuTbiDa3fgbnsHeS6Sl8zT5duDxlOXLk2dPTnodqaLqS8dXWHZ+tD8Sj6XPzHUfSYf37nBGnqv32DyUy+18HcaGyzZ7NzSavuTU0e/vHhm6z+/uvnKL56paVo+3ZRu11npxnyG+V6jt1DVl4fbXZmKHy4c7uiN7613NzaH766fmNW7WaXByU5hvasjF36puALjtYhHlZ05AhOxDsd3eBIhpAo4+wY8STKhoyEJOnTuCEUYCFFgEuCG/8XF+eVDh/jC7NTMzHe///1HHnuEb2l0W1r2ioS0ZoKUavTjYWVCBs+UW3UC/BmC1jtEKorGjePK/AS0EXlU1UbpyBrjAWbEmAQ55eLuUZyFNFhTbZ0A88gIAZr3yS9pPSWnjGXoXNqqS9hYz9XuiwAxghcNj0Jjw6PNEvDIDIDjJKxZlD86ZS9Ha9FVoODg22hN7ZkmdEqiS58d3xkBhwtzll5fa83gM9OhnUQPROHDAnx8plyiad9kov1TVKq7u7PeaTfpInw3ab9z8c5tf9+FjLO+P45DEdIziCQ7OzVMGLiyCGMbHric9yKxJw7uwREJHbjswToQMOK4T+FToBIN+l4iTEgBMAehxp2cHnIj39GzJ3jJYLVQOnpsYXOD9XQxkgzZKKeETTzBjY58zqkjI+e00P/GxjY372fzRf70vYJCMV+ucIrl45t38uWp6ZkqVqZvD1hyqBEggy42woimMU2K74ZMcQaOo2xKkMh5LA8ey2OAOAaPisrrtuFMebpTa/3g1Xenjj/87dMVDvMiDNNtxoEa8smWo09xxuTHfqETxA4RDvTyEjFe3SiiYyaDrKvmOEVEl4kM2DAyYtVWsBohE2RNgd3lgb0gBwUn4pSVC7p5FGMdtVYFuOXZritUA6GqEFmIZdzUp4EtY1fB/YMsOvgTuWF3oPtSWOqFMhWPfp/rmi2tVOcX/mAToOdEPz3V1TsHWssSJzHM2cxIy/aBN1NbPhiY1oE8DuHxHcZcIccNVumCLZfrNex+zUSAgnLH8Qi+Lths0QrUNnW4irWvgt13ZwMG5KaDRgZWfmR7mg6KmXJUL2vNtFdo81ZQ9dBiPZNabzeXZquLc2W+h4AqB/r+ghy2q+4AGB2E/9lcgEzhbBgjquagjHmYjWl4wxNJ9Aimj47QrAlC00TNYfiljVnH5BeLIEyMRp5cGpRJf7Baf+fW9iavZWVL3JStBQdzxtrIxrbq8OCL42dx3gN6LsbpQBm1kItYBs6ql6RMP62CgpXf1JFq6sL5k+3djWvXVu/sUo+HOY60ywQlNhtUv1onzQdznWRlLRBVRfCDsUaprGjGMK2URwhGiO9wDNqZfq7bL/WG2C+Fqfx+Ts7ns05vor+Gi9ej+7OaMAvZd+yiqIPtBoRkpD+MgdwWbaskpqpfWfzBLugokHeaAX5Q0gnWAe0geEDYG4iSxIXF4325ByJJTA8nIY5GUwvQbd7DIbkHXEURmmmZ6oQYOEfwAEM4MGmyGdtvdxr0drUUbRdt8ZAl5mq5oB28sob1qcU+kweuD8Dn/iOa1FzBmjzdB8hpGa2RxXIq2zb74mT0qF4G1i6A5De1JOWPspRog2izHB+fEEmYU+KTCueLWd7MRWiGThT5DQlDgMzS1fEZRrhzolt5t1e6fLhsvUnA3T8QchFnVmgBaDJOkInkT+BMJIkZmTYcLSgnjgtJZEjgBO4hYOUYG5wlIypn5etE0AkngG7evKKrJzrTHHy+3fyYcq810rlyqV9LslKOIp14sTrHROsDcQjic8Johcsi223WczmJzAc4GK6de+xx6L/xzutsxm7v8i0ifX3bxUbOP//zP0cG9jFIjgOOsfFt6JMnHmYvg71k2R6XYJo7d+5cfXgVq2Mw58hQgB0Iuu+RU+0p5WtqdgmE85ee4VbrP/y//I8//Jsf3rw55A6sYaHKQaFMYZ0J7WCgU9IYq45CFYtQ7vfSHFL4nd/5nT/7sz9DJPJy69ZdKHM4iwMjH7+zwTRAA0NznMkCmRpx9MQlTkOwBv3MM88sPHwJUv18FYNffHeZTes3f/k6UZurmjBz/ovDVo1siyk69NHJxjpfmdoAQVZCcXmJ0XHby5HUTnSMidOQ8t/5kmt0FwkRGxtKE07sCPtOigYBitJkiQIKOKFmBTpEhfBEAA0TK38iQkOrvbARkiZ5wY119qNUXmc1XoGF2gySREN5Z+oEFGHUyHyAaFQqJy7JTsA0NKoUhjPyyMjowe05fnYW/pQMx/EP+juRliIA4n6cKeUiFOMEvrMBnwD4D8o1xlNCHybGkIksAxZOsnQM0yUMogIjrMd429/O73Sw6p4tnMXko1+S64IKc0oVXy3nWXAZgCdTUWGhCUHHT0Z9vmFxt+1VFw/i8KVTCP0C8HFb2MMfwYUSqc2yIWNUjlRQchDxDE5k02M/ox+IQwee/BVKheXl5Vyx9N5775uxSLz7OmSjHcO3aZ7QKSDLvvLiYa4Z8Sw43H2n7GK4f19e+yKoLGwqEmzbeYEc5cKSBSBPtF2k4lwnp1yPPf8YPYIUIFtSp0NGJDYb/FocAlfIirVFXhc+EttIYZqUfrert33n8nm2Pw8vLnOl4s7tDU4Kb91Zp23PWytUzHC3gVaNTSL5xlRWQHhfaUHAqEjCS/EwxRlmZCKeXJCIlObx9FlA6Bzps+5srr///vvZKldkcqmyLkuZ4uuEbPDPzSFnoVy+i4gffqws27sIOebK8Qlr1sSg79Ng+NJz0Q2xgDtb0citU9E9m2QGYKetvYdgAHCHiERN2A+PXjc9C+AEBwR8tEcHjQMOhBeV6GHXNjdYLp/qWns+0Ed36fiQhIyA0+xqMb1b18luvrwEfFCs+NtFf/VXf3V9fQ1kNhyeeuqpw188RGPCBufywnJlfpq0TLfpKHcLDfzba9fxu7OHkfBc/xDbuu/zwXTe56vpJB1KQKsQJ9fbazsMD/xUOLd+ubTguIWgcChAilPDl0tqEBhKUuK82gwRXMhyCOwLDLEHBSZSibRcZE4IkzxSs5cIqMIxPauU7FAFjxQlPtnnHDe+tGRVmEOaDGPeu/wel4lpgQeg+Xsp/4ogcMQm5JsLgc+XncibKjAnL1A0MMHCJQlAl2QvWkDYGwjIGA9mj3NIyBQBIDiP5dEdpAKcsAORM1BwmYFT//BBBg2gaoed1PHkIS0BtXHWseKHLGOxbthQJuAsYqDIejE4fqfdpk0wptC7vwuCTaA6BYhPwHlUVAwPmaWWuQAT+CZeZCcTUfd4hIW7fQXYN+EnxNR8VtMec5cuXfrH3/0Ky3m93JB3HCFFqzU7ywbYDktv5AuH5ik4Q4/4O0cgtLSEcbTkhYLurGf8T5PoxAlQWDR9HAUtldVOcg8UBKHiCITdtDAxYpO5AyE8htAEjiMkMUGApkPwxTE2PwI4kjBzoP1sbdz64Q9/+NzyC7Ncrcn1/TH3EAjckwGnEMssRg4JOCR3CgxVyT7rezyiQLXhqQwDQj6PCzJZdZN2ZBm5zdOJIhwUgdpBMy0pX4RhpzbV3ATrIMM9AhJXSog4EIaF40tOIxuSG640Rj4NqFQ9+xA6AXBHUB7Z1uXdwU5K1YHLVsiOrZZ0dSdkdKxEm9lGSkzta/K5oU5GQ1c9lDlnylKdZ9zxjbtwUA5vR80dnQWybp9fzs6U2NYFjkMKfDSPw8TQGHYFZpKIc3GCE76iTNvAnXtAMIIjOjx63gOCxOZLmy0+zAnbwdWruv3Zv/ni3J1CrMmQTgEvWQcFUd0e47SKJOxEHPNT+M7IfbKKfdJ7Mk6gjzH65Dq9vLzEe9LHluc46PbT1z/g/WmiidVNKAnLSUoSBHNpP4VgD5IE4pEZShUPkkI4D4IJXQySpow1M9oracHajQfl8QnxRtmgLUKxljyqkJhdkhrVQDVhPLdJnGQxJBN6mFiSJ1M7vnyb0Doa9Mnx3oFaJJPtabjQLgk0cR4myJ+LhGE5QZDdOYQwcBZBQE3WGyggCY7k+J424ZvZ7QE73xhNOJ6WhtGA4uVBQiaY4LxHjRhqOiPRiBTcZUsGCOOKdryNGkcWrOvT/ptnkyQRR210m3ycQrWbhjjjjBHtZvqlaqEtQVK7eQ3gdrnZnisUOEzKScqpFF1kd4FrOnLlKm98FjJT5Rz345XTnEMdqvcs5NjE5XPMbMyl07SL7IsiPtTcmskHAmhGi2N1clx1IUdib24vJI6hO4nsD7PzwoKk9t/IF5TpjfJavoQNSexMtDSGHtUAKtDj7BGbfqRlngpAm34Sjkfo8K03gKS1nEDCzkqDQdCnc/osNOFgIWKsKsAcCR8H6rgJeAVRp+WYhsVD1I1Fj7CyIcW4+VukRHJ1xgpNMJCaJYxsXGhiY9G+s4chUSW5/4IpnGyZHQEKh2/ODnihiEgMbKY6w4ink07dvXWzeXNx7ZdXTp1bGk6d5I6yXs4SSQrLBeqHNorDdyaR9hgOo2Kz22Km3anttkrr77z9UPN4dWnul+vr0xcvznOFZql45/rHrL3m+81BI12oHM52m7lBt5JLnz125NGHztZXb9xe3eYGaRZT8sNOtt/qtzb+07/7V9nB7zChvXt3babCDbi9QoYbuHR8bK5SrOZSX3zu/ObOh6lcs95em86Wr374Yb1+l9PKbAYzxW0P7vTyfK04demLpw7/n/73H/xv7/zg3Te+dPH0yccv3cpmlw91N5e4yC1b32wdyc/uMumtTLMO/q1vfocBwZljK7/1vW82GoX0xQv/5s4fMyq80m5wB8KavtZYZ7uZT8Gmi9ONdvr9rdRiNv+tJ788XD5RWD529uLjqewcJVqvt/objVw7fWLh6NSTWkzPnMizenJrl22dFB88atfSzY3+8cWzH2zc4XzWB69/nOJ8mA9gGPRqQ1iq7tPu8qndbIbXEVI5LX03C8M25wBs/7Nrg49GXjUg281USnqTGhcXHmMy2hMKjWoZDYtl+SrC/cwttmTVLHNe0QiSIKp0HhH5jqbRXmQnY7F6iAhZcj8zyIJ4AkvxtFz4asaogYy7Zc46aghQ1msnZWR81iSb/MJnpwifX/7TZlpIYRMWga2OE2MVFihnqAnHuRBf7x2kHuUvcoTU1iCP1ymvXBYPIGjGsSkEIzhKTvsDhEaEXLCiDkeKA2T+iyi/NDlWaiaYmkt6dJfEW3xK1pM4i8CxyVnI2MU9i2nXz1FalD0jvx4wEXzXiNTD2qvhRNvi9uDbgZIEqZAY+Wz3HkTpChvB0TxihyKRbvbzjXZ3hzdi1BqaDo0mSEqiJkltqsE0PSCANqBBHh1O2GPx7d453j1t1LbWh13eZsAEyEzY5HDZhZ5MFZLfIxCUBtO9aTWrVMMvqXBOJ4h9D7JRKUb5kzkSNLWRd6XDDmUPMcK9SCUUdW80j5Vq9TFSurNUHtVnUsVh6syZE72padpwXv49c/L4Bx9fJxatUXcYCggzz5vpk90N667seNGusjzHHJiVCJSATpgYM8LGaa0zzlfEPS7TCVH3lum+CEG3IQBaKKNkEkOQ+iS9hhMyJ9VEVkKZ9NGP9Jp/+vPXTiwtPvvss2gAyevNGpZW1OXP/iJIutfQSEZXG9qQg7R8px6aTGcxBu4rEXfeGCummfNy3mo92+MmsWKqyBfYKwvVUw8fObJbZ1K0ce3O1sZGttlgYljJlRC4ZMvEXsvyPdOS2bPqSOx8EZxOHtLYWINBVl43jnFi0lCoDvyqarDbhliUrJovG8U1NxrDxoCTySWavc1eq12vcfA4k9lcmmUyOXVohSyl1nduvXONL8fzJhofMMxRsexUo49MdDDNDAAf5XFNZA9Tma6yhn3zo48OnbrAGuUmLx41mnwohCkgM2HoozuSoyhGdPVOk4pvL9rQ+2R22k2kw8h1UAvHWiaZmWaDdypdYeDHehwUUnxUS5/Vraau3rmytVnjM6Jc2gf64qG5zkyR96lu1na372xgXfmUvhFSmdFVXbxxtpbqtPqdjV722ke6suPu5roMo9H98Kc/aBZzLPpUi9OI4+fU+JpqtZpupYcLC5Xpuel8Jt+9tcsFYoc2ynPt/HI/W63MVe7e1PyI+TUfVzhznJs13n3x7U4qPz1Xwex3dG9jd6qpwxNTTfI9bPf0fZvZ6fIuJ7KGvKnFYgAtGTyt2VGDhNWo49jbkqDkeziyv2+sjNNM0QiOcO5NnzUFEKDJeJulGYiohdeQTBfRcIhyanauWKnyDiHNrTqUYfrQkWO8WbV4ZPGlF19isUP9jDoBLZ7wVggyeC/pbde+on5qoOcl6cvWZe2YUqIPCAz20ZX1FHgjDUXYk+kpIqqxDkFz75SaskAVuMYmcqhF2gvM/dHjDva9D1I/zjlVKPOhTXxqgPRvOsRcIQtEWjUHMlE8Gll+5QILwrLw2IEcO/EiikcC0EdY/ihbSt7hCbLM8cDToI4ouLsjFQEtl5hjOwpKlLvQfHTHwBWkdmt7bdUZZfVmlDdKaovE2sXIaGDhkgN35GRGhGrOozC4GDABFxFMFH9rc8fOazLh8k8AkDROhYZgPNLTBLHJRxIiNbnUXA8atuY4iZR43l/yiJ2Ko8gcULMZHTPnnRD5fJQily23eXMl9VvfvvRbv/Vb5w7r6r9Ua3PnfO7fvV3HEs4/cpLrhnY3e3x3jqaVbK5vbNGo6mVGaY8RVjSqrjdbXSpggUv/i9Nzs/QvzWYNCq1Ou9aoo49iudrod7frjcVWL5sr1bWDyc31bVTPH/KznsmAk3PWUHa7JGDOshHP+2JgVHbhcSLg65gAvQjw4eClgXlyhh6r0Mi+kO9WV65stP7V37z+m7/5m4vlbJs7jmVWKgDLnCtxsuRogmigtJaiV7MlnKh7RmwBl0flQgWjtFQglhM6vC2rLU3tpuj6z1aL9k3vNUSbZ6rO9I0mLWQ1VvYsgE51RF613VK63nvjSYUp1lE2CZuLpLUMj3SJdDECb7aqD7dv8CiXCMsMAmHhCQ49K36VF7bZ5dUJafJpy+WMmqngniMbmftnE/xeac5Kg2mrzWq5CeN6HbpoWQvTONIajIzol45CN2FL0wN6ZJB0KolX+jQ0Z6CsCUlXIwTmizr+1ckO6FULM9VjZ0+Uz81C7Xq9RnLmkviLU7kud0CjHLTJefOsXu5CS3DxBRMVOEMmtGB5JI2KJlqvQLBoOiMEySlR3UZcZnzg7sAhygqRHko0LUNUMJ0P4GQ5ve5Oau7O6p13bm+s9zI9K1Oas0AqGYg5CgZZj6KsPUCsArElIABPySSO4HBPMvLjUhhByKc15tBgrQQ1F/Js+hdX17ZUZj2VL9/kmJ3OX1iuXjo888WHj7fPLn/j0qk//dM//cnbNzc22g0OumRRlkjqw+8oOZJU+vHxZ8xOESpM9GONr+oLLtZWjLbPb5Rr03kIE0AZNIz6ro1sfp+EI5D0JBcb3QgfiJmYx0e+U+O1uWyPz4egif0++zuW4l4PaGXfaK+D3hOhlICjOVt4CGUPJMABovQJiwxJkoGQ5N5A0OTM0J2jF2oy1UQ4lMQEfO+jFZXK50GSSIr7OceZoBaAHoDKBEKgGvS2LwJAdwHfA1YbY+uO44K0HnDVJSTx5kOHdOgvIasGk4WtZlPLkUXtwk1N6Xq8bIk3YiqdwzQj2X4+w7w0O13hwEim5NduqNnt266dXsy0cRU+jZbRVCfB0Eh87dxZLN2Bv+AT5/6BSBYRckRDilM3aaNA466ScpvhUc6aQnLpUoGJjTp9h+CrKwXRtxYn1SlcI+Q2ENEnkRPx2CjsKImHBFZA/zQBBFAyMhabYjwojDvj+3EiIcXNh97pcsiwcm1fAS5Pl7n3mRVbjgixacqQSCpyavgxOwuMshxlX9q1ZZi4Y1BadZF6fenu5l2mfPOZMhw5aMwUSDmwjXcmooSxq29+85u81LZ96ypnja9+cIOELIsgA2jcQ81JL85PMQ5jIPLdwyc875xELvR3Sc4hKRZ2Wdf++c9/fvnNl1jePX/+6MWLF3mTmms3vvRrL7AYvbq9C4Vjx5b/6T/9p//X//P/kfMFR7pd8tvt35QkJxegA0eGgzrOn80S4IwDI0im0zdvXmPazyFlzlD/+G9+xDluVQSGOfZRKb74hJzojRPNrOacOH+eF8i6HC3ZvQOp9foOB8AZLFKn9A5yobB1dxfZmu+9gx42W1vwRXXUMioU8qAoICjbBuh0HTypuK3x87CWLaPiAA8E+cJznQs9FJalcIiZjLwYtg9aiPJA6ON5jAqawDhSklcSfxxr9BSEnCRk8icpBMqwJoxP2rjKRsIHqUYM4hBJktRi8CgjAXLvgIvhMsSYIx0C8SjLV9RkKImuQYqHjJHOrY5EpRPV4pggv46vFinmNeICxPMy0l4i5acLOqlw0wssknTGcy3901I4EMtvdzTApe4q1UjMEQHDjPBd8iR9D+ODFijQUFDdIC6gDdkhZ5hjgo14fIYQLNxRRtHhZy3rjCgSS0ex10RHGITigZ2n8xPfqpvmROE+6e9fAcfYJR6cCZuDtGkvPPtFtsQu377Dyc1Oiq/86hL8RqOVqOijwXqChoLJCkKn4I9I7rbhAfKlts6cF5mH3Vc2Y8Ulw0mcTxqGTiAe0k6wRkKa3L/+67/mvOfc8SpoWm3kdV1eZuZzc7yvatMeJOcyVGJ50RUKvgQT0xQX7z017GcyzLg1GiSoc8Qgaa45zHv+6Gn6nd1bq7Tk9c1dojgzjOVQfojh2XeZk2GI8+g6wZ4JcPswvnes8CFMRqHgcx+XUzYBWVYumLRwXRqTZ20zMBUSNToIbQxsV9Tj3NkA0xsZLJAwSYDzzjABdy4Pdog2eEeHIr5T38ZmyBdREAe/3WxQl1EdFFxgJxXTiCjTcQNx4UFAasaBuakK9sYBbJ1Wq8j2pqu6sIteTEsGXNahE5q6RRQEgJ07m8BTLX1xBF4qJg5go5a8zv2RkDK9e/UOPWNn2Gcdp1DOkeW/+Iu/ePHFF7k2ky6eUYrOG9rtmbd6a1BYXpkna5eOnAW+fENSM1hFzkOHdGVHq643KjRYtdek4Hvpsce4spG7NHn7avfKDRvt6BYFXmG2JkhSccjM9SP1mvZEd48DZw9sEjChzMnoxDOYewkCMeCokeURzKjcY3N1CCXL0TZ8lAmaG6clxz5S3Ap94ugJqsz1q9fRs72YrmG5Iyhwv/YqIexkUFYcN9qTcfbsXFDYZ+FCLuK2YV8mqnJYNc52UuL+xXCpAijEk0U6SdAAggYSgAODmL3VR31YDCRXIMRxXihOxyEgOFO3FKKCDETBFOcBojzW76aAFHBqJQi+wAoEBMgCIQB3x4Egdss6IAEXyXFI7mj4JHFSBGhIRMdeA2WUS70GCPKvwsHIycLCFu0A6OoPnOlnpHAewYwkAfzAEpFERmV+YLc3L/eIAnks1lgDwdmC45B+AYFPnlz49V//9a8/fY4qlhpu00qU0mlmFj+6e402iiaIW5trWze8aUWxnS5mKEHQQWx3tJ+aOECND9JRXhwrZl7QrO2ARkNEO1ziG2fMtXNq95i/UMT5ggRCGNEy52FynATGkWO/INwbJ4ngmKbJiDLcozyYALSixHLA87XXXnvu8VNw8ka729caOY9Jai4H+LZArc0PL2LgLjmP4HtCB3qpo0AeeQOVXoBtTVpsH/NzkZSQE4bBo1eKwJcAZEnlYuuRqzTjt6AISB7rHl0GfJcz+DK9ccUGIZWW/9bCiLWtlhBLWvpuAnwRHh8EIF5aHmsklRC4M0QbIcyaCWGcpRUdf3SIawljUXpLnFQjKtSjbbQxXiXso9C5xTm+ZJCf5mMQ1S11qrpLCoJ0s/Shh47OOxeAFBt5YZ0TjSm1rV87clIMwi45AWJpigg4EXyVizkCIS1hLwUTXJrBeRacCI9UBCDIjFH5AQjwnBSxHnAftAlIiHWOQQZnQeqQJATuSyrQJODsAlkgmGKQ2RHoAVgZYKEAn/pLLMPRf/gP/+HhN6/Tz/7y3WsoDSACTMiQZPQZw07fiRAm4LxCeT0QfdJNVgUkV9KIjsfix8WCsVC+lKAKMb6U5oF4fXIkchQSjXaVA4iA59whhLV+z/lD25/bZ7HYqpwnSSaMCEaWpwrjaV2pftV6jBP98iPVmXzB9HV6Kwa64L4TFWsUylZvIxqkVsUjhVNwavJjXSNknAtVA1vk9JoS6SXOhUlqZGPIqBpIVGVtIomw4UUVNu5Wk+2WLiODh7SSzbBAlBvlNCIWJI0TGS/YGcdIP4SpTg7BdJBFbUS63+x2Vgf9tbU2JyghXp8ZFlYq3Wk1JXNHF6haHCGhU6wv1hjfcIpHddKGE5KEM8MZrSH6liEkCXMTO8TFSBzZLoqaJxeOeRu5YV9lXwdN4O4TQAanHKttlEj2gdPtQmzUUcJw0mdo2XaCNeMxInXSw+s/JClfM0iYq9G3FkqjYbZw1DrbNp0a9D5Hy6hYbCvqgl9ei4GytSOerSgzvjdFLsRLmVHe4+KIZDN2UCY7cXYVgy0pg1YLeAyOGQCOdfoAIQAmLk7uJgSJ0TtH0AdLCS2npnbnP2rupQmnrpCwocgfnyxjILXbaNWajU6lx0most1Q1mp1Ll9+tzhgVzVnSlP+TSqS6jBtkNDp+DnxKPvpYWm6Up5d4O7J9buaOV/ttf/onZ/+b7783VqtfXigG5Q4lMattPlctdUczBSqXN9SqGYPTZ3OP3SGgXKHGz0Hg1Kh+Y0Xnjy0NEPnxFkqjZV3NlFFf7jLweL0sMvQuZzn3oxhu7sxPZvmWw7rO5sv/+IXTHRPn1ikubh9/e7//G//fS4zc+nUY3/4//hDlmn+3ve+/1u/8ff+7Z/9m3fef39qfmk708n32u/dvfV85+FWcXrr7hZTZUb93/r611E5N3FhFLz8zK1tsOXrRujqS89/5W//9m8Hrcb80jJvRVer06fPPUNnf/ihR3b8W1UAAQAASURBVB//8pdT80f5vFS/k2NBPFWZqZRn3r78fi5fLufzDAJ6O51zR858sHN1rjTbyxa5Gzrbyp6c5/YobpTOD7YGpU6JLylzwAH6TC/67ExbSWp2p0/uonezH2xdBwEY3mLGOZl2wqkaZEatNPbgNc5RaGZIyzazjiPIKtjXtuSJvVaUnKAXBUfA8UjVoNjJ9mw/E9IHOA1KDI14y1vEV2Ft89MSklG1DxYbEXcJnWgSLggZkbO0qj16YVBtF3YOI8Xg0JUqkQagsSWr9bOKzwnEuE7ToCUkdzmRSRTM/uENhahFsbZOUbiolTLZ7N2LqHbraIKdIAJHm3Okd16i6a1lzNEkRxbW+0yGiK3qGjqOJBQv4+t3Ydtj5HGEOjx6yOmMoCHaJU9wCWpR7qzJIZWyz42H6MjvjrR2ozXMc4BnozVodtP2jWovRcrLWtqovGjxpF5nKOLmPKeuTwdGUfbGDtWeyyiZeYBAsZE4JBwJ/gChpKYCuhRvf0AI0xbi8xa+Fl3IIRf70ueaacTNpGldMgQaBwYodajRFbJPj8ya59gEw0kdRCCuBQeSnYhwann6pGwm12Pulz61Mv+tb33r5PGjzAy5I3j1ow+ubWs14ejhI+zY1VqcmaIUlIWDNElTHLj4zNnXKbziUPJql6JpFzT2z4pn5KBY6DtCyG8IEIWRQDQJSYaDbEki3IAPTj+l15N/cetu9qc/Xyh/mUWBTKql6kz/wBtWHKsYDIvDLNpKc2wwR5tnq0U6PKYeEMeXD+SsnO1S/XRBjSxDDt4E4yZKzrwM23bMt8MRa76ivHDq4cfPNHdqtO2rV6/zBgzv1SD/bE+vm0SVPtKQfmjp8DkBg89ZdepSHnSNT+TkW13TIN7aJXp9dJjuapGa3Ssk65MX7kjRtFmjE6qjlo1b/Vpnm0XbTJfzNDTnVFg+Jgy5DG0c1HyBSaSQSibN1y3UyLf5umIm27L1rY8//Jjd0OEULxENuNy50W1rEUFr9OoYKBUxthGC5NZXBziL3Gbsxz0gnFHC9aezhcUpLl5r97r9YmZ6nl1bTn5h+F1OW3fa9Fx0Ozr+3eFF6vzgdm83vVNLNbWIQG+IYS2WZ1iMbvE5dRq8fIsV6Xo+tdVvfby5RmZyxVydg4e97rA67A7rqWY9X1tLcUzaPjFHnwfOdobc9KvFHEvP/83X/8GZU2eWp6uME45MH6a3nVssFCszq7s1Zoa7xSJ3c/Bm0Wqt9t7W+tmF2dy5k0fOHzvx8KWP3nln/advgt+e4rBh5yb3AOlte+0u6Og2Z/vUKUXO7AXOcoA8MOFHqBNQe0Rv/B6UcCLFBFp49ACLjeBjqxKJssKyspnKVHVpZXlqZlqdoUHVEPvcxQ7QzS7Mf/t73/34w49+9KMf1bZrPe1y6yoJf9tExsSowhpQVctP6BBB7aCkGkssPRk8qbCQnQdlYhSSyElqgsPFGKlCcaSGw35cxrmnM4Cvl0KSlIVBhYfXzj2R3mMamLPPaAynahAXKI8hDSy8XY0RRJOwZ5nystTCB8Kjw0FgemVodoLSmntLpBIGTrcSWBAWsp3iJ5osU/FowEELBMmL4LGaPCCOYsunUbU+urZ2l84vOhMdqP/KAohHPhhFs/5sw5jITkxGlQvugevHmJSWuz0mEqMY2fgh8bsvvG9v5tEy0u+V+zUkOjaXv3Tp8e989dKpUyfncvUq04LGVpVSzA5OHj30pYe6P/3p1XYvf3h2anW2SgdKg6NPKpgpkRzT0BicDCMlB5xLhah9oXHMZFgQ7PbbmoIyAOKz473S3MLibGqR4zVrq1ssR5ePzDKZLWPMtlxoOXVbVUEmciMjCY8eTpolUQE/iRmShICafqsL4LvVEVCSTH9mtrJ6a/fHP3tvfqqiKwRb9UEu3+/q8nF7uSiiEejLGjWej+zcA0GS8Ag+YYfT8hZz+W6p3+nmOOgA12KZmRYftSpocILybVPWOZFB9ZY0PDb0AmiPkt8qlr4lJcIC6JUkuIBgjFxdeu/QUomeYplPJNQIxPtBxtIki31gVCN9Urhgs3VOSZOaeQRpbeFA/Ez5Rt8aU++zNMKgd7PT6z6G1y6HOfCZ5wXujAAIp/U2NrdLO4rWQ7i4HceZZWADXoHV6Vqbx+l6MC4X1+crpw4tUCSbnVaqt6M76Mmylm0y8wulhSPz3s7oHdOuBpMoj/RkVC2RBt7MQUgBm6hNIyF8o8Y9EiSyQJOWEqMN0tVnRLpvGkfVekEEYhpmSbFRYoBoDsuoD4vNdP/9u7uvfXS7RquAhTlPPwZs6NLBuJ1HVMZ/oBm13qZbkoSEIUAKEziqCB4eJxM9KcrGTgSoAjRZrD6zI06g22sz2MszSBz0D0+nHjo0d2F55kQ119pRu11obLP3+4+ePf380cJ/+cmrL7742rW6yqihcmckI/q96LpIhT8HZ/PK2IgieqYxWf6BmovLYq8AByaZQLXPPnP9GzWoO9BRBpu5x5Y6gfz5Pdo4PpiS0d1bkA550JwkhCNhyAHhoCXM2itATDlgJRJbUJY3CbOWZURs9AhycGbBkWkChIb7yYoHd0kSuz18ZN/Ox9PuiwBQxmg1ZIRAzVc9HwEIBZECNQIBKFRTQ8TSkpoAaoztKfIcwSV3kIXVbEGwO+T9TDFm7xX4sKKREAuvxLL6zCdZtPCFMGVVJNohCcP/CfmNrnPnvKdzNNh4lgx0kCfKn8RJWm/oaSSYp9GaWiMY6LgYQRjhs1KiEYGWomhUQy4IUJEYxcOfEEmiVwOMIGMH4JSsKO81rz0yC01uMjsOD7mM0cbSawXcXLKgwUQkHMLje0ICjukBxxEkAXdM9Wwaxso+IQQKXygxRADpo0eOst8OZXBY1dWiQ3DNJjt/ookjIUmVwujsyZ1j4UOBVpEmm+PDNNlAmBZyMJm7lbNN3RzEeQFmkmxxQ4uCq1R46VtjsJa+OKGdRijUa3c4dHzk8Bko8IUklpVr9S3OR5956Dw45MtXTBjrv/vuuy4v248cowby9//+d2DhCoELmOTrD//wD7/49FcWFma+973vvfrqq1tb+owMhs7kkzUFJrHb2xscSeDcAQvKrL+zGD2/eBppCXAX59Ly2TfeeOPCE4987Wtfe+1nP4XgQi5Dn/RP/sk/4drF1Ky9Fr27+zd//dfNQZFTSM888ji3Lnp20C2UOezFjJsTNHAEAW28/NqrWB0Ce/cGa3Z60Em92YhKU29Xmcbjqg5BnqmhZFklgokFeyLOHAgegG9ER8+ig0PhbkWEHWrIURILRx5pCbmfDCRxLBzZeeA1afejBCOCzj0pvI2pRuxGicZDe+WBFJK6H3ADWhIippZrNRfW5CrW5qnok9hYcwa27AdQkiBA1/oEUMSUNHKGJoAT2YNs+o9x/ZeioZZFhSvQKLmi/k5cyHLg5hlBfqoD53Ta7agJCggElDszor3JiXIKHpXUg8LW9lLNaRM8KomQZPHg4b0yAEkCw6NXQHwnDutxtBFPL/HR83jI8iHBZUjjUZ/9KYhO3YU+r1bTCn316cdoN5i30sRx1IVW685bl9EhNwbT8DZu3SZTbo0HSU68LM0cNQJqJMF57aCsIQuCO7C8XFw/Sd+V5rGgBQXuhYQo0CbsOUnQJNrfc9k4G0zrDYVXXnnl0Ur6K1/5ymJFtYMrthCbCxSQuWHXStCfgkb/5/TJneZSTEftCg6mR2C6lnQVBj2sTSnpMpRrprv2XQEos0sCkNabjcxHzzyEzjdu3NW7YtfsDO/A5uosB1sfDX6UO2+XbDYMKZdB1OKaAgQRJLmNqhSlawR0MihaCrSjwVR94B7LahPEyT05dXackySK2qecjs9FvAjw6YL9dmB6Lt5so1Pb5eO5dvaZxXRIiULcSYQwgUAB80D5evGaReSFWXq0VqYjyKzek+sM7QoI1vwts5bTqJUAwrI0pDjbRqmVciXsqlKs0BtCHAo+1YUsGSeK3q3V5erPNlNbUpEczef1xq5uvQTOejo4vkBPZ8p3FP5r978+/fTTj5z+BpsxTIWFby/7H6pUgfS5tNQ+yAbHt99+Gw1cXHke1uyRS55Sid6/N1v94IMP+v0dWOCUaxvveXYQAyAS8kj40znXZDJtErI3DMSB7ntCF4MVSIoMheC8y7Gi0QE3Fx7fRfVH0hKQ6vp9Ggpe1Xrxr1986623dJi/32fFx1mAQ4EnJfwUYWyQVL4A58mD/DLyz0x/f5Fiwck1OUItWAhbdUlkKcQ3U00byaiDwkHygIBphTABNGZKE8yRkxYChA1lolQxk2MLg4AJUDhef40a5Yj8QAI+YceEiMOdmkjbuUUy69Nm0BxiaXnyNSUl9yTAQUAeeFALGFQ7cWKVgV+x8xzRZiIwfHGBoQVHWQ7wBwkEMkZ/RPMeaV2SeyAQ5eIdOrT8reef+dKXvrRU0T1FrYGmM1X7vEBO90NkebGAxeLbVzdoSdjVoy3ic5DgUNpwoXidl1OjnaQRoRXSLpE55inNdp2CYDscHFgAxsaYCLQaDSjPz+j9Y065qgQtc1gxYUrfKTudpO/wiViI45JoHnZgENIfzRcyjHDBkICw18s85eWXXybAViICu8WiMIiA7GRJQgA6gSVBB7o14oMP0H1PFdJCB10xB6eVI0xdRieMbVjrwnj67HDqYg3nJUaEnTgBSOLjDBIdeZFs1iQChCmxzgu4J8QnTKEFSVw2L0XPiuL1wq5oenZ8XMEKMHAIiKz1GsQajlU3G19wphvHEENRtkaV86/ymN4suU56EHAukIOal3jPLs8hCsciOKI6nWhlxpB4VQqNVWZnoY85kTbLXammBAhinFgabxBZt6heGDQcBCk+naEbb52AW2xUoNDB3BxfQpjzJIpK6DOO1K8jEIvzsHx/zKStZPXW9UcfrbJn00/xRoFckkIyHIgkgXvDXpqsEQf8EAD5oPBeOuQLh3LwMT+GJSiQtksnDzRm0FVCS0t674FazzgKuYFAH1LgUDv+3t/7e0T9yUtvvfvu1STfvbw+OySwDqTg6HVvVAND3MGBgDxWDqEOh4QGwcJx1MfBgHNzgA4su5DuswdoCGXv/Kdg7k1ur1KAeJIQCBQCBMsxoK2tWCgwCjhRIGiLBohEEou6rv0e1AEnL4GAHJJD1aPwPeBieFggqyYO/Oy+TzbifWbfx446D1jRLLrsxt1Ht6NhrgHRthpNhMbAhY1z34zeWi3BUJQ3o8mCIdekDXn3R5A5MUE22SBrpYbcAU37Xj2yALxd0rSgUVRzNlisNKey3WETCp4LJaQQTQhfb2BFC4iOWcaFK+ESRUOSpIuPGyZhLq93SLIQGmo4Ih5CusV4s5JMw7YwQzvdUUjPwgyGNeU0x3URwhaR7ZyUH0yCklSnRjbFgSJRZsBG+85Vmqo8fUalfohoMOQmZB0PZbrJe7CsTHEFtIqIDosVK+8qrLGOX5yR5JH0YiKZAwTpFE5MCD0vslKJFM1kFDbnJYtgToF2nYDH4pu2EVtjH3YpIeXUON/laKZ+o2wagItRNc9GBr7FyT2XEOvSk1uJQe+Jr3/5rVtXG4Ob3Wyn3dhhjn3lvQ85K9ZpMmksjFoVRCCr2l1IUHYeKFbSGDzTq3V2uxVZRXmRtnuae8aZrv/RL//iH12YLxbKnVSv2Cp2ev1WKrPZbHcLU7uD1lazTSvPeS/8cilDg14o5hhoMPdMM+etZI5MHc3mly9lLmQ6s8jP7UvoLz0ocinM8tzSTHm6uVM/fezk+X/8OHZ74uQU8/Pnf+0r//y//1/2WpRmj0PW773+1luvvchSxdNfeHp6dvp6u3VkYb5dnoPXW7urX3rkTPv1K6XCVPf2Rrc+v7Nda7e6h1ZybCA//PDFP/iDP8gUl3/51turjToLx0dOnWEQyeequJvy9tbObrc/V+tzB9yVm1sMFhuV2erMdLOc+vGbP19r1YDMVen756ZK5Y1urbBY4dZQXiA/d/Esn8DW6nm6c2Rq+dlHvvDm4M3t5kZ9q17lFsrusKFNILvrDUPQcIQTSpQ7usbXETk049YyKuiotCkMikp1gKReMc1KZUTA7A9UG4e5GY2TiKubW2Zsh4YpSwwuOmLk8yWrd4lYrwsxrvibON54xObrJ6atDumyOLUdwvQ2zXlFlUrNGKzVwvuSMYoAk1Gf8K3y6bSXcJyCrNHrYNw0KQNR+yn+2uYXvtlz1MtY7cPIRdnDhIJDFKtmpCIv5jvHSMmOCI7I4kSZ0nJqhhNVcrgBNDmNEYokBU2NaKrF0TqYCSCZnQLZFWV7485PNyfrOInE0NjGrZNEcEEUkhOOu6CHGBD/2kTIsqaDk2oOjUQnVeh0O5t29pkVIBoCbwVc/17WyA0yeYgVjv5lcl6mKlvJYyLG3ATpc7Cl3281uKTAb86N2kbDHLeiRLKDg4H7wSjRzAoEzWc0jZ+Uysp5QnsH0kNSs12+K84ZF5TsujkQP0TsLYVkfoklLyqzIadI9dJSoZs6eXLpG1/+AqtIrEH1e63ykC+k64T/2RMrt1dvc/a5zjsQ1cJ6scQ4GEOWbKKzT16IYhpAgwkHGDEGYD4JhEYYfzDsMY7XGxX24e9kdXANBz0TCMUagJ7H5GMyHDSQRNuLkIR4mGVZxM7kuBJan29Hqj95/e3W4qHfeOISCN0Mt4sOij32TakcehdLAxPqTE87eVoU41g06ZVfqwv6XIUKATpNu72R80aKdXNgcqUFXTKnV/q62UGdBQiUiZ1P50qPneTwc77WYdFh54Yux0g31P8We7pNguqLPBrNktQWCxhAUJGxcCtiK1XeA7KhE/hwIC3tHhWeAPJ4N9uwpWHWaSkFBi9gZviqESs4FA0rtrK0QXZpBnvABpjIrX18w1YxWC7XwWtIkWMmTCrOYaq6sDB36FAjN6TXYyTRaXI8KsWrBxCEPsLgTBgxYvbF1wW8IgDnjV7y2CtiJ/lMtcBFk9xVymghW8k3+lzIbF9L63NUTVXB2zcqdoaPEmT5CIV6hy5LCvl0qdfPlnP6CAXjsGyfjr7B7VzcFl3kWHqXgZeWCGyavZPS6gbvwLdT7cJAd5IUKiXky2QLjNt63N2t5Vftl79+5cNrG5vnL80zUdx69TIaOJ0asOCeWpyfmZ7PHDmemp2bWZpr9FqXvvS4aFaLbCp/9OKr1JFj6eKJh89sTldWb19fa3PJJveMZzjdjui0DOphpUQ3CDKxj0vGRgqk1A92CEykpxJpnCEDVzBRBDFatErCI/SRHx9MVdKYFEDKYmp2hoE6vV7UZcgOQSad9dG8C5BNN3ttavVzL3z12JkTr/z0pdXVNY3CCtwtajXCWrJ4zmJifR7ePfV3EINxHcamCPa9qPHKhq3DUnbZguUo2aRGLbPnNOIrnWtWCzvruaK+wHGku9ip1LgDGp+T/l5o8KIscADxAXqYR0fo8rKCtou0YAEQ50VmFURJcGACxJgpRAL+CAJRngQITtZojgTMAsioI4iEkYWCEUfOKCExpHAijqwumpc+Obba5jCIVq4jSdRYOOLn5jt35xsywsjZF9jRUMTJf01kJfGi+NykmCSUlGcijqj0UC/alwfc/5B64ckjzz333KUzx4rFRrq+yepdle/FcFEtOxu8VSPVDh89lO+cnnn9CmfJB9tVhuTsabE1SOFQuJExaMmRt2gq7F8Wdhup3Z3W7Lw21eAOL61BsxHYblHghIHQUPM94Uxn2Gj1d7YbC/MrPXsppTzU8nQ2zWlourNYe+N5ILmTTYLNaoSfDEwg8IiBOdCIyA4JYJNklrAfUxuUyrmZ2VffX8vNvffC05xO4zIln0syPmFQG9UsjGqQ5qJcOEpQmlEOuUHEzJvWTHxgB32cS8UKJ2ZLEv2RP10ArSRq5E1R7BTyWhN9EOcfwHFp6d/BgYibMfRRQGzyUW6i0R1tor4SKaBVEwWs6otLVGNMqeQBBM/JMEtXoCVt+jXmX2oluMzYdrTF0ZZzeAccAWrc8ZsZ5vsqVitxjcpYF4ZjoM/qA7FZy7evDJAECMkFtxpBO2dh3j3WBVyWO816DNHaL4ZAlqRn4we+4Qs+vHZ7g0J9sFReyhZ4IYlGR3NGzvJzeXF1priwMD1TZZc+w3f0aL/6TX2AmnEUtKDsxCWJ/RHAqRumZbTysXUVtCSrMJEkpIuNjzzSmCY1ciDgeyxhj/JH9o6Z1PIxBhYXWpnK+zc3Xv/gWn2Y75omNV4jucZ4IilasUVZ2LUh1p/aBeGVZZX7/tSQFkeGyFy1Wp6bm2H+3Wk1Bhhjtp9LD9iAPzmbf+To3NJUiS/Y5KWDIY0ygpFfxpXz/fpvPH3+7JFlruP4r69f2d5O1ZxVVlsp7UhVnzofUUKxxcl+aW4izZvOD8jZ/tk1Gk7K6OFRxUbOw+MIKI9zg/x1O326RRtHU2bKGLoapf3MoWCcUBrbUv7MlCcJUOTS5SdxZqkqcgsorUMIGDXRCgE97HFWWp58Mo6E3hw4BSqYIOYmUeNnIiHofgyb/A2xBFQHDnAhI8l4ZJB976k2ofKDHGIJ0H94csK4JE0eGQ3YwEu3+/EIJgG6xnRR0wlGtEA8Ox6bJMVrrQB9Ipdc8nCcT+fHXMze3eiNkOfOO4+9lD3WhIm8aFQYN4imsVHlJJcazNkrM15N1dKY3kjvKsIPTinVVIlzLOGkFEJIuICfgEUCmEZBjvCRzXGipiSZwMJO2X36lZBNIiWt4QSxXby9woiLdWZOHo0FHMLs1GVXr7ASwXXgGMDd23e42Oj6O5epVyxzuork88+MRL4FMHQnOPLj0Tb0md1x/mhubvGrX/3qWv0vWRx5aOEsmJgWI61Ns2TmRIww/viP/5hB2PHjJzmLdPKQbnWcmmEnpIUwSM5gj1TMKrVWwnEqm5NDBO4aW2R0ZIA1bsy1mNdGpX2XNTM/X/oX/+JfrN3emZmZJoOQ+rM/+zMkYS2YReHVzY8A0hPguNaZMw4cZ+ZUCBNobofk/TIbKKsWFLO6VPTy5csMgHgnAOTGzsbXvva15x57gouruCGE48wfXrmB/GQduwIHzB//+MdXrlxZb2u/tJLXFc+VQpGTR4eXjiI/QPTD6Ql43V2/iT88WYLO9GLl0Ucffe2lH7OJTZaxqaiYLOAmhi4E9EZmpPdRKFQHAmjPH7mzjFLT0MmcA0nDk6UcK0ePDZY5Im2hSKRE2Ikk4USO1Qd7AkGY6vSjRnKCcvIxUHPkEGW8xtoxogyoX/6jG894xM7UFVI55kHEo4wbs6Aie1IXg+6MfpQzIzWqR47m9SKWRzCqCuzGqLFSK1KRzonyBZcAcS6jJMYwSTPi9dl+VL6RCCNCQTMKWCx8CWO01Ai6CwKsVSqBl2acFDQ6UZ6CnE4B3/sIYhQ2fAPqEQeAWkM1RB5tmcQ4MeHP7Rd9BpOmXiMnfHE+wwlsJEBUKQJMAYeZvGNwfwBumZHnmBAhvA9qQkX7xgagi+F06MYJPHbxzNe//vXDs1Vw0Bm+syALtIGnTp3iHZGsbQnTDHI4lHintq8gyInaHQHloBOIeOvKIw6OambtmKozCjly2cAJKnU6wQ+YAbI3ICKurL1x45DAzsn6I5lDAC7051bKC1MlziZ3O7JSfZ6HXkOXFWbLpTKZwomejkdr5QinFQJN2+I1nVgMYoB7t6p5viRUUhymEvpo+PIIsJzTJ/Kyp9Ocjtm5tYn+m2s6UMwCNDh84QcHmvG0NsS+vuDcAELfHXkRjvhoU0TJLCGfEwCBaZsiuPbbek/yxRIYaKy30usVpmUP9Lb0j3y/99q1azyqNvHitrHgnM5uRdu6u/0m3V+vpLdKW/qsoxgCJy/0jzBypgAjsezHixhG4PN6EkxJhuVwOZvGDLZAwNlLBCAhOG5IhAmQXZKz1kyYXk95MUdApZLTKa1OTpdpBDhRyjzZZ7UenWd0gKukFR++el2RfbIYzRJ8nzmsnDBT2uTmOBVSbVrXudrQtV2Z5UXyPsdrAXz2MJ+nhyX7RV57LxTo/dNntunNuzs1aJIQy8EqoOb5NdpecNKS64oAYoSw4wRfaa1383qXREvSDNQmEPwxAEMgpAXiyqHc0EyAQ5D5MGMnlOw4zgLkIJuFbTqQzpNfkjMaWf7+93mHYPXanTtrG5WcmkRc4BvSfi6BfZugz4VyRCTOK1njTDAWwi1mKIQcuYUk1RW0NCGA4BOgxKOpR/FuJDAi7KSIckSigOB4hKMH3HcED3vaAAEIcgC6wMQGsk4KtABhB815qeaKm9jhAww4js+jOycIGgbAOVYCOKs9Sv4rdS4A7Kinqrxykww91zoS9EkcdCDLRosnshypQfhEDvHAD0LyMvB3vvOd7z37MNWqMNDbGOw1Ykt8GA9t+4IsBe+p2Py7cCHLt2pAwHEzLI0J9AhDE3ksCdNqf2tBLyI4L7OdIZMFtso21lYpfdipRWUjlUIcZOhS2FNEY4tzlTgJbEdWAfAezpS8n6LjNMjmOA7wIvFMeQsWI4oj2UFCAsxlmq06708cm+mePXuW3JHfgJkMBAGSAdRsjFT83nfwCFkWoMm1Mm4O2fj1RzAdBwj6YS6Do7l2eQh7LGggOLKHvTaDADXNgI0RPhBHEwWLhasgvupmCF6nWCYAfpBzdsQS0FDGfgxZoWRvwiN8JQTIRhLdC8cgAC0Xo4UdUC2JEbNRCo2NTN1GTH6+ztdmKRfScpeV2xsq9XUZT0mvx1Bw5fACPlv20MQkiYIv8hAAgoMCsngSHj0QfEdAVwGSDCh9IglhpwwOYc9ggPAIL3a8gaxvrLMNXKu1HDPQDMhAJsJOPMkupPp8A7BAn+4QmMFPm1cV6nUMRyKlU1RbVgw0ijBDoVhc1CAbAUbgLKT89m//9tIjN//qr/7q3bt1IAdVls9LfsRAQP+7D03Kf7KolcILE18GIhsx3zH90WBEYRLYm3patntthBbhO8KvwI8WoLXWHose/35ibsmissRUgBExSv0giklV+Op4VHXMXol1U/DkgcuB1EzfoHkqBWwnnB+sCvr3pZCknGTt4RgSZUfUop4ragJCcniB7KepaJEEpzOWFYy5Cc2QilqNC0guMGjUH1gBT6oSPCj2efEinap1mx3bvskXs5mSBq+d4oCvvHOIiKlButzvZBocToFUeqi7Dn0ByxlxRMckS/CdlHSMc2wwI1kcYntrqFqJXVpmLmNFSIQVuVf1aI/FT17reFaOV0rIaSqtlzjYhcS3t5R0LJowmWIRlf1G6dYOQQ2zXJc24JCpzhGIK5s33oMKJ53Sez589ZSbDvj+LXlmy86vZlU7bS22r9y4zN4qc7AKSvCFgskryyHMOMI4WO78tV+7x4koHFFWvqZDV6d1D8QQS602HGlMm7B+OSMZzKoRR2/Wb2gaplVYoyZ8FayoEMZBBCPCPMiZmYmoceEjPSuxXAP90fVrZx8+X2290r3VoRGpTFUWZuc47tTcaqY4Ee6nSMgMzjczOGxOmDtg5Uxy+VZCgjAzH3LVM6GdfoszFuuV5eNfeTqzu/3R+vqZKfZdK8X6Mqu0qfyxv3rxlUG1eHd39ye/fJuv2J84fY7yaqxef+aZZ/5X/+wfv/zyG+nsHGOy6pJekuUocz3d5TQCI49et47wKJvzSsU8s8sO3+ro6QPTW3dra3c26wzp5qYWclPpI2dLLGL/t//rv19ZqF15+71cYftQeebU0aVbrfd5F/hOts3Q9cPt+t/ceec3Hzr02iuvfHfu1Dtvvfrwoy8wPduu8y0vrVIxaz157km6nJ1a/dCRo1/+nd+CPhv8W3zAqbF9/sQxrunq5LP9mSUmvenZ4aDS+/Nf/OSDzo1KtdJNd1uZ1mp9I1tPX958v1qaYkq8tHQYu+WzztX5cuNqje9TLR07PlOstrPFpflDjz36xI9+9CP/7nCfkwOUGHXcRkKYAYVuQxgGKFYo0vmBTnYSXUcnHFJQPcyIZeg4NXJyXo4WHGstHDLyUcjoweqCPzK4SrY0lI7eJMDZ5kFIQnJP77+R73Xf+e6TJ9kyFEhJdpwU5uxbtE5B7HC2CSu92AiSgvNajxBAaK/EOxpQSgrXX8TQ6qlT8+Y/yEzACTpvThmJlVU3EwwCkVSi6Xnx85VGgk7EfkkhTGPhEOmcAjUBnEIki+g4C9Mh1ZaMxxyNmHk+WvZnz0t0h9oIxaRR/kcSeqQG1iYFZL1FVb+jls7aDR2Uiiykl8px9nm3k+GDXk0apEwO3SuV59ExaeYihUS86beMPnxFCSh2Z/qPRrEOFJw7oDrcMdvi6n12zt3OIyq/gh+ZUDTgVh7VPMZdJ2JG1hnz5XE8W3HEnl9qE8VJrkkCXYuX3UblagCvcZ703nWXWAyAu//wWUqhI6pm09///ve/+ORDkrZbR2xba+IgrPRZ7Nex7rNHV26vLLx7c51bEqaqxY0cA0RxM+2rZHGyuYTz0TBMvGYxu4MyM2TmLawzgqhvbzDd0StBtIRu3CpS73xEU3ZjpBNkPQhZAu7viRwB7ovgqC4hRQa+J/Ft71ah9Oa1G3/y2hvfXTlyvJRvpnp53t8BY8iHy4v5SinPQmdRN7fmugMmAOSOlR6sX9XK7sbtaSnWSo51VdUzlRo+IHJmK/9kRHkUEB3ypCqpyyi2+PpAp1vizv7l4uLSoeNfOt1c3eVtmJq+77XLwR4apbJh5q2V4KCIGgRrhXTLsMYqKiROSePzcXr5fHtBO69av2hGp57Vy1c5pspRLGup6A2Xjx+dP3qIkqIQ2QrlNueZqUqfI2J3dPhM/XupeO7cw1MLi6w3be1ukgOYNOqtWl3nndvdFgOIVm/Qsm8oQV/SII+aWa1u+SN+q9/hfd4iuS8OsnO5FN8ARKp0Kj9VHhZ00g0cb2F8wOImlOWYkK5EUdvVyXbJIPnJc2co960Xs6l2j7N0XGsNcpcCSw9aOY6VUnb6qgF1EpoAWevJMS7llHc6pbXjfG5zs9FudCk97sOmGHIl7u/qKDovdaHzVFfaYxuGx0a7c2d1Lb+9Mz0330p12+keNyBzrVxxeWZ5+fDSc9ODcvHq9ht30u3rnc56CYraGOb8PIXR08Wjg4INDL3cIbjXeVRAILOhPQnIxEaNaQwyOyKj0R+KMgrWyKvNiSqUOi0bFgYIA13oE49PSalJMX9x5dDC8grDOdksf/qnrt77CN1orV6A9SNJwOk+LjBh4pgplJ772gu3rl3lyxbrt7dKOmOvkhVRKUFieLtB4L4uamGMBaYlOQ9IcxA8Rt8TT50xsjHCvX5Zu8EONQrlrL0MWe0w2lDR3Csdcap9Uin53yOCoHELQIDchUds1QsoBEIU1ROgw90PRemPYOKoBR7AB46o/ugBkrjzWOoo7zPQbFCCiIFJaERo8hBmyT2QIlWQkzCUqTc6/8jFTdubhHjHhfaNqJDkEwYSGrV2ySV0+5kgRV64ZKvdiva9xmIpn1gECjoOjqHc+4E8WDYsj4n0XppeshQaCoAOfpxlH495Lqh9vOfVYWz/u9+++MwzTx2pMjDfybQaJd0dxEdrNP7EcQ8sLQylhD/stVZmq18/P9f6qFFvlOdzw3qmm+EdGGu9uegpX7C7/rgyqDfk4zpsWUoY6qau/m+pDua0jYedsBG4XdsuT5XZcaP1Leqoan13t37z5u1K5Ww+X+A2f4SnsYyF30clXsqOgO9O5W4umXeiPH0I8JgIK5ZEBlFbqprB2VkpOl1eWOaTvH/7iw+ml84uVau8Vpsd1tCHtx0aEchhw6px2KiT1VRa72eIsreH0djYC8XSKJmcqiuWjA+E/gvfs2BwNjO1ykpXbisF5EQc4eKm7zljxCKImCOXuiEEoTro0Wo6JGFiG5mIaW2vLS+q/WRqDEWg1o6BTh8uCyO5SYsy1HHbxrALZpMP7jPydGqlqZq00dgcId2dYfzkW03h1R64QBQnTppeK7P+CGELyDL7Ji3I0LGRoYYLqIjXiXRgzLY68uUiV4gS3WHlgtvvsVUOG2UyS1PFpYW5w4uztAvbuz36Ge1saFZox7nFPeroQaAVkqjmYIcjSA8iSewTi+iOsMQw55jBBw7lKFVMJwA9yZDbnzlMxjsG6cEbH125ttlooWktKSihPmEhppIhKsxINxETYqHjfuCbDES9lY+7D2rEkwnGwuIrDav709hjqlxidDFTqbBgtMXYsdEklu30cik1Xyk8dHj+yHQh02siEicCkMpP/lKujHP4jgj1dbB7Zz6b/f5jJ55a/v4fv/jaq6++/sGWbLXt6ypj3EcPEOTB1D8C7htC4cAn9uxi3e+b4oGA8FfZuyOEOPgeiMH8dhlucgKa0xD9AeM9LMmT+agjgRgFH3wsQYVVGi/HBKFPfwLadZogNRl0BFfoZJw9m2mqMngAmAwR7X82fQdqScpBAID3ECmgTQQCzQCXqGZVARIC+/IlVkTMBgkoPO5G1CyKtgOIAz1Ag4LkVoyiFVIHUgRa3MBhLT79X0AgOY86nkKvyTgurRv3ooWbA7IQ0t4j4LJRfvD1sAa7sQuxMcCGwOHByzouekfGD0VDeEQrkSoZBIcEWAvKUNiS03q76pDKBQs+mMgKBYd4AJlJOEHWoxxILPghnMQkLO6Wa3DcAaRnEZo1YTTCCqs1i1gznCabNrwCT1NHdUlx1+jrV4Gm0prz5PGTfi3jI7LIqQzaKXjOcC3eWNT2+8KClnd7PfuSRocleHLr9FAZAcv7PTVtGqYTBhOx8bnJ2UfhXF7RuNZgGsl2IgvQs3NnGeodeexpdtRhB7JtJ6bvtLa5jRE0zhT89U9e5z7TyiJyLUwv6Yhf+WwZw9YiwmBQnNH7Wdtbq1qkrk7RP8hqC4Wt9hbDo/yJImsomosMh9PT5X/+z//5xq1V6Hy029Ktajtt9oTTTQ5VldI7Ka6G/M2n+VLR7ObqJrJxeBmxXWl3Vu8gT3nmKNQOLS9xzArHabudm7cJTE0zOpVuKZep2VmoreXznF/mZDRy0nsxuERaDTEzWc5KzFRnuYH6zp11ZJhJ0bXpAk1PjmxXbl+FJlkA2Eo3NIlW/5Joh80qrBSsLMZNUahRGWkY52gEHOgQ82XD2IyMZMyWLX3sgekWApqTimPC75ipi1rsRN+eHDiR3B8ngHHS6NdjJ1hjU04Q3bplJniqkSOVD2o9YZzcTVeUk/gTHMPjXsGAOF9wglqMmmtG9ANCoLNvQGgmxF4uju/wCWoUxL7UPiMwcFEgYQk8KsvWUlHFMGBuOcMfamo15pxCoCPhLXcRhTinPOKU0v0EDeBMMOBikcIR6z1oiRT3CZLWdeh4Hp5QIEAcQPgmo2BrOXAK+0gR529/GSCVTAOL5OP+acahJAHgRUGYvqmcT3Os9btff/7ixYvDvi7QpFrK/oc6z4K1h/yiRlpIjelbauJw7RobdZYjFlyd6KiOChLU7nxpxGh5aLWgT0CSmAPuGdF4wIqGWuCCE2BBY282naDjJMNAQlqPvYfvZCeSOwUvNUWZkrmgXxt7j59BVBZViIULuaDrIQAa4ay9oULuRDa2dsK+50GRQ9m3ZYgHTgQQfBMAMlGL6gKjCrhgQ8SiAVyVbzXX66VC4cknnxzU+zqTe3ODLcl+g68bcJuFztGkfG3cdCjiChgXh8SmG3LHEMTzCIXdXfX+LKlTsssrR1S+gwEnGT2nEIcjhyHpPtqdBsVH1wk+nZH3v7CzHPClQ72CwyMyU6lRiGUwKmvC7jyb+HSsllZ3KzMqJAwC6gwIjo/MWtgXF8WELBAmm/DiYk8wkRaIGhNN2HQXJMdVlcDQBDQEAo7MI45JKvnVHWTi2yU8nykDr7d0xUF6V7UAjthzUjAeWdaRkfd6vGZERra2N+D4yIVT7EVwy6rbP3q70dD1jsOKiGDNLs8D+rDei+nAe0cRi5OizBoJ76UDxLDkuZ7xLVGiqRkOaSIocYsSJqkg6UkCTRRlYVs4s4PqLJBR+rzvRbn+5L/+Ne1Gq6vra5IO6aJ0SegnCSOO52z/7H0SUuAeoKQEFVudp2QpdlV6Oy7oqdwmXQ9J5UQai5rIBKk9QcwYmNZ/zcUqjfCcDr4HxN0KzaMd7lFRAvtxqdwHAM2ASRhHFM5LH5qULGJgxt6UUcd9zENawhRWwE9yCXwhSJuAdoBA8wEUmiRzr7Cb374YLjwCUx/hq2nQhLNSU+4w72joMYHxOT8q76EbiGl/+ctf/ta3vnVmqU2NaLfX0RLtHRrjpXlQMjTfrP6hbwNSCmwRITMDe9zrb90hFTtEWsrjBgVmKCV9jwFGTHBaLS1b62iTfVcAzG7PXoUUsRStN+ed2T+jInPS2iHMUDbvrjEj4MUaq+BRn0usCa8KHsIEgpMOzbm5yobGOy/HBOgI/uhJnHKAIC1oPBIAwVd1UQtzrmvXPn7ppZe+/8KTKrXoVjhPJx/k8AAFknv1ctkcIckLiEUpEQF6KFp6Fw9SBEIWCODoAqTDlszJly9JCBd8Z43vwhtru7YjYoHZR52XcGxbgqU0MTZ7AIgAViyCwIvSFoSzEiyFW0i2gIuWqRU0tpbQBpYQQSy1QX4Fh5F3mqiLOLsPRFdqemJ86JsvLLgaXMwQyqiJuC9xKjGOW+5p3Uw0lxwtibKVNQHKaHlxhqk0U2zvcEHI8YUmytRVqrJVevNH5WWsI08SSAaTKmglgREhWP5d4R6JAASgjO9h911IrJrvQrElk05XgHuU+zF+XAJOLvbBgaP7MezT/zpHZXCPwwKpsJQ4WSBAw2WFL02USgXq5vHjx4mCgjsIBME8gDZYisVr9ZuUBbdCk+RPf/7OBx9c2cNNAE+1b9S9ga5kHSqKHbo39cfPB/1SRONZJ3f7KWM8PUnMWNzCGXNh5wN9ZduNdhz5c30aW4C2+jKZgSQ7laupQYHYJcPA7NEVN8KJcVV4yVwJI9YrpR7QPLAXEhACUw16Rw4KVqutfQRs8goDUiRRKjGJkuhRDn8fUYlAIto5x8C3pcRwggC4J8QfyQDNvWIbZDJ3EDQn+tq3jgXzZQJtqUIrWACNINKqvTI6XgoEJaLkqvNmke7fMJdpcEvtbkFNea5U7NJup4bTc7PerEOnw9qo7ZtF+FKUHImjptwiHOIkI3+seVVXrQOd3OJv+DSfJJHk0PFNHOsz2M8MjJwO3bxpzzRoGdUVznRQuaKVU5FDPJxzJoZ3KJGJfQkbN4/IRHeV+sVLdrdSimtrVESqtpyFxtL45oHYsffCSW+NutPDHtMkLhvUlrXNQ3lxtChZrAMQqs4p2Vu9MpkhW8D6se/XiyAYsT0AhRfNFbMkRZGUsawtNDtEZxPkRrYR7SPBGjoZbkOm0KkOMk0Vq+6/xniUhtI2+pbWLdxKWSuXwpAk1COafpkFrl+ib+z3+iwKbw7ab/38l5Vm7asXHhoszLF02zt+jg/o6bSMJnvuzJbgQoVQcSnreNFFsBKAvAk8bPcLU2zyk2/O2JDb9OyZh7ZS+csz2emp9JMXj770zjuXNnrvtRu//pvPPf+dX2/2q10uoxtidcXLVz5kKXxqcZkJYSNV2mynr3fq2cJSeiV1tXd3/drO6is/nP6tlaeeeiqd6zBz6mYzvVznp6+9ynj693/vn/3oRy9n+lmWiS88foRXbf/zv/tzvpXx93/nt7/+9efR5MbW9srhqXI1dyRffuz88Z9dThUHu4Uq89VhbyV9d7D9dvta7nRl6wMtTJfau6vvX108dUL2uZg9tFCo7d5mGXq4dYbLNziW/bPL7y7wXl65fPHU4ebmHXuxplOaLjNMzCyW//bnP6pxToLZb34XhfM9Z772U8qXrt+89cLz3zjytROv//U7jY1GtTnDmvuh4ycYem7c2mVENTusprZ6vGZ25sSF16+8QZF1q21GDHzXhHqiD9qY8pFwF+VTm7RgYvVQupfDlmyGIrBGS/GYjADDNbMTdjH0iY9uuwGOJi+yj5HV8eQUsSjqALaqYY81icGPAyYPgrkVAo3R3PgNoLSRJZsR8ujGRCosTEmsRhsyBysBjzpSkwQD9qqhTIXcuh3qe+VUUdeLDQaomtRCCR236rJCne21nBqOniSzZHOK1FkLCMcHeS6PSy5kYqxmuaZk/bEjrQU9xnyry7SfqggmvLGLE1jd8Z7IY1UC+lP2AqaFlSRoj6gQVoTXdIXAcRnsYaw0pbq9qaKS1TEcZc14SXJ90wplqTFCq5o2cPa50U5vdzgykef8BDg6wxK3AyQnre9sw4VHbAJnzY7/AouKTyGzJZcSXOZguVanU28OOm0/BEoUBUWCKI3U8omc6T9OIQnMEYC5tEyLTY3K5dEexzOHPZrEmBVMLRgnkiZwsfhOSUBBPBEtsYHhChDJ6U68K45GRxbrYkSiiGJEKv4RDe9JXZPup/u9ajF16dzi7/3eb1fzWOwaewG6GZCyxra0CEi/xQW6/RJX8Oq8f+PM0vTa6aPsn3F2q5Lu79LSI5eOHYmndWnGE1GtnH1tEfFoTKCAT5ixMnD2HGjf6K6kUF66UV/Xpovlhko0yeX4YGZy2jzj+zdA0Cf+MDt6GzfKdazN5EDO8ku8LM8EGnkiFTunED/pl4ySEx9qdzhNBq4R2Oo0f/DSW8cXZrSapvO49I/9XHmqmZ1niTa/vcVCwPLRw5XSTK+wQP/UqW3SPteaO+SLqk8uKAMkzNjSEtrVEomXvnzlDf0wVlEeY5FRV1MHpFNciIyorU6NsU02p0XSbDHVny3OHDo8kzq8u77Jd27xb9VuabwySE+ziMFSMpdLcU1HZ4a0PW2ocU6V/HAySq1idIYKUSg22PeGtcFgtjJbWpmfW1hoFzhyxpG6Vq3X3u209EYOhTjF1aG5w/n8Ox+8zwS9vzj1YX1Dt2Z3hnmGc9g/pskgopSv9flqIIJn6+1OrdVmcMdYKsen+4p89YFxRYYPaqBonb0aphrZ9G5qsFYYlGZKvbJKR6NPDtc225pAurHKgDXcJJbT5lyGCFwQGwFaMRNmLschw16jt5mf1QesepluhwPc6rOy1QEXVna4BhranOGiQDrDfiFforQRh1XEqdlKKUdX2Minubg8M2UXjNS5TbGUnymX6Oxm0r3t5k6q29DUUeU0LKay7VqTsUWq2Z1ZWDxz/uH6IJcvl9Ob6bs3br/9yuuy82x5rdF76c6HWJMOkaU4fyh7yDfamHVH2ZIB4Ly/4CHpDB4BQCX/Mg8p6YEcX5AXXoRv7QBhrwIibf2jmMtB31oJ3ixrte19anVyuSy2zSoDm9+YKPJysy/6RGkMCqn5sh9KFNV7XZN1Am72MAuah0y61uoUq9MXnnqc1+oH27oTn8MQcFJNO9h5+xbRTKIleqUkOA7fi2aME1Xq0aOpxMVPAg8KU3zkot/Lcic/EwXywn8U6KbKGMl0MtZTQMo17DT99LeHqQ8WoBIKxzHxVRoaQemYjrUfUrWHaVswQh5xPgxzZEqEYTdpKQOOoIdUEKX35NHveHBkYyoukIIvziXu2NIzZIHQVjOuU5R6dN2Qaxsok0qGMk7S0H9wAWyzqdfJoxZOfKzmyvzk4vaNQDx/EQhJLBpvkr4lUqtgNYAnk5S6pH/qqUhbqUxt7WzwlYGYyOSvcmgZIQv7cZjE55nSQChPqLQuexCTR5sYe4tN9ohBEqTnj7A1rMxUtNV66PDi448//u2vPM0xlGzvFje+8pUh/vhQAo0ZTQ9r+yyKkprjmthUpddSReoWaajb+e0vPHL4l3cbm9faxV5xqOrZz2czpw/PTU9XPvjoLiMcWv5hu9PLtgbd3VZrmXkK3zQsFgBLV7MzxX4vd/vOtRs3bp08cZ5Jcp3T07PTs92jzIbowuiLq8UFMPXNuNSQy52wtFjbgCOHFZmxK4NkHc2jFmXc4BGS6oJUZ9ojD+pqQOMZnbhyvAS9xCc2mEHiXv7y9FQhe/qdG2sLv/yAawOn+eQCp1vTWqb3lQ+hiRy54w+qTGORRC8lIg4hcUwsBCMiU3h6dhSCmYY6hZJ5FrKcrxhoV5jLhEFkQxRMVSrVJj71QG8u/aimq8FUKpKr0DMa6UkauiQbgUe5zmu5n65QNUSNH0fuwEJEmQodBNQ0bGQdINWT1q0WZPpT4AxTti1BS6kE2tHJZ6tSGs02A0yaV94Q6qgZ5tIJULz9ETI9C8eRWZpERbGE1rbzEPcCkDReEp7+1s+g2fsN3H5KprQRwnaHuA/9I5awRa8lOq9sdnaqtDhTWVyZ4s4Sq3QZboGk4sMUjtxjJZVouQQz4fYEGhC1IR6LBuBM56FXoSwsPL6LYAszqI8HKVkSi5qki0pZtQoisiccccqCl4Ye+ny5gdWnVP71K9eur283hnp/ChyUG5yZodZw9nU2l1FXKLbmYq0CAyh5IoimdBGVECA2GXYKwBSIcBWqFFnG4MW24dwsNbXC96jYHFIpMI/Opw5Vc4+cWFlZmMoMWr6kZIs02p4ya+HMfltTWRTakybygxYqqRbTX3vy/JmHzv/gBz/4zz/9Jd3spqbvGt/gbGRpgzL4u+ZMINeiZIqyS3B/BxGsN4oD+X74Iu84+2GiQ6RQZcW57/pJIttXZPhqjO70yrMfQlFK/3tdXEaTMQdlCkJC3RMdLUCT1UlKBzxPFLY9KgcOD7EhkCTjlQEfdqNiUG1M6mCUAiIHRY2Q4hCY7mKAfoH4Y5BHlhEbMVEeBs0D+CGJJ7yvH5gmqIrsvek4gjMVi/0VIDqO4z6IBFzaJH0hpXUIxaUljzgPB/EY1jhEqLFzUjxZIIImKQNK4vPgEKdPFL0JEOl0vPPkcYJO8jEuliiDllpeJIHJo/oCnlfkqDpHlhZy5w0jyZLE6eF62ji07lKcbKppNyR6/8hUCBy10nC0nVhyaRREXynp463JpQOzgMgRUE7VVcZO6wDgGw6TaDia0N5ojjITchUHQk5DgByRnHiHeNh9Y8FYTLE2MEYQDccRRxxtycnPfAGn2wZIL8seqY6/dLtMY3r8L+lgb+TI9QHCOcdEOegMBkAyT1p+GTYxnuv09LV1LutcfPgszfmN12+wpX/u4fc5ZXzl+g6syQ5DZ0ZaLPKmGlvMpvygBBS4RGnlbBXBlgftGzduaNWgpksbnT5okGXRHMhPfvKTt157m/PLv/l7X//2t7/NhY9//ud/zvemX3jh+bW17T/6oz86f3L561//eiY3df78+WcqzzBpH65LSzjo8d70V77ylcXFAd+ePrywwMk1DjyzKFwsFhiYvvzWNSRn4otsfJEAHGTjzDL6hzV5JIN8MomMMLJnQRkgkD7XENKpp3XpXq+tk6R8kQAJ0TwJUTunvI9NL+kW6fWb6Bw0OK5vrSKSRrfmYBTZG7+JQvfYCR/KCJBegryuSyOWV/jlq+pl8wXNBDgJCESrS0ZNsepYRy7ACZilqfiRbYSRCAVkYB5GhhDvkEjLMkhf52VUI0mIxQ/IHgACaAIecxkha7kDjlYTGRKB4HzJk1EQJlkOcOVSZMXEcRziI4YJdkI6wIXcxSLtgwe1idi9kIlkshOvobFCoHDfVBNE7vso4aPCULvk5kSqIG2SI0AsttXS2Wc6YaL20k8CPW2gEGjuTeVR7mNXVAQMg0eH7MX/HCHoGQldSLRh8xYtxQVlmG2IoeMcyBplBLOOkaIsJBR1HyJxQtDi4Oj38OF5DmR96clTVgf9Y4lqN5wvAeSHoxl5VK3ApMHkvNXGrQ3bcGI5GCuS2ZOvCQcEAtzeQIuGHiCITyqaYvx2uwE11j5cNg/DUVNEs8wgAGQNR1mAiePHwAme0WPAmYi+rwEchMDyADOEn/3sZ7TJM7M2pbQ6SGPIPYOptV0W6e5urnMCJT2tdo98kRcXw9WI5AQ4D4VIhEeMTHE84jS1xVlZkZagf7AUm1IqO8QHXHTYzrbtXnBo2Jmic/kG5dJe32U1obuxjdn76JpvIKDbeCYufZCEgvApun/1niKklOcPLbLByaKFbjee5aS1Fp7oehYqh8g+5Ug94gNzbE9yNSfdIo/0WblyEcztXlfbnDvb9LZoAI4woqC93kkATeflICW12E41WkJOuJCqUdb5L8IkjK+MkLRa7Q+pEgMFJ9ho1OAe0bRLP9vdAX0cEFoVtzoW7XGoDXbeCqADkgMUfdMzOkSnpADCri2Sbzea+AC9IzMKaSC8ISWxzCGw9LOwQE8AL+R8+KGHS9PTlDSdI4t32AyDE3rzOMXkr3PHJ8KyPomw7zNackW5b2mlt+AsPsJxYMDk0Zkm4S4AENAoMh+qoUN02+l1GVFg3uC4xmR8cTUMCZ2aPcpuXV1QQC1sA6JeTIsR0S1dc8kSiNZGvdHAUqAXzw+czH180kLEkZJKQ4ljWrgPmcnoJKnJuIln0xJi4BjCTCjBZZsAOgE0sy88kPdYHxB7kaFAZ+RRAI0pvyop+JOWMLHACaPzOEqPQVEg4LxcAOIc31kTpYCpgORJUggQ4SSUa8TcaCUGDgipWEzE19mjT6BNJ7+P7/kiIhLPUDw8QZ9cE0l1QyUoYx/mo8EI1Kj/n8xWJtgFGZKCJTMAXC6ax6V4c+iFX/sqp5jzOZWmU3MleypH91wAUVLTNuXlyDRKDz2UYaPxw40d2rFcMUuvypyAW+Y58w2+TwS9NtEu0VLRPlPvMjntLhBNi8READgTHJ8TIQAIoKE3ddNTvGNToYMy21ChO2sJY45Hh/hjQAiBgOkI/kiSYGwTyR1NRK3AQnLPNQ0rPRFfx6GBnToy48jBh2YIOwVXrGvVbCAiC5rTF4KpNcaJDBq+sVNdBscFgAWPhFk9pmjsrQC+iChr95oiZKt6IUA3I3bGxXPtW6Raso5bxbAADSb6Jy3rrvRW7bZ26Omqcf2W9TIsk+K817NCCLl2vmQECGF2o/B9GY4UwDEgfFtOjfQkUghsS3heojx6HsFg9Rx0lrYdBz9KZtYokViaSGu7iygM6fDKAn0fV3kprenB8SECTRzyEDY5oxYy0AQOQaJEU5ERL6cDPLD2AHAIssrgNAHuxQEINV954DMV7777LgpNYIrFvqmAP6AjuUvqeTwolXMJmdqLRt7pWKnFbDkQAJNhJDSZT3NGEYVQPR9++GEOqgHcmzxAXC2yRnPOl2bhG9/4Rq0494Mf/CjWq0aeIONkm968SBuB0n0CJAyqI0DCkBaSIXwfKuLrf1FZPEhCmGFy5BFfGoss5b6sPiWCehFMLaQ2lYWnsQDjUnuOSihg2vYQMUHjLNJFrTnQ5Eq5tl+cBC+0GFNKhAwn86hxus6zGR4cFa0+3wf3MA18nRSPKp/Y+SO+Z8rtyREi4AhX1ThOp9+JxzhqlOtkXpQz7ROpwfIFR5ojaHg4Tjv69f6K5yAtCZNhQwWgvBJO21Vj9GPsV7EfFkkNL646BkfPxp9fbVMwaxjyGRuWxkQnz1tDTDiUJWu5RTanszA0T7RWOqejiZDixcudb1KLFoI53HwdpjHnyFIsDS9Rxtd0oDEhpIGD6KIadxhqwU77bIpVjHPxDPBo7EWIbPDHQQ3wlTG4WJzLB3W4s9biMqhYnZcNfcxiyLDIDdMc68O6GBRyNyGk+JKpOHOzIfThoaDA6jPgojqmvkNLeCST/Oa4GYgQJUTBkQuQe806sXH5mp6V6ahk0JGFDW54sSeaLi07aB4kDf0s/6GGnDqtwCEO+t1AzhLHtE0k69koPpPOexrZj1ceapbB+3fv3tZdjJX8yvJxBhNkvt9tl/OLnUYHKuKo7WL0LRXb2XkgvhApyemj8GUDSCYP08tznLzL1i/XidaGy0cOp6Zn1tqdt9I1Psb3WmY1/cyxO5dfu3v32sYf/Etk+N6X/sHKbO7hk+cqmWJtuLWzdZdb1RYPLzVYUEinN7I7q5ntylxLi6rVla0prozJ9PPFGm9MDYcr+blqZb7LAa1euVisPvfc1258dJN15K9ee3I6PVfuLhVa87vruetXWn/4P/97PrZz++H5x578QnYp0842v/71p1gU2L38NywKVIcDputXr9dLt9469sgX7l6+Nqx3suXsB+/+Up8NXDjxnS8+feToORaLH37hu3Tqr779Jn61zFc68319zKk3yPar05XMwlR1aebKe+/f3txslrXw29RJrm5n0B5kBkXOtWVSt9bu3NlYnR5oulgqFbn9lmvxjh899s7a1Y3dzXI2vby4tFnfaNQ7M9NLyJbhBml2n23Y4Z0d5sUfNQjLjA5IUchYu+mfE3mb3WZXawjpQUefeMqkdSaRzXLVEVWIaLTBtB8dEoXDGonwcPKQbdweKopL4fE9ifuqB1iYda8EcFQZfCdlj04S39JGHIyXc5YB8c9s3nBJzC8L+SFlEMwgI7jzkiqwU1tWZYFBAptpk12EdZI+lvK86DZ4WhVvmiVyxDuuh/474jKWC28tgxwamDoLgWLMEcTaT9o+UaMRiRUMaiSiUdrjgS+t2F8i0umPdX7J2ETYg94DJsGuAYdYe0tbpadYcgU0/NX7FZK50cvU26ndzqBjZ0eo5taooFbPkRLHvfyITpJaKDsRtvzTfAnVHO2YzkC36oNOdC7D4YFChPfZfuDrBAmoCti2hIAw1wJBXPLGRbYkq7YWzSrFvsI4jsvlYVFBL9YUWh4Bx+buePv7SueasTNoqUI6z4IC1+eeOHH4t777Bb3lx5IvLQwX5DIrsLM2XMyhak2bgvB5ZFXBACkMugyQj83MtA/PX7+9Wsno860wYDxEttE7+eIADX4yz0wDGDvSwNKmBRmZzg16hWajnanoY3FSnY0Q+Jqcyymx+aNYoe/JohYoouGZCgQ/XcDNJlkE0ZgqHhkGsnaKJ/vO9a2jV+4eeeIYcM0TM/nu5na62VhIZdo3b6/eWats1QuntVrHYJkjTyiNCb8vesq2pRrlJtThwJraqFVXMhsbDPjioDMsvCIm8+aSJckTV1HrBwXgsn++JNii012en1tZWnr8/O7GLRYmVq/rUoh8pscS6mKda6SzOg0EPv+Z+shUKbYKTLu8+w3zZiu1uZXu6aaFfHdQKuaGOR1J4lIOuekqS8ysbg/67eri3O2t9d1Wo1Atr9+9SwmWFxfILCT51l6aD36Q8T6daPQGgMVYB4H46lmo2LSUmQYayWV3ODVZLedmMi2+9OuzKW9wTUD1QjhVGR2XAlbMZbkmZHZ6iuXvzZ1r1LJ6rQ2LPEe2+IaV7n7m0pI2B6q4XJ5YbuawsQyah50WgNReShXaq+9x9Smjn8L0wvJ8t9PYamzzeWG+ZFbpDjZrtWmS1zpTHd58z06lWlwUVE930DTvNlO5p4vTp46cynZzqbX6aqO5efXjfHH6+KlTqXJp4eiR6eWlynvv/eKln9drNetJPT+fwA/mQRoP24xe+guQJE6AA3Q4WfOARwWfcbzP1QPEqrjMtdXtaNWYJUWKMZddWV48dvIERUwtdmSUidoUT7HQOKAO6/QpAtNq1EdTrbkRQcMJ+xQKpvLwo49srN5h/Ut3SWv7RL2xxKNH0EnqIMso4L1MbPIjOIVoObRKZfrYizPC/hWE4I6WcPbBFBSg/pQTr7CK2qsDmO4tjqTkIdYD+OSUsiDgRekZd9qEbW5ETFTKFIGtlKkTdApASI4DAhqx+BSc0jDAsK888ahnQxAaxqpVdS0SOY5FiqBEtZro9AWhNzLxGEISBk6D36zX6FPsrKU6QlIC/9wd7FwwfERFG7u1BlN5uiMOV/pYYoIpOJ4p5lGjVngCafzR22zMjdmbNT+ijhrMRSM2t1KfN3qU5lM41JjSGz/f+c53Tp07gWbQhrU4mmFpNmqakRLjvJBI50Z95IQtZbQQzCYiq8hfemh24/38a5eZoPXml+bosm9v02R20xm9nc6jjdOxwAGvnbDp02pst0rZ6vQSMqATvrBKA8kS9nZto1DOcpZWmipxhGeuvtbcWK/Nz9ezxRJv/oLPyw6mHxWcpqxqMQGMKQ2ZgbtLhoEEg/HYpNFGCexnIpUejSRzfJJwG0Vldm7to7U33/34+PKlcp7PBGi1wXtIyt4adAAjMUil8SXOKg69NpIYgpWUzDeyUk+lxNEWu2zJRnAaSAOk4ijOGygGOtjNkC8I8HFaXu3gy1YDDkWrqjMTcP5iG0niNkBz6MfhREZbyTpcrkaC46yIaWfMOVlMMh4LGV4Q0o3AvDZHrUvlffHUKYksRWwM/EN/9Ik89jkGbWd+MjXmeZCONaH5N5WYE0rSRCyVje44Z08+RTAer7qUyKAjy9YYkXVQ7OtU8lGFWgJphMEeKwOpufmZ2bkyL05gFWQTM6eCkCMLSCfmqIU6uStFSn9sySusvEPGmhFkY0nANEIafU7IhYWR/5mY9C6qHqG4TX4rHQYYSuA0ldNOOstXNd/44OrVu9udVIllJcayCEPa+7p9Wwyltey4bmHh2nNq4TEEAhcgHha+WZ+3EtBjx7xS5gWGAYd6GWWxaq6NartOczBsF7LDS8cXzi5VWAmiaY10abScZqBMQLVSTTW/g0KvSf3I9rqHp/K/8fwzGx+9/epHq3TaO/aVNfYopHmUPXKxuYwge0I2U5PNWoyxhoKK8F4OoQNtAsk8TCRLxnoSkBPJ0SLLz0whuGMUbahDUp8nKq5P14bP+uiDJsh/0ketRR6URgWZcPFD/GtRjiOf/zH+QTStJkQUCR+EluA5Fgz0CYS0SZpj2IkHTxiSeEyglkBU0OHB5pKxE1F6jI0+ieZhYoNJJGMFj1M5QcUaqsMB0vYCI4DMhoMntYeEHhuT4UmOGoUPDl/uJaEFrFe2WFZa7VezCw84slN2SAh7AB/HmCupOiCIJohEI2zNjXcbVnlo0wQ34QxBHDFlZxcYIZ7H4iubNPD4dnYmlTVRbUjhDTQ4OKw9SOLLZFCzGMkAS8Iab3D4ywd8UgbvhOhQsPMlIEn41p0dW8B4mKgzraJhASFUcuDcJwiEmSSHC0RUQ0+3/IQfQYyNsYjoOzPzfWBkqvKEgprM8kJY+XJqMU2iUIewI6cskERNpDlGoUhFi+i5A0aAndLyyRUWbYkivNu4C5AosikyEPc/LzujM2GlwCKG8Y/o+/0P6bQOH/HGTb3O9j7KYUWVSz9WLl1iqNfabTFhZrbDOIHjA2wqDsodlolb7T6zaczP5UQwlM/kiq8ykQGoAcfBFwPGYS1+1Pcb3/hGtVD6j//xP3IjB6UAEBwS4sOXs9VHjl6EFN+0ZpaePtqlHDkKTfhQtc8ZqLnprWvXrm9Mn0GYxls3SDg/NcuZ68ZWFwjnCtm6rJw6hcCIxJh1Pqszd42uyp2MoL0ub1Xn84wgEanVEpwT0CC3u1riyfCVT00NVQrNtm5azPeLyANZln5QDpJMFXLQQUV8kpEkXQbEBc8v6FGvkixjQXFeRhZEM354nCfC8Aq1l0fMBhcVGT9x2YWAURMHkPFBtkDE0wl6rOBWizW3MwcvUng4UIgDZiSxpYwYE51wggfxYniQzQIjSbxaOBaM4e61xpsOYolCGFKZPaqR5BEJwXRBHB7QPOAER+FY5gA/KOByikPCGXDUgIcYwb0NDKD9AiawIjwQHvfDfVBYTMQFHcnmcFOXoqhZfDKHekovEbhHmYvNxlmGhPtKELOLiIwloYL0+6zB4WNptMMe6zKY6ki1L9VPD4SsU4ap1ieMqUEiSZ0jD45GXg+UYY9tgIl1MZvxtA8iZRITE+11dBrxC09d/OpXv7o03YUa/RPKCaQAUKMRj3WPACQAHeoOmcLRXh1fa9Do0XDqWe2x6gMVNEFJqRFY8cMhV/LRHnqrCzu4sKSFDdAIEfB2gL4JuBiNG4BzN2rKeIhNii1msUvixDD9RgWQBCXCsD6IoKd1IbnN//lzc+QFIK3rxu4ucNpd2thWo6mr+Xu7MOK7PC6nGQCAUMgyOJ9nO8x9BmcIoPlXjMi8AtW5gJ4j1ohF08vFlvwcF59UWr2mmNhy6XZp7S9cuPDY+Ucw/vWbNd7gUTH5voIMzkqTOR6lFbFTPUVUfK065nLsFmMt7X4bCpnaNvkF2UuHzoiit+5D5xxZwaAca6v6KiJDAEloNs9AwGq33Yht2VD+rBRcM6TFcfKu2WnC0YnTuVg2hOnO0WytmJ1SwbmXkzO5UxV2hYu8PITYO9sNpOraXdjggwM15Pdc9zpuVKZ560CsE/BhlBYCSAJfJEF0bR6oY2yzZwKF48ePUF/oP+HC8UD00LRawLFUwkvHj9NfN65cOcmoY2WFrpnXpObeeuvSF5+ln6XRQSocOKn5KDsP/uMZAT8EQlogDnQ/wD9pwEvEi8NqsC4VJfvojQBrIVTY2YV59AAjygg4STxMYfKIDSm5tVQujFUH3vmT/mVg1sujT8yLMRjXl//4xz9m88GoafxAcvmjFuiTZSKy30+W6MGwsZc9LXCUkhqH0LY0jP9g5PbBQmNk332PRqWCaIFGHFAgzvUPglia/qXzaPgUDb9d+QAJgA+aU6YcgeCcDmr3hDwChJ07zdCMEbE40uqyH934rDYfCD74wF0lhLET4BEQcNw30TiEIaJomUOeOPigv1CeQBXziP+oUiCho1HLaIJIZCrar+ysnYQsSci6r2KMWFhxH1jgljZwD6lcHxOiSkuRnCpcXnxkyN3iZiAaxni4DBwX6BAgCZA4YTJGYaL4UjqHWo6/W6CXYcqjOpXJ8wJlW+9pDFhqdU1QI2HkjrJgAZpy5BHKVGccCVHU4vwUemA6Ta3MtKchSJtPXzxT0gslEg4d8XFgnOaCks0LMSh8QkQQHAIyAYgQYKLkqTwLwSfgaEkIYXdOCjlp4elYmSXVaifp1IgN1CbEcPHwaaFAowoRxhGO/DgQWDjcHxEGR8XBJyk+9N3ykwhoj/4C3y/l6HfVu3HSzkiZ7SSUIFL2SAAhjKZw2OAhrGm/VCvrBU2trp28TpVUMYt2BM3nOwzRJIMtYuqrp+ZE03TIwiiDK15WIRUL3B6LluxMkQQIuSBHOFu0UJMCph61sAuWhMH3asoSs+gjnC0941Oa0CkXiiTkkfkpR3iN16jQwfGWB+7g4Iu4hQngXDZ8YxThwNGiFOupCCiVLd47Mv5EcklsDkxicTx5gLk2L0DHTw77u/BdgKSczhX4KOd2PAWr5jUyfIYeyE/V44BavNSgF4sZvOEPB5rjByLJQMisA52pAyEIqUpl4dvf/vZ7//o/7uzUS0W9bs4+OyUYddWe7IF9KLueCaBY1y1+okgPoEXOo2IRQhI/KqCkasAAeQJiJWutmTo1XQ58sEO4vfo/GH2fGL5xMoIeRMv3ImI8r5CyQq1/29g6+Bi0oxmpSUuNKYx+9+R9VCWg4NnTbg4pOECAZ9rCvvgjrMIxFwKBtCcPFWxcsLHaFZLsJRKixFnOmtpkqWqBJoIzZjAca11iwVwM4JJZiJHAjhl8ji0IJ7IFtSP2CUryrTbazEQWCWc6MMMUF9ceJ2usX+NqK9k7DJjT8MfsgUkTu3vIwl5gsVxiBGttsA2JnHf8uRie1EyjUiMKmuKRQy2m+kN/AhBhErDGlAYTSKwdhLJFaEBuuLanlOrrJJC90KGOCgcdkWKtCyJZfdGe25/xOb9MfnXqm1j7Im04AEmayLbIoMY0ajSFJicZtGuk+sAgLttnlZLwUJ+2S2W4HpED0D4upIykEFSdTXXUOmjrj0M6WhSIC03kcnYTNO+75IctbUIKUblTEYqylxddmPIDfandwgjEvFVC2Qq9DJo8pnR/nK3b46s75NJIcspN0PKzBTooLswiTXRK2gg4Fe25Sat6EmvrY3x8w2Ob5SQ4SQqdyjxx+kzp3Mov3nrj7upNxk87XJS8tFzrD3SYC/Xx510+9oaGoAxVS+umxw5mrFLuwRQvbmpgKw+Tsszxsd58rlDu9neGfC02O1zvrh7JHfriNx6+0b/28Y+3ysvlammu20r1mv0jS0erK6z5Fl555aVchotG24Nhq1DZyhXWuqk7w9xWMVfIZVaLLE0XssXCNI04G8xZDQfYieTezVqn137he08+/ZWzr/74b9IZPnw/rM70M7xK3t/q9ncbrY3lk2f4DvBqu3OZkRw3bFx5+/3tX6KT2XKmON179NeeXrpz9Ocv3blw/sLC8CjLwe9fvc40tbO9e3vr8le+cubUYw81MoV6OvvYoePHFg9PpQd3b95Kl1PlCp/0zabyBa6XpOxv311lJYLxirSR4ZrvAXdQ5geZgq7p1+qDVN/T3s/iwgwD38FOa2NzjQn87MwULeT7Vz78Z0/9w9TT7bdXr/9s7Wdc6ye9M0XHxjXsoNSwTpmyGgEKEkVT2ozGrFx2C6lae3ej32KoulzQIn6O81+UZDxYsfKy+uJFaC0JtiCpvF4aRO1lbD/266Ue1SOvOzZCo4KSFhFlt97mYJVGTZ5bIP0jYR3kNlT3g+XodJIoKFZRZrQeDr4jhyQGB9FMDso6gGeakJi2fSWh1HAEHA+YWQI2uEaSpj3VIyOJfiWAa0Og+PyyxZpnDVi8yhTTFx1zTjnCRk0xcAzuQIkcaR+AWh7Xv6dJ+F7I0ar5GI7LaajOa0TPqioxkVwjcq7DAOaRAa6iLS8YFqHmINtkZ76TbnS42U65cO6q/gon+PIkm6S7RZNSbNKWglqUzEo2+Ar0e51Ws9eupQfRZVC0aSQJZ2yV5jO7CRmQUE2rCcOAbyI2yc3tGQh5ios3GR+Fk1HoBVxTIZaj7obKG1Q9njjSoWlUMaWsrjuYncp+61tfe/LR47xAkuq2mZ1wLzBGaDph88rJqesZ9PX5NY61wDOrN3iI0uRj0GlVc7knzx7buXHl5katmM/S/NGN+WiDhMp9QiZvkVjmXl9d5cA1dsiX+9BMt92hv+GRvkOv/ajJUW/AyRC7f9ZqK9cL0SzZvck0RtBFIFlAyFUiz2Amnj5BUGpMpE2GE1S4S3jIDQ2Du7U337/9zDPPlArdfnfQ29wusxZJo9ttT6Xz9U7nzu07xVOnURd5JHPss9BUOh0Zg5m4a9ltwyqmbB6gIFg7SDirFyiHtDbo4ldg9CwDs7v1vexZf3D6xHLDMb73jmClZ6sL0ysLF85yCIYt0rsfXWdtqEB1yvCJPSaM3BCt8UOLqsLtKJ00pUm1aWzvFjJpeqg5jlTPz29sb9XVw9WhvNlvka/F+YVTjzzcabK8oNwx3+vlc1gJpZWfqrQwZ5az6aQL+U6rzWFZXSnp692WB6Rt8ynHVGq7kL68frsxrS8PcgZI27rdaJdIqjAHU/9FDb4FPkxVajc237m7fb1aXTm+yFxufmWKGxXrA15eYqA0SJdyu7VNFFsqcKS31xr0Wq3+0DZri7MzEOzpgjIUrlfJkA3Mte1a+6NrJw4vHj++cvfK2+inkmrlS/kjuW67XjtUOUyqncFaZq7XOTXDss5ss7J88mQhP/Xh9RvUi5PHjl/8wpPtXGrr8se3r9+YmpmrXLz48ltvstF7Z2MNRXO1o+XCM5WoHp7JA/ygBOLdTg5qw2MCbgnQ978YfM9fzIF4DRrD6656JcIv25epMD3GPNnJZr+fwbCO9KfSjBexBA0/aSpoJmxFAzo9EmrdipF4Ppsv5lMZipWBPkmguHzs+LlHLn3w9lsUZS7LKBS2KnE3/tDwq8yj2mw/sTHcMx+fa2QoqEiMfYjLyBnG9/MsevkpQozEi8yMVmWBuvZJGYMMLX6wX1SKwnl5JkDBcbSooVA7qUJxRxiOzhTf2pxo6R+r87YXTJbMHB8gdJyC84IRCQEGn6EvYVto0kKVSjmWTaVsjVOftwvs81mQNfNO+TIc2tpYW0ctYoERWsFB/F5aCFndL4Aknkf8iXiPAmhRGTZ62DaSsJOIcbq42oGPk0nGEGEcnEp1SVnBOS9DN3m8BXYFycQ1UyMCQ7Y1vnSezdpHn3iUW8+5FZdXSfK93bxmVhoTxfJrmEg90LHzoY6suqOPg5CNzZh5MmQcTvd3nz5z6Mvnt3evvnuzSR+aaTOLsbkeo3embUZQWWIHjf0jmvedna382i1uv0EuYtFlqVhp6r7ZrempecpOi/GZTHV2uda6s7nJBUozlfISjJkTYhAM6pXxgb6TZvrBpKOSlAKtcGN5x35NwfLcrohz5GSSZHiU2Gd8abU/1ATOa2YrM6urVz+4dntm6UieRpwLsq2ZUIfiN+PGiTVC0Z/VO/WekYR0ufBCnfKt1DSe5AUZu6ZSsoHAkoS0IaVTjuQXyRWkzSKhgXlkVdRzxASUd5vzJfWb0Q3RPe3b8XqQiWOENEu1XhwW4q6qQw2CBPRRDaMdl10XQEswjYV48UyxdlrZW0XdDo0zyYmxWDNcuPGdpf5wZrbU1gts7PZHWaaYYG3a0IzZmxM+goLSWIGhMLXzQO5sO0qrBqCqARhm07aQ7WZtIw3KkNaDlWdakdmq7rbCrkjqKpJeMQnZLwR1olcKMcNAG7CwRgbbUxYsj57jyB54QJ+Q8vEjWdR0wZ1KRfVNssVOItrgj3woSK0QZYE9UTdT2Kx3drkIHak4R4+KIov11lhYceY8hfx9x5OjaAs5Dgp0F4st3hP5Sj5OECGK70xMlwuzVa0vSVeDfqPe4l1ABpP8lTKpkyfmTh9dyg1a6kYhHs0WRcmzGTKLDEgFBjaqg/+muQL3bw9Txc72haPzLzx96S/+4mepgqYh3XSZYqMTDsknZNv7aOUyarnhRlp9YiBWQqz1PUljBEWY8erXAkE5PMbj1ji5YybSWs5SPY51ZwZdXmrr9ji+z86oJzCbGgkTAceLIyb9oL9ay/ikzrJksstcFTBfNSQOj5F0HFQJAjoNcYSpZOExBEBTvfX2KGTPGZl2qdCimVBcSEuAKC9yD7hUSYTPHoa+Z+oepFyGgDDxGOATASeLzN4CM6hUOFYyRKI35BPJiMURwwegqPuEfYAChDBtmfSc1z5Pp68TNNIuUUZBTWNwBqJnBUA1c73Jp6swARyRMJ0vYW+l4DdOIHrScEi2aopK4Hg0RHDs+NM9pDUsZsXcTDG29RGaMkQbuU9hu4SO6dZCGL3howHyqLeuYO1ftjUZBDQnfGvbkJANMWRhX7rd0Gkgx/FcMXsEE5yE0+DHG6nksNWFkawwsiVmaAoT1UsGezXJc0eNVtcrrapzQ8NWfJGuTDyXwX01eSY2Pr0LPiLhLEh2nL5OTECKjBPF1KUwLDBto9CZw2yvbkGVOPxR9TNGE55EcmHE0XRgXTg9G5hcpMTkE/raLTTHmBjt9dP95557rnDrBlEMtT788C5642QTZ4WRhPsroUnTT1mTHEg2uwXED7UhM5IjNghMrW3TXZMrWGC6LH3zEap0rcHjysrUr//6r2e5ELRQIFOcKPzSl76EFNw/xVGCs8UiJ6q4/5FYzkAxB8usZBiMlo8U+YTX6ZlzwDdWb2iSX9BL6CTkOGgz1Ud+Tlqxp50tcbqhyAlOMrVy/AgUyDKxqJF8kSMeya4VGACNXcAkI0hLFHDWBdipXqnMwYWEZPDi+YcRj5MFSLKWahO7ldo2zal9i41RJg4FHAHqJ8MLf3QfTKhhn726Xb/jGxv71QhlKoLLzmOqImNRTm8sbGJYMnmRGBHe+I9ksybF/fHIsSwko6CPZpLck7GxtCNY4EIq7yvIBRqmHZHxJ3IdK011SC8CjHI7yoXjB5oHixGNgJEjgRxJ5ZRD2sDIMUei3zMUUgU690T/HCKdET6Ou1YxQiya4sBUgZBPRVh+YXZQXkBxvVpASfaVzMjylcMe1USDPHVT0Swa/INS7UvqEwER2x2pMA9Yu73hI2kQ1sMupNMnUyH23hyTdO6NSSzCOA4WizCEf+3Xfu2JJ54o5RusMnCmBQjm6jjSUjQcUvOIcskCTu2J9b8oEYJMoACym0hL+MHOkLaOEQFVwbPg4iWz4zRp4mjTaNCYDEMECrSxxtoXTWCilQ58X4xGc0gV5DdMn/u5sJ+PH+jfl5xjMkLAolhV5E7PYyd1VJaWkLR0DF7WlsFpGnAK3vWpPiPKyP62OsFaxjlqMCJbVXGogkghal7s6KXCAmjggA8douABBcYDQGiFADJjIDxdrnJYuHDhUUph5+46XVJzawfhS5kKancZyBrdZYrlYlw2h4Vwjwd9BPMj9eP6PkWf9RV6TLoPkOlfSAgCpda2AmW+znnhVsdOMdu6Od2W5LFpM+KJcuT00dobNz52LXHOeqtTk7RmjFLCpBMktJ9olcELxPmuHfKcGbKhUkQtSAJ5N3Uey5Wi5NleB0h6fDKFGOw1E3CpKETyxZ2p5BGHTjhqR1+5qG8dp05MLyFMfjXN+CFfW9Rp6EeLMD21ckR96FXUucUBuLXV1elTFzjqmH/4qVdffZVPCyIJ1zhSO6BvVSDahJjM1p5n2AWnLI9q5R7UBICsGXICdL+gJRnp2Y0Kad2S1bNlNEpH1bKWGveI1BCGis/4GBzC5JEBNQndoVhBbMQOcZyXuGeHsCj3+9jh5p3bvEZm6zyUt4rcs2mdAMU0KTqkQHAovEJ0AAbI32UAs/FMYTa0iiak18SRVh9QHs+IZ1MZtPbWFQgFAsGHI87tnFTWs7GN4otWbgPiroKIHWF/BJu0XliWVpgIr3DcW3l1AI237ZxvTEZlRJZZv3I4OEQBJCCIScWOLzVCDUXsQIiDn8MvjCAYaCYD1FyEh5u3EmIWOKMYCxPrukJmcvPgojmmcWbnSu3M3swgGw64FGJzK+rOs88+S3uCQoKoSquFMznHd1KWOkoeiIODwNEELp3mODDdN18deP+N2yo4mzNG+g+ZtdfLUAV8KQsG/7S0c7NL0CQXpMoN1PrReIKgXkrLjjoHvbt1A/zlpTnE9hIkKikhjNQomIOaR4FjlIXp4SC8wyf8ZOzesNN0OBlHDOTl8erVq7w8Qb0QCylJztjFDw6KgJbVcYjL5kIShrhDwALo9ZcADoirFJ8VTCcD3IGhKgEfDnoSr6/JV7+rIRZLvU7cYkWK/4G1KTAiDh3nBQ57oZQXsSocW2oBiPPtK/XwyrUkUceniqb+hcJHPz7v5goOcqQbReMiczFkPJSLWgitbBtVoxyjKVM2TrB8Iy3oMl6Jx2kAbhvPpxjPFHXLS6bCd/PsYLvIWTsMF2eEr5TmYBBi0QljOsAkxyUaBuHAhYTkAQRSJdffHOhwcIglOUk8TB33sD8qS+Z4pLdickHC6KWwOOrv7NdFcsmTTIGQ30qlRF0jjCMLjLhwNlEQbrGcws6pmBBxOk4hhD1AwiTlJE6EmVZNZ1ECbfzozSsgMKSBEerbm/C+kKBqUoe/+6a6BwJCQocyj6ThR+Ufu/jRbRIoAqiKydkYI0bc9xfS6HbfqPsCaWtGlWRcKNL6Gh2BkfadE+tniratjQhivWOQwwKydZfApIsexiWNuIeEzksLjrwRYN9udgpsBylg+0i2To83lm3nFTh6KvcTxGljrMYn9DWRMCAnScVAl1ZNjPVEIm9oypNviJDhZEIXwH3gnnenth8aeiZSC5QUu1JhMZZbpU30NyKkRysAhMqk280OrNnAFFpOO5nazPTV3WG6bB/97durKFRK2hIlosjiwY1YmU466n10CkutD6VsQBozR4jEsE4RT6Xri6pKhGpHIwwflHCvpSqQbIyk0U6dMBk+0Z6zY6Y9Tq0u+fEyr+ec1SCRmZjVP6nB6I+Zk4Fs8C2ptM1p6gKsRr1LEgY8qISaRCsAG0Rgx1vM6QD40K4N6IGCzvyFaw3poCAFAYmhvbxotZd8WYsQDfKkYd82jMRScZFICpRK+eE/YanSFgp4kgLFSh4zVZawdd5cJ4ujXoo4WPOnumaIloKeWztX6gZwvHJLk0D/aRCT1m7dApUOleBufSfH9WWFobaxBoNTR0+qCbEVwKijNekQUPkkjPM5qFinSmnJHJE3k8uw6dzpZ8pTcOfr0rwBDJ0ib+MOtheri+eWK6s3ftk+VZ1Znv7yd5/hm1G/ePnVv/jBX2azyxwK4Ep/3i579OyZ995776O3Xl0sZ7/77GOPXzzdqL492yktl0uPLMwX851cppkpNzmHhwiMMPIDxODytFyxUszkWp1u69wjp+jhsoP27/93v72x0ypmtp/70lPPf+ULc2dONob9v/j5i29c/7B1p5RdmebCC24G4TbMeq+7VeB144232je2B9uFdmV5ZfnxLz/LZHXtWvPEiaXNRqtxZ3Wr1eN1s5OHD9FtrNV3dGAwPY260sXqBx9ePnziHBWk00rdvbu207CbN6f6zP+XylWGmOW2lnU2GzVGITndHDUs8O3Lza2ZysxTZ0+fP3vub//2b589//j0oPDLt95hEpj96NpUdnqDJWtdtc3iFOWqAY+MX9bDyGWAObA86BvEGAkWu1PQcIGvbq/MVgoDdfa9tnbIuX1LZRedMrZuDps0yzO4m6a321Z5vcGiBlixGo48M134OKYsSqYrJxvGEQKmGrGPU6xjy3osjf3Kc1L4sthElHO0NkFYzkIJDIf6B4RUDnGgwmxRAY+qIPEg2DDR+ENzJKH6CAzJZIvkFxeSGK+kvIGJcaDyWjbjPXyXIaHDKJPSJ7zxuXJtjARZMBzqr4nkeRnLETK4tLSxTm+Cwid9jHRl2QphAj1rczhQwsWsO51Uk/s3dK09TS4qVlWLSi7ml1Q7RxWhZyokp6Y6tUmRDiMuVsQ0LpBK+5cJWtvD5hZ3pJP8oKxZEcg2Yraf7JeETiFIS4BRJlR8wATCyBI+CW3ZzIRQPBoQlqpaD1xcrB+jFHZ+aU+effJcNl3vt5sYjJ8Ep4dALk1RepE12zQewcU+3R0wzc2yekYzy54xy3ZQY/TTb188ffzaxm7t7q2GGhC97SQ6JiHC82c7pRSbehTSsoDFkiXLha4xiNBq9fotzmpxxEqHZOlrSEgXxJ+9yKFTqjh1Z5HjjNKEVuKYe/06x30x7hEV46s0aeIo0Exa73zsNLq1Vj+XL69v7HC9ODPyMln0bXD2cQvMt3Sqk3L3oieJMmHqdBVHtTJmwK+sNv6jhpsuFe09ryiY/Uuxtg3ACgcJIAvAhlGq1OrtXTtWpN4m0NKAtG5jB7VKU9VspVqkg97ZTW1v37h2m2EF5+hY4m6nOmvt9eWlFTqc6at3WTydOTRfKRR3t7aRpLI8f/LMKd6AwdE34RocYufiwkp5YXmpvb5Onzgo5jr9HpdZAvdNegUYLTAD5YvvaAof2fhCdT69Wd/+eEsvfdfaLZazKXf2g6d3pCG3SeUO0XlEIBUC9qHWTx0T/Y2+XDqo5Uqra7u14ccszSyvzC8szLbarbWdndl8l9Xneb5t2GgsTleg3zL10hAzeuIrBprrQk69GquoHA3jxEHhyvsfMvf71pcuMuVeSDXgfnJhibWPpbMrhw4tbG9VmNUdmro0fGQh9UHtnd2tncadxnSqvbP5i1deeubwFIvUqYX5p57/6nptpzI3N7U4+1C/cfnjK1iOpI+dGjw61UQFpiQpfSlqvM5Hj9ZLempr/MZrgI/9rDVwdcV8Rs2a0/ESEXdzQtMZsajWa/HEblFDWhcYLF/voMekelLi0MGtrBzhegYClA2nCClNJOdmVKouA31aP8oL2iAA5EpTTI+VlfSQtwF0oRkNxcVHH//xj3/MqTmODNrN3bDW+gsFIrLj+fPsIAxRHsbXCp7VCDUyspHP1Y34WCEl6XsUkKi2Ko/8MUWQUqlyUsuovQpiCW7GHCB7A8qgBuMiLmKYfSIJYdctAZw4mQ4oHfRPIl8bIkoE0LlKQUta3h+BxiIz9wTTyKJhkio1FsAnTmxub8UeFT21ku/ckRHSGBcRdMo0dGrrdC2hcgtrtTNGi1SrdzcZIVO1xNRz4Yae1CFs93MjFvvFBljIe8AXJJ3l9DM2qF4NP2BPBCSpIkliqe6BmkhJ3WQGw3zVZqM2c6HVkM7dWeNEpeaWNiZ2Bev9VEGWDx09cuwkB0xoBDl3xK3n+WGnwDwYrTG+lqmj1SHKxvaRJ5Q4YVMv5z8YxPepVhRZ2UzsuTP5xtPL795urK3tdAsqSVYWwecVD911SRFz/c2wQ4eeL7KMlW7VtzfXM7Mzi0RjJuJiBrC5tZrJLhZyU8isWXalnG1WdhrtrZ1t3k1ASLKW540auhc6Z+ShdqI3kyHKtv0Q5QXhivUowsnHJP69w2hAKTn6iZZRO60LM+VMemOjTiPEhesssWo2jpOWhQM6uEJnixR7tNqHTKBoPiOcaAxGfbIDzejWGz3xEpFxB0TO3sD2fkeFocqoJWOcpVIanWXHz7JKntf12TSDbV05QmtmJaxWyu41ptKJF+u6JEfUUNC8tAoFHb0eUH/9CJEoM0wGn/0Ce6D1JRdacaLBplrbnaZ0g/0cr0JlNaOZm9bBPr18Yk0BmErNwCzVLxSyHMsGTF7VBsjo+KqQXkHWqXssiEFLhE+TqgVuaNJuUOwM23Il3fNSGnbUF6AUcAeZ9fXNqcoyEONCIegsTtCPyazSoTXBh6daA6Rho5p3yCBgGKZ4DUNVfa00dak2j9YEWhZ0kYgs0AqcsNpH5YxfkYi4W1kbyVQ3nb+zucvIgAx02noxJ6qliUL+3PsLxCCbLkDw0YmAiRkZL60V87lqucif5tksLvDdjU632e5w3ILGGiU9fLjyxOlDlCV0sqYHp2sKiPIbijjw6un8eppGQhA1/Kxh9bqt7nSh8uzjD736/hUuIt3e2oIFgrrSQtoQIFUI7w2QChcPFvbG7wdxoT0moR7ojLA9GGJDALgqrqovTnbb54Wefl7X2cqNKHyuIa9vD0pyb6nvmzKgEQiZ9zBKD1kOaZP4UgP/LWG0CBvwPCLGdhxgKihTsfsT6MnHfcNOz9PGtPdFHAHFL1GmHpEEEcZqyTxRSfiIxH4h545Pebt1BnlCwNP5o2xG9iIuzEbw4eUDIGdKrJsOuzHA+fYUjwADcU2hIiIibFFOU42XvgMRI4RcWABE6/QtjVLG2QQeMB3Ioxrq8ZpkjNQQOn0jYIoy4wA/wMXIHHQmiOvRohweierYGoTTG1rutHJlm19WIsApFvP1Qh+VTrG0tcYIONyFYRQI6zHqPpNF6ZzFDPnAgbvTNf6KhZDC460MXIiTqOIa6VAB0THfxbBMQ9BwRrJpyAUjrY0zCJY8cd8hGXK5IudcqnV9WoG+mylidW6OG5pIImbOxQLQlWz7OZCVZelI0TzKcsgOL26XdF7JCGgDme5weXkGSYBsbG+cWnkSM2MnlOS8MHj79m3WD7iSYraqOTSzSpZCTjxx+NKlS3V7HSaTOcHRNu5i5owPh5uYuBZKGkbDkUkpZLne8ekvXOD2yWFzhxxNlyqEq7MDTjqfPHmSsuM7AhxP5luCJOGt1RdffHH+kWss9S5MH4Jvc7vJcvPG7T6dOpdFQv9IN/3QQw9NrVQYuHP0AJptPv7U7bJMw9Ucw36HJYBzD59l4seq9K1bt45a2aEKENIzslWmdUhIxpFwrqzvKc/nVsjvoMnR6f5K9SgzegakyPnYw484C0rhpdde4Rg4rDmL/fHdhNJjI5K2rfRHcQkI5FlLohQOH15Ew40r18GnAPGJQh5LjpEotYf5dVJIOKJ5cIisQWpcBlXkOLly7ZRBi4HwGhG38dDo0VnFwugpQW1Mjgk4xJOpjNdIEqwDARwBDRnyGLXwEAlpWnKa7gcEAhFOBBIX18NezBhZGXTN7qUZiz2phIj8ODsYATedh/hPGdhXWmghD1GYN0dvOBZpq5xj3b0jSOzYSMAn2YQccb5E0MlOIDgQ++eQD3UKvEQZTVLbm/YzQpAZh2wIgCPghTiRj/AIsulGmQ7Ag2QAATQsbrwVF7oz3ZsQuL+zxrELDk/RUGixz/uChA24qCArkIj1KYS3rqSl3tVbOl3CdiltFHfc02ZubdDXax6lLCgzCvDHAz4E0QBtFAhITo/AOh0UaJRpQ9jTR0XqINLpAl+9MxUAH+S1gr9vabmtepaT+YVC8jGED4IHhGTgIGTg8NUEyAM2kqG1R2Ba75TdY+7Fh55p4fkoXrJpggUJVb+kGWU5yZRwgBDr+Yh+ElERmiVFq46AVKZw5R1I1JHba7wUFrE3bt554403Otk5TSb1mk2bWxE4ycvNYGi4kpADmuSoPdSH+45vqUBBFk6lguR0l5gBG+lw2bWvEU4XuBx5lijAdK/gV5bmYUpGCUOEYlVVNxf4eE65/Zvep1AugNBkjwgDqBSgdmRa546jXMDJHJBShTVzpqeiXKwW6RPvrt2hR8OuyEtn0CPcaO5gXVN81ocRwrBDr8eHCumdLz1xCbS725v6FoWNi1iTg7AdXOt5mIELuegNWgwAuM0ZXc3ou19Dumk698XBHGHsn8xSvmfOnCkMapfffpuqRCq+aU+H+MMf/pBevlRdRF1pu0eVLzOADw6xqcPTSP7gzrUEPhSS/dqDU5jAFMG4fhD2ISc4BpdB4ihr2FFwqsOUZqVy5MgRLjmjjByONrhPvFZr8soXO98kYWUEHzMjlVOASCg18k5aFqllpXp3V1f/o1s0zDWX7735BtYCvhsqLEgosY27AuOOWFgkYeNPyZj7hSETa2MSdYyDRR6EzKKkCY/PV9SxOrCVhXEKEzJPsht/Rgk4p4PSPBKarmEC7oAHss4XBI8CTllIJLW6urMVx6Pjq3CpX4mmku6RWK/p4MCdykKbRioecZDFVyqTJ2Jk551BJqHjOHcqJkDaHZf8AX2nkER2jknIvuGkNkIeR5j7FRxZkYTWNqIq5Ww/tBERC4GGSKYGPSsU20/QDwHI0kCiIiotOuQEIm2aHUuNxq4usJGSql2l1IjsQG9mOFNwPOC+eFm5sBJMFN3uF7/4xYsfll966aWaCQRTioxNLjRAEgyBlXiIEyaKO41o1Tn8SANuseICHRolUi3O88Eh6q84MlmggjPpwKIWK1UX1YRR6buzRzPyhAXGkdEvBOGO78ghlkflZY9LoklgeyUFoLfMkALS4CPJ5ki9l3KSpLNwDQYNOwKPWsn1jlLalor2dXB3h4qcoKFF5UJUxMVKyqqC9hTA0SK4ap8t9drOELoFmQ/yioK99JFsICAFmBEajFIDHeLBWgDSN2EPHCiRs6Vnrs0klkVqVITsgNlakpBGuMU+PnWcbY7E2hx8KQasi6NgiOf9CAvdSmQHmfl+JTRpr/FxykJPjRgWhA3wKjh9a6YgeJ4NKSAqeSFr5HN8CThP+Aij9LFiHUeCm0MhQlBSVRBxiY2HKGoLcBCNgBGJQ1FCIXtqI6FxcmRggU6cQq8IMxLgMYgUon6lAURCuH1ZGFyxiITT0EvKVztMHaRD9GpLHim6ixcvMm4ZdHfBdJquGZoAJ57UHhCghiDWXhAwos9lwQVIp99hPPO9733vP/yHP+Y+LDDZn3I6D+5DNmr8RkX04KmFuVcxZDZR4JaN/ZXnRa88oiVrGWTJSSPfVxRXmqI+ocw5OycS0ZwQKfHo5i47TuJHyfzHdx6iJUGBPLlbicvn3fBIVkvoCDIXdTHsVkVr7SiR4nPa+DFfOzfqpNmDwAg0DuPXa5RHhET7BGBhOprE5NkEG8EjiA3uXWbtDsnZUmPcmIbssM3m/IBoa8wwzR/lIlqxMTLJkqKhANNJoQp3lnbMwxRISixQ+dpIS2W5TLdQaN+5iY1x2IEGPz/DOuCwsDALC9obzOjo1AxbPsVyhWrWVZOrI8OkJVYMfFPAGjWd7YUy1yibENRj4pOiIiTSUrnJi8sJREDISLqo1AAApHw8jmg728mvGnr6OnUCOfsaEqdu1aorLTTUEGjAJktSHk3PQCSVnc8ysh5lO3RgmiTGmkTqJHS3FHjIwy4okqK4LqXHnRncRMy2uUll1qV9M1jbHb4mgDzJbPVVl2ygDA2bTAfmuwnBBZWahNb4shMvyXWHdSqjfHGNBKS42x8c7nombKcY2JBUKhUenp2SZtOZWDo4fH75b4rUg0rc5NSZbQYu7NIJpMLhlw6DfhPq0hMv5y4vst/30i9/wILp9tTG4UOLzYbuDK3Vt9h+T1f9timhchZGCdyGzcIzPV2cP5ta2trYOpE/Aa+lTfZyC42NLh3q1rS9Hrh0aHFnJVPvFVPpo6XBiWp6OdMszKbubrwF/vLs49XFdL0wnJstzVTmkLDPa6SrG9lG4eTyYa6FLB4/dfjU9LG5Y61yiWF0pXTm2xfahUx3Yab4hS+f+sIXvvBf/ssfNNsfFQedbKpXbuRe/rO/ffWHP/3t3/7tx586k+PGOz40nepOTy2lCoM648fdxlrr1n/+y//8s6v//vEvP95e6fD220eXb19f3/3ac09cWDjcLqdvt6rd2dpWt9GbpgvaunHjLV5I/18c/Q6D1PZ2j9kt8+eZwgyjhV62P3N4gd60XZnbqXdfvfpeO1/c7aenypXtjUar3i+nB92N7cJOuTTb5wrszcEWYxNGkIOUvjdYmarQzx1aeOjw4cO/ePWNmbMnz5584je+O/P2e+/PL53ZWv9PNz6+e/rS6XPnzry2+hJWrB14zJEloQw3kWuHQ7v1OMoFG1MdJKz2Jj1dzA9LN4e1j9obDxerX/veN//D//AvuVKzqlZINLyayJasNC0gQqJmtmQ1KQrrQIWZtyJ1QC4aiNB0YLdWic3koBafAqNkMTChmzM0iPtyj4AiJU6q4aK+vxOvOMbanPiBX2MRYrHMiGN8fxb4jKFU7cZaooiC8D2nCZJRMIjnz5ZfBRPCRJj+Q50ae9bDpLSOkKQchyPMERFvXa1+7asaWok97D45wE58kyeJYYNvSJBBGcdgsN1ONVrDFqfGuL/PGsVIh8LQ2RC5uBAVdIgZRtznGs4BHltibgRwLxXyfHF+0NVWqDudjkoQBzjxGCN+yl+ywOCSxLSEDJjw70EI1uAnEfzJy3w8JsLCsJjIQBTNkU+MmIrKA9Yum7PRjudI2kQYm6jw7XjmLOdOH3304llO7Kb7HHiSHrgIMcdZmoGuMvDaajMm9WrwC9rGKhgjY4hczVzh/Cxr0PU6yXk99ZEjKxvnTt/ceBPudmcGk5lRneDEh9ay0pqu0DSp9W5wUnadhS21VJkM42+IoygG4T6fUj+FlXK+MstpGM5xmX4ii0WovdUhqb9PFkY/noBAwvKj8d4ELfrNTpeXPTqsuzEFTJdK2d0Gf4cH6UI/NdeVYJv6Km16ZnqacJulSd6S6ebyfa5clv1z/I0Tn3zBCAVxSRQ4nNM3Hw+Yj+JUazRc8EVcSkGEVS/4R5ScxhLWYpgvc9asWqwJ6nItYeolWi0lZfM/+vmP+GhV/9gF7oyioHlJXbQ7DeJIQh9MapakdSNGTz1yoSm19LNb2UK22up3t+qz00W+voeltDt8g0AHLckaXxvYbPcG2zulUuXYserG1hbFOpXL1puNYVlZZn5cY1e1183mc5gmf/ligfLkD8VfuX1rh63RbBEZS43UVLF8qrh4bvZclZPTViuRRDk3J4iuhOHglkYdlWL1eHUuezubqWd3ijsYhcZjkjmzudHc6GwSXlyoHj0zu9Xu3NqqLW/ceuSRR46dKh9eTr/yyhWm+lRM1kYZj7HFbDMyrhBJNbdbhRwvNnEb9XaptNwpaOHjl9evsOJzabHMJns6q7XUfrFFTgvzx6cPpzjAv/FmM3WtxYrTfKNT29i5sboK5fwgu3byZHu6+srLL3+8u8Ho4pDm6nxkk26Mr3lw/kmZtNZH2Uw6MogD4n6IioAJ5XgUZC0wSSckJCC1q+kQjr+VyHtdOBpHnYAbakze6eh6Nw3h4k6HbLLQzN3fYFLr0Rs2ggldu/bR6urtY8dOMnoBSO0x2diU0lsQ/s4615pAR7Nrfpj22gIo/Ll2ByDHzY8/dG7t7l0GPEXrI9ANdZBBDSJKWhMVvpFRA6LyWy5ACxICnHD30sIE6id6hK6reTwVtQdhTGlarnVpaXfBClnwFHExjaff8wQ1dAU4HtioCkxgJSE+FgKCC5qBNSIFIGEcQGouPmhqY2ND0mSCQ7a8gqkWXF/kpkzhDgLCqHz5tVv4PUckV9pohKYujLkDQKhQ2jTvADhBS9qseiezM5Q3XjBOREb5AA6+YhI7F4OnAGRPiHavXtfNP1rOioYAliAUHAQIH8TR4SMmETPYKsZi3SSTwiCA8sXKkS0X0rBR8LwMA7TVS33hmS+ev/AIpTngXCbTW6C0rrz7oXOz9JPMpMDm7LxieMVSo3C17mRAES4BahR/zV7TvNfIXC47rB9fKH3zqcO33041dvVpOC4ftgl7NHhDt7yoQ2eVy8yVCsV2p8ErRpvrN8vFVDZfoWTNPjlj3G01dobzS9WpmZ3dVqFc5ZOz3Ux3c22HFc/ZhypFLgZkBw2RImFcJJsnRuqJfkJB8IzACIAv5cSlRsBLzSGhBMfJ6Il5FS0kCOiNS5ZZmOXNuXqnmx82N7Z3puZVEtaaoStaG6qeQeLmIhCkrcDJFGS+qrzQhDtmTUAGAoL1pEpvtqQAQLcB67AoEjuRKwRProCe5ANB45YjwahTQDqFEm3pql0Gw+cP0DbcaTPFlEm/TfSpL3Iq5dRsgRqWX5gtca67Zu8Td3u7osZlt5QTFZOzvCnGbxx1YVMbp0+DFrADr4YoSlfya/2EYYNpzuZpLrBm8rynymhD1Z52HsoYAS2Fv4/YsVccmrY2AhFy003p5DUUU51umo90NNpoCe5HFqYXp+dLnRbhXLrNlJ/9ZSo9m69aElBWeMutwDW92SwLrEgiqfiwlKihAmqBKTbDp4w40488+pP22LljCELJE6ZcTJ+ihiOsk/CyV25BV5ZYqVH/SSYG2vLUSFh3eQvRV5bWduotrUdQGry4rvewkY5YxpROc8JHMwaJqttE7Gd6NOuwuTM5R1E5bsiemuLtM8lFbUa2za1dtGDmwUXNqYdPHL300KFCareMhfYHjDkQQH0qo+tYFDNcHqIKhd6kYZv2oAE0S8azpdKgobFlnmvtep2nL567deWhP/nb9zkpwc1KaJ6yQCfoE+d1lEDMAWGjlgSIHa3WK+jEazCvch0h7hM6IFY1xf4gIXWQr2RinvmznAZweHLRWMJkX63b6edzdGQjOyEjY8KH9J8q4Gte+yc1ZQWp9sd5QKi0b24vTY/CB0N+gqG6iLicEuCIJ6ghNgT2yuNoSeSAk2S2V7CAlgwkGYVwCDhm3IdR8HulHhFLcg9hAmr71KsrbZJyMuyxYHr3Q+dB7XLSAGUsNCIca8LZPVaMa5XE4DQUIoVyxcVMn9Zfpm/SumetLehBEsIJAUw8r1RWah4lFnLCFGtqkT8bXwuiEWWQ5nyUTeNC72QInnHRsUROUPXQIcHXc8I5a6SFNWHqvCLtU040nhbULcNUdYnqYllyHgUxdgYY8yBILsZAiQcYuX7k20cFU2l90ZgFaMtdQVJxpZMquyqadcc0/MopnSI+3bVktrx7FhQjTBX9XtYmbOSFWHsmkWaqTMbawzbbej/+8Ic83vjoxrFjx9iihLjjRwoV+j6OlQ5M5fknnmeI2elmOF6x3tMFT9dSLRYvdrtdTuDOTM+srq2y0rowv9BPbWr611N+dUjq7t10+qiO25x7Enar61fp7LFMBOAOGCistz5mcp5f2WGudfrkEZZu126vQZPTwc899zgXNHG+j5VcSgqC+Jfffxf9LCz/AxZQ6Db+8i//cmHxGHuMg6kiov708k/50BMbmOTO11Yw283N+g9+8CJHnh8/eoGbQ9+/8qZrEhwovPTSz88enfnWt75lV1Rr7sHy+rDXRh6qBfKwds+RBD2m04QRhm6Xx0ZGLwiTF7RR0phSB8z5RvbFS1/hlue3Lr+NADvvvcf5L7LDWbPnvvUtzj/eXltHkxxr4iz23NE5NE5C9wlYJYsgLqSKRGWvXwuobJmFtlttlUVmnUeugoWj1eCoSgaalmzM86gxkMkAHQc6ArUOCBLho3mi1GeaM7OxWjOWUHZLvBtVoOZJ9vWNvmiSxNMm0falAJqNeaQi2g3HARgzHWsenZpnx3kFjoFRgAfIgwfE3ZSWbD4ETDgXLAGIJDS0qNxdBseZSJ5M+InCE3T8keVYDJ6lToyW2bGA1mJ7LD66ct+VFjg6Ao8Bh3DAcaDHEmawK3wCsKvXqVDejjsC/md3gfVeUkS5Q/MIgNuL4xCTkaByfRDOfeHKqRtjotidID4Vh8kX9slolhaDVz0Aer9sCSMdErYqoIrgcOcb6PgjmqTKQ7Na0anZWlNX0tOcQva1O7sffPAR8wC6Js8N4kg0OhLTvi9Ak5ZHdMLbIbSfPOL06r4KS2tVLFTStkAWOGfCgHc4OGQDVpdB9TShLZfW5QTBHx0TfwIeHh0hPIZASLhvQKtm8QqO1i7tqkGAyoJ6ZOUTII0wK9SElTfdNuAuEgbIhJATvEIsgahI/Tce7UhaUzEB4ZgzAcQCp6uRTRL0zKIvEtL437hxc+XwQ+A6DggEeHRM8/GsyGI10p9Cgcss6H3Swxl6Dc4406fcqm3qZPHsPL15uVRVqeV1MQtZw0K8+HiEPo9ubJSaBLOPf0jUtF4PokOkL6N7gksmrW85QFZC7HEup9s5XKCA/tXvz/BxqtWQkWQ6cKD/i1/8Yn5Ob1/pDo2lpalOCo7+EgCvE8F6Y32L9ogZPaKyiESOoK+T1I15yEIQOhQopwK3slpeZ9INkA4aTOZPxDKwoTNdf/UGXXNrq6kKMqNDFTtbO5xPvMOHNtttkFlmBYFxiOZe5sAhOUF0YxlURAB6ScUZ9xSfpw9lHJp34yJMSeEQCVVIMFb5p6ewAUYgjTbL6zJvEFCL8p5KoROGRhyrZHjGAj16w5EDlSbLyNaSoHkoKy17VbYgxSMTT2eNPp966qm/+qu/giM4ljwyS1e+IUe5BuKPFoiALnz08P+XH3Kj5S0pLdeX/ZB3BAljA8LJXOwrY8iax3re8d2FJK5SHh3upoLPd0OIIhCnMAHiu1MABrKEQXMKzLkcTlocklM9rVx0EYqXBXBw/JE1LGINN1peJIrHUCheuxkDYxgwAi7W6oeDyTvDB/Kd7D1QQcDBIrhWS9cBORCtK615+xKx1IomeXL4tC9yAGJvweRI6AwsEBUKauQRn5rCeJ5NL6oDydGhK1PNo64dG5OMWFLhXB4ePQBe0HBIYpKnaI0fe+yxCxdeee8Xq+CQVL6qYJZrSEjuMhCg/m7XNgnQ3FHjZuc1BwQNnyLDbmmaCGAaUPDWu9uu0Y7RDFKvC3bdE7kgiUuFbRPY61x+4C4/AVcycBc+BIhKhidIeaqAo65EDdKAe9vQai6n6U+oX9BBea5kpxOSh0fSIpTLMBEbWEMn4BPwR7Kc5LUXWRCj6KkDC1S3urbFY72hlwOcO2ohrCtiZEYqYl/Wr3FPBB8BXpnlndR2T1t9HA4jp05Zj+TRZ+jGBpFEKhiJlTV55GOEICcdCY27jIHKaloCZrddWOWlb9pq4rVr0RvMqu/sliBgtHxvwxgt06dSN670jhyZ//IjF2QJQkzRiev9GGsuJCRHCbtdTMhao8xubRsIaEThSxIL8Ig87a6qauTYlWd1Vh821C61SMcOBC18qs8QnIUJcgpZHO9sKPn4+JnY3d0GPm0DOPTn0uTn55DQ7R/igapgCTfxSIxSkcxUxAiE6kZycsWoGFGRsNnkhh5GjClsgHevibWiU8Zx4OjHOCrHrkyBTKvWzCXhlDT6hIu3h6AxYmGF4a2bW6urazy6PB6IiQhIOHaJcBw0znG8iMhO7uvGqMbYKrY4bUQHLjEkxhr7BR8DRlf+ngcb/LLDX4EbW4BGreN6kcYDRGWIXGNZdLNIZMXX8m2vyaUNyXlMUgt5AQHKMhirDOO59DPHZhCjgonYodUgDETcJgLZvYFIEiWKae1F2gMZu0ieHSNzlhGslhxpeBcBzXZVsoLwG50vY6PME+3LFmHcgeNdPHuuuiTIOlHl0RPTNOggbirnytBUE9FQmhSGrbQZ37IrxTYfuzBZNTrDQk6vphRy+hZwucS8M5vnRAxq1kkxXQ5ISpMJIEGd9AFgZ3URibDVTPKspsqQgUXOchivlJHW5YxurUIk2/8zazFS6oMjRdHoYcqQjscBtJBKHS+FE3R5XAbnR49vgVEL63DfwZOsLj9pbBEto3uroKSxAkfEqU1KL9IdVgccP+O7XMYXZG/pjJJ5KlzZpTVEUrK1szFNKceYMfrnnUm6nLROPWdyupaBbht/kOagMIWvGb4O4ojayLqjBWi4SAC3kCgfYq9rPOm6IqkFgTMOCAe7lDU5Atro5/4quof0sDnsHVpYHG5nlo4cOtU/tLvDdLG/u7u5unbLPkFs9CMDcn06GfnZgeRc3qn+k9/4R9/LXqAxPdafKswXGoUejfi7mTbd/J9s3v3+7JM3hqw495fmiu2tm83e7SnEwFxzfMGeKyw+eOrYeZZ9nzh/Btn+4k/eGXAGu12DwsrscrXAVtpsMVd89cXLWOCrg7vMabvN7bu33i/PT7X7O9/99hdfe+217o0Sxzr6u1fPHi4M68X26gf/0//9//b7v//7S9NLv/zJy9du/DtebcudTTPUyz7a/fv/zUPPzeQvX77c2bzJqgqnjdANJzNee/3q9Q+3mWOfv3iBafzGR7XdVmsnV7hbzL64u1n/8L2Ti6cZIFYPzVNona6uFqk3m0h1fbPN5LzOcYVy+d3Lbz5dLpw+efTUiSO1+gbNcb2xS8nO2AbDEycv/oPv/t6hi88C/5/+3/8D88O17bt3VrcPrTzMMvW//B//n88///yZ0xcYXF48f/FHt350+9a12u4mdZjKwKl2FocwRZasGMBiSjl24/lnbUVUvFZc3HTGO8+72f71Xm093Xjr+oeZciFVzKXZllbbrP+xQ+WRYWAoVrRek9y6PMrDk22gHfnTvr3Xe9LK9M3ArOOR9XrHLB5y4slPqNf+yoemF4o05pFVe1i+JyMAKdQeIiAVwh7wmy4tg3AZRTpTf/ZU0PRHCEZDhyhadT+IMiIxHgrJZTexGwEnIVaLDdPzGN+PrBzFK/bKZpQusFdZWY2L+oIofm/GY4b3/x3LLCp1a1DbgFnJ7XYy9eaw3k1xXke9FM5wAmkA3ucGCAH6Yhdf6F5k1mP6wNQxQ5Qe+dAraLoMr99pbHNsxGMxbgIe9lSflw/NYAYEKHd8tBEt5cRsJD5/k5alaFP7pGiea0V76SUSolD2Y0wNGK1HCxFnpKyjxBR0rz/JuM0i9fCZU6ePH0pzFIv3VyBtE0vu74EK548ZNtDiOgX3lQWjxtEKrszlPlDaohbnPXd2yWCpWmlzjEWtfe/o8uxXHrvQ31p9526d8QAvkSIQbQhMgsiMG+mSGAfXag3g29s15r2LS/PcudHhUCm9obTV63DEh6v2NShAruwgl+HL1/DFlJPVMynnpwu7lkgbAvel02XpnHW6waBUKPS7DSbo9Z1BsT2Y5jgru71mXc189ujhxWJ5Gpnr717DDmgs+fpum7kmU0SbgrIWJ4PxKbFPLDFN5hjoJVRPlwaIemKrPV5ZXaF2BAX7dvnJAgOlKCOUSE+LC13uW2a3jAXBwbDRz3XSRa46YYqqC0+soMWNxj2Tbpq58tYL1FpG3y9NvJ0eHj99cuZ4l5JqZ3Ift1u73GfTTTWzlcZGfYqj6bwMni5sNupz01PlbKVw9CS5WO2kZ8sVjk0TyycUdgZ8SKHAOI/hA4edu/0MXRrFvb7FvcyLSDjdrHX73ZnS1HJ5fjk/ne6k831r02Q/yn6UL1mUcgQY1XFHDCOpuepUXiYmIK8Vg0wBkYJvC+oYYXn+xkbj9mqdmdjs0kqtO51qcCVG9+ShBeZ4X330KVbK3vzFW3wXYZudFFokptXtfnamsLm18/GtW8dOn+ZzQeyHcFyMz8qnszPF8lyhM4sN94cljPy9wfqPP3wlU+MeqsMnvvfEyXR6/ZWPuZ6i0N6udWscLuTeVs4aceKXF5y5krK5tsayUW5uysraBm+j4Sqyo38JT2iiIbBHL3gix51KLUo1HqGnCTpAoqMGMjTO3qshYEwHvNvRRQooB9msumkfidHI3OICtRG146i/ROH8ET1w3JX8bqyts9bGUWhW6jnixToCR9VVyCwp5NWlkgTicKQo8bNp8QLIHZXlmdlHnnjy8iu/YPbg0kKcgIdJG0o/DktaLIA/3WRqXbFA5kIgBnyq33tQCVHJ0rBxCNnhTRtmMNGs2MsFKROOTCWzk4gZC2robhnHbglTIjhXiPsQcS42YFEsLScTClTn9GlCSctjJIwtUYWwM+MQIgmDPCCThFwA4Zi7ywAmqWBKLDfNYjWpTHSPh0tIFI4kND+gEGaVk0khsf5OJ80NFgAjZ/ogvhMMmBOPAR4C4m6jtdt3VrlJ1W0jxB4UsKYljqSUQsnGsMnfuCTJrZa2NPdVTw9r5nUg8xqRFnXVhXIaUd3Ww5ceO376bJPzlz6NFbZe4qAx4QR0hhYSxYKf5pw47ymBRaMFKf6gan2xj71V+tyBKx0yNqf104dZhsNzy51vPXvkjWvNW7fW68zCGVTpdn3uzPX7clXv4ECvqwLRJ3g66xt3eGSOwyYlX0nt6kqHIQuF3JU/N7uAHQ0xHN49Kk3v1uuce6lWp3NlWQUfgaCvYO0DSfjnCp9Ukcnt5YW/b6GTkKi9CT2Vwxm1ugpkUTab2NmsdfiSNPZFd4lhjZ/A1F0jlkSZt79ktVMSYyrBzVEaJhuGSfFFNWtSJFt60AgZw4+NV8Lb4gYlKXx7cSaKVEsEfbHo5Ka3u5u7fepTntPCUoXNOzzvnn1JBbY5tnGpv9vXNqYOnc2nyvlCJTMolvOcP/arq+jAKUDjw6SIN5YoZu5m5nVtVWlxpBkn45S/HaoHZLZCBA7NiCWXiJMZNAcBTgfrPafdXp/bhze6vNyWbXPGGgpmqIxzlMQkxKRU9c0x9Nu6s8si+Txv/zCWGPbvbq/PH14q55hwy4mPemA5RLNbsOGm0QiP1trovQpNKTnYbcvl3uBgVwygbPFItQtSWoCL3fT0FDKUrREiADUkJLD68a1mt8mwiXCP+keVot4N+5vbjEKpCYxihKcKayYHRdLGVP/ufsktpVNgXSydKRWG1bJ6UtjTZN9d3+B0Ox2vPhgyTK1Mp566cHyBUxisyLNCg0a98TSpPRdJuR2C9RMgawzTsWjCsgc0y2Xh9LXWO2T79SNLU7/361//1//633V6auR7NgjUAOB+zrkgioao0ShVaR5El45j1yKM2Fhp6JGAI+ALeL/CQRKyY9fzaO8cWyIjLt6I+mcOjS1Af2Zq9yGwNwNASIPvAYz7PiTi/nVfRTgRpwBCeBR11/391T7i7yzcD8mJnmDtURPAERXLHbFJChOxXrRGOdJGEmEi7HSo/wTcoTPOldCqggmEhpjpCLWOTw5JKqh7u8zrXfaaCaNdQSxx1DSbcqwXUHd3j1LwbJJ0X6ncwD0u2L1jhiSMk+Ge9u+0mk2z2++SC9Mp76HvREZo8XMgqwD/LWH0I5yosSBHdBryrXVW5VYT5EUZNQoHlaDTBB+xlcoGfGIhB0nb+CMjfEhAC+t49CKqq0QI31L5JoRrGyDOF6BdapvUAYtmBVG8euyxNTVoulYJGM7Io4HAMaVhcbbcKXNShq3Rjz/+eFBcAIm1Y8TUQPV+jpPI3Lf44r95kanOSq3Iuae50yssKLePzHHw9oXHLzL9u5W+yhLteqS9lAZY5SKTnKmquq5nn36Wmxmnc7p+hMsctbDb0JzKKfft8x1b7TXOQT/7zLMIdnjpIT5RyBkrvnT/3DNPwr3aTLOYy83OnAj45je/SUY++PgjJq7f+Mo3uAJ1c3uDPM4e09vNreEWOa0eqXLMaimz8Prrr2/evcuRz1JJ5c66s46HtCrEnj/0KKe3PrjyAU0qEvGxxKvVa+SCbw2z1M7gA9WxOY0Yrew0y+V0XEwIW90ex9nIjqjV54kFE3lKPV2tSIBFcCoaR9J+93d/9+WXX/5Pf/pn0rPNK8gap56fef7XmCiCiczwRVfUPS8ECtFGHXoiFT2aF6vskD+zDKJgweSzmO4x2cgfOsXtoivNNOWS2tLb9JrKxw4a5I4kbslOzYnjx27/BtZfVUAIS2v10ZpiJ0XrRXIM2x/hAoQwLBxCLNzxiRLfyDxHsTH3SDbHtFT7yxNTUDrPiBM1xajWWU7FxsUQnqnRNeCPe30TeAQOwjvI0xpwJHnAUYC/cRdiAzgJSYYdIUCSqgtpP0UAgq4f0hpxnjS+wJ65C5RayddPgFtURN7xycwEO8fZk0UnqwYruEANUwco6hHHFhAvUQcmEoXUn2eAvFgt0PQPB1OnHgJJZkTuaTiT8XvCSB8rCdswk4+eXYe+EEAyE0N5hT6YxWKeA1PUFxfJVcEjCED2mijJcU4HbYJJJ06+GPzRztCS0HrQHFGazLRpDLjOlUvqr+68U2d7IXYqdi1kRc+0OUYQeoJwIJT1KYg4IxcJ4nApFvSODg6+VPF9xXOEfbXqUffwnSMIIeBFdo8kHiV822iv7XK3xFa9GLWcJqHGP2hpins2WXBstzl+i3jpSpH2dvrQEhrLlzX+afWiO5HdPpwyAkxkU2m9CGJDB7KvhMCJCPiQldbM6Xtj5sKKEk+GLqiXTFCCIY489lzpXw5PT6mP6A3ouaBMWbPCwdCOVWk6gnwqD04hWwTCC1ckpk+hEDlkgAy77RqxzrpQ0NlAmmliUQ7AaqWgvrJahebyLAuYy7mOpqmasCeEDPkiuZyVWq4YLZaht5CdkAoIdKiH+ExuaXnoyrG3o4eLfISTcQAnjx4/d5Gju08//TQUfvKqYjN53e/MTgl06JrZps1U1XIgHhSgSY7oiIFQy3gkp6DdvHwbhCcWHmFMwlCBE0zZxjZ14cYbV3j7ilOZGhXofpsUBAmU+2U4UuWAHOTIZTJTB6El4Ul8wu5AIEfM5pOYhIl1SECj+CgXH7GTBDh3bXBeG9MljMBYb5lvUe7yypkWqYHgKE/KEQjHyUlLsZar3NRSzRSpB3kKJsnFySZbG5lTtkC5bx85wnF1VlRUvJbIynkk54T8n9sjvOLW6bPQNIVpSkwecdgJOv7UBL2lRQMEIBWMwQP4AFG+lZ3KFyfW3FDjzQVTLWYzOlGoGudiWGHJqEC2hJHnsTwQAIckkMLHOZCA0/dH1AVlnASwrUES6tlqBOZNzQLi1EmIIvCdy4P7Ea8DEjhxxyGDIWtURoQHbrk2/YdCIBAKOg4gGGlHTJI4I+hkCP2RdTUBsbapBiChcqgx2YIsVYmDJnS4QUikBCf6bmdcKEAggvMsuE+kA52xZ9bDAQ4Lwmidbvepp2bu3v1LHsFxVRP2VPgoRFVRX1Pg5ds0Mx9aIWYZ4ABtdnQ1GaloyqaqMwhAEhW9paEo1dHPHqNqDwyTWGdEYEI8ZxpiXZKA4wH3HTMJmUQz4uQOzF6/Qy9zfftj2hZaY/UsabXAySSILQmjd5sVE8TbNxxkc2nBCQFPGB5DQERjqw5AAo4/wQ769Hq0itK7CxObHI9R8riMKCAGSVCgZ2GC+eTFU6TiNTAgrHWbPeMxpVIjzLIsydl9DTJIrNipFmpZU9RiWPTrDbs/EMtIG002Wx111tZo6a4KbJjLXWj5bfBAtyHZYqMiU3wnE5+ejX7TWeBjIVP5aAE6Ypb4AcF7cxIiM0vgKqm05qr2mk1kxowMEV5vj5hDA+SdoNsA6+EkzZPM2ijC/gktzFgd63heoYyFgwmaFJdYYkAU4OPoJmuidOz5s3owgnuSCo9kh5pVrf7/iPvvZ8uS5M4Tu1o9LVLLytJZqqu7q6q70QLdKACNwQzWBoNd2x0jzEiOGY3kGs3GbP4S8gf+sEaucQdmnDXjchY7MwAGM0CjtRalVWZVavXy6Xvf1YKfr3ucuOfe915WVnf1MvJl3DgeHh4eHh4e8sSpUljCxMIn8i+VZ9FtHqm9c+eOsTWezTZ5VDHNXBN2zgGFQALxR0ej3sF0OYckmnXBiUZBjNrJ/Yknnnjppee/9ctL1o7G1MB/sIOOlCb5ezDyVKyJYix1qwSRehgHWlqQPFKjmDI1Lq7n0XsbcpDDF4ufhjtgAdq3Q0wDdR7K6vbBvVqqX3Ge/JyX9qUf5CiG6CeFdkXZnwDjb0BRo9igUceE2Wvj0ZPHQEweyUbI/kA6VcQHmMaMcAHD+bVUO7PdNlQRNBI6b6T3BuFp2cagkC7VSDnm4nZTpbEM8Bh1sGdFIu0yhbukVXKDUP3WkZOd2FFTY3OFD19zSoXsmP0zHh1hXFhaq5TYT+Qky8LSYqmq13ipTnBIQu8NdQA8my8J+8qBbbpQoCAE5aJhKrNvUIKz9aikkAkw/hq+UkHK5RCjlLsy1kKtKt7263wM4AN358fz4qOzllCSUTo5+Uk4AiwChICjR2kveHwDGUvBdrLi+KQs23ScOmLbDxtEmaCvck0QNKqxdnhKh3kMJbIf22ulJNzaxraT/Ey+hOXj7LP6J1vu57VpUiFufN0nnTjESb4OT2DGsslMlT3pQBYnaKBiXANFU3rF0IdGkc+fe+TiydOP7370b+p7944/daRyofLOjTUmbN0B3w8ss2nvDWeSMMlFJ9/XxRf/9Zd+//73L9f/9bcXMplj27q/n49R3JufX19Zba2sPPp/+GefKxWvFxvD+/dnnqlw+9RCdkEdGDuI1Rr3VR2vVR49fXKpVuZyM7h95aUXkUOppuF1pczZry5X3mHTfuf5L5xbPv07L71Evv383pdeeXZjW6+hzeSrM3PV3rGN2uITza2bRzILJxaWMeXPPP8E6wtvvfP6ydPH/vjsq0zPnnv1UUbAP7z1LVJd/cUvobP8+MzvPfHM5x57msXry3evI9rdMstw3Zsb3bUffPcPnn314pNPZd4p3Lu7sVWu0ZVea2w1127ly+qr2LaEwkyuxhvo+XybkddgsFloFvrd3vv3P/rdl77yk5/+5MjRU0iPz0ANev3ioDg3u1TMz/HHvanshS7MnfrG757q9Bag/MHl91lE4HNPjz36FNuiZ86cevHlV/KV6i/f+A51sby8ymoCZ8bQFWmDvdNQyRXabPHTirt2npSGUSrxJWTiuUGBq9Lvdtq9ev314Ycswc9u67xbjWFuZoBumX6iyWH12S1JVOloVBPIWAOVeWLr3HC57zrOFiwIfnKQtDhsrdhV523vyrlCCkfWw474a9Ug0mS7BYn5xFi05DTmjtYSmkKwgThhx5jyE5ZkgcjETsCIB3NQlUEj7CtA1mcBlD4HN+6D1HYsd5Lzl8JJcP1XBM0meEF4jCWCc8MRHcwI4XiuGT49Eiu8Hx9Mt2kyPonMae3jsOe939/Xk5p5EDWXTGTPa8H32Juj4l6nu8MXMTg6YX0lJxSMZ6txs/xWk8oPCk4k9lCRK+c58GncOiappi1Jn4W+xqDT5Avhkq/pyv7S/BoQcjwwFTWhKO7HszuO4IfbDLXklsaeTEocf04vFmQyQTrxOGx5eZ+CrgeiyB8dEg8cqtIHFaQbFB8D/ezTj50/c7TY5/Znul/qhDG8/qhvTltgZkk1KUAvjSbVHN2gshj5YfcgCHK729verR9bXu6NhnzUG+DZ5Zlnz5/46Na9G637deuwWXtQQYw1PJhiVkZjZLeXm7FJwhyEVzjZPhSTzNuNATpAhNbrslXNarXOlPG6lFY66BpxpmPgfypOgjJnYvt4kpxe46TazIi50LBf77Q2G7mlWoVxjX20h88yQqJ6/GTm6HEuudrb3FkozLLj2N2tb96t73x4d71SWTi2ynLb0sIMmF0+SjTIdBkvZbO8J4R+MmzCD1oUrIFqkNqU741ZCiOFwg9j0FAIYXiJWB+mkjAZHKwzrCwLDVjyYZ6zxQW+t8FahGNSt0jewz17V0wKBB1T2W5hdpuz6rutXEPXItOrcrUjZKiETIEZ8pCDxnt77VZ3sLm5DT/ZeW3xYgX0YjdFslOZHDcozy2a5mg0iLLpmF2lUJtfKg/7S6WF2sKCeBgWyh3m6wVdkj3yG9vHBfMWgZ6rh/Li92TxkBhve+VaWjDhUnMgXCuPzyhIwza0G/vLP8KDxft3e7trvbvX766f3OLE7tJgdfap1YXSyksXv9htVX5e//l2W/qpo12FUZM++trm8oVz6CEnzlbp9ktHc1nOEmrtZpiptNqtRrWcP3705pVr29v3Ljcbq+0rLx1/hr3hR48tnHrq6JlnnmYbe/cHv/rwnXe45oPpe6dV/+DSh88efZkjSAgdUcGq+wSQH0/ScXMxlgA5ugQIH+hIEeHpsLfoCIEOQpFDMKwIeO9jtz+3WntsditGebGKxwcn50+cOMaRbcYPiNf3CRjeN3Z2mf4xbEBW5uXZmYDc/Y11rucu7hRJUp0FXd8YBw1jQF58jwJ90LyX1SK9a8wBL63X1tsNRqmnHzl3+/49+hH1iDrDhYhVIvGb+OG8f+ivY3F/44BLbqxrD0cwyjskZLGDK+s5cNEfcTlrwjNYiJRifCxRE3skKvkAwTkpJBkfRTDRHASL46pf5AaQsFsJGTQ+Oqa5DEE53TvPGJ23XtS4NbZUH2BjKU/oeRF2fHKBmusDQAK+lgNjGl3bOTsYoU49CaddQMDcMfolZGl1MjVsgLkFG5ePRA/lvLCgRvamkrlYWK/qc/g5fHHhkHEUuU/Wg7W3sDlt6kY5p1BCbtZsJnJGSmSNGHSxs9UIvwiBTCg0c5+LL7xw9MxpDmENcwXmX8yDSc/3dikRd/fQofJCCLXGGiP9KxuxvrYIDtXjf7aeHUY1UQ6aXFEKy7I2GjxydOmPPp9bezvzk1sMq6gObCwTWR/lqielyml6Jj3ZF956qNc3d3Zol2yDjSq6IJZzs8M+b4W2dtht4juIGpiVuP0q097eu3Nv48TJI1T6gCulmSBobGwaZHymJTLmMAU9EOjxHpVGiFUM0KsVIZfzM3RXjXqTi1+ztVyvwz0c6DDyo8ZYaqUz4V1bio6G2Ut1GHwfoCob9ZsxCwvYeJsaIA4N159NxZNqd2Q/GSZ+tILB6ETmkii1MgmfjsWpMyAgOTnbM21KHS7jgxpHjAf5YU++FkP0vo61LHTDZuhqZUie6505qktzgSb32oLzs/fvcqnFsm/gWTtlrAFiqZit6puQI1njOWH2MKBQMc45FA2Ee8BZyuyo1ekScpH3vo/Ccj6evCiHvSfUzeTb2ewO1yhluW1aM37emRZNOiCS21Q+XLljFCgU4zJkhQFpNLOPP/nZ0/O6rqfSa5Bq0N3DxyExSi85SboqGzwwG1XcSC9YIAlw4Jq3wqocCbYlZuD2KQoSq+XaoDSTp2zomzhBQhJwpa/1aV4JwTiyjkFuQ0D5HSbIjBNphwixN8rv7HV2dpu8IkCJeLOKob9al7U71VqoKHE07VxtwlhrOvLXfDZqME89cRt+MT9aqNXKGmPRQkd3760zOuKrx/yVs5mZWvbzFy+s1sS3sqPjxI6ibLRajaXRvmBATcKmyaE8hm+qTsPgF3mIgG1F2JsxfFCMMwGlSr796hc/f+Pe+ttv3x7VJAtVrBwksUU+ojSAtR2bvRLJuJQKFRrYgQnHOtz3NkFRKcEUFqREZxo8hRUeHS1pYWIUveYcDGsT/cKwVD7A2quRJU5N5BM6tS6c2v9DuDRaOvwQSQPKgakAOuuHxVpiNTYQ8HkU5j6hHpjcM45RXs4xkcDXAT8hyQNzSciOpUdBqJLxcyLbNCSdWVJnruKKSWimsQIctsEHwXEIW3ell8IitiyMOVaDGM/XqmWWEZmOcvpJNj3lIDLxbFFO2duaC9zD8O9CSxE4OAgaDiHgK03yA7Y/YCiZisE8jyrJQ+hegnOYFKc5UZ5I8iBhQmp/qafSC8couJ+O9faGwgqYsANaqJWkdsSwlSvQ8aLvOx3jFiZmB8mobiRUd/txLqbl1kVOAXBymYkHZ4fFXTbLDBwfNYDtZludU8LyAXTRGpSE6yxO0evoRULdMlmpaRmUACeM3vn7v+cQE4F33333woX+fG2+MNohFvrUKf2SXnq1dRMqFzY8CuPMbRlhKdNGBqyDcDbKRuVB25mjstNLLvBArHR4eAw7XmDtvN/f3mxy2/Kl966yDTu3eoQBHSrNTdBfOPMF8r3WXOa6zA+23+HE0HB2lljmYwwd5ufnkGGrtY1IOUBNmMkbXK3V12CyyfJQNjtT0R4p7w8QhmfI0hcThnMokwSa+HD7/gdX4K2SzXNGIDuzAiNsn3Iqef6R8wx71tY2EDWX0OE3mnV4Xj5ynFp4/IknEBfJGausb1/54IMPSA5xHLmQj1c3WePIiGZDRK7ErRu4KoDNrXXY5uQbSa6tX4MU+9NMSvlaCD4f2DRSSo7uQJEkQPBF/YHOMSMKFHyx0iGRlNFxUgQDfUThydUcbGodoyJZxosAEaMIJlw58YjDozcocgfZY6d8osChOQF3cYHpQMMMLE2lmno09qZgQffSzDiGIQcBeo5TKSOrSUA8wBJotFlPTjgG0smTJGqK43Aa4yHCLrSIaBmp0t2x1Y/e8p05Q5seE4ADPo7kaUsIPBKMAUcWppn0lNgFcTQCKDwtjgAupv3tBaZy4ZGS4iL8MC4cni4FTMZmGBhGMLEQiUpCGZj7iM7r2qTIivF4EYSEnGR87rnnHMEJwhjTZQ+7VXHhO8TDTlNh6zM9CjNIgLkFLZ0Tslg2hvdQgwjW79w53ve4vyvDIDdVCtYmsFEcmQFOE8SnglCMWqVK48XKQYdU2EMW8MmIrHEsxWAbWT8lisuiwaHhpcsCHCGASeDXcFOkHkCBXOAKa07ZcTCvE5wmak8FD1hguKlvbnJQiD0/zL5uQeb+Izv2q53Iy5cHhQxymF2aA4GLnrCqtbI245nyIUZEYAZKtldzPsQSGvJ0AZGE1b55xgEhfn1MZck1s0XCjulMBpygjtM0HceFacaAyxQkcuZ8RFE1Ji7lEgVO7TBXBNKxm3wJGENeI6IPJjgsPxLG0bvR44ADaaKoV+hbT6PpPb2S75tN1Yt4SCw8RHgkLY4+mquuoE9Y+Vq5CIMD/6ChMiQs2IebOPyANLgQmxO7zTtbnHr+8ktfPHf63Je+9CXofPenPwFemtGRz+3tOuEnHzlLWpQcf8jyanKeCh0mdncuQ/1SHGKRDDU+ur7HtxAyjz5Bn3h2fpURQreZodK797coDhXNoUIeOYjEehm5OLcw+olcSHVQmhhFwMPyD2kaMAANEDCViJ3SEVZJdSNwnsEbWkoYYTIWcpGyRFUrV9hoR6UpEVNa4JSLhCSHDgHKyEcLfcyDhvP9SYChRtSctYrlZQcIEcJ8FAQDdendd1zCqvpknElaLyjIMTxVdMqXYE3F/K/1mFh7ykIRKONhMt/PkEsmDaeYSAAiHiVpWCNyWbmcgTicAGL3MAlhBAg47oDjIimaCRh8SdXlL2xbeQWBMHRI5QRRDXCcATSc6gaBqCnfy6uxcT7PhA4Lj1WsmGLHLEglm5R4+8urqMPdFL4xgCdHlDvKyNo3DBxOZl8MHJnaoDz+9zAqBE5Es6xl3Lz5soSMQ3zI8MSZE0x8XJhIBrhjMX0iwGPkRskNosR+vtUgESEGYnktOdJVP0iF8tbFK6+8/LO/fCOBa+tBtMzRrpkBWRA9UZHbrTYdN1fmWD7qcQhQv1gw8nINoUWL+WKR2QS359OzOxvQIVtnH7EBBIIPEN8zjY8exeNhbgqBRzhxZIUlMzlmUpTCdEy9A5Apgk4nUiNgTIbhhHM7hZOmMEXQMR2Y9knCo+nxOHXMdAzKaKMdPmlNnhxm0BrCCiiBaCBeTytWLbEjA6RXatlZOLYoECn9Jv78XNXUSWerd25ugkbPpm7UemenbJUAdS2gOaO+UM6jNW3ly+o4GbEADaTJxgM6kRufKBcCDJqE0Vs9+tEQQtDk/jUz8krF4TkzLMCLZW08Q9acWy1RpnMErmNNmDLbXAcIBbh1/JhKi85kamtUOiWYcqQt6+ucHKArYls2NzYg0jGuFk4sI4H2QOeF3UGcmSmmgAAFMR8CeW1QWd8Emgo45fZp1FT8r/FILipR4gjTpSIQH0sQi6nk+AU9LAvoCIQlCloZLzTALXsL0Tm3kefUI8FQEK99asBTKcJdsrYDTVa6kV5ptkS3/sorr1y//j9vm7Q1WDQ64nafYJyg15cpb+RLgST/CaAX+sCoCbx9D8r/EAYiriPADzWLyUKYHgXzXoqI+ZsEuEwp9LiiktqR4M4CAVREcWoBoeBiOAZoHhFuQBuw+hq/lzNF2WiwK5EkAWHa1DiK/JgFjcukpj47NPiABR1IhGwCbPonNDNRSwTvlCN91xC2+oD4OTI/EZY2ClA10Ytzj00kJkjCg01jrNnTGRp8wnMNQ4GdgjMAUwrY1IJxI+RJY5gKWKaqdtuCkwRw4ItnpKLv69rpE8wJp2y40xEw36ixTazSzCzX04CmwxHeN6uQkhepvdRJaxJNN9ngKwuLUMgegoz9SSdTDnGwmzh2I8kiXb0USrnYUuNwoIE19Gm0gRM3wVZHvt/I6pZn6JlB2HljBgkkdMyq1XAYMdBxbPHIBpBODav4VgBRMOc86oRaLL9HJLH+5PVlqca1CS0YZwaBredeIfl5vmyPoMva5uX9K05bWyOAXdISJFfN+RJnokYy0jeiE6bACew5YuTWVCJGGbFwDlq1hhgZzdxYf+ff/vW/vVm4Vpsr37lzj8lJfuk0H7EtVWqNJh//USdHnTvlKZ9rIzlZfKxf3n776is9datH7FxwYa9f7udLmQJr08P15p3v/OzCl2tX6zs//86Pvva1rw0yfL++UobnfGZm0Hv0xMrG2s252vlhocfVVZv3uIWS3nHI94QLGZ20miuUdMtFplJmcmSbJp2ChmLcw0UhuPuMfehucVbi4q7qwkyG0/z5/GqttZrLnX/iCWxho9ugRkr5eSRzjOsi85nVypHh8vBrmVutbuv+zhqLDk8t3uSKj3uVFuO80aI2uHPV2Vs33lvOnDyxOL+23WYNosU1lJxz3mqodmwPcybXQQ8HFQ1lCrYfzietm/n29y//5J/+03+6trZON7bD1Vzt1vq9nRs7O6cWzzTe+6B4/CQdW5OXq5ayzWbn6tUb2ASuIqWHYy5R37rHF3ghyOTwm3/wB9zxceXqLZWXfWTGAWgkkuO8G5+tZEucYWif6y9pDFxipWupaD0oFV9W6nG8fjS6tL1xvL31eP54vzfq0oY4G6fDiiiDFu4xNqYt0lKE6RoFAdRL9jloWFQhab1022wOWkFabBJpdW0pTnDUdazzUlRXV2GiS0JjJhDoKNMxglEgBkTtzDNQIm2wXAlN40jaSEI48U5ECY00QNdVM6VmQ2gb3qIDz94jTOuz5yNjibMGZ2JJBCDohHO2ATkD4sTLZlgqJCbWCu7kqBWLGUvGUMiJfLAFgvvWUWKLjAlLExjycBocYh/ix8oVahFLLiEy+seMKPtejmt/G1vtfovjOBan84li3mrHfBeEidd6aAhAhD+ro8BayCCk4sdTSWGsnPLN/qBdI26Ya9W7jW2AIuu3QlvtAvmtOq8yuogR53yt4uDPmYz5JryLY9eHGPWQARcPgkfvTYRSGdLaQSp+ZcO9m0LWTzx67uyJYwiBLpc/lkRpmeDTBHUgNbVVnMqdcqiPhihHS5EgTQZ77kuHzFyoQd0BnevX7FbQQXd3pph55uyxzWvz9au7omMVqLcnvM1gEXqdcmmJY2I6sGRwJgVYMC6dxIW2ZuNcDkdzitbPVnOUh/XockZXKpn6qEZdniGJckAXVGQL/vregynQi1ZnK3PZ8u3124v9QpH3QDgXpi9KKd+9YoHlucLCInerXrl6/dr1m6u8NXPxqcFsmaXJ7Z+9RX9YmdFiJS/LI8zdtR3+bJYUjNCps3r9lgOh+LrElUkJ79KrXmk3KBNFlKPsOAK+KsR4Bmr6rHyi3sYOXazkkC/Ni6Y5WiR/6v3NToqy6ohflEEOLE/rtZYZKaFOFrFCoU6Bt+BEtGeviPq6CrdusL6bGemV4QK3O0KLObNZJ+mieOKPE0hQ1hINE2aRtINjNqrMFHlXSVWaK2W5zxnhcJGlSi0znjjKC7UclxbbmUOo0vjBYawzX50p3h5WCtmhvSDcAwnHaCfHXdKSkkx8f1Qu6bIIlvoVmVnZ2+3dae50m5dOzZw6tXz29PzJ0y+fnB/N/O3f/u1W/T4z1nyxOtgYDLayi7OrC9k50cmXOeDGFdGcAqsw8pjt1TevYNS4vZUDXB1W3YbDe73GTuvG6mh1a1i929TB4a3ZXH2xVL+LFFRLg2Lu9pVrJ1eO1ObneIYZUXafAaH3fQIk/dI+06HSGb4HDvSN5DiGR2SFA8R/HsnIcASBDVoWukQAHKLQzhKX4FTKSJtVdw1uGRrozW/JtlStnDh9SruJHKrSaFHv7ItzW+agf9G7wH2+qtTSSnS9zi4Mo450U2UzgFNf4ANEI8HfatS5dgc6UENDvPKdHwDQFPNWCsnmIOdNX5GHIBCj0k461/ZJmMtoGvbgZxqVCZbxGz0PgzfQAfAnsciGJs55ODjfBIciU14bPk00Aa81xE5lEYvzUiMoknhq0iFVongEiKPR8gdBwgC9IlTLviIjaXP7oFo6dHDGs0Y+7oBHZpwCuTP4IJbj3jhIUXHgoEVUN4+6nTsMSBIbAJVfy0F2fzqVysoixvRWhHBYeIKBiEzfJUsz5ZKKSNdIQJGOaT4mP0GbSp1+dMYQAgE+DUAURpA/Ls5jmenZ55+fW1hoMF2g68pzNS1XKkj+WF4SFNh4s/ZCKiVXR6u0U/SVRcIKaSEitWGGjWW0N/msYeQeWex89bmj3337+DvvXGUBnqE3bdspUxedDh9k49zLKstPne4eo3SiWlzm355j+UZk/Yt2VFi/iylDG0jFXbp86CVbne+NmnfvrGELVk6silVJ2m6DZt/ILLAz7fpMQsqSLoWHPTbK3OsuovmjCmsaa3CFKSfa06o3b1y/BWW4zeZ77W6HnlFsMwKRRSj0+dIthlNL+zrw63/UvJixpViQkBp/lkqUDSxxo6nk4r2h5QsQ2an1ESUNt0EzCXVBMRnSRswW0ZdZuK8BmNLQ/tXi7Jw4zHHSnVFRcZAp6YA8Ay9bclDPw4PVDsiks8qiBUpTh8zw8AuzWEb7iqSe9N9OELda1e1hvtvrcGCpw0SLWqa73OVabFBwY7ErLnEueYtVA4dPXzsyHvK9bIWtwJ4ZBz+73ZMQBxzKBRkVw3e19KFkgW3iQba9O+CNrkfml5FPwfIt2DsfIKNL2vANu5hcXaVtZkowYMDHkofsEjtVwdR4IW0gSkl0XGy2qPXEwlC7Wb1Wj61i2jTwm5cu07orLS3UMnpVRva5ws/+V783W6rtZLWVSyuDZcZCjWaLty3bav20ZjURXgiAT9YEdncbpEXD8ROnMqafE/in8+s6j46wHAPLM9Xw+cG9Tv/WvXVWnmGux+rBoL9UzHz2qQtnF2fz/bZN/WnoGq7Bv2rN2Ul+I3MquPin7FS1fPYNTR/VxQ+yPempJuPUOyP9YbbVKGSqLz55bu2lp//uF+93eVNac7ewuhjJxkCiXQZQ0xqrVsRJB6JoCUAX5xD3JxKnHoj9OMLjTMCkh9Wr2SVW7NSL0fuMoz+N0EORc4NlJXwo/Yn4zqEeXSr2HKt4in9PNZU2ZjqVSopiborIxz5Cn3SHoXls4CRBSuNH9oIgUuVK0PVL+08/psNEuSqngTE8pp9QTuce0WIAagONkTTEJC3OpkCccLLe2nyifCpLAGoJ4UhjIgCFiefkwRIeHJWg6FfcjsfcIWaSJihy6ozDi+ESCKvk4t4LYWUhMYB0VZEqZEGU0Q6QdNiXsB0idsSNGWtNAzxM3Shs1GJd+OMkq6LicPMDL+LKykg/q+R6MVVFCGP6FJOgIY5IJOYFhCRGXR552M+EB7KnnYCmHowlE5El56DQ3/zN33BWKH8+zyLUXnMDrWh07sIVcxJJ23KUET3EsV9HElt60BTX6XPXpI+wScT7Su+//375954G81pfn8R57rEF4Iy3GAjCMEfS3vjuG5wCrs5rvLW8PNtssroBA4xD9D1oRo/aSdPVKMaEVr0kzMSFVsNoPxk0qqdFWOAxwOTNovkaH3DItvotCsUSLgnZ9OW9pr22zgGRNQeINnLnuPf5/d4dTkjtttZ0pGh3hnvilm+epGv80f2rpEIaOPoMwrygLd9OJbS7OhnNAjR+t6jbM/kuIieG/uzP/uwv/uIvGJDANxlxWoFj4C+88AJXiVHq27e3WL6nF4c+MiQVOLREpoWcKGf6CKRS63CBxo/u/MpKLo9iMp0hQF44eCCMSuFLY7M6KkVCfpghsQQCAhu5o+VjINMZgOZdQqJXIgIBJ2VhlY425QiUN8EkqXpR+Z5prA8gAn/MQMFTCc3YhjJhiHumCQO29Kli6sgPJXK4qJsTb9bl+2NkMsYSMKDofBxHnugA31mNFPzxALwEZFIK5QI2xfOBkCSpkElBFkEgDGkMEhEmSuEmLBX3MEHnJ1alPSqdlwt9QGfa9q5DuqSEjRNhxoAekoRpZIdH39N67UQ0BazusK0+S8dnaSSm+m0HKIU7MoIZ2HMO3QdoqvUgLhxB9ZNyoR1MAqdwEL5D+CVpjCXrlZUFTlzCGNou3bW1Jx7BVNggrhueJ1EWUFGk4noOebuoWXzb6eyQjkhostKEVeHaDFJhdjiQdX338s7OHmsgZOjERML6UfAxkiiD6sqounmnPU7qj47jcY2wcvcpui2s0PicyQN92AP5wKhfDxipecGxnzDf3dMVxhSZHq3fnwPixEFWb6U5f4duiLWYR0+cO3rs2GiOWzpy2dtb2OHZI8sY5Htrdyg1BcTnQl2SIxbkyT4lGbE/CoTXTyHo012OHpE134LDJwo4FhiaIIuyJKfeyNmIvvPsDcGBDnEKEULWCB+auJg2EAuaFyy2eLPhPkmE6QvfNrGk2QEwlUistxGjEZDKO2t4dqtrpbZtftNAHo0BKSL3DjsPkdXJR+s1eMUGxqyunTiSBC3Nv5UmeMSAxgM+eSEqJu8wQzVxkvd73/sep5UvXnyBmRsngzgb+K2f/ifPlPrl8B19tDZjtUgkQROFP2MnuHmRiNqEMhCYR9Rz+So+Vc/wY5Sv0TSgwCiIVAjN1Qaf7vvzX3gFtmFDZfkkTvipikon3x8GImC6ZpO8gHu7hnPEAnuGaxqVCy+oIYG5hUUQ2LOlmNwQqtS83sxFZwsLWvJravIfBG1LVy5hr3EIMthoNHZiG1FqwweNGz8QBXNsfGgCZ8AGPtySUFMFC+A7/+NHqHwymYlrhJYSm0GmyMjYBbgCnyQLq0MNJ3BerbRLd5QlIfqg3zQaYQSIhfEikwyaTpwoRMej4zhFBzoEUZMQvcKRHM0ETgBMAggZCKQI4BTo6ZQl1gQEh1NFjg8F54Eo+MGRxFWF42fEeq3BjNcvAVVlYgcIOP/u8/hJXUxOwkiEUng1RYjH0ugOpv/AqoyVE5pIVICDaQUoWXumHvAwFS7eMhnG9hcvXiSAYEFAhggWdfJYIDhicR6e8h1u8WMPHE8OzTHURE01c/v8Zz5TwNRsbnOzgpoYyO5jjkjCG5xYqsZecWNjCxapSuqR/sjzAhP6+DySUHlxrJU9CTqdQmF3/RqGbm5lnlqmEMQ6DwQ8OUmAePI0bzHsNOMjASC4CHFS8dER/JG7AXGzXJpYY+9Qt8+jZuAjjoj/gKwjTkLz4So4nSyle5PgoJPpgoAAa0y76KlHrSZCtoKqLpD5gVISkGXDxNFlYAZZgkVnvJqgAE1VX6PByS3grKhaRkqjziw1xJ1ixqmSPGZNywfIwrpE6NefEjInam4ubBqGBgFx3rx/d2rIH03rPXKSBRz/ghcK7lHmj8tCVvBDjUHeGYsZwTPGheyoD/qRtfv39WJcV7tZW/e4sKrdbujlRTs8na+wbQNjHJ+gNeX4nFKFryyTF02e0/35XW16OQMIB8ejOWWKxiJGDpKRNsXk/xpBMvc8xYC9J4QOE4Y3FIOxB8wBJww3XJNDK/ZYZw40AipB4nsPpkIJKKWKUZ4EH4JIADkqatJWIEOASHtYmfniF7/43j1eBbseEz44YDkGFAplTE2n+E0E7DQPozyVE7mrjObshM84XjIZG4Yx/BOFwgK0l3mqVFNqxOMUhJxc0AQ8ylBQPumf9o2MIkz6jTPOmeumHeQK/NtQyyNVkwFtzE3QeGOSWHRNbRWytrkUEpIXCJ4joJB1qD1SGUvGk9q5XkMQDtbCkRk7OCF0zTnURhxhK0SaMtkqoa1n2knAsPMMDAquqR4mlZwNRBzuWbjPVrjawEh7jLxWiW9wSqGlR5v9sT0TuFIUx0soIN8gNZ2EdXaw2JcgL042DXV3ZLZf4PKbgr7JXqva7fk5XtPikzN8Fpyaom2KDltstusofl3aBtarner0hOJOo+ZxLYw5CfWRoMVfqxMEQ71EmCiAD8BJsT9NHGu2+MNwS5GyQQdAoDeGT/LE16naZDzvXPFOuaVSWo0yJp3T99dX9c1B6YPtmRoalNmWIqgaMTeRWtWcSGMiwh/EUuKUO7e8ydIVKwwaRnm91DzCdGvWoDeymLRSJLbkxYMdTXIVoQeYUgPnmTaugNVFhEAAIuTlegHLxoBy17qrJIV8XOTIK0s3fHfjUmmlMv/kGZZHl08eoxNdrVQ//PDD63dugO/nfJ1+cr+2kTSv3OyfnlkaXF2f3eJL0nP9Qf/6sEu5TsxW66wxZfaOLi89Xpp75813Fi89dnHvyM9mzr/7bufC47MQ7Lbvl2aWy114mP/xtc6//tf/+Z//6VcZGfz8h7/UGL2m09MLqxpgLa8U+TIgYy3Nryq85MV5rQXyz2WbqnfdeonUOM8fKreveQFC1NSRX+NU+lNClKNMtVOxZYJZxuIzuWVOSHULW0h4ITfP+ebzxVPZR19qcWCq01k9MQsnlaXHH6083rn0dywK92r6YBrTdMiWCyXGjp2hPsFRK85ibHPFGXIEAaSZxcX/5a//+qX/3Yv/p3/xf/zv//v/F20tWyyxLv/dj35WfGR+4831jy6/VyyuNHbrM7MrLzz9XGO3y1Ubt2/rfrHr96/84Ec/2WlrEeTUmVNwvnp0ZWNrvbW7BlelGa65HFS2e0dOFPd6jXJ/0ObjNtyuxaWerR6HneCBhXuaKwstNInbteHGnUtfmT0DnZN9XsYqs9WLOSjpwAaL6QhFwrJWI/ngXGjIFhnzGHtJpORtEBUz2Y5tsIvZq8DartLxHzT5so6i6o9AQhY0V/uqBhCdpzBLqCg3ZsaZEithaEuWUDSDwTWOwwAx1L/YjhmB6S6BTNuhqcYFMsKAapLhmOeJ3WYVLSy2pSmYgYWGSMC+MjWj7+3UzmITq5KqrYMkk0PRxmX009CO42X0G98iP9TLZBmMmigiVXUwFoT0GA4wMml1Ic7B63M+ZdC/X2/V97iCVnXEkEgUIg3xr1IYk4GwZWRFGwM8NM7RORGrOKs61aBKihS5saxPY2Agyi1z+lg9ccrBtFCUJssnyJiynn5dR4+BPYErcux3WjpxzFs0tGjYxXqY+nvRxSk7XzrWaVzDgUy0Sk2B4IYAmEHYoYATbPFGAkR0FIo7p7X6SGNzMaqkqh7aVb9D6OnHL5w+fiQ/bHPGyQ4893jRQUmQlC1npOhKDq4JKoXlCwQJQ5MzR/zLtXK0cYwnMyX6VOwSm3CDXtdkmOVS/+cfv7DXzfzoR69TOt6R6PBfFCQTXo7kbf5qrbxbbzhtTvpwWpDJFaXWNZhUqCJ03tcEIA6pQnwuz2fhccjpW930J3vr9U7gYZzJZBoRssAl8GiDDIXssb1UJZNwZwOJisMM9xbmt3c/6vQ2+9whNtzMFk50B5175RyWc2l1OXNqtZgtX795ozecW1pdLC+d6ORq2Vw1W1nKPfv07PJyrVjmA4531jeZ27eGnWyllp/RVJPskCRfXCJ/DprK6fCVlEG9+ajWaXNUBb1lfUfSoOyGJG2ycUooGvjAyxVrj3YrVKeSowcZ5GYK1QL35dVmSpyDs15a14nQBbCcxcWK2EnojGmiUNIhOtt8p2A6aaMggB31g6wFcF+klq5Mf1VxSJL71SCHGnKGm+IQhju44NpO8EHA8TX7XGEG5aUVljgLyDEdrkQ1m9DuZTv9ri6O7NvONGMXO8iJfKgiirY7HKB4ep1WN5owP9dhuGp15ujciWF3OL8L+eJ8S5vKPgtBdcgXR7mYygJHePi0M6Q6a9/D2Nkc/t1f/GTxXzzJB41zhe6zF16p32lyd3O/3tq603njzofdK8X5+Vl2kY+sHEWSywtH8Dmg/8ab9y6/cY0F02Z9NGzp2whVXn4qiP72Wr14rnL6xDmmypdfu77b0ZG1LofHCpzJxtyW7m82fvGrN9mQ5uCWRk3JiVHEyIlj2Uesq6pYcnKrbrWvgqg8ZtVDWM+qKuTjwcN8j/ZUGGLrhjgWNWAaj4NtvQ5seyL5wqg2U2hgtq81qpU5LidZWjzqnwaR7DjbJ+vFCGquKMXvcMM4uyMYOtRYR+W17qAahCmy45iVluNpVKYS+NyFLTZskRk+GAVBFgvBmeuZ0Vyzzr4O7Z95jDTPGFaBCeBTilCpplEU1ppCqtDJ2StAwX4KUwjup1BJKxfR0lETYfCiNbQUE7H2ICJmvTlAzp++A6j8glWfwpfwxQ9lCtMJghEHtcHGovyKtlMICBaHWKhlBEuU1gflRH9udoG2xoidZxISBRBntoI9QCYZ2nexYUzQE0iBYEQkAxaJqEf4QdWdNwMOWeli3cwFRzVBhKEmuTOlgIJsFNaGo6kcv9hr7G5vdTbvceixzAzSnHTSCgk1HCIE08vJ2N+BD/bBRzE8MUFHhjYB9fQyk2193JR3NSgl7RuUh6MMBVgBXX98LafEtzSpOT06h4fyBw6jCs4IJ6MGJlqUbJQrIcNetfLcK1/Jz1U72VGVz7lwMJzbd3mXJNuiIHPDBq2iOtgjd04/WomozbDrRpmMnZAzpZO+20BOBdcsFojqXZzrlRStIHMXcL5S+vpnl+68N9r5EfWVucNxaxa2clr4pm02Wk2aNpibm+uFfIHti3aTprhZr+/MzdZoYkRhYkfdSmNrpza/SlpfuhpwcoUZQW1pu9Hb2dzlIq9ieQZkNkPwOakCpl+X5DVC/YbLjjn0rc1nVTeY6riQqbV3MC1HtVqViDIySLfhgRNREoY1xA9zG/c3rl+/CQHMNfqfxzpyT32mgv75KiO3/o2qxX5T/GTs3dMissxWeP0N/IwdtuW4LAoLEcosNDgfaR8XYepVJG78H1sU0pqYVTBex2HyDLd9GGTNkH4KVV9aWZb622YqZYRn1C5yTjrc3U45U51jmYOGzNiGEnqbZdGMfBlWKGHowgkOGMDRUrzWS/PlHu+P8Larmp02/mhPijVLKGnZqWOiyBQnuDUQIVOoxBF2SAyww0rpwhqFbeL64gN9DpWT1chKA7YSOYZ+UwSRFv/Zecanuvjr5iqvvfdR9tYe1qPK22m8FTdTYiF4dq6muepcBWVjCE7uXK8uHmxcJ0rmYJBZHusA9c1ttnu37m3Q+4zsUqCyzanRcS6XLmZqEjKNjHvAa1VaVoNXufl2VLZP42ERF2Jru/nywmq+Nqqzxaur1HPN3e31u3utfKXBEG7Qa6Pr1j1UalWW9Xl9j+tNeG+DGoQNKFBQcWgTTReX1mcSlzLJAQQOQOk9LTMZERHHI77Gp5POJcDKAFwsz8/OVpjwVyn1e5c/6gyGM/NzslfdPoP533nu6adOHi0O6kZH9SgF0ZpSmL84YWMbDtE65cUjrq/2ImmLN86wa1o01gTy5q9k7/rqRmx7A6M2P/uHX/rcX67duLlrPFsDDLynJECVC8ggDbkgJbjVgp8C+NOlDenDD7GetZ5NlfDomuCNzkoW09UDkiqEkEX5IRxotBqNx2lY9n4PNGgIByY9DH4gsgM//gS017f7ngY1naIYcWLAEEwSU6j7Hi3JwwnDlCCxDjIHOKc3me90HhK5OUebigbo8IiWLmCMIgCCPapcDncg+M5JgjCRA22V50jTMXl0Cp5pzNooSKHRmQkq9qAkiagIO5G+3zJmgw3slJNiBMO6IWaFAEDHJAnOelZ+RdF/EpL7MxQnB0AfCKKAxv8EUsgvyVFnXMwZXKyQBAefJMfXgxfVGU2QBTYHjgemfM/Ie1mPAuKpoCyTZnnFVJ4jjwpY3xqjDgxATVnzYw4cDzC0AU44nYocZfXIMVabRYuCORiLdCJEgQkyBkhK4Wjum5BUokKx8NWvfrV5OcPBrtXVWXZNqXcWQEfWAfd/dcmGBaG60xRiGFVBT9gahSY9lvYPbWzNIJj+DjSmRvRe7JDXb9zgdmMODdPfs7VIwsHOzurKKlm89dZbs7PPMxxhxYSzCdevXwenn9UXilmAplv6R3/8B/B28+abxNaqi1xMsboyhzC8l6Ln1eglm9N02vSZfCUi62xU2MS5QYYsAsZwS8PZr4VQQWe9Gb+AzD0bzIS7gzZddSlb0ou++SonsDjODA+N4TodLUu2xLJqBQO8MozQ9PIrS0WmChAnDHHq6Lvf/e6//Jf/8k//9E91DppLmYscICsxeR7NnERoR448SqlfPHWB78sjLqTH7ZPg0P8BR1zkxbvhTA4rq7rD0cthll1TFco7sK4BuKsQOO6YeyBhOngigFA1VPFnPvMZvgQJz2rNiCjMQdw6BYX0LFzTjGZoZWO4dX4anUlp0wonms6GI6f9Q+GG5LF0cDyRNQLEt4KIW+pS9ZXkBdwpxwDJY9iiAgLhNNzRnL5TeIAv5KT1OXuGPJVRIODiMianSXqm5k9H8WzspWV4AM4UyPOaAj7kY5QGASofruCZptpq84uEbcyXsDPmPEU9UnAYOASmgB7lwCgTewzIhNFhVBTnaPhJtqnMPu2guDU77DpA84EBMuGRU66JWinXaI8thR7RQ5o8xaFJKolWBJCYtaOxuimtOyXUTCa0NbI2pwaCi5UIjbNnT7/88svQ53Ms0EQ0UKDxgmMHWAUkSUJYv558Cii6VqEUCiMDBdH01yHDEVrlzgyBlbpTpzonT15759Y2pebDffhMsT052WkNsbjZ6YaCUVl7e3iD2SoTS2Y/Mhz8wAlJxI993oqwjF7briTyDYMJrsX5Yc7pHBa7H44EqA7YIABjIJA1FQoQK727po/oSpi2fI8oiAKBgjND4ipN3nHhQl2mYVhaonLzNYjcunuLfoobDaAAWaamMzNzdEkAWcSEGmWfKekdHe7XNl/0caiEaTTzXB8KCM4swBkw5tVx8+iO2FxDL6B17aD09igMtOCBg2xYe9cxkiAWl8xwa9vDym+y93dlgLKutLDqMIH4kr18hh3ybZhBWtQDNMpCkig6rQUc5Hye66MR4lX7diCItNAhOXJWp89HeNttZEUuSJsFaDLq8DK5TYqQPB26yHOqnhrxqYiY1UTaWJN8CBrI9VD9F8ckyGg2U+BjDJfWbn3+859/6uJ5toHfe+cN3tniJglyvHKp+cYbb7CmxzihxCJrqfTo+Rd+//d/f68x+Mu//Murmx8hZ16Ug76Iq+Aqe3tnB7ZPFZd4G4Czh8DdFCB5AuDTIu7wYnvpPU5KwjONGT6J8jD4wAhbQAe4CLswVcxJ5ziTsId9Ii1V47VD2CsRmSwszHHum3UFMdzTlza2t/Qe1ekzJ2ESM4D0aJVkwwCFhPlS0YkA4dGzJ0Abjo8OJAvqq1peBJ8XaXlkDEQtU0AqEYuJhKl1Hn1xEwT4gYxTBpwQPMgseh4pP+EFNgRNHqGBhCELXZkn4AjcMkkndgOcgngwYE/AHQYpuKWm8H2EsY/oOJUV5OBSUOMQwUEQgeMIkxKx45MQBEseOkeEJkFxGVDSiDyWR5MevyEAQT0kpHiUQGXYpXsWHC8DQNORgRN2R17iB7o44wo4CFgc7BhRgNkAjhlBIYZB81ycbPSBx3AMAEzg01IC7jQJuB5KibnazlyUuSrFyUGekPsGUZtLVIKArJjKeOir6CQSNSPnyAYhUeCcgNfXU089xVdIWaLyOhKGcTtmOCVVopiiGab3wmavAodu4d3mC2SUQnYI2QkaruYLZ87McZH9B1d+eufOnp2nkklRFVn/TloOAKlqrNf2gvDoFokmCTVsLNaMUoDj2REgio6KmmXljikDEwxXA/Ch6blHxiDokCkfZFw8C5KOJa3XIAjAIU4ALULN1je2OKhE1g60HrnJI3wS1vasnBRbP1IzBVqNFpakXtcXRwI7YUEtKDOYYf5r7MfeJ3IVVl/t2ctI18GTh7d2tiOmZ619j8S5KO73a4gLHmiwdCJEsgE/1s+cei4GffhQUI1YO3IaMeyFAhgDhJ0Hz8/Dnoqwo6WkYcqaJHE6xCY15BIjkXgYk7KSOs3ogwN1pwAmBWHQ0shqK6Leb6t2GNZQC35kjcVUMrVrxNhzJdb2WUgnrpUffbe9N8NrRUC41oOoKsenTA7EMnbH1w6kjmFpnttq1MH0iyk40oFU2bHEZ6pL7LCsUQH3HuFQYATO0iRwnPKy94M5Cokm84IaE+G7t++gRWQHAhaMAGiOrDSJJMnxN3FOEwoQhDF6T4YEhHldmCWItTXeCsrAjA9dFherfKsJnH5zy/khSRBWwgQEPUhAsYkwI5CsYhhc0NyHA4d7EnzaF/XCa5EvvfTS9f/8U2vu4jPJIaKHAFFEJsY0AuFAtA9PJcxU5oyO9eIU3Rptk96fkqri6E0OGR+GnA76oWzUNW4w0HpLOOZ2GCsHUXgATBeKSXEn3HhJQrLQTiBixQ+vxaVxLUoCjQFiPYwaG6Y1knQaq05wKJhj7mNgCluGMi5IEcejIE4nhes0ASQMKACmP6aB8MbjVLkdAWZ8Du3D/WBF9N1PTLYqgAklviyhdbrsG+kbEsaGVDGlO9KyhNXIJtlamNykFax0jcuS0iAHjtWZ3KxRjDI2ZKQhQ8fG+Xt8oofOj4/TYEo478n3eLMF/rAqLMixR4/mgUlqLyC+BYxcZOvjAl7Aj8Max1u5Qx0hOUWEHR7NmmgYxoN6QQptYdM6nUnRUhzyZQYkuN1aCP8QCHtoVj3EEysEqabIuws9lEBk4R2n2MD+adMs6BGloauUblhHbOxpky0oDPjkRSUHovZDjvolHZOtQsmaTVGbt7kSB585QsT0UC/zMpG28kr8xkWayFTY+XcgnQoBZnL4rl8ExKHnKyRTLlg2vTJfV0UQ0cv0dpq7jz33yJW7l1v0HtyeOa8F3+trO1ybyR4Woy23mfvPPoswtzP3SsdLSzv1FmeqOieWX/7KVxaOrrK0ysQeE8a3GT6sZbY+eD9brdx789I3/pv/pnaJc3L9H1z6gOnT8UK1kam2c7PvXF27eLJ66tRj1Sp6lz9xcmVriyGAFli5X6zb7I66eW6ofe1nr6OTg56+ebi8WmOSuXxEjQGZc/SkUHT7o9MH7sK2qQ9usr61bnVqZVdVScM4MMe0oKZdycxsIcerMVrVmMvUOK1cbutTWzOZpbnFla9+Zm73ZuPY1nss2A1HOge9N2yzAMz72bSqVkntpdlt4dtYYFTqZb/5zW++/6PX/t2//5/+9//bf1mtLPw//x//AxXHkvVoUL58+xrL6FfuXdvp1Y9eOF/cW3/tjbcYtx09dpIF8L3m5hNPnnvmc09R0g+uXPn3//7fMxdcXFm+W78Hz5h06NAydctShjOPHGTQIZocd/naJ6fBYd0JRWJnetjuNMpF7uz6zsZHmf6Zr9aOt3gpnY+g8PpnF+2WTmBx/RvNkkdwHggthNwAm9on0cEIBq0D6tf8SRNphiGdkP2GMm7U04MtMSggaikkg2D3LQuNdxmsSFaqJKpJjYYBlDNgvGhEAtzSTTAGhPZrcCuChfVWgPBx/MQyGuBwD4nCohp/4lwO/iQG1d4V6+WFSRogviMQRiYmDfkhbSg0ABLKFsFvQv5T+50wQEGqMo9kYPkqo0FOU5r1Rrve6u/Z+36k8sLQ15jYxaAgH+eiWNLIvnCQ2FWZa5WZtmWTEI73ttrNdmsPhWVerrMKv2XnTOK7o/3CDL6OSqWqGC5MwYLvwybmWlhFGiMtlME9Vex1bKY+cI50PaEHCKvMUEMNCKmPUNfCD2EddVFG2fnZ0sWnHput5FlMs7MvujuNoQLNmzxYgMMcoYFg2rBBBJRQ3oSjMTkaLPX4ehq3emsxrtBX/rwyhP0mrXqKYk4fe3jy1GrjwqmPbm2z7sh8BJ69XqCP1a2WinM1vuPHoUh1I/jMv/aauodB5q2vd1x4JzzOxNAS9RNIMperlXS0Np/VUgsX6+G5Dpis1HEL/hs7r0R80ylVARN1fexur3Hv5o1Br1mulfdYGc5zko1v4zD54Y2Uxf7S6b1B+cOtxlv3GvVebnZYXL9yiwpt2CrfRqsBEQ6Uwb/662kniHUaExFJC7E2Z8v9qJVjwB7lVd2ZzYFV4L4tigDJt2sX/bftE0PdXDUzW+3UKcGG0CwtEmZWBpFWV+Mf4F7eA8XoC9BBq30JUlqnniJKyQP4ghtB9z1H/CkHJhCyw1GDsM2jNwHHZNV5Z1sT+EgHU+1RKZ9byNVn5Uc6SNvXddOuDjoWA2VPS10yIOWRUupqNc5f61sGhZlCGUXsvH/7R5f+w49L9pliTrqxyl3iS8fzDA76O/0WLz6XOKEPh73ND19//2dXILy3114YLJMTGijfehDsNlnsDDb+41/+7Qc/eJsPkZ06crZQqWYy2zCc4wZtmIOLQp77Jy5fujno5x977DGOG5NcQziITDY/mJeMzLm4kqeJXyVP3H400bTDoURZz6hxqg/tkDZNkqSuCQTIcfnYidUTp2hfei2aGxf7fLmRZR0m9hl2WSpVuMUAqZbZKSrNVJkLotOd7V0dKLQFBefHLjVlX0FFYlUW2fshD+wzUsIxTuCedwI0DWKZzIGJaclzKWZLXxuDXWLTpQMhOrMqMq9k4OXf365ARhNtqC40aFpy2VMRl/mxb2tEoocFSGjqGvyD0NTRsODHl0R4U6Q3LJT4gIiyIyN8b92efei5DiICjIZA29TM3PoR1ZlNzPCRRlKEcWKvO44luLgczaPBJxAFGEl5LFmw/AkQHcCHjohrWikXsyOKR8+aT4lkWeltY4TBU4PS+5WMEdut5s42l7KptiVdidfIRJn7U/CdMWV3kAOuKH/VV/UGN9DD1ljxLZVCeq9zyDX0zRavRHS1ISrF9HwnKfsTvmwvP8GKGmHe2ZPlRBlcV30wmNYllwgZ4bBSoNGEaVZ5G0mqPefzu402rePRF5/v1zhgy/lZZrvW8nj1gVc9rYVyDtEOoos29+na2iuFoOVLPXz66NIDKt5cjBpnqlD2iMcQSgMuO9IOD3xrfbQ6n/vS5x976831b2++uZXpQZmP0bGgQDmRG+zho5ZkqzmN9nc4B5rjM7qEGamDwFslrb3mzIIycovBGSLVQb9cml1o7G3dubv+yCMnqTg75swXZFUDqKaVReYraYUA5DxfAuKVluFbPoluWFkgJiLoJz5O43z2IIulK7duf3T12vr6RomTQJk8r1NiV7jJv9EdNVm6otA5HU7SyKG9183OMm+6c3+bRfZ797c4i4M9wqQwcTM+7ERjqs/11gdNXLolegsVNHFufNOakMbRGW8rZoJuYylkbu8MoY7ttfu0LMro+oMK06KZpIEPEPmgFJRalj8orWL4L6C5GPBH9yED3KOi6JIoKIV2ZxComRaG9OOiWO7emXolWI7WLJBJJOKmQz0TdSQVLVCXvND4w03NFos2W2TDlU3EsulVt6uXXeDNVU5MaoQaiuOls6w1boSCR7kPH2JTWdFNgsWbXyqLTggrpYik35ke3aidubm5sLTI+JnVTdZzdzqVWxleB9lxmaN4kOKAVIelEN5xqFRn5+bOni9xQ9ru9g6ZasRp4sJ3HsiCgAMJ/3ou0pR+SasxCflqqcbG3K3bW9dvbLa6mXK1wIcr9vqdYj9z8ZFTj59czLc3y0xYKLk1WhsKUGSIxQr1litpCErpfOxnqzFAwWOtCqCOjlkdSpTYFq0fqmptoA7NNlaIK9V/95XPXrpy46OP7nCVuLnx+kYoeNBMMeB1qKwgLRZYr5QfmbMnQYwdBaYcU4bl5UXOebLEUd/tMMiiwtWg1OgDzakkhz1KQVge6bJ2jR1jPfEwxF8HfgAxahHnxIIg7CeorKQTiy8sQxckBiImQOn3PucU3Ddkqc4+rACgtgh5pamKExcpe1pHi3QIOCRN1mPxnUNQiI3AMQXjhfk1semyUHc8kgrfc7dHsANBmbx9+Tr9NBueKXCcFDtxMAAkedJvLEIarrDUyC22vgNrDQBcJddAJ/nWKvxofcpcJOX0lXzc2NJ5foLwFIWpx8i/w71kHoZhYuEh4sRyizFjAQlLziYf60kFJ7kKr6ZIMIhLoCQ9OB7Glz1INIc0SmD6kxaxskvkEBM6ZXI3RhLP2jo2HjQMjFc9YdU9Rsd8oGA7HX6cjtJbNxM11ilaAdVBphlIeHGUsQ+OoaV5D7HQwb322mu7J3UAko0+GGCRBXw/F0YGjgqPLs8x3SREH8ZCaq+3SeBzn/nc515+OTNb03JFLsc9i3v1HsOOD3mXjFfL1tcZgOrdn9Ho1u4u+laaW+6c0RyGZZ0zT5157rmLFfu8JAedUE6+sMEu6G59zVpHhoRs74M5NzNPEuZduOUjS/fW7h1ZOgs7FCFhavrXJDDdRiISsZhK+OEdYsIUlijeg9MgFsPJO2LcQJ0t8/VbuLq6dYpSdLp1RLTR3Aayu9ci1eaA02zZ7n1NFAng2nstziV9+ctf5vQWF0p+/etf//Dyte9///szMzpHiWGnLMiJg8mb2/+WjNbWN3nF+DMvfp5OmnEbOJSXZsi+689+9rN3P3wfOd81pu2Vej7ZpFxQaHwNHwibMsOMwjr7MmAEyGY0/Qb0d3a2uIn7K08do0Zm8tobF5q0iKwYuChsAMvjIA98iBuaN6agIC4xT0GXAxp0HDOSEUgu6GFgMnmOjxGf0TlcKYFKp76X9oMPEN/hjuyPMSEBIJZd0F6PApKocxr348MJ5wHTH2Om/hjllhYFCULawI+VPQjAqQV5RswHczhFPDD0cD8uTNjGxSKggZoJMLOdHI+A83BUx1hJknHxgCjToAm2DJqgE0Vz86zB0XhJ7W4sjQTxU/6l4MjQi0++8IAjD1pHxj7qEvPzCjVfw3QchpExtK3CSDOh4sgqY0rZkRwxmBByYQZKbKmgRRyGnUBwVoljS8Wlcs8884xR8IaoKHDoGUjrMvSEhAlEDg8LgENC+OTuP/Fd1uFfRrUR30dCHHPgeMWZq/evXl9znqGvaudYab9fWyR+odnhhCmjAmyJUhPLl2A5V80HmuxEpK7Gc5ZICcfki6OwUABu5EK2wCMDBKZi01EPGYa4lxQd5lgKj7CENb5z4yZ2DxtLLEZKSx4YWRMpPQgdCj3Rt99499q1a9ttKTlfTIJbFqCh0MrKTiI1iqCdPVtdolw84rPQBCS5HmfMpg87kBI4pIYOrBFN/kDcTwSoHFlDAe4i4sSZ8tKnZImwS0v0Pq+Kxp4H4hU/tuTEpwUBEiVMczx6wIE8+hYgjzi0QLVv8w84RP4OxyeV+7Ch3L2nsF7PCU75omwTclJ5iUgFe1NoD37M2QcG/Z7HoWaU4RC5T/+8aCqeNQIxVq2gwxW+4sSXLutNusJevUm+haoW4Lgpi+x0gxRvAtsJX22zcgo6V6a6uaWLeucaZJjkLDbOTmuo+igsNQvO3MwRfLrC733vewvVZeiwMoXAuRRUffqODqEzGYOTq1evsWjymRcvoj/kL61Q0wzLMSSMBSfsBQESAzH21wjAOdlRFhhDDl5ScqFcvGsPPwP7EiUnvEHjg8bwjIaDfPTYsmLtMneKAAU4Rz58WEPHAjpalMzrreMsX+zEhzevYg+L/8yQXHgETo6UlwCnGpk3Ix+oSQF6+sw9OCBA0AtIpumSGkENtE04siHuXHl59AC+/3FdOgiRDc9aCjthP9I5TIahMs5jMip5Ut2ZzqsWCdtN7l6KBGX615iftr2eHNTIrYd5xJEkTUUZWVEtMlgGD1OzYIJAJRJAtgiTKFc2IDwShahVazRpKyP0cS52IOiI16BTAJ9YaOJ7PYLDKg8KD3VPGFnikVTRib5l+oD1BscBjUAMQ8fCJiiD0w3CIWec4K3Z5FrHdszFA5M5i1ysPuMioGN+kAZWVGW0kaGa8aRzfFdDfPJ34UQsb0T0tozkkQ/RUBtwb5+ZBNBcLI4fZCK1FB2cmUDVhSA24o6UPQA8Qg4UKQgc8PzCF77AVXs3baYAvlHTdIbdUxo1FQQ/5OLtSHSsckKz4stNdoo8ZkSAUqArIKA9TEqOHGGiMI8+kTZsdtqKbGhCYTwmApFJeICItCW5qiLSl8CTuRVowrT2snV/i5c119c3YZAWL/ZmbNzC7T/mSNgbjOhtb69v4V++dB31a7QHjPq4wUm521WTlBQ2vHcKcxmTMN+CFdwkml6AdmlE9giEaX8CklZbX+cAX4BOIvXrhsQW+pnYhSojL28DNDMoID1KisoB1rwQfZrWVJFKEo0Dgho8LkCDAymAkp0FJGdz/ki8kgRGlByXRMGUkGmODvFY/ActQGN8bIyx2dS7emU7tDfPi1/c6b/Hq1rwE/opoyySaQlTwwKZ8xGFhyMyj27rXfJFTXk5KiG5OsS/kMEjSRrrTX1/KKurObKVCqPKfFlnz7s240BtjB/ssW6L5pFvDtARrywtnTlzZr1cQZ85T0HZHU05Jc6BydNv9IuusqNKj0mAvh6G4YeLKoFAF65mZzLYDR6xYCiDdEybaNSLGAtNNOiD7AmpBHdnKkvQWBR+0HZ7dmSC0grXAZOMoWHvdJv2H/7hH/6bf/Nv9hrhJRtLd4CHQGjctNfQA49N6QRyykoJPvXo5qVSqXrDBAHGpP+f0DlZRIcxQPHog7ir5VOssoLu3IF7N20qBqbaS2PSNwiWWwWwHQaUNtSAsSZWiHM2UfnJCTAxdFuKD+aCoARhcJOFJlRs6AnujgoTD/ud7aBrxCED7I1dSF7ZMHFwqv10xFKwAgnXYZChsnjuKQVSFsotNGbLRRx6XRLAqXxBaw/Kz2QYI6BmDV9dhaUV/dQQIVBDLIYZ01EvViPiXd0pPLHVBK29FtcyceukhDgqcx9WPlOt6A4nr0muI04MkQrChEapA1kkaTy4XrqvXPa7scQn4iCUxncKGlKQl5qiv1Bi0wyVUstPOuPMGj5ckLc1VuqDjgFkGGOUjDSpCDhj7g6CCmxtB+I+aVFdq7Tk4ibUKPMIvJiXvvGKnAqr8xaWY5bzsGzw6fIt4Vq3BIIomO9plUSikYIYN0R7iYiHunHiXUiWy/oZTJWGZKcv2zKf1cFdPykGBUiZnFUEksIKwqAtuPIHsrBi/LPESJZmIIRsOMpX9/rZDx4yUDB0GYAlIITWH6i83H55d2vjbnFr/tjinf5Wt1ja4q7AQmG7oz1SBAqu0HBs+5FFaiEDMJDiMLdUmx9lGmUukX755Y1O56MbV7go45HTF0qV2dJzR5b0/Y1H/vov//L0xlrp3Zsnzx3hEotMeXGn3ftVa3P9vcunj+U3u6Vz508MhiwWaJ6zfIQLMbiZNX/0+Aluk2YsjRAeuXCOV2Wxj0wtsWgbWx/NLVAL2R//+KdfeCnHeer+sGH1yOFZBpWmu3YPKUN6tbNsF1ns7NymdNUZ04KRRsMc59NWZJZbwFimoVuUdCRgWobkfrTIdWZ8zDWXmy0vPP/0i8UPtFhfYL3qJMe7tWzRUOc77O11GV/Vm3qt7P6uFsfX93bf/+Xbz//JkydOnPqLv/gf/tW/+ld//ud/DvdvvvHu/NziRudGrVxq7LUbmc79G+9rapfJNrpb3Ki2vXun1d1755135s4s8KogTfLC8xe/c+V7DN1gHoPO1+lRn3JtRj9oD5ZdCojH6RINvfWnPUxuLNXR+gGzxszo/qm5ne76k1feoSsdrQ94M3eo1j/iTD6L7H2Xhmk1pJBMdNKESSeIWU5yijEmON1CJYWU/kvoMW2w/tbq/WY92WNplHSeJkLDicMdFca0zlRaPYc4oGLwTeEpjlGWBsoYGIeik5hig9gUhY8bi1gogsIsik4WyBsRpKacJVRWIhsKOrb5EnLi0DZytCYbQCQxsmAleCFX8eysmoh4GtPxxMpuUuaQUiqPfqCPTKfQnBSFkKiUFZv3otbsD3f22rs93rLmPkjLkWvhlK+znLbPynKKpVgsxSlN6C1Ac/715ocOXGCLGI07NeWrbXHyY1zSbNpxPOVtEYr8tJyzsZ+ayoaDAWZb3ONn81gA0goTXVJblMj/kH3gziXgjxzI8u6KUmrmp+ZAOXWOTwGNTSTxconGpRR+OhXtdZbIkRc8WH189rHzc7wLiRUacgNHF5aKKKwPq5CdukJsj8tWVUPyyM9U6cQeeVtT4tUIjmoz6CdfLRXZTaBkCUGG/JDJDTpnVhaef/Rsf2f9o11dJKrJikbbfBu2yUyZ+TDrWWudbZb5ZFRUi5mubk/QjcAd7uLU/pZWISkLYUyTM8an6CmyvVvBGxniHMsr0pNOMv8NHAWBAh0B/QX5MjfA8DIl7jQaWqcb8KHyYa9S2m0Prre7R+YWSryCs75x+Z13ub/hZp2SdbgPFJ5R+Vwl381rwZo3BZnrQA2J04nCn/pIFDY5cEhh/ExWYg3GBZCFkCU2fDsHjYLxABCCxCigJ41pZI5l7qQtuAqvQ3EjP/HgD7VgxKojGx6NBhWhlWi45e5dxZrQXA9Fa/IRrSEvt5lwTdiTBMPDQ+KgABqikwRsN0gdkDngdFKqXLo/q1MQCKeTqA+y0XhC72N+RWE0L25VV3Sx0mqnwKYvib0ggAkQhd8adgsz87YjPCouzMHSwso8mPCsJemRvmAMerFUpHTwU87P5LSQr68scLcvmFyqa/mKNxkgDJEOQ+o6Tl73GeXV7/crWj9lNXWWw/AFTZbKpT4bwIuLI3rwjXXeJecNgeLGduPNN6/RFx89PsMt3cOujRxoDykXBeJFIOsYaR0DNa1Sp+GO6WhIwXSBKlNvB5rLnLedOJSPSFTucoXFX3oZpuhHj5wU59xNW6qy9KyJ8bDKZlOn1+YkwdUrN7lI7cSJY5DiPQYEP0SPuLRzIcO9ljtrG9TvsKtlwU5TnyfNc6soOmpNyQtCNcAYPOA7z4S1VFpltDQiv0KpiPXmQmpjO5Z1HKBe+COVnV0QOV9EtttQAprpb7CZfLoK5wbS4TySjOOI1As7KYS9F6FQY4tC9mNJB7LTPzJ4cmOybCLwrhtk5WxqtL/PFcdUgyU0Cul8UEivIyMrpUWkLiggMeCx+JoamMki7A6BIxyXOfUbGx2xRCFqCHoYehg6TAO6bQaVzEQDNHyvL8+RJKpHDXmUXaRARTDi393ZopPhPTklTjmQI8OEPUbc2hstHhXhMd2BECHbHMHfe2MPVl8A1+JtttVp29U2qkdaOJjqe9NVGcMmea8zOKc109vyJ4MKe6oSWof4hIhrBWBqEQjbJUiVSDxuCiBnTa/IhglFfzR39vgjLzw75B5bzcoYS5Ncn+gcZvTCOwrNoZMS7wFYRwk118ZYZEE0tcBSa5bmZxs9Ni3DNL5Or1jLh2Zp0Cvnsi8/c/TNJ5aubnDV0nCjop6Xeh50aeO5+fmlEydOc+eehu+a82oYgVZRHtoOtcmV0brFBcPnosDKUX5cgd6BjwAu7LVa99Y2C9ygw7sKmmPaPoSmzuJYiIz9PAlitLOTcI4jSmKz+aZkikuGszwifBw8cC9nL1eCw9ff/7DRaKltwr0+9F7gzn4Uqz3KNnqDzd3sXqdx6xY3C96+cZ8FvXU27aBDaQuFuUzRVjAwxapB5c6mL7nDjystqD7mwY6Tis5Gaff1ODZKEmM4DTPN6YO/zr7qVWYVX8TBsXGvn/L2GbfrDMSJpdHggyk521vUhInSdqc5kd83UKXZArakisdpbABBOht+bUAA98AjEXiCrFFWjnHaAETpDRNeVfdqA9yipnbiWUQcG54I2SBqTXBKnestOswFudFFWpb04aBt6zJphjph25gYp+AW1YiQhzClWvL1w7Fmk6GwkCoC8VhhiCf5dsk5KqnNM3dOjRgeGYJCASlxz3qm07zT6NCBYnhtaqBpARdv8O69tHLAGnSTS/RFZJjhfTXWoBnL3b11m5lvqXrAhrdKkQjNs/5EPmMPKHA+i7WuhVpleW6GRa7GTqPZ4rXDTLWWO37iaH93javEnj6z+Mzpo5Vuk9ejdNc22sgBf1uuEQMmB8mft0xMSwHiKBx64P2d+2iCK4PFEsbIiuUgSedeX3KU4jEIzBe03n3++MI3vvTi//dvf8z71hySiGV0feaRUqDgWilip51n/7S4ZuKQFvqEkCxHV6e03kKBR8bFOzt1n8WTkLrD0bqJmiAiqh/jhI+0uLSL4WSJl8qwSLSowBKJ9+vSx1BMRdvqsNhX+Si/OcUTcDQpaxJOw2PYY/HBdDE5BNE7BR5pUoQdIrR9jihP5WiEY/KYMB1AmlABDWrePacpREyAU/BYHMsucAj+AxyYzj84UHNMSz6WUhro4Yjpj/gkSZgxkjHi4QKeo6gYOyq4JfTtaJcqZ7uIdd/qTQMgotAYjIUlDV5kzwJj4e/nJcEM+gBCAhFuSjUmkoLjLo0PRG2VurM3Bq2pQ8EuZrJXXWxOpyKKlk/XtbyLkTUcqoEBmZlXbLTlF3TJix8SikMbRGowySqJLtsiU3wyhwdr0DQbM8fJTM8pGM3gGbVxqWUbKK9YMLVDvi7ifQ3EbRPIysutmvWI8BDpx7DhyEBEBgjEWLJKNC4mnQhQKHCYazHR4oJj4m6PNtmf53JMzuEyn+f0LnwAF9uH1RZyt1M29BAoD2sW3/rWt375wTtwcv7UIyxDf/7Vry+srNBxQYFjw1z9nHtEY25S0TPRLZHLleYVplUsHxxZXhr2NbUDmdLxbTSfIfCIJqKQhTnOl6kU+GfOzUOHbo/pFkUgLwZlYPJZHu21GsPuQw9q7kiryXzZat/fFOZ0lAk8QYG2ql8dos0oNHH12cVwwP4tK93s3/bthWjg8EDvhdgrc2XOHfgtV03eAG82r63dQZhcJ3ry5Mntze7Pf/7zP/vTf/4v/sW/+L/9X//vDMuQMJz4ZRrYd3ien53jk0ecYPrGN77x+OgJ7sX+q7/6qwsXLpx+/DEWI6gphnFgIhNY5Byi1pWCqvAroeFLAbDxpmiEKQJJFG0HYaDDm00cumQUSKxbQuJEk2l5Ymcol5IkzuHJ0/gXtCm9cB6kkMkHlw5LC5UY5QH5tgBBlFFWcVy3YZVYAxMjPikXPm2KxzFDScKY3KMcx/xERIm4krQTRCwvxTjt+OjI/rg/C4NP0AHfm2SaQpomYYsSV+6m5JmAf9NfaUWqJyVTJrQsb9HuGOq7ZAA6P/hAaLLKNWHNcD6eDRcLPo605GvEJBZBvJs3LdXZOU5e2xzbohzh47P4TTAoI84rRcbFJqBA9tOUbmkSyxhpIhZWSY7zG5B5NOW08kmpVES31mwT1yo6tDjkJTSKyUYQ9jnJiUw5Sfn4449jbAlLUEaKeB5x9qT2S6okkaJiOAYcSHJLJVETgCDWVXxqWVjnbEFgOIg1YKUdsv6ZF9bUOJ106/2PfNgPeSY72C6QsRUYVWzUZr2VzhYyIGO4mOvUamIb+uQYWSWMrebciklmuoU6285tLMKvESBTcqQ48ImPJmNs6UTmuZpfSqVikws80OkA51M6dBP3wGu1OuU5Gc98GRztTiJtGxSwAkGYcQNwH2kkjI2rID1hSGL1Cw2l1cwaZ3aAiaOpN8+sHopmqg3CG6nCxMMWoMP4ZDBkLZiVMXx7dTGsEZesU4MUZFxbSE7vo8yMlB6NPhDCNLIkMB7ou9ih4JIBAQqgOp/ApR6mvvjq2rzvMI0iCe0FiDufeFtGwVP5DNOfwYcsPo6MKkXdTOqTXhagvQjAfRLorGq6lpSF0pCcYzNA8j0xwyOpAqazygI0w1RVIMdYZWz51Bg+rWyMadKAiOA2WCKMgxAFKdX8hdYco5dySQvZRbZWtLyuz3guLqzSOjbWd+mY1tbXKPJea4VZsW8JeOmcH7d1FBzKQPCRhLJJikPWQUz7fgKFg+AkpzF6QyZTigBv4HNvNUMRBvAUCxx0WaUrZkGm9weh3WkysuLTgwzJZmZ1kNnJY46Y2LOdTltg7RjKNGB4s+tAdUrDHTRd4yEriMYbUgweYUMVYfaCNW3Cndb0sdZYFPAJ45OJGX+xkfBibcQebbkQsDh3FykI3+oXyTpQeCGYxkqFwUDak5aSJEm+wqSAFMc1igZApgDlH06aJKk8xk3Gy+hRTieiTT2CuV8NwAGOxsEMjrBDCPDomRKgrwTiOpAmQhh8tMLnNf5o2/8qo3MCkIEVNpBKj7zFQERzCI9TbEfMhwmQNhJUWYwFlIe0GF4K4rXAHIAwQPGI7KNoeY7hJD/ZYk3mcKoj3e2gJVFlZERs4cdMGbGIjXzZU/DUkRkwEQN36XDKmM/7iZapQZKJfgHiSB6BDnE/cqnHRL2g7yOcmFFMGwMQ5MwIPgzg8zLHV7/61R9d+f61a7ehREL3qWKaJ3tLHILe3d0QvqzUWCc9J1Wm15EXURJURwCQORIWgGkCfcfJlVUwdZ5GJEKlKLhfvgmjnpfzCQxB4EMWiA3lhnyrFgirz2+//fbd9bqIueP7kMkpfjKFeyYvzMvu3r3N+IG7EXF8P144uqew0NHVItzEL7bZFkuokNlY8lw5iEBQbHKnDOBQgxKRzXo8iRazoksWoAHQ18CGO1bHCThWWIA2CXiLw9CbTGykmlQE+PTXJCGKsJtc5CAiKQ71ZLMqDyjWnPfpmD2efLQA55TCY52ryBLAuADtwJCRmoVy5BE2HOisBjpR+IYTifsYg3anhKalkCUM+14c1aY5F55nGnBSC9DObjx34/RjLh4IoxfjJN1ulZmc+8qXB3yydkff2u5pAw8w+qBxGG6oN6FbLa0J8CVt8FeWFmkO3LXFpLXRamD9jOyn6TlXUKY/ZSQ56OhFcHWLwwzTeZrqzfs3yJZb40EYdrZhUzsDYt28hBcvy1QNCkNOSP5D0EQ9FghtPKEx/gUHKLwx5KLVEHjhhRfeurr+9tuXx0iTIS8IIicp9W72gFzU8cIANeC+J+KRPzVrywWmkgAdvb70wMWjOLcqae0VVElE7cEOHBxo8OMNmXIgPQM/OOlDxapDADHx3UToEeNvcDHosTTFJBBIx0cCeqXRCpMGKm3AVUPADuCHmjT4hOlJGQVv8J5U+JKU1i7lWzv0KHyr4vgktMiAEk46qZ25iDMZD4c2gwlQKzvTgejEfzAHnhGqReS+fGKC31qA3UGrONhotjnVm+8X1NRnF+fQs8LsrGao9pEWXme0NxD5UBsdharIhSCjg1zd9sRa2sdvSoaqO1yEeB2lJenDFEej5VEVbPfymOiApEc3AAW2ih1Nsa7j/pzoAEB6XXxe9iRmaGeYwk61v2pnp6FZmLbG4NvjIuX7d1k/HaCBjU6H4/P1dDFkb1N7EaRV6g6VQtnpcq4i8qFFS+PQZ9WuWKC7IglCVpgpMQumfOlJpwE4B80ru5x9Rvl1qsE200DXC7xYD/O9YFLUqH4O4lEIViOuxlPK7N2ki0dTCUMO5OxHbFOD4rJXmimWl6ucHWsuDE+cPfntH7wxc/rc8VPHb965ScMFixULrv3sJJUBAZI7AyLWR6DFza0NPqHX7efeff/K6dMXoHbn2t3G5t528z9yDid35ebO/Y2lmULn9vqp6kq9f39QqDLR642Kc+W5//SLt97+6NaPf/zOkSNna+TInyqcgxNqQepGufXYO1EOLSl3DYnKJS5t1LWkHFNqtrZz+X5nuFbKlzYb75+onsjwPhhpsybPUcnkafVV5N3axkxeaXUmWn4D6fUyu11WbDINAL0hszJOSzU08evVsuXZGucL9LKeTiucXjzf2+JjIFuiqXlxPlsmI211Axna7ZblQeHU6sqxytK58srPL19aXDh6+vRZVkA++OCjp5546r/9b/8vvFDTGdQpxdWr11lx3t283+/X+63+Fz73zLOPv8jwkfXvP/vmn/zVT7516Z0PfvXeu1Budnd1+ILjV3wajAN+JXURWsLOdbgEWld/KX83YjJ3bH9itPrs3urVBTRqWOe18sLwUq6zffn1Rz7zKt3MUY6+6ju1ssCoPPxQHMlknzMFjtCUKkQYgpRKmpmTeshcuFqqRYi5YAcIB2NKiIGtTIuFwKHq7UEaRqSpGWWBlMCYBkBSPRVTxNEKGLezLwAhAJYpuxgILsgkPsWIBCH5xYYbkeTZGEg9BK4dx/gYR3qZrLWH8aQzmsaIrdIoiJosxJjRCdypBz9fQG85Bd//mCao+Qf0ZRJVw0gMrkiC3nPZ7Xan32XLHN3VYEqUsGAgyDbg3Hzsz8AgTieJHNcnNUhiDDAEdXCD3Iyy1gMgaBsM2FKOY3V5/6bb4W5E5eXZJ+R+S7/0AlSxc06O2PZEPw/WZ2fD8cG0pi5VhgSdpL0lo24K2dKxJGsFFFGF5tBJrcqJw4IdsuYoE1mLnkavKDLLbvncYjn/4rOPzZU4qMXhY63pwJLsiWEiReZIIANRZzSljfY4WQugJOrEMRgNPAcjJKyhCYtz3Hdpi7Kc86L74FCZvZh/ZnmucfbEze3tGzc2OV6Bo/33OyMWsCrV0tz8zJH+SrNzh6UDMzBq3Th9kZ28htxfoTUsBu60UJ8rwhIOnpGY7cap17YxIc1VegIcZHAIux9q33TD6Yt6UhbHcciUTy4+J+HA7uXL7GI2qJehvSpFbfG32e/yRaQhb3Xi+I59odDm3qJKmSgOTtc4+2Zr5WThR5cx4pp1YwfUx3hbG+u2TqVZOw/mSPU45sin+jKCpEULUGmSctIOpwaoQRWCsCczYVITCimIvaETdKOX0fcMXYD0gORl/SDVJyfurPsz1YV9NVP+yE7R1o4MoiecssBZg7YkrBRoHVMwcxZEF4LZp9KIsjNTwoETsKy+eKQsEAKi7PxcFTj7nBVZhQ5k08vKGnaIXZ0eNGbtnBhqFS0hpC2iqtFaocMHLWFOPQBKo7vF1bz64A8rtkagLoDljzxn6NTLIx4HvD6+AADSE0lEQVQ6Gg466+oRGwli3uDQWhOLdKCoy4AGbzUBxyQZP4yKuQFF0XM1LV609dovl3sM5xdOsvXLa7nbO82t3a2dN7jIqv/0M+f5PmWnw1dUWFdyPVG5nPO0H4VzIDDGipOksgxTvZtGldx8bTeSszJFK7NqHDEtPnbiJBdyUFyu4ICHUp4L3ytDTmdx4U++1CvotQCWge6v8T7b8PiJVebS6oxZz6G75yLOI8ujSnFnbZ01we5Ag2p/s43WBBs48q3ayTUSEUbFrAJMbYp8jYqqyZKW9e/lxWNbd9cIm5hlH/hLO6eWQFQXdlloaDs2WObsIRWhOgbZJBFIKGuZL45/6/1HHuGAOL2FAKWAlaKttmC1IDpBD/djQoc0FI3XTrQPxzUtiUVKaPmvj1imsgkoSJhUPEANh4k25vXoGDw6AhkZRKXDuoPgECVLkAlEI+8JgYAGkABGljDfANMSXuJA8+QIvIcecPaOz47wYqO2CjQ959JjPmvim1IUd3d3e6++pc8K+NwqcJWQS/1CluQAnH8Pp+LHupoG7g+TUBVrDi1tdelhsvSMom+aQmtFgi6d/clV11aPsIGwQRBBjAILszqUaqI2GWC8EIZWsGysiRVnwiILw4iZdkTHQ/eUzx85fvzRZ59va5gHQe4AdqOncTJTN3pFZnHYDV6DSTNDWha7ZU8CA+lIhSGPbqrLNbnBwwRGAW6xcvrQBeuvamq5xmefOf+7n7vzH+7c3rV3EAe5GvXfaOzMzelrruz7Npq72uPhK2DwKsWBJmWR0gJJVEXjVR9RkL0O/pZr2Up/r7Vze21rdX5FKqobwK1PkqQRGZ4UlYQwia+AtSq2F4GQFT4J8WVY1YOpfnhVjJXiTq7Ex9lee++jjU3ueebkKPh24xavhPT6/BUr1d6odH+nubd7E8qYxmJltlzD5HCZrU0u1Gh440g2U3KTOQ7tBUh0gvMubLHMXwQS4Kot6QGFSC03G2Dc7ngccd0sdQEDnG9nAoR9CXN/oUlqLBwX9FYBYVGz7XMrrk0n0DeficMk+sPLv9oMmNCKqUwhqHyt5q2yVLqIo28ZJLN4r0F8nGftaRksE/Crojwh6YHgHBMgbOAnCR1LOB4iwLs/hJUGINQsBpUhSamgNQocuSoWZzLh15DVnGgx0g3FgSZN83cltB6DlMKiG+FktGCYgZqVHeaEaYLgow+EOUaGjxIbXK+jAlERWIqUHgpnpjqDyrV6WvzFddqD+/fvETnodel8Z+ZnHn3i0Y+uXGYLrVRMbVeQ8jdwlBQHAe7FrhTyM3x9Kp9ttnvtVhdZMEA+dWSZNwfZ1D3F5wfPHJ/lhUXm1MoRIUs4hDTDCa0pnH1WPHCbVek4suSqXBzfBoUcAPW2JUypltcoRDVkCrNmZYEksQAat2fmK/k/+NrndtY+urKmYbrNrEIfauRFiuJoqJRnVCDmwuiUr7aiB6oWWVsiKLSVGwDVTY4qiMHFJxWBeWlzBtyuj4c1yIp5shHVBzknDgZkHRefMRvXdDFNSL6N+iAKDx8XFqCtDKpHr0tvIYSTEjo89WxiitkI//BCjanYyDE+Bo2PVJIACJHYYTgJrmrae3cgQTkS+fLoecVATJUOpLOYwlTyhBXnKkIIKByj0xT3hZ1sOiPSohMwPwU0mtPpHRiibDwkbTJnSo+K6oHugQMgDCLLdpcidcJIFCC6qG4LXtU9M1qQFvtmqHVaelK0+dCJLMUmQZyKmgjWkKWdU86FZbNsIbtTWukxcpQBhb6nMoiCMeAPHiu4yZaxG0kwo0Cs9UHNuVXFkBaaIFIuocVmZl+aphcCgv1DQ+jWLeyHEZJKNQpeZE8u+Zhu8+iB+OjGl06NKNCQqtQrcS4OnsSVUXCfR3fCTzohkuMcmVjS8EjA/ZAAOiYqFdAJJvrseUKAKJJQOiqdMNuMH330ETaCpU/mLRyUOLm8woUSkbeYoxOMZAmQnFhSMWzC1jD8ffXVV7/zne8cnV3mxoyfvP26mL9+HRymUuxwkhd3IneP8qoNy6aSBrM7knM5MoeLf/eVi9D0kxrFasUG3CovDjjY3mbx640mszICsA2rFGRUGAwLQ9ZzyUKDI8x0v89jZqTtTR1UNvPqDBP20oEHkXZf3xbr5nUhNQvQ8MwYCobzgwZNAyYLGT7nRZ8w1CEjkyp0KCxRPQYnaKm95O57XcgWCMzr/NHMDOxVq3NMAjk78OQTTxLgUqf/6S//38jnkUfyHNA7dWyVM9G7G1scHHjszNPwsNvaO3v27Ktzr7KU/73Xf666MFGTozNOFjAAywNeyiN/G9Zbb6c9RxQitDnUg2g03wQIfrvd4f7TKwvsE5weNPUxMedcgcRJ3Ck1TsDx19QrPKXDBjIAio4EoCnJJEMxGE5IhFQWK5hn54+E03B/BOLU4As6PAL3KJ6g4JCYMMko/ALHWZmmYn7NR6jBdTqxQcYAHr28BMQfzn4JGyDw7HBHoThTNC2ZyjsVmGrvHnuYnxaX46AGdrDOF39DOtCS9f/Am/ETYl3UU1k4ghcvQVa9qMgGdc7Nlz7QkvFpbjiaP76rKcAk+VQOn/IjcnPRwRIMfGy+KkuoL53x8aIFIjQqGpdpOPXGXI8FbQpNmEOZlZLuX6aAkmpKT7yY+Hwu7MJTpzmAnEle53dZGQJ8Befl93w97QMkQhqP9QAFJAkmF855qRh+9DUREzWcV2dmgI9mShzBPrPVuHlzM0lLWXRaDZsJhPOSd+5tcDDFbItQpKSa3egPRZIZN/10wULf5YOlInemVSA45ehLJqleDLhxJT8UwFCnyuupEmQd+qO3olD0HVeuXOdwFIeMSQLnmFxnhlhSwSSP3Ijt5PElCvWCYpUwNB0fmOCyRtEEpTkKYS8vqdIL0J7M2aPpe14iZA64lN7GJJ6XTw4lRJyNTMjTEfCRnlefJ1dPZ1oEDhD4hQfCToq8cIR9hmgIlEiE3ZHC8FUosJymowF3CJjEOjBJF35BkApZR+/IhKdwHvDoVpKCeiqy0NQ4qWqspEEkB4Dg4OP8kYEo0iAMnBUMsWFjwogjNMvAiSdUjT2fNtvSFbFetFhAapm07hOgXN6PoA84pI1PFaBOvBZGP35vTaOUre2tazeu8dFDWu7S0hwJeRWAtCKlejEdTpjwUgB03gjsd6SyrMdCAMeLTHJvX+AwGkcUXJpHmB19hhCgaTDgKjnQBI9CaAVZzS5bGpVYsPYRF9doEqZqY+40EDSqnMlxWLLeqpMd76JADT5FM3EwhhD0RDM2B4K/P0EWAKgOmBmxDYUUeradH7RI2OBECXgZiTQyHjvhg+A03Y8JnQ5A4lmP5TGRrpLLjUn6c8onalxowZ24Y1AUjYNtkZdiEmVONZgicWgwaib4Xl+OChGnQInSib2AB0aBBhv4pAUNRxhM1A9fsrXDAdxnDoIj004I4Dw7koNM3UkBbP+PqtGqH/jWnrDCjIRNSYJ2+dKbE/mkPlx5vqJ/uBOaFQQU2hEFAZ96BA7jeBaYTD8pe/Bp7lY7QsZ5jg5BLGarRcEou+yE4+0afKOuemb2yudGOdIoIjYDYlpJmI00oSdWMXLjtQDYiMrXbHLSkdydwJYXj2kUUTZHIMHVGhTLan4T9JUP1oj3vKIW0bpxTIKSswwikVAKcjCqDnd5auWIJXVerNxu7TBfQB8gyNXQkpVpYqRg1EwmCRXY0w0NVghnhjD2kLS0L0ihTRiTy7eu8xWZ9V1d7WDEZSuIZYKN3iFkL75kZSdba7NF+uhsVWjBSps8XNunpZkoM8jQR4EJuIMsEMIEQqMKV7qNEULIfpyyJ8EXP27HLNblzDodTzBvugQWW1zqCrlZiDAL0M4kCDwqXdLijEbwvLw8eMDLyKMlCZxCByVyIvg4R3Y5e1g4QSenWxb44t+cI6tSTJ4h00nFg5TDlcLYtNLbEcLAmPQNp1lhYnmi2pI2SeelMBJGKZCztTiDhlJ72MXlZXYGpnBAQJgIH5XATLHAqbyMmgNr8xqRYsRit9VutmgIx44dYRTKB0u4Ob2+u0faKcr++Gv4lBQ5YFe9e4UfbBTLEZSUrSCyxmqh3VwZz5KCqw25xNIpkAgl5p6O9ZUox3H5oJkRISaxutATqwcR6GikUr7WqfPa09e+9rUr/59/iDj7A153DG8tO+ofZQAWq1e88IdKwAjJI/sxQEKypqcg4GHY25/RQ0IgS97QsV5MlUueMS2UPQtBIgcx+oGBMEyhcOYgBZfpQZ7ARjbk549O04uk4XLKXFPsNE46jIk2VKsezjVQKi+GmaNIw4uQrmDCXsIIVNZmvIAfVkCydmZiKpCd5yTJRJU4Gr5zwrzApTwugqbe4g4U/YJnUrGJCtXj46pYjiSTg36hwKgwyUoY41wOwrfs8Mi1y/yTzUFYYUG5w5kF+z6ybe/qgiw+yK59Q12Iw2JdLl/m5q0gZzeApFJ2WsXTqIBwMgFTYXTMKnFuiJ1JK2gSYb8cjJp45sGW8HhzEuXn2DUJMVT4vnWL4ZLNnTyf6MTlT3ZFIuYNxpiWhVB5xRvDafwgeJ+c6CEorY5VMFpT5lQLh2VgkvIiDwJD3q3VTCm17yq6qlKN0XVCgzB9mkSjDkwbfKpo+STG98/v6ENDdG8FDQ2HGfYkURXEqs4AiDK0ClW5Jp1ySTUQIeMSCI8TSaBkU01QNPRS7LhtMmFi9jvg1dVi7tSJM2+++WZpZoVN+Nyw3thuLIyqj62e3axv7G7tkgGnkbnys8UXuljAFVe2x6NxyajUqoqlmZV2rta5v3301Pmt4W6n0h9eODq6eWzx2BOnH3vs73/12n/xR39yY/UnP/3pT0+OZurX1meG1Uqv1NiuMwVqF1s7ne2F5ScGs+Xf+70/ee21S/OFmRdeeOLvvvsGsTvNBqVeWlqhJ1ie5bwPA/0eYe52pMOYqRabe4O9wcyNtW7/+g/P/c65fu/nC9mF4uB2cVAr9OoY0ewM60C7N682V+Yvtjtr9H+zmcqN2++fXtEib6/Qhn57xFurgwo2O5+/273Les29whVil2qznXyn31vWlXm9pzGdxdwMHy5ZXKllM/P3svV799bzpexuvdnns7j9fmVmDpx8VmviOnuYyTPNo0M9Vqux7pyZWz17/ny/16qv31ldPfLs4+fee/Q5XmR7451f0t0+9+wjL3/+2dsfXv/Pf/Pv3vnBWywNHz959pVXXnnuxIUj2dqLTz5Fp3hzdJsbOd679NPicMBba3OVCmef2STpoJQ6I8SmqymstEjKo8G5NMTq3V5L1AGR/nBrlslA5/s33vnqI6uPVkosDvGhJ1KwNEXDoeA4b0EeFpFpZ5QNSELQCLr6KUmEoyrq7WKstRTjDZ2PJBNjILSArIaIygYU6VhChLDOtRh/2GQ1USOooC1521lvpXS40/RwwprsA/jxERydsOPLNrZM47n6C/jpAYHzEyybDN9kizO74jvexlewQlgxJ+i+OAkSchsYhZPGUhj2JpNOIJDFFOVUdEq2KRJkDU2aMfq2wXH6dl/nP20qpux0gCavY7NWEOXuxiih68mjn4D1a6wSI6f9SYSPNto4higXtTPiC0+ss3EAuttp6q4+qhPFCzJJU/00w84hPi7DJ8L54cZz22sUJNEWMWmtJ7JjJVJNobvzC7N8wgnDWbYeCmNEV8orpVh8bozDAlAW+ofajN6uYNeSPozWZ/ZdOiClRaZm5wu5/ury7Beff6Y65AZVTpnIstHNcdLBb/YAnxEbPs7VycOm+Ao62w5M+66lfFhJ7cTGkbwzQR0QZLuPj/+IC2vpnM0lPJMbPXLyyIv1c1vXP/zF9QaTPt6KovfjHApoOvaVzZw4spwb3tvcpU9UVnHcRCPgPGpX98DqOg7voSIzPEKfuzWxhZrMMQb1CY8RiWgEJGTzKVRs9YKYBmJFsaiIN8qBAEmKJf3tNdq3b98DgbVJJcc+5/K1LMd99MgrK3RcdznbYveJyT4bVxQC3vogkiFfyHLttC9FkK8O11FuaYkFEt/5SV/N4atWIMtpXEoiiEEujE+RGFKK6oQyEKW6kbN2auOQIFi41/3fypQRobJHIVQKqiSscsK5IdjoiOyMcwlXn+eiQoMwoTBTrDopF5eMuihCXfQRITnobJ9xYXScq6BaThlMr9a0NorGpAM54nsM4nU04DQDlTomQf0QgakRaA6ONKUzZpzDqgmdG5bZlgb4NomoRf0bE1TIL0Yj4JyIoM0OuAjagAkTVKtJICnEmAEgKJtPvXj/DzZYnIJBFgA5hXzyxPLCfBkjwAvCd+5sb228/eRT57lci09FYlGT02HiBGc8BCl5SROgRSeeuhHVqL5faackkAvlABr6SmcGva3UqqyM9PYGC8tLx4+fYkeAJRLZHBuv5pnZYUN6vCNf5h75Yq4wU5jjAHWxqE8ptpqja1fvnT1/Frq+IEWZeR1g5fjR2sIc+99cMzIYtZrtFjaLpTFm+PCv0ZZthIjFUBRWZ7QOJUAhX5mpzczObzf2Fk+dzM3NvvfGm7VahdOm6mf4D5JVs+mxCFBKrzpXBZc7dUOjRG1dAaLOkDrKjRVCtInhDQqKTzVhbaI6Ba0lQeIgEqrcIeTN86RhJ0Z1pAt1/fy3v6YIKi3PsCM1O6zmNUUS5zAQtmW42DSUr5UtojnEldy1mmZo9J19yYnZALYNHHQPn7p24gjf6ThNapa5BWHoKEebiSgx8wIvi/kg4JxCWKnh8zW4VnN3a5Mv2Ck22J+khPt+IQtMbKcwHZjG3Q8h1uirbofc/48z+9Yf5fc4U9dDimrWYtEUxDnVUCPII5If1ypzNmajJOL4Pl0WGOQLYcbc9KdIRHnRqwNUd8WzbKa9sEOMzGm+IH2+cOrpF1/4UqYyi/QarQ7zixLENCKqaPCT0ZePipkmSlYZbDofyshebfR+WTM5E4zH4ltBgrRjkimxoBSkYqGbzHiXVIrYq4Pz7ONzv/vF8z+9fo/vMrJLS0S/02UszzCiVCkfPXqCQzV8NE61IJ3EmLGZqiYJ/8JkR8nOCzhPcALOkE+OZ7Kzy7rE4/2PLusqRa43yBYG1IXakLJ3TRZBnBY9R62uPkzH3fkAEYiN4YRZVM84ZNzAvka9n/no6s03P7xZr8NSkel5WS/3hK3lSoV3p2fZImNLt1eoFrTorA2Q0myVO8iwJMyqOF8KTQbZsiqWu+aQMniq0+BcLSyWyGgBiBX7yNEqgEe9cGoOHNQAx3e08SGIb1XgjZGF7Ikz1Iq1dQAtN2fzfsUTqbCu3vBgQQXjM0JoPwZLLypbrY80AyX5Yc5j8V3Czp/MoSwKCd10EBA1mi1yIED9kgSfF1UZP5JKdNTJC819IHq2xh5zp7hRPiDgiMKfQov4/t6JWUaNz3HBiOoVCfRf/NL1yld7dK4TmsZQYnNtiC8CGuzHfFktivzoHUeYUQfg8SoJlz4zhwbexuT2B7VZXfoq9UKlB6P6XquW1UWUJKDfwnax+LvbqPNJA3SW4Sin0y488tiNm9fYmPG3n9X9TToIRkA6HIEEkKI9elPs0Dq5Im9hdmVhbh4zeX/9Hl8HQFXPnj6+UivfuLF2rJZ5+enHVqt8yruNWeHuTQbpUJCFiQYfFhGdxjXUvhSPNS5J0XQ7rHeZrEy0YXBI66PS9C9xGlsiNVvqQflY2WKphhaa7el98EFz5+nzp/7giy/86Eev7ykr3mRVKaiHhIAUhghGI3qXT6812fFnpGKV7dLBTHrVIy1BLH8S2tA4x4vpUEA50UmAbBnwiLqmsqBgqk/TuAh+UEDHUPSdZG5TwEah/ZoK4fCYdqIAhMc19yBK47gwUFYZrD/AV+XK10/00wEzpmMSDwg5hTSCQdympMHjsCdRtSQuhgnEMJHORmwtQNKxPMbcYyAhGX7BPyzKk0/F8ihIkorwmMsp0qlHz8V5U/LEOecikhBMYg7+9bRe6kiHLgFsKKBqAOnVfKbhQMI4wqRKC8ozncom0hzDjdkD4IahS1cmHVzAST6r0y6aifO6GYezKJ21Vx940ahwMZ1zBYTexOExluVg0Lzxu06Ok1l6MZZS0cCnyTPSjwEkIATsAs5MDFGxaIwYFdZEju7Mhjs5LS47DxhdkDEeSmI4XAph+ILwAQec+nvwNc6ArHy1bmMvlssfBTU4ODg9hNME4dHA5iXyl3z0CoZaeHRKJ751XIJDuOxpg4Y8WdsFh5EZj2ykx7wIiBP/M+6cVEzFnATbzS4l78hw1obYl156qdqpoFTM0CAIcZDpOcBczGj/n0d34MADy9NcrQjaa6+9BpzjmRcvXnzr/Xd54ataneHEbnt3E1ktLy9yHIl5gy33tAkPinOoyjvv/JJjPu3WvcwxHcojfKxWhXK3LUXi6AdZzy7rTBmOTg42GOhz+ySOXRYoIw2KOV+ZRybbA30kgUeN4NVKdGYEHL7Up9dVDU6s7tSrb0Khndd5Ul6VQYBLi8eYSEAcBFJBByCfE2SABybC+dGPfvQH3/xD7oZllfkf/uEfWFkGmUulT506xR4vnF+5coW2ubx6HP5/9trPgJQWdRI8u5h97rnn1rc+unnzjsbj1valdWPDoPYMe/rTSGK6lVEQeCAJAWbR8MzLcBECwypq4vxxChgjkwC/IdUUpudCn5zGdJ4NolRJEZzCmNswPEontTSwakmU0AI2D1H/bCZdKhwGAU48+vAGvtFQq1FbpCnbt9Qd6I0JFaeWQypDB83lI8oPdJF+GtOAQT4xdcQ8MHYSTWkjQfDTg5WIeWAg5GKD1BA2gXPtDadj6nttNHPEi6rYh0QrDC30KQob4wSmatazS4vUEgos4CQ3zrxTJsYxZeP9U+OGHJNPJv1Nn5zDKeZ5dAg8eL7UOEw+mAeSuCbAtrMFPqlISxTtl1Ux4Po26ITCTxeBJD75IdXTTz/NfZSeNdTceWMm1uEEIEEUAZy3qWmiyTNoCmIcdexaauMQN2XWezHq01svTK3gnG+RyQbmuce5xnFO3sD4YIvNsKDne3tNMPnmFSQxs1ik7cYGOVBQqYb3nzobxdpcB3sOe2CSKUx6WBybcJSj39kh/sYuMDwGHByCPhGUIhbfZynlSpbr9e/c3mYEA0Pi1u4sRqedB3IHQphi4uMcAR9OLdb6bp9cpTJ3gQPwQJpPUlFGoqDmKTwWuON7C42xwrEoR7AmJbI4Y0DiFYo1NsdxSNp3au6DE9E8AClRsMJ6+3M4AnE4PmGSW7Ygi4tIhHB0BwKJnYI78zGVk3U20kAPp2K99AK7DAmIMv/NOT5+kF7KlDhkKouIf1jgk+J7Lq4z4QCBfUOJSSBR/kYql0ajThvrW5s7m+wfYxDOnjuuEUW3lWaDAnlzTAMJT7FkaMHAJsdQVFnIgCiI0+6QFb0/WZAcNjiLzaFjYv0RgtJnG5fqrAC7YtzYIrW3i1dt34X9ZibwDKL49gMUSIKFoBQ4xmZcics4Z+3+De052VEPEDTesEOIzjA8GKv8SvkJWyMKxWGEA5HbV69AgXtRDAeUMYckcYdtUHdtc1f3zVoI1fFBiznGvJCkSqQROJojSgfK1rMgufL2loAKBX33yGkfZCSGbaG8nB4hFXxMIx3yTCpPm46HIE50khKJk6SFAvRwOpaiUVJPBTI03Yc+cHzUADNBZVk7Jl7O36oBU5pJ9ZuLZAm49JQd/23szXiVsNImHIJ2mIPeYVH74col5cKjAQnDJKVAN2ApkqVwk4mQl5HYV18wYsVEMlrTccUD4hkS8CygnAD5DXNt54TcSfXyyy8zqdnqa1kcYcJMb6CWlWJ8Oggd9E7kzMUcp/GsWp0hEGMsGXktREg6QKv57Gc/++Rrm2+8cQk+Qaano3aY0PLIJIL5FEtwwLmywBmAGQiq+n30YhIkyosJccLgy1iUy7u7m8x65o4dAw4wZu1h6HjCKd/RABLwvFg0ZEp1+aMbzBPrHV85VSP0hozpoPev2eUw3M4M27y2qsX9knpbY21CN0joOXpGB/qxOLB6mAAfTCRSOJD+fuCDqe3HT0NiXvsDoKWPsKRTufwd4tLG9MSqkYwmq5VHYtNA0kZ84GnihCMzU/D9j1M00wgPiEqjxfBUfR3GA3CnjIagP4iCVWh6ulaHWxrC6WYCODZPcUyBsR60Yl5fYBmaA9Stpno08o0FPyyvyNuBAZJjARYW1NagDxFeCQKTzT4WJVhGZungiaPLPr71LJQjGwT0Q9rumlBmEORM+PYbjHCwbMaBw9O+iyKyp8cUWdcT8oQ9doSwYJxLu3PnztvX12QY7bxDTEsAyk5QfJpWEICkBYXoYMVqR0QqpLJYQg8IKVnxwE5iaqylW7/rcQ/0Kb9JQJ21FFayUqeDoyPDLmF4ZBksxzQl5zwNeXCYa+tkwnDeDMDGXPNIozNfyWMg4qSB+5lQmsRN2q1QftWNI9jphgTXMmL3ULUvU+sVTACZhrDhc9tiTPJrByJxp0AZx5D9XNkuEIyAHKXhCcepwlJR4E1wFUTOcVIJka0KiDy1Fqa6lgNBgg9unByg4HyHE3Ww07gDqzVSNJsNCZNPw40GnBPtMr4u5Pm6p9Gghy6hJU5PaRmzJNT9N9lHHUs7zcEk7sc/eVructXaEyfUtB5tbYnTYTaWUDHUZdLmgibAHsJR/dpwljxSLI6lTTKi/HgJs2fTQy2LkY9lOu6YdTaZ8Y3d9ARNju1oW5AJpxYXAm3t1rqcJVnTdhZyacOkpVHZYvQwLDHrVTjGzCq877lxHzGpKJ0Y0tTXv3/EhpdBbCDlC9aehaWUx+jEpEGQVLQyAd3BheJFgAgPKKxHxwimhygQXLYqPiPdYaa+1xzx0fdCfqOxM7uyONT1G7urK7V2a3NmiYVdbf9ClVKTv17DJB9oIBN452A0bas/OFOZq2y3843W8eps8/q987MrWBnyLs9VkcyX/9mfFpaWSr1msbtXznY5Y1ltD184+9hfrv8418n2C712b9Qq9Fq5Tr29deLskR/+3Y+5A21hcfDIhSOFWvb6jctLyzOtdmVjbZe+5+mLT+riiEGPM8Xvvv36t7/97e1ujuFR73j76vqH7e57G/1bJ7aHN3fvHDmmdfeSvcg8X98prt3lpXMOBXGL05EZjjC3uFCyh9brMtoO4Lw+apJfyZ3UqWeOM+b6tb4W6fiKdHXYyXVv9jqjQq3Pvv7e/Xv0iI3djVKh1c42QG7qLuvR9s7m2s6N3O23Kf783KLe9csVGVN2+sz2ervtu9fvvndk4Sidx1/9L3f/yT/5J595+um/5tQAn9+d1aDt1s1rp46cXD26stas1xZmrl+52mrsvfOBToLXm9uf//zn33/3Ddbo//Grf8z90YPMXr/dyi1wlhNljg0AqVMv6lWw8dqCTRyKaN2antvVfD9X3ukObzS3L86vMNvVdEYpp50U5iDncNcotJgADkTgMSBVEU1RUKOWA8Sf52NpmAzzHACGgV4J0RJbWGwnzps81MyqhwVEtWhbbkMpWVO2W3OFYMyEEtijqDh7BBQB60nvK7VmX9byUsNXwY1zmxg4z47M5b1GRfyLomgmxQMSgoJbt54INWxcjQsTkwv1IVzA9z5uspc5MLXbZwrusWKXT7YOB9vNzg6HkVRI5mCK1NEYJjl21omg5JZQVKJJHVCsAfHBSnzJ03MMEMXQP2lzDXSsB4yDz2ELjfPYY+TQQ6+LaeGkcPqYleUceE64+NR+YRLn5MSfFcQhUVD7M6MUCEUnRVkO6PYojKwhpg8DygagXbFJY1Mp0Ewan0lSEGRronSBSnOQACf5RqPV+fJzT54v6yu3nIDmHIdtntjQDPMObfVqh7tYiogCTcL4jBaZ82imzpNVsKoaZmTLYcimsoCIHGhJt1CqcPSLsx5Pnz9z+fbau+9eZyEN9AEdrzOvsg+PHVna2eU6ZRSHuk6yhfVBpsM8XuMRrZclEeNfrCpXJyA5billnVgRIb3YcbYj9tSjdW7hJDhFpmRiWAsxQy4G3N7eW7+/N+oOWEVjVEBUucxfUQvQGrxo6ZkhjQLJwWcC/MGAalOMyCXtOHKhgDhJRRia2gJwL70zo0crjnFu8vJUGvEHMSknsyaw4xSkJyoL64YWZZgR39AP8GJeMS4NsVaLyilTY0ZTX3IcP7o9svVHZ+PAHAFGuNOJ2aUDMcoD+JQojZAmEnBMHvQITEWCTCg+moqeEeXSMvGaVNLEPkGYvHDp3KcSx6gpOI9qJjoIqwPXyJMVSS6VAB+TxWt5nIOWeWS7ujBTq5Ur9/N372588P41LqHhsqxKtUpX79WKMIz4w5QDlVDZxbS6LGkj7PuyF9SQFQrHicgC47ThsFydWT163FfNQKZ+SWctgoGKhqMj+1aGTj9xM1gPNioA2Tpns7m+27p54+6Zs8eUKserUJwmtn2hXG5+cbFSKrOd027utFhkwi718mRJRjZV9FUn7jpTA4dPWUTUlxxl8Ib56sLc8vILX/zGD37wgyGnOwv5QQfbIvb2C9khKjXiQdp8FlW3A6n6Od1McVQPjCthT690kXW4Y5tTqLzTJVuJvri2GC07hectyWk/yIcyAsERwCFh+RinHrenGtyWyN1uR0LgTGRpEVS3UzB6Ysip8UjYs4gUCIDsUaKWOESUlpJHUemQwpzyqO7S9hjwZdftJfEktZldI8pijWfhRphUAtOL0FthagaDxta9YaeeG3V1anacv/h0alCO4Ug/DUkjpOERWYHkgI4DKYhPItudfmOvbSsG4QZwPmOiTsrkqipHpyYIhQfKjGpIz9QWdEBUb8JaJPMrLY0omQ76qcqMRDDQNqtCcsTu9fpPPPLowpnjTXaKTaN4WxdBMb3rc9terUz2jP0QBLfZQY69JuVgyouNtqv7rPpkv0Plot2wJLR9Ll2hFj6wZJliv/noydVXX3yqdfPS65sd+Oft3wwVlKkNuci7WGDflyt/mN1wtpQBARfbc1qQCRec79a3FxbnCnbgifydKyoIBmn4g0y5Ore0s7d3c21rkVc2mGXYZxvBNH783KiYJyGp0iUAwagBHnGTLKaj3st++NHNNy7farS0C8LGgWSBG7J1XV5i9RlX1JYVL0DMz8+W2YOy7StQEsbsGHJiFcnCRRcR0gxErpy3iOw4+xkGLoPEz0Q5HP1j/AOpfUyayWiK4AAPuE9dAXTOJxqboaYzhXMcqUADThKC+GGSZJ2iK6PpuFVYaK/Kweh5naqB+ONhvn0lCArT8Toum8jO2w6EKJUXLAmQF/yFwjoJL2wASZHED40TTnywaYsZ1sXDq89ZfExp4+J6s4U6Mb3lKHSJ8ekuXzvosdwMEWbQCIFYhnO4ZqvBgnNmQ2fLWCk+dfIM68Lk3u3ZMRo3AdBM6mK6hJPPLnN/0TA/6tZKpSMrc7UKw/Zso97c6zQxMkdW548dXdy69WElN3j+0Qur1cqg2WZLFlVjXKDuT3KUNcYEQd40ULZM4pKd1ghcLiVtWgPPXguOZhjBI4XqfdKJlCio9as7HWil4uhq6Su/8+z1m99icYLjIzDhczcSg+/bV+jSJKXwBNjbNwiEHY18xa+tqouAbYCJlPodBg/aM8btJwiFg8C24pZopwotzrStzgiKfUcNV8SEFF50zUrg76f/YIh119ZmvBj4MfDglDHWc/VUETj1mHD2CfjzJMgUmoTdOX0nDiRm5zjpxzTmfniEQCSyGskScHiMAt/CeFKK/Vlb3Y/5mUKI2XnAaCjopZuKPezRNTumBY1c2HFyCD4NnlezsAU+4EbfgEhRPj2Xzl2CmHYC0brwNUuUIdZZLTvgC7NywXYliQUx9SPJfmkQZSMKImXW3Tb7eeQJq0B84iDoQQ+IYZkUS54y7oJbndLVW6xNdPPiGVwVwUayWr2Wk00xMsJVWjMADJjMmcUxguStUocIS5p4SjlWEKBj8Y1ZNWTRN96AxygCwA0AlTEC2WHuOXqMNc9vblLjjGLZM+d8DWPrUk/nl2Ez4UJKo7/QMQnsNBl+sG1IcvTnzu019idJ+N57773w7IvQ0Rf6DJMxMZjUKY5OBR9ZUXE4sZ3LcdiZzT1O5jKN+drvPknmjJygxtE8jglzIemXv/zlc2fgjaH56JlnniJM7N985yco6jMvvsgBn5UjfXJkGAmpk5lT5MgwnIMGHPmksGSXZ6lCnxrsDloaMMEYuRPlAYqjQ87F4lx1DnM/6mwY//roAevySKY7V+Se6zpftRqyAjIrAfLOXrOZ7bcoAucfwadTBHnt3joQrl6hmHy+g7QkQSb359Zh5t133yb2v/xn/+fPfe5zP3vtR4RhjbKQEfR37/YgzkzrZz/72SivW/OoJvZ7iWKKWFjq8z3c1977oeRmr9hndOI8cVRQuspSXRqlcyTUEIlVq0W44n11hAQpGEhVbEIt9TtObrpEDIwFgkkgDUwlVRBkKJgfYgjjptD8MYGbrpryAPHkBFgkgFtKA9vOlTUptVMpVYJJlLfaJN+JJhQzIhV0YNDRgDtPibQCg64k4SH1YwwobQo2EQxRCYIeEwGClyQ8WA4giDd186KPf2g2E3kavs3Z0mBS6+xzXS9lc6kOJXJ6PlwmF0ESlqLA03kS9qqJgSn6/khsoOk8p5gmCscwBB4oGg4m0kR+S2EyhfPoyNcYUdYA8YE8OGtUDrOG6XB8wvqyBg6pmW2Bgqmlz+6miYFIjiCwFEuYE8csWg1adWcDvXUGiPKUwKdJHP4cywIRd752gx5Yjj5+FBY0HBk0yiwLaZs37Xyfk5V86Rvzu7Wulz6kd1Y4y1ZjA/b/6vWrbMzCo0vLmUUm4KSQlYJcgLjDorJGjZTcwniUkX0oDyIRD0FhJ1kW49KAyx9cAZ4vl7nNpVjRXZMzM/pwBRuS2DeyI9+4AO0UkAZOXNnkIZJ9QMCFBgKJnPMp/qceHRMBOE1n3n3JPHGShjmPSsAH/ELfEyZJAuXITEwDGtTcAfSEXt40Dgus6bTgE4t/mMY5QqQQAxAh7Ll4IEalHyMCqycOB+KOIRoBVlEiPo+HZZcmfmAY+aQlDKlI1koX5HZgWgc6WiRCwGWOUtHeSyVdCa0NJ/prvvlXKMzOLHF/BW8sMT65+MwFTifJtDJqNdPqBYGNKcYewADIXgukxcGAerS87nXlkdwXF5cYBgABDwikfKmasFi19wh1Eb3pDByihoapMepw1GRkcvv2gG1s10/h2fo1fmVe5xY37t/mnCxn0cBnD54BIW1N7ci6WB6RA+V3Bsid/gThgABvvOvGKx23P3iDQVCZsyx9TvI+SOZpffOywCs0jSmbw4fmwplQaQ48qLBJI6L0aQogyAEysRAMCJEFAmNDokq0BLL8Lme0h3IBV9QBpB1d7Akh0XzSwpg/Sj1MOA4hysvi+FFongQgzjGdNMiRDvXuQFcnJGyxWo+mCkwU49kZdDwtyMR6mOTe0glgohnsAY9RThw/DfEw1NKxU49EpSEWjiIO1AJNN1y2taDjjba0BH5CPp1zzHCijhxKpSM6u5JR9JFAgauXrBZcpFGeYz4Sep4ZLxd+/etfp+GQlhVygDQNBOVC9nlakmLiV8zauAhfoZQj0/iULoklCdUBgqUSX1PJgUCBBsWUh5tt3/wJL6GKHuP82cVZZ4xZAMcwmctQ3Q5xOoQZyDHvmC1UpD/Gl9N3sQDkEeGAQ3K6db3zLqMUlAo6PHoRImOeREzY+Ud8zj5D4fLV25cuXdpr+ajbTtBbcWbnqnDIXfKQ4goO5llzc7pZnptCSOucuM8jCsxFBxEIBEeOQDw85XtURCALRzgMfyr5gx+drEuAMMhTjD04+f5Y5wo/aMWEpuxHHzcTCh/L6Jw4dgTySGUKaBoYcT4VOXhevw0/zX+avsvZITRG7BIQWkGJHZO5OebTdKDEuiRN59VUOVYPHOdkCdAuWEy4dv0Krdhlgh5F4aRzfEAYfIQKfdYWyIsFJlZCWNVlL49TbkTSGFkM4VVFxpajXWdsTM+ZdG6nlJi0tEBFuSaYT2/giUmY7okcGNqjT8pSpgZkeMMGjg1OLsOr0i++uPHTn75OLAkxAQTo6snX89CTnPoXwuJH52dAJoiTrPCV1AwR6MmQnu93ytoQTUI1Ww1CNMJ3R6pA256nHhMs/ToaWYhzaFnrkJy56cByNxw1GaLSCR8yzLBRG+0qi4nTCiVCFNZIjA10pOilio/aSIDR+ExjSz+YIqYik1rbd/7L1udUQlEz32tLIHNpIjGsbVWEovV6IQGHPYsNxs4xTVgOj0mVwMuSJIGGCgOVMSTZoRon0+FR0oZuwDONsf6IjxOpfbXilFP0LS/jBKArE9RUpoOSc/jM8tK6G3f0dNtNWIZhtpiLtUp92J+vqF8XznDADXOekTehdPm91sI2R6q8acaSqhIxT5uODWKOJBIscqcUOidFafSmHm1AuPyoQv30n5AlQyI0geEfC31GcUpi4fSxOKQQVqdWOB2YHLsQhoJOv0BZ922JJn/UqOTBdMPtgiJEB2QcS8z4mQKfcqEd6I0jbpSyKtHbi7pTQ23PevpwrlNpR3wH2QoIJueIRcFagW2soUQgTDiI8DzVLly9gVu5YVIZejKE4HJIQzwKCKe5k3g+HE/1D69sba83Wzc/+oj1hWMri91mfWGhwESlWFrZre+o8mDQzjUwD8BK0WjgUZcL6fSKzso9Ul7c+uDGMc7TX72fub9bWynOrzWv/f1PvvjMc6+//Ub37t5Xv/GNbH2vpi8ODvrtdo7vBd5bLzNJ4YuBJfZfGb+gmU1WWSnr2XNHWWa9t3aH9zYy2e5g2FrfuA3Pzz/z9NmzJ2Dn6tWP3n7jTSZRz33muT/45j9qdnvc2oEt4su527oZbWahlrlcv5ff2uEYHB9sn5+rrnQbe5ff6d4oMW4bVMr0N61yYWG+tJfZpj4LGS2Rl0Y1pFrtlGBnnrvQqanRUo9TfZlBpVfh9AEnt3LbN3v97flRu1gr1nktd9iqzfY7vb1hv4k0OsVml9v2crs5vvtR1kxgrz5Ckt1hsZvplru97c27mftrTPCOlGde/w//7gsnXvzmM8++cfIRxseDXOfiYxerMzUGowP7tlF1vrZVLbaH/d3N+0ePHT1z/OTO5Vunlo+8fvfKcDszN7/MjKK73V1aWmTUKrVCb1BWbrGjSCNOTLCLLt2OamAlkqJw+pQrI7Nz1dFMuTnodfrdWU47FbJFTkIe4lyjpiIBGnHPRf3cIQjWTsWb4tWmhTnWat+R9rQ6gyZnLUWtz4gS4J/7jmc41r7A55SLnZ3CarCMrrU3kllPrOmqEyS1TdolpJB7Qkl5Je3IimD9CxDPPEE79NfR1DDU0U6gHUghAR4gsYnEeoB5/kwaJgkZiFCEfbigxgo07lMYlJ3b2Xs6/szNC/qIs0wTeqI8sLgEOQ3EDcHJbMyZxE/rj9CcjVRAFCQ+YoSMqUUGdjpdEGLdKUpmnN0Odmq6g26Hdw9kirEkPnJKISeJPv1fLw41xdEF/nh0SJrVA3PlrkAOymFAVdSMTvtSGFLp4ASH+KygXMyJLU+ffQ737qlmTMV0eqh36vTRr7/0meqgPRi1ZYI5yMDZrmT5g9xNUIjlY1zk3PHxcSwoUw+hOuCGDDg2CNs68jFi/ZtW0wdGxwV59VBsBjDebNcKucdPLK+fPXZv+1qnqxsxe+0O79ZCU/3vaHR8dWF7vXx3XWe1cLRUbpZCizi3ovepS3p7yVrdWEmcQ3LUueRBX/cx43SthzdovWYEwNEUNemUtTnm0jZyZUBMX5xp7O7dvX1PEkVo+mBFqVJhA491Ccy0FuxIhIlj9ZnNbPUvdrsjbBNM8tJIDEcWk3l+zJNYslTQcdsCzYRIaCz2GMJT5EAGYmVCqMZBqvgJb1OJwmPUENDEhhUg+s4DjzgkADLUY3mBJExK9riIn87MkzuEcERL46TDkSaBGAaBtDDghSXgUXa/vEYylBt9StPxJGkKU7EP+UiOridTBD8RZYZ78Ow5YqBk0jhskytgtHQgWPu+6JT+c3KDYdK9e1kmrm+9eZldpZXVGfKS8K2OHpJtKhM1JAnMIxgCXl+s2UGNSSCOAHAW0XizykvnxMknjBC1lE9qXvzy5SqVgo0YXjKWjdfnCQsslO/utG9m1s6cOxJVkYP4qqMhhSmsFE/1790bbnL8WNcjN3v6SAanBfBxYMID5xy1I17fYXGK81ac04Q37GEvW7v42S9iAd568818n9u9stwqG6sDbkNTY64bFB9q/hcOWMhCadgH51gXxAxhlZUenbVUhjVM2ayKrcPx8psvVU0UiiAKB5+p+BCk7UVEEIw6Fa2VfRWv18dYMLtRFJnJtu+nMYZoNdP4o4xAXWdI64Ilyh1RBPDJYZzYgBSJPyQHnIQeCx13YiNJSFhoNoNzoCPHVARwniO+88YAmkyzXETHTfn17Tw3RNn4yosV6ZAwzdhU2NEOxPF5d8C3OQJh8EMHYRF0k8yOeLmePUi9e0JJUSN8et9kr5ZSjllIZA6E7gufIiBS9m15gwZMgkquVQb1NtQ1ukH/qc7HqGQZz0vHYGREvrTKP/rmn3D5DGdhuHSMW9JZSaIVFyvlsk3Em3ltG8z25FcG4omeHp82Thb+UQYYkBxScqL3EySqHQlSlWhPH+Pl+CZCNvvk6fnf/9KTv7p95+bN3fag3dnbnl1cZQBFz0am80srC43m3vYWtxRgV8jBsh21Wpwk2K7OLEBB01+VPdgc41PdYm15icsLNvcatzfWzx05xkaRhhkU2TYCdcjWjldCFhKkwupQaJjGCHH8lNy39vqcfX7no9tNVp85zqr8WS+RmGdnascW58k8n+kuzvO6J+uHc3kNgxBd7BAlAZOSzj96j+uPqtCkX4CIA6fkBdwhIE9FfeqPzkPa/9SzSBFE4Cb6WEKLQwhRDjGgGI2QJ5w6UAD6/wlcMtz4+CSH0rfZx35+nKKXxpZwVLleexQE7QKBHyuUV6VYB5+m3en2N7d2ZmZ0Og1HXWPoMF/WsnlpTqMI0IjioBU9DickOARNcrrCxx59gmNnm1vrwiloyGFiJfJBjtfVxSrXOcPAcLg4O0OXyVCSQWy9vgezR1fmGOv223VezL74xIXPPP0kfScfOeDQw+56XWnZ2B2NOrw+naoE4FxvrljsltkuxboKYyglCJ0y0cAXuExHcKIiGYl3XqsQ1DohSh27GBJSRlIp0O8wDPnmqy/ubl56+xIfJR4xoIZe19udPvSEqJWxRu3gE6c/M6fGkfeDGDjDFGvEJ0OeTIE3I1UGTRgwgAWJn8M31Ehoj8avSD7YSRLmjHOVi+Lg2HczEcnmBAz7MdGlAR8T1suNoHgyfAugZkm2qeRw4E9kCYJn7IEp/PTjFH8pehNBSxJKYklMEQklbgL7IR7SPDg6lCKQ8EPQkFimEBECPRoKRHKxZp4/BoglUJTcwZm4JN0nLXiRsakEDscXsvqYiXohYaen+5w8FQ2MfQ6Guc6Pv7k6RXDqMSEoskl4jJKGeMYTkMDLBEskhlUYQK/wGXxIDCYHRClls27SgEBt+ueL1Ida4mn6Y/6SUJqrkJkVhwxgxrUWswC6S0ZN0qJIqBeiaMNFvS3Czc7CL+jaDQbeIuvG2vB1YQUumG97V84gDg9GKsQK8UAXWYVPsnIc5yodNZXWkUEDx8P4Gq/YnIF65+0phg6VY/MgIGQWTJfnCxxYIIvt7Xo2N7+foDKFW6sbUvF9WK6VYITBYWo+4MH1FO2fH+VMNLMy/Na9XY4cQxaCWh4ZDm0vU28vksQdEA3D7VrYP/qjP4JCq32LxeL2oMWOunIbjfSxdUaAOb2eA/7ly5c7/c5XvvI7X/rSlyjFWuVteMa6kYvN13TCmv15vtDHaQE2MDl9/MijF9jt7PT7nFrKLy96uYqZIt0dBKvVOehoXcy260UKccgNgPOJDQWbWtogC/La7u7RHUIQMIXCuYRhFQrg4NBZmDGSGdadca2Mvq5Q7w4vXLjwy1/+8pvf/CbHEzR2W9ZpAqbosIqDghd56dgidM5feITzBd/+8d/8/u///vrrOoNZLmZ+9atfVReKpBo26yoLolEbN52HB6QmwHQTgCxWgwzXb6+TqFk5hzzzvMnz0E4FtDJ6eWMWMeASEEeJqX8AbZDVmCf5dArqMycd8IjpxBEOFe1YHhWqw5J6y3W4sTxBDmrUGnUVspvEAOhwwxkndLjz5mENKrAPiaQ9lSWY5t+pOOdjir+FkPOAcGDemSQT1FUbMCPWI5DsmDdHA+iCIkAqgARwTsp5jggA06WI1ID7K2meKkku0UQI9aVhiC7i0IBsuptUnp+mi/nCsBcKCPnCBgGAOPKDnwfnKuNg5wJAI6HCamR6LxgjABDiGoImajBFjSREK1U+w6eBaLbk6LjOD/jODI+4qeQHPjoaPmwojTnIuFR5coKeNokOlHn0HPEJ80UyfEb52KW37mzeu8c3hzje2FhanjMB6ZwIm6wYYb7512zqQISGFbZwwOeydZKlKtsYS+/0PWt8WqVMJfufanHBCYcmOk6URKR+wYEmaTGkSM8X0W7d0JYkKfnrdjoLi4t+9tnXsWAUfL4pRKZwKd+yiNImR2fvAVkT5b2k4zwAM8WsK7kLVuGYyrLDU00BhDsLTFjdiJwmmA6bgqHDUcaKjKkIeI6UlzA+H6oCwVM5ncBAolzeHDwqTSpCDgNGBM8UOlRNBD44FcguBKElOg8dJ+VE0uE02YcJU8YHoEHZYx+MNkXBBesWQE0YZxNjbcbncnzkgj6UDxNyJfSZs0c4COz8p/0pgulHcYLhsCaMTz8ujbVDiIyICOhQIZtG3L9RLvOaAspDEiApEy56ZAcQh37Zo6wT601AoAApJtHA1+6zc71dqgzhk5yJxd4rvdkBzpqxtd+cnWGe3+1qQIVjdARLYGqzJ5+/ffMWPnNecuTiESgjE3jzTYUXX3yR0cVHv/rxzk6jbBN1J36YDxGi3IcFZ8N9r0kKi6mmKMYCpSPfQIzA/tp2eSYY9gtVqVqwVyCkMuWJ7iuMRckF8RMbcPZTD3T1Q6kl4aSvJJW7FIrKJfpWQA+D7wgBWwgCOJoHYhjhA0HyEUIgnQthqsJnEFCOUZ6LEWaDsMM4nCGrECaTK2PLmoQe3v/ocM93CicNdA7TvglcLJAvPCCufMGO5phMwJQkkmwlhCRMEtiJfxQQCUAEHP4sEGrQMJPKisylAl/4whdeffXVhXntkcBAxCcAzQdvMAiBKZ2N8I0Z8Qckygqa7lIZRk0ewyL+GGQh0vJLLE3mc1fqd+78A90jPSm7O7Qmqh6DQ9m5Xf3a7g4BLECkQFqGc5SI5MZmGK3x6GQB0mPSofd2N2nLy5UaNCmLa1Sk4wxI1uZctuQFGWYrlz7kYNJHnH2GLFESIB9dLubnZ2c4xFPk4Bpvi85WWArkFl1y5GuXwpE9S9WlURbE1NGjnMmQu+f9/w9fPFurifx8Ui5iSaFAGIJQiO//TVGLyARQ6nSmHo4QpxYfYdIpTxF8wGPMy3GcIOFI8wFpf5Mo8iULFyx0xIbVu4fTuaNm3iTRSZQNhFKpgooCNDTZ/LjYDUGATI25jobFBG8dJGEsXa4UmdED0RARJGtWRH2sg4FapUaX6rKi6UGZjo1RLo2F0S/Z8Z4iBIlarFQ4KHZ8WS202W2SXUvr5PoOAHyKVXoE6xPdtPh8kI6L5DZB1BRDYW8Zk6M4Z1VlTJj2UsAY6EplCkAAACKiz6Wn/sM//MON+nfu3NlkMQY8vbSN07A8MY+mkC5k1cO4AcZ8kvzsFwTQKBAvPkUpIlVOmEzgffIH115KAecuB5h0MjHwSanqCg7S7PcTQtYU7W4+rYXIqZIQkoWnWwKyd1IW641zLKZpe+YkyF1VNkZLwPpVNbBAmQYRVjVM4JNpCqLexSH4TmSKgD96ewg40qlxPkqv03YxncqOruO7KiADcYfKas0x6K7xYApke9QxMQHPJQbUPdgCYhrnwWHdCyPJa2CqJs330/giBPzkNWTJVPI9poYFPgpvoxnklhvRqtFoUqlsEoXK43Scn1jAyN6ErC06Rj2YvRgLPk5XF2qe5d2tlktcc3i9DBY4ziUcVn8ZeHH8TGIEgYGJ8rfzy5ZCbI9P/Wj/Fue1b5S90pNSKC81Yat0wlSP8N2ZsWD4zRNHMfAZpYJZKFbVruy+yVFekwR6Z3HlJ6Nd2qb52m2Wk++Lzq4uJIANX9Q2BNcMmQ/A7jucslkN6ikySVjZJWWwKM9IaNHpLWswS0XUqzDKxcv7Gd9gDk6eWW12GpmyPpeeLdRa3R477p1e89q9u03S2SKmW1A252RmXYZ2Vxoze56XZ+ay3X5xplqem9m529hpd7/3D9+mXv7Tf/ffkW+lWd55/e3W3d2ZfqmY5QaxbLbZ4k8XCGIf+3zkdZCf73H+kg8O8Dbn+cdrjz41N+qfarW3+rnu73zls3yogL1HlkIGQ84nlx+7cGa2+nssKH/w/tXTpy48eeHE/WPHPv+Fi6x3f/e9m+1urd7e6XE36V67OLsw4BD3oFcZtGuj7nymuLR0pJnpcH/0+rXNM8sLe6NNao3Pue+2tmt9jvbUcnta7CirfsutPSRUGhZa2Xa2ubbdpkPqStXm5wqDTPdya223vtvm9ua5XHVYIPdecas/5O7DbaTa5RAFt7WV5nMzwzl1Zp3RUK+1Pr60Qq823+p+9tSxGi/xv/PuHz1y8Xvf+978mZODwSYHLJ/mSPORIsO+dqlRo0NtFU6cPn109cjNmzcL7cGphdVXL770/c3vr23fvXDiycyFIj33qIEgMTmuU9bGVUmx/l1hNHx3zaGhHD9x4szy+V/84hdrO5t0t6iRorlG0gjR+mz871NcwdRvmk4mAwJXM+XoUURafmP1S2ll4MRRMC4wmygtZLFLemb3WHgyierGQxpv+colKY962AknltUqQREOWoWcudsSDaQp0mwZFshKOP+W1DFJpjXEtKQsFq0GYWJBJZVKDcASWn6My8OQwmkaIt4UjxJsyDSRlIgkxfTCxceEiIoQwyHgVlGmbsIly99mo4wcfb1hSDJ8TB09uV9nmbDod4Q5056/DsVg+gybHgqTEknDAMLkMV06wl6cGAsCYWcKfO/mLAlpQ8UFCfCSOC2DNUusDYeaEjEn9PeVN7Ly6wYSrRun16lGe1cdkCyYVEV/bmMdz+yc1XSSrsIpWs4R25KNDk/3BizJaqisiQQdJm8coHsSvY51E7Zys0RFGKuLz/2syOnJC6tfful5Thcz9ZFsTFFd7FEB1IuF7i/J/pBf5CbJm9M7hJqiy6k09KB0lt5y7fAgQI7AUQGlsP0J0eSUH0rAAnQuc/74ysVzp/s7795pZXp7u5nhcejDNlYxN+odWZ7fa3euXLmjY4poTKhbrXHwEv+Yx7AQL3YExJpwzWshy01/OistkXD4XibF3/vRM3mEIpu+mTp63aGQIMIAbnN9g81OhrCoGf07k+yVlXkQKlU2fZGwkmU5+2wXB0TVBSrZWruwrA71hJZyPO6HiFOhJYWfSJFKnAoafiAFV1TQQWRTCZzhCUB4IG0aHB9jwFslGkWAXPyRWJxVZWjLkQHgMRwDngWPxKazS0M8HP00GmGrIImGgOerA3aUS7NT+XpgU0b1GCyhSKk5yilsQdcBgx3sOYfgHxydQNPFTGCH/WL0ZBBwkbgJU+cKrYdRF8OJPh33o5nlRqu5OawBCz3Xrt4bDcvHji+hq3REpiQiFIUQaYp6Qt9zoanSCDmeqKLbtQm0MY1O7KPQMMBCz8rSMvE6fSmq46qBYSsgkJAd0fDQxwDIwoxKZZ3g5ttgC4sz9AVbG63RYOPk6WVYIzuxl9ErHVzOD9OzS8vF2szuzl1OTDf3tKhkawLoiXL0DsLHsZXqTLGkb1bTJEvVGb1Unaucf/L5I6sn3njjjfUP30II0ciTdsqxCkO9kbvDqX1sFM4VBqCrCQNYXiWxq6L1iUOWEq1yAjFwYuWPJWKRwEUhpRr+mMhKSORF9rJMxT4SM5bQpmnNtyQpQlZ3yAUKlB180lIQHJiWuenwZJi8PFaZyqEk6qBjEgJednw20gZd7ilGv7SkBTZBctQ15HY4fL/OUzWwQe7OP+/r6MxTr7W7dZ/egINy6JPTF4cpYZHvFDXn0AtyqK9Tz+NIFcOe1YrpGC1GzZiTyN0OnRFZ4CiI8wBuTK2ackomY6IoLhRYey7xKktOHQfx/LdXfMSt5CIS9mdpzfxTLi1DlQo1jne8/MWvgNju6ONXnRFLVCXYoBJsgEZOyox+BxzeW4JBtAuInfVV46MnQRHsTyoKWvRDScnd6o8o5n08oD8QYh8euKrscBdyHNWPLhb+0Wefuv3L7//oei/T2c6POs3denlumSEc9cmsZG5hnk0jvaFm1WQ+jYC7VoDIwkuLvA2Enlfay0tAM3ML2+0W773d3tyY5ds+hTIK6zyhQRIxzKvIcC4xypnQ13e7H354+72PbvMOBlMLTqKimJS1mM8szM0uzdV4hbJSyi8uzrP4zFpYni9Sjfo29c3xQhdkknJbdcaHyWYloY0xE6Tk12Nd5g9AS9AP/X1wLocm2xehat0HdIDgXharBR2dR7DgU3qTq8UH5aEssEQbwGec587pBDsLhomFKAvKPhN2mo455ScN6jAGp9A/2aNn/fBpnO3YzPWNq1jLzqDsBhZNUwaMN5RRYL6Uy1YmM25WPIlFvb1QxNKtQI0zBRg3YrF/XIR+9/4dhp10iWfPnK9VZ2/cvEa/Rl/Jd7NtRkO6/dJQq1T7tupCusucf66yMKIFVpLzycOFxcLxE0eL2UG3uXt2eeHc6lLj/roWnbO63q1armj8yqvu5eLcvC7mwvhTa+q76ZWsmBq7226NYm3fi5c/KBHckou/YcOtA+pGkRTVmqprEEiL5SGAIxYhEc8bSTRTXkbigQUy3lXq9jvnTx77r//Lr/2P/+P/vHZfTZlXRKRu9n0N3s5WMmkZH66QiBmxu4UwIyqZ44STuBgkI+WieToTnB4CZzStrXC7FDdBH/8mJZiA2APaG8afPEKNorlDVvo2jO9pjdN9spC626kUXh7JNOXIJnkKs9nk8eBfIzKWSxrJ5DJBPB2bDjsnQKZYivA0sodhO3K+PxDxiUrTtEdFehLZ8Y9zJHcKU4gOD+KyiW4kC3Gn74O/mNChTo2E3uajgIgF4gk9iYVl/tgPjxDaFRDTMx3jhVqxpHOaJDUGhE/5CB9YuliWdMktSaLfpg+O5nDPesonChwYIOvInuN4qsQ3427NBXxLpUZrVa1mk+bHk4hI0g0oGJuaU0/5D4gCy/PygPtAOK8ioZmceUcRCEK3WG8dxq22XRCPy1N+cBZk3MNjzBoCPHpFeDjBNrz4YAFSTeEAdqATRDIw5Img2W7ZHaZWVTyyGLrZ7aIAvOfJLuJgUCMVjip44+23YdmJAxnnwvAt1fCdCBOVR7LZr3/963//H7797LPPDh45BbWtex/prE2j+L3vfQ/rDk0+hQ0p7DKpGNOgzDxi3AWx7xZizTuNBpilvO4mLtQ0+yqXa5wCmCvPgqaxYyZz/PgReL53f4czRyxAy/QPh8dXjj///PP0Isf71XfffZcssPiwATHbF7C3eFiCLxa4QOrK/TdY0r1WvQflxQUdqb6/fZ+B1FxmRieRGbgruWSVtxVdsgBCl0GAIwrAUVHCbJmSCpbURQ11JIHxHd0kzANU8nye1x+JBcJu7Wef/iyxLEDDVafeYQn4y7/zu5w5qjd2KClfu77HC71VlqzLexxg63brrV0ucWv2R2wbQE2lO3784sWL3/vOa9BhYRGukKHVrwb4Mj88uslNrSQawlgxoHz27OM///nPYUNM5vSyj+NM+cQCcX8qKoGbElsczPBrjVFpHN/Zw08TIZywLeIx7En2+xNpJzWcTEOOqWRuu8gTyj55YwwwlVE6VZqBdBgce3QJKwMeo4N1hZN808EElogu1ZSIsuKMS50uXUxIIF20NPPpcBo/hqeKhvKwhdOy94w9yu0kDDspfM8r3b+A6RUK2akcvaSRPYbmOH9UE0hcSiABBASy4BCwmU+C+lv7DeW11049E+dBDNDd2Ov2FpzgAOXlz7XIIygdbQTfA9S6pQqe45gYJ+ikH+CE1x3+0TdfoasdDtukRGyIInA42V7SCQ8Mkxw4aZ0DxyEcCToEBBgGTsDtrRKqdKF5OgVuFoUrtABrxiFoltLWrm84KUMgOJjh+Ek+z0VNWPW1+/pWTHQo2HCFK/y0IeyUI33HMcnoNJn4sffAnB9iifIiUJ6pVJ6WVD444c0Y66R07IskPkrhuCW8gSNS1hRhAz6BgOalizx44OF95+dArh6eyIGY8J/UwDjeM7KoUDvjuCREiQiCiQOTsEMIU3B/BCJn5+MQDpj4jiwySasHhSTECpi4SDYGPGFEiwFSEOWxhIFDDZpOyR+JjfihwFZHjuO5kz84jgkrkaDjRN+5jY8eSCOnETxT99M4MbnnuJ+IkmhKmOLSkBwf33kGQZg23svwoexMZmlJSop+Xr16tdOtcxsyCLj9pCIPUwE6ZfSWJC5GqowAoxE0GSKEOXVIf20EVWWBB6OSFjsAI2JtxF5qTDp3WWZepYLIxvoOrSmb73AOWnNTc+LWekmeGIFw1wfjw90dvTfT3gunoYliBY9YhivEcvsNfEKfMD4UGKTBGGxzue3Gkdmf/OQnAJ2+cS6jis0SHat3U0Cv9rHMwYw1QIAIKbCNtE3+FD7qDHIQeaiFJJYd6UO2ou35T/uQAuSiA58scPpkamp8O53GZJsGRkYpplcfsZb7RNU7JCb0LEjijlh3ESEGAk3XNysSaUH2XCIaAS9ImiAQSsTg1keP4JDW0SyQTi2eeSb5BDT1AEI61vFT8YFChEd8coQHciR59EmoDA/NLRAGHwkYsgj7HwRdLDGvNBskQau/8tWvc3krxScKZCUpaLrhupAuSEzrwARFssKR1roSTxe0jiSusgQivoDmHBjDPDrEA56LQwjDGLnA6pNPPvmTm2/xeRqA9LBHZ5doSgUuU+eb7Csrdt5ZdibwgdGxNw+IDcStRZBWZbSagiyc0xKZnUEQVzsyvngHSo4MGjyIEzM4jIiY1Fy6dMPOPsuYc2IAo44gueRqZXmeEQJnmWjvR5e19FytavUANHcKRxYT4Mf+OidpNKcZ4TGQxvlthC2jT0zYufVkCP8B6T0WfMTu1QRyTE5tAqQ6HM19p+Y4DyKd5BowEzbSRBKUj/lNJ4Fa+vFjUh4SPUXECbrmRxPAIz0gUXRPaBd35NDvQM+L47E8EotK6w6oPb4X2AWHN7Dpqhi1ch2HBqJc8VDUnmg46WCGEWlPsQZZHL0hvkcxuSbMXT1s+TTqmwB1vJqvo7U09YYsxNtNHSNjwgwEbj2tF0c5mmv3tCRNEL9ty+hcJIrjvJkw7UZ7HVmhkaqrYbU52PMpDnkk1omrFcpQqM2SLyMN5AB9DmX/+Z//V3/396+/8877pYKNDWz5mCM2JCc3KJCQsNrlQdpDLFkQFRFABwh940dx4pJeezyxs5iH8EgLWXhGXhaWGBknSD6pNwBcjA9BbwLFN28DiO4bKl5AzynBTVc8skAqEkoSK9HgSOtaAMNRIyfpOKLMfYKrVCJnDjgr9JIxFzsiMtv2D2lUEwEtKiVRXB2Fz8ZdRCMQiTsQOkD0IWoNS8FXwB7wzIlwIE5IN0ZNd6pWRl0dGZ1JwUcMCThyaNu6pg4JOgxEpxzMAUGMrlsJYjKWsi4ABNhyxkPFOB5bMJl8u8eNMTkOdAmhXOjnM61+l70TO3vFS3+YAK7K8kXqhEWS+86eFVcJD3dMd2DA49OIXn2UEednKCZpKIKe1ZjXbo9jeu3R7hGtNxjQREqVThIOBbB/HaSq6vC2ksoY/VRGfsbKj2CZ/HXk4uNcgi69hTHxpjEDZ7pqNE0+56VzlqoX9MRqf6BFvXDS2Y79oPJRKdj3S2eoAps0SBLhFA0XH2MgLPQcEBNRFBCHJn8n4mGo+zxEpQA0GvG185mZhfu7HzR6uwvZ+tb6G1xt3B1db3SO1moLm9uXqeJwXxIF5vAF0uNvwHd4iig5FGqdbLVaW+wWStvdIjclDjPdUe/p5y/OXuSrF3m+sY5N37p666233lrEHO/tDcpdbk/NdivlzHwzr1s++kiDD7L3doe93SynodvDWn6Br7qyE8LdpJlultzYDZkrzmbbg1qBTMQ5b/KtHpn/3/zzf/z++9eavZ255fLWh/eOPfXUS3N/sDXcWsxcfeT84Patrd1+IX90ud7vz9R7uWGh3O7dvPz27OljDJvOnn/k0cefGG32q4PSXKM0Vz2V4aK8jQY7frke/UKZI4rFRm+U6/EaGyvKS509fXOM22t7vQaH+NAnvsmey1RKermV9nLkyMre1hZfVsv0dYkV39VGYpVSa7e9W+4U243d0m5nvptdKiwwvEN1iiQp5zf3uvc+fPMLz1745S9+MVvlrEqeF1fbu80nCoU7vW6j2dbZgnzu5rtvsEhdGIw6u42Our/ihXNPcsJop0bPlplf0SWG3eEcVc93PuiqMtm22kV2jhqslIvcmXXv/hrSLmX1IbVquXT7xpX26Yssye+W2cvNb6yv0xNnhjXZNl1bS0dnlgHdVE1LowDi6Zvk/Hg7Ij9FaQRMY4K+BcDzF8z9Sb6ImMJEEAK0sJJIq6Hpym1Qt1fsnvIUiNpvbDnpxoJBEDwBQZmglNxaAd1rQVvtQiFLTqzZr3fnthzGs2QlxbLMD/Amo6AQnOccLDYseFmS2PBrNjPCxKYRsDwDqXTOznbETwcU5cbCituPskFKJhofdpCE4iMU2KZgXL640R7saRNGZXRLRKRR1lIANYjvZfGTO4yLknyERVSsXB4jh5YkqR9ky5BPamfZasvNcpjy9P3B9nDQHg07wh27WLcCifFP1aETZIDN1ukmGw4S5IpSz4TsTIcoGsMHwUwD1eNTWBo6Zh0JSHqmsIx6YVBHsohDuRXQqgp3XJLUZYesoUO/AIVBlrN1md/70rOf/exnV+dL2qvyhVrsS1cdExNFtSnruKkKcYAQ7A0kD8MYDiZhw/SNJ19dVT5MHyV0HwGr/VvnK2qqDQ7nibS0RZUCoM/t295lQSYnIRAHH34y5NFjy43zp9Z2d7i+vtvWJyu59RUHz2SxMFM7c+JoZ+9Wq6XDlThUSusLum5Il2eqaCgV3CoyOHpA1qq4jbZUrdS369xJzYEM/sEF8unrpAY23+4yslbPTAw4ickR6wq3m/fu37l9m69AEsFBUcbHXO6vUfJowDk19BYZcibCl6pVOtNbfNWg8eOswB7E4Q147P09L0dw32IpnkroxUzhiLg7z8jDXgmElTZs+lIElcL9NDIgP8VqcXCiNCEnQJ4lAXNuMnRgJo5FVK0ijXzCQRo7URLzg3EWT3QWjcsNw8sQoqVclJVz5eQhotG7Rlcqrt1yzrC4p/uyeIZtHMnwyXAyLY1J66S8uqSUrBKaFAiz4wM6qzf0OC6ZwkALqdSd523UUEzpvSDOl2tUKD75p5QI7gyH2iQjUriDJgE8xvPwxowRiA91PAqgB0Bz5kMS40PhCWEEsjBvuSvOW5y3MkyD2MUUZDP23Q/maTBJg0J/ucLyCGvQt2+t13c7Tz71KCMNmquXwhYXfCcgFIqsoUksv7FEcGhMqlCIyBsCmCuLS8ePHAVCWN0AaShy2FdzCdDuJHLEYyqv3TWMBJyyiCUetA+hKi6XysN+lS3JjbUer0XaOWhy0ylIkU5cLlupVdn/55CjPmiB44Iy6GA+YKMtg0F/WthrdeYriIDhCd0swpSxqRPHtzUee+qrjz31+ne/xWY/WZLK3w5BfkENpSE8abxO7iRSuSSQwITrKfLiEBk5Q4BOjHf/fYAvdbRkCXpI5T+qWNWeuSQgyhYmI48RU2YruGN40NMmLTXJYIRawLKBRu27i0lcgxkbD7vDZlNbiaVyrjiLhVN9wShOnCX6BqkCfY3OIytG9csVLpxGpkBWlSBLCsMR9k2paI/DQaPVhJppl6SE40vVhfzAbwanfVuvwdf5yF8Ok6OMbOPNs9aX+waDu7dv9joT+4XEig+TNj5OLJkTHWsv+OC4wXEROQK+48RHDwCcgvBIQiqLb3Oz+iLzop5KlpHKxFQ4PjmHvPlxGL4FsDqy6ra56AxLSxhiaxiqT/NwYJ3T4KzIkADKzTY3yVafevlVbt44cXKFO0ecK6sv6tEKaeOxQV5y5ng5bk5XntL3NbFY+dEWSZgt0LMUBxnOS8M2dSQMXGDUH8Y+vEkzM0xVmBlBQI1TmQHSj5K5FkmtBFMzpI4pDW9HMXxfqnT++Buf+3C98atfXc32WvlBZ3P9PpcDUH3oQLU6Oz+/xJobCaOYMXFINcf3GOnG1Dagq6k9eVtnLvYQXbFSrc3xvlb71voaF1ixls0sA+lyZBlq5C3erNX3M00qbLcx+OGPXl/f4aORYKEDFEarz7VqZnl5nm9ClLkOt8bu1EKtmKkU6Ewx77zKZrVrymwqrdxJqXKak0isTxSK/dFDwDZhPZkGKqeUcysJKEApeyqWoD+aHssqBtsV5iNjVJFVLYgfhxokTDho6f4YiesW+gQY3sryRyUGV3JzOlYdevSARUJG+TkCWuGG3Sc3nk7ZUV7NwpG8Jxr3UJ4QHzSN0kEwEGIilxj7kAHP6+OQEWEYWChk7E0ktHxVRufEWhBBcAwt4cpGbp5XAvIE1nqcrJfGAAwYiabPNlJYMSG329plYYmZgRyntngrWrsfoeBMHtRsiWI4SkBba+3GaE1scDDr8ceevHP3Fu8iY10kXmuVZueQnSyPZ87hMcIz2d7q4lw1z/Swy/l9WlO9tVGd44DF7HBYHzbX54qFM0cWRq0djhbDWneo5eMuFtEZ9QpKrKhrkdqKRjiynEDwGO3olU8yVtNUNr1uh1Zpp9R0QZUQrXRmH1hBshvtRzY+NznT/OFNsZDu9xjgaopuF9bTJ5RGg1Ory//4j195/LHF//g3P2Hy0h2qA+pyHTvoVKvGdoiK+bnVsVeh7BlmzRsgFgMM8S4JaVAB/+htT3z6GiNvUBWKNmYXhyI36UL9TAK9/VLDuk7aRI+55bXw9l4Ty5edZaRWpl9EJoCtdoKVnSRz6FPCXIJAAZJ2J1m5WJPIiV8ERMEAOc5+TJEy53TsMUAicgxMkE4eIoUEoF/ylb1L8k1HpcNQ9nyFKflpMo//4BzTFA4LT3ElyqHhSxTpWMJwi/IBj27MQyIfzwiEmGOaSAROoYHDhqoDoelDdpq008EHAnwq+a/3mObtISmQBCdtwiVp0uWyWClYEqtfCdONDQ8W43XNUzptOuy090McPuWPczP9ITs4cD9GJYoNSZugiISCzo8T1ONB7jD4QbgfA4MULE0R5HEsHysCECx4Z8h7J636zTWWIHnCjp8+dsrHPahApAMyjibkeTspYjmAxllgJidYq+9///tsErJz/sTZ05VajZkxH6dYnFmkO3nzV+9p466cw2fAiEllPIj0vHvWN7zs5Dt7j53NOvhUJt3MgEUefUiHcWam2emUqjU2EYF0e9Zt8cL4+XOZXJNtwCv3PxRmPru0sFTq3uMERLkwz8JrtjOkdIX1hobydoKGkSUnAgZzqjyOFJEKZhRr9gEJwCFGkYI093SuBzYYILJ4D6v9tm6yaw65d7QzYMKVVCUFAXN+ME/nx3cNpA8aHBe4gRp4fqbMa+OUnOV4EiKxDJd37O4eXzkGcaLYdEXsrGrRBsl31NYGDMmRxqAi3eYFJFIhZBDu8/GR7W3GpsBhBh5AlqGwUTZWip6EzkU9SeLIV8j9/jOfeZ7bAKscHSwWyYjis2nMkJTkVOK8fYwxSTTxSxaiigtmIbS4MVLSFB2CQBAsYVIIYp2xR7nvcKdJGxHKPo1N46dTedG84LEK9GhIaTqE1UJpi7Btp9fB8YSCpJzTcR9eCGA9KIICcpOoBlKsdXgeHfyAqEpJpTkgOIGQQnaZTGY5Fk66dAcQNRCMxdKh22gLq4jGsqIJeNYeMH8CbjQmPKhFbsEnzit3AskeiHUED8RUERN+aF9s40TIbymwP+uYEbw5/+A4n+ZLSw9zGir5hNxqKuCbVhD2VLIhdtrU1DlQIhVFPnZi7vOf//xnnjqLZePTpDEJUbGynOEYNcUJcEcQ54k2AsSB6YHop9MC9Cw8Nk720zhTYQqLRTq3s/fBBx9i0LAVmCZ8CggRkDkEXa/3r1+/BS8aUalbkJui84BHZwbzxSoMaN5TY9+ktyZhJIMBZP2OTOEHI3nz2g3wEQJp+cAglhA4ztsxaESRxPkUzaReXG6e8AEseZQjPxgNHG9f6SJbwlBHU0Qi/hRZOExjph+d+XRsTOvAWLoIJ0CUx6aBETMdlc7LkYFEtXJML2MEp5N7EmTuEohRBCJ+JJtmJoadeHwkEImkgQ8Z9kxdiwiHRyvQFEtE7c9oCiedaYyyVErrxO1Rmo8QUEWSMCJAG33LKpvdoH//xS9++fjjj505cwK1JJUPLSg4jkdcQiScvfV8AYKgpmEjIvSZMFF++zMCjzwAhAg+QKMnLxLxAM+eCz4jH5oY60AQZDgBV3xziXPQ/WGD8RLZQQcfOAiQomj4ThxqRDH7NRzlwi3YAmb1PYyKLZs6Muw4svvgf+UrX/nwww8vv/m6JNAecI5SW0isznGOQcyJmnPuvj1GI8eTHEWEFAG4gj2t3cFDxFI57U/gfc4NexJlQtKDlUVdm+fLI5Txcdo/STmxGfNK4HRk4FNBxJYrUgPCRELN8BM8+wWTDC2jQJlwsHg2kMPi4ZK0OugAEUTuewxOEAR4I4yzqDBqDZmacIjiMfqMfrHeSL5ga/bGQJC2BGbIE4wm/CecqN6nEB78CE3PBTTSkgNlcbbtEYAQHCfmTyCpnzF5SFHdOJOM4NQdmFBT8myGEXKloNh6q8kHVo4eP8JR4he+/GU+jMmpa8/CUoUagaD3OAgxxnp+8ObsAQcNZ9OF8cjH0aI/ldzhlk7sxceIHwPgxLATcZ+3o156qcn21V4mw8h8sy1lQCMpPk0SSKfD+RgUVGMn3vlPE4EmmcI/QMKJGRAnJKfhM3Nh5I+RYuI/PzOr3lPXvyTSMHmS/Pbt2zfubN6/v9nW2TTxxR+l4WKD5eUFpM170XTKS4tz+MWR3thIs2EtUJXuFWSciE8RMmc0jW6SLOIAIJyAP83fdBa/Nl3jLbCXJpjmOcI9EB/TmUZRAEyndRyPHfuHy8OIpwl/snA66zRLaTY8DGYaOWazP1WMOjBwIJEpTKeJXcVe0Zmityyfqs9K8MyKassES5t0u3q9g0cMHfN9OjJ8fS3QPjrl6YxsqhhGDuJQQKVJS9dCjujt6dOnUWwMJhrMkWqG69gW7dpSWdbHakXZlDn6HgAMDv/RfG1f0EKtTQ30TRxS6vAfo1bjXxS8gah1KdkB/YXgRg0f1tPZeSsRQTpo1rXZQpubw+4tLpz78Y9//N6l20DAVxLb7JIJpuvRGqbWoAVWouBAE8+2HUgUf1qb0YK3MiUKPEsiASaJxr/AiD3Qkdw5j5xACYJsZCNz3loUgk0thXCQOwzuuHZwJlloCMmTvREeGSNEjm11H0Ytm4liyL4nJ0E0LCPhdK5wKereCwbLLkBqv0VPOL0nYmqSlCdNKlS50cGokkT79cKEMpLaZw60amEWXYtjyhDnJVJeE6XwyLFPbEIZoLE0jhyHxNIB3W5A8Mwtq5DdgzMd001CUPAkSJAAuw7EUGAKtFunm+PIqe4rzrHXy+3F1Qqb8n0+oc2SdLXEji1JXID41FJC9dBfBq2RQw/ER9KkwwmJ6ToFh7yMDB2blGE825bA7Uy0nUxRaWge4KgBoF9UJGH0D5nS1DR0hhYU2L51UkC0EeZScA60ZwpZpBP0SjiHOIQGYU4taASTs6tUsxzIZTOZ+3Nhjo+yQIkbjRmUG+fWdL3Uul4XZ63DRs5TeaS0eirmoMcpHlXSpKYiuhcEFoiUzWQjzqYxwK0hYGBHpSrvS/Je/qCb7d1eu/uZz3yG5QZs2fz8EYw+B9wkcBsX+tYn5Wc3j2Iibm1bcW1Lc3ByZrm01TuWmZ2fG+1sb/YH3Z/89Ee/uHSJXM4//iiLm6dXj0kLuWOcYx9ZthH51DgCQ5RwpVtgZUalmbmdTo8Lx/7mWz8D8viFE8888wz3UaONd9d3CpVarlAhTyurNvA1EJOqsuOXnVlcuTg/q3rhHDDGtDA7c+TMoLtRPH6cg/2FZrM0qhdGpUG/nM3P73DnCNuo/ZnMcG525izLc0tNCTDbLo7qmdFOc8DB6UyRzGCxRI66y5kbi/Kcqsq0uxipfolmks1XZ5nLSR6ckuBu636/kp0jmkQVzv7YOSy+Xwj97dvbw1uN3Maont1eO3r3hRdeqMzPswh+6/0PWfRnU/Xqm+8+evGpD+sfZtq9xcrMXL9lU7U+t2JxXy6karX8ubnZcr7MoZsXnnnx/bcvnbtwodz7abatIzVgIBfvikY624dc7biJhKwGBTUpRrf75LHTZ+dXurt8ZOxeK9uplWcbzVyDUxSVGca4mbq/4kcZtDGfmFw1UuZBCFbi1nhUg9qoaQSkbG75k3YUY11/vNYCpsUpiWyC2XyZJSOSRIWzDEIJzs1taNGWlggnQkAsxiZgghCjgqC51I9sBTUM/0xeKZoPESiIEyEWNHnmECm/3jsIKqcIfiBiiJGz0IMEDDixGD/vYMSk5qRKt3ARkZPvCYFECrJ5JhmPhWcZtCgrBVwMwMYOq8NDwhYlw8zodvV7u+3tRrfTg4I0AyedsKwTNuBBywHxUbVsLgZiVGRSXJmUHNNfKDMpcRQbLGmD8WkScyT1g1QFx3Y6HPTSqw1WLhtBBYzf0o+4NX68ILqT1D5zzyP6YUMGiTRdaygR6gL3iJSJGgMmhWTo7XQDZbQWgo1gH0oHBbTyxCFfVo5YVzAJ2ElJdrfOn1v9o1e/yNt8eV7I67eoTRz9BTnCGO0OFlwjXLzuIwpx55rHg0nUH1Vb5lBaahKgO4yzrHOqKj1MdmTEqTGMFsWyCqJ+KJ2oeC14G3LtLQw7J1bmnzt3qnXvVifPaxzFjb0Go/y5uRlYGvRaWKQTx5cb9Y2N9TZWXaJgYaQ/yJWl5+C4fvtpR9dMFg2UmWkp57B6vQ72FGapByQgItAgtQYqmhiXeE2jxD17epfo5pVrN2/eptJKFVkhjE9ljolDtVQtalRiYoRzjhMSRUktI3mIRcwEdsRYjPrNAl4WHxaGMOWGpmeBT9Yxixj2iojwwwIRP41Aubw47hN1IJonSUelWdovHGcpLRhn3nNBnjG7yAwQHI+qNzOqaWZi2R0N+p5QbEg7dMWoJwcu4EEuEklHpplPw50aPq/YA/ccPXceIYXzQMT05A6PYY/d9yieXW/BB8dThYDqhIY/qlY4B820ua9Pp2SrmewSI8Gtzca771ymo2dkxQfAmPCWy23ake4RS9QDMw3DbMjjI0uI4wgzlWGBqChpcSBvyOrz8aPHKiUOd0t89pKnDjY7t2KCcWdCEwoRTphI6pHhH68ZFDnby3BFJ285j8hV8KNGY7i73R307nNiVFPxkZY+0TX2/DU4o3RJg5IGZrkDNjPoNA2uCWCfhSqMGac/+VM/Mi6a84A/qlWf+8Irjz3/zA9/+MP19z/gpk9u9YWlstiXIkHNv9ngjYi5sEyTuuRIQwHgGAhZWs3XFAcVlcuLDmBam6geFACWJuiob/KMI1iaKd45J8a4ssD4x0hRMHhzLJdkTEEAgWCjkCENgQAlUr3JaXnTdcYepzxwoI5smeoryhdZOt0WeelWe2rBhrecEKRj4d7ToBJSC52WMB0kt1D7RkFMSjVtH46wiYcGN2jWd1t7db0F0dfoji//RvxpaRFhDjL8uh9AKXgaclg4UOCMsvgJb9YjLuCKMp2nLImeWg/i3MSaskd0C4WDCCNYPh7jvRtyc01gKQM5s4qkHqRSXV3NP//Kl1g22hvx3fHdak0bA9SCNrw5aKw6MonFXCUxK6aGRLycgx7rJAwqQ2+C1rA/Avu69l3q4fy5WCKXQQCxjaQFYrmnAamwHf5VLjBjJ5H5Qkwxl3n15SfvvP/T17nbdtDZptMeciNBYdBt22pbZXFxmc0eThCSihen8JFhiqjaEW08KZYaL7GgoVEc/tnTQaO1Ru//19ybPUl23Xd+mZVbZe3VG7qxdWMlCYEgKVAULVELxVHIHCkshz3hmLAd4bD9oD/GEY7ws8d+coRinvwiOWJEj6ghRguGHIoCARA7GugNvdS+ZFZmZZY/39/33pMnlyo0IGLk09Unf/d3ftv5nfWee+65/XPLK9zlnVteVulEWVCpaPX3H9y9ceMGd1tYhCS4kUA836py9vxiu6a9z0vz6+vLK0ucwFOv9NkbFPt4Raa2JuoyoJdLxyVu9JtbfhoN1BKcyRzxRxKXllOQFUWUU/3yYXVNqsIRSo1Ra8JapZKsuk21gozhgf4+5TEB2AycLhEHJrnltFzn+cl5XQPPZnddwSQx2vwoI651GX62J0vtstAa8xqOlpIgN0dZ1XX0pUW99NgR+MK20Diaoo0L8BX9IRufgVHaaLDzgydM8XHLsIWduRQ7qdRqr0HTtLd2N/nYHMsXnKjJmsOFCxpf9g/3qNiMG/I/WWGiGIzIZ6ht8fJym3FoyF8Mnier5xYuXVnneRnvVKy2ak9eOtdkoNULJuo3tGahkUR/XEZll7eQzqVDrE5T9ho16HO4/4oRkd5cD3T1tJYtcpw9qqfLegzMvY89jLXUEwlxwRTy5AGD0hQBLjDQ8xY1CxOsPyGBm5PlZn3lmYtPXPnef3zt3Vde+febuyxR8FUHtUG+rxVrVioBFgYwBDtRZtGsl/M0C6EuavRjOi+/xXNVmYQEz0xUsqywQaFOS/SIOjPgNyREbbH0aLD0ayrQ4xZ6mVOQx1RvLQ2NZ4otEovXV08jTXX3NKETBEkO+DMsCHdAQsZOtRIaQhIIkNqM8YWvx8m4yk1y2RtTei8XeRactAcwZskZbNYFdW5Gok91MWEeEkjSAGxYTGKUWS4RW4/jd92YkQmGO0yAlAvDSc5pes8gOCPpNGnG54wUqS7LOTFlymVuJCy0aDXmKH0ITAM+rw4TLHH5UGWEZ1w6zN9tnnhlEc4UwpLpdzDMyxkutUJ6GPFQmiz9lDjPi0msN5GT6xIv/6S+ErwvAOjIKGXMY36GtTFH6fV5Pn54eHxBZ/npiEDcWwqVJ0tYnWAMKsxa9LzxntZ0L116jG1z/+GnP+ebzn/1H3+GwJ///OfMyxdrOtHpQhzttNvbRwYDhiRz1xH3XVDKxpiMo+Xv//7v2S/z1Ree4pkexvzZn/0Zr7A8//zz3/nmr7ELYHFBhx/RhUcpy6CVVrt7MsAA8LRziapUsOrmezeVHBvo5hpdlHKXw+PNbk2vb6OUJOo8NnDqhTBH3Iztt+IV8n6cv7x7POARKF01018cgUZePkUktzyuY+jSnRxBC/L6WDxaWFjDq0XgDRMGQr2wL6sePNj86U9/CvzMxcuSFgcmUgoMnGx8YFK4dTdOrCYtVlJI0meEpFHPUVl0YQLK+IoEYuOBCTJhfFR32RGTROzOEphHxMtXtcN9b2MPCfgByxkK0cglwdK0Aj3eHzoVUYnSGGLpigwCy5JUacoWkSgngMQVgOpXwpxGmeOlqwwwJt4ElIlhYbrIgGCSuQkI++XK1GoiqeCR5MhprjqTNwKhlNsjSMIpvbppSE7EJctIFHXpU9WNqKMDh55Avdrd7dA06BRlQmkPxGFRoRS4NGNkcy4wEUBmRoSbwJcpBkgwxIQkB3wYpZ6HCUjC/ycDbI8sj17CdqIdQB3amUF9S1BSFtFelH08irS49dIwSmjWdC5bt1d8XwFVv/VbL9Mf8p08uE6YcOGEWHaHHXvoP2XGyElFuSRboMntTHAiADONtPCcFzuZhMraWASYyZJkAtDG6Sj4POkWq77z83zCiI6CU7nwg8WCZB907+hOt8vtvQIZjN9Pj+ClYw/DqXn6ZeUNNs86uPsFRguBxTs2s2x+somLvPqMFxcWWnTv4W9RAhBjMH1v6posHAkkAZsGOAUnpUsDM5FnJ+UsCUbjhOR0qepX2pyQE/S+TNJyepLAT9AnOYmlAKIPT6kAZs8xwFE3hKMqS7jHi5LIonIHIoRQpuuXS4Qk7RNJXJodspk0pp+QmZATLNY1IcqXiVL1fNzCZJLxiRJ8Dudk4DHb+UrCQE5LMJlrL+0MIXCxB7Nea25sbH7yyQb7kp5++hr3xtBQ+bf3WAXiWAK9TRUnS/BcX/7UdsRYpEMFAV0EaKjVzEOIEcsDKzAk2lQITOw42Z9SyZ5hNY040IAWRFOFMeZdSmWVkmXx27ePnnzySZOjSFy2KrZOO4/gFWRXlYdDtOKdnT0k0y2A9kzYlnMJi1UTMwNcaLf+8A//8PrlK6+88grroXDFargUkSOUhcIiRnPyeRLCuAEl54oQmKwkF5m3ICvzm7gAwEkaf4UzdClEqYPs2IHIl8flc3WUwWvmXF4BU5RA8V1KYUrHlFxaYo6J2UiRfJJ0hf1i5G6ceR0L3/iETgwMBkhaLJdA75AspPShzN1rgjSyqCuOfS5DjnHb17NDWwKZjeQSLeEDGQBeP+MhzBvDT2PGOWZchZHaXUhargULaaOoLYqGZFlUBtTGjCgehKhXRzXBySo7D9k8iGCBNt4TZycHj3kef+7LblbgySmMTH7A0FywBKXEbleuqxgVYseyiRb7FoMNgIEst1+XgQz20uzy104ur2b/ypIyBSG2liGV7SnvbL+N2SAdoIoKqTeBwBc3luHPgmCq+GwqMQSw4yIaO5daZb5/v7u3T3vcX16m2fLCEaqZKOKcW7dvMJqz5ublMlvHoMrpgqjmLk17n9fx9Eq7pSU2y3TJWVGZITnQTjA+WZIIJoBPJZig/6e6xM6JukIGE+60XJggkSWfkIvkt5SasiZp0SoElFoMJxoD00gwufCc3knGJO2+pMioVjnxQ8J5jswybYCbkmuFdUxoT4zwqgerHNHBQkMr5iZe7dqjUjFeaLZJDXQnDEwv58bOwsX5C+skcb9MhVfXyqih7genhFsq7OifX1oifYFVAdBooY3Q+sAc7m2h78LaBer5IM4DKe2c7RlXdQwQWZAAEBisyCwOdWeuJlxOU7FKJjEWxCoKjT9MK57PwWuZWjUug420Fni5xEiRuRdiOSb6E9o1pw+RkR/88Cfvv/8RuyKlJcxCmjIa44JXyTQAKDByxYK0uhQw/kudU2EBGgkIY79OaZToCeZKyARIdKTiBk3xTa2BUj6hiJWR6I6dXzOaKwk5A5hYgLazNJ9wKPpXuzjOCzMeBckU7dOTkYWjwZfqJccFHoUoVqwUssyGUGVAF4yRXwEluvhV/gM5SopN9dqGGWFCJo8wmAmQAp6iGnFFYU8Sl5KpElDaugDG6isYgmUamI5tTF4YxjgmF6pMqiLF4BfvVDEI51QysbyGFI9hRmEJ9Ydkqm23c+DzmHj6w76RfuyNm19oANBwmBk2F5o8X5nIaSn2U37N9Vl4i9Iv5RY+xFQw9pKTCt8W5NQdQbRedoFC5kA1EWCnqMIoR9Gmoo7wdAsk7a0sNbUebC19JnxZK0p78J/WCvkCMLfvFb5DyiOzqvY7sNdV06Rwb5QJlIVxtgEu197ifiG0jNZxRwo+I1RaO83mNhK5C+fIDnmDpk6t5o+GHyZptbE5z9abztbhg/n2fOviyvyl1aPtzfn6PCep0X3vHhxGTuXKOEuSs4VYd9Y5dnIRN1rVKmdAP9pe3/3wTv+T7bUvPbdUa77MJwi/+tV3X/+AT2osffUq238HH95566235g4PGmwKbnZ5TX2+UdUhe5QCZXdcbTTr7ItmP869+uHN4fZutfWgX33vxs6gvn7cOvzLV3/ylRef+8Vf/eXmUe+P/uiP9rY7Aw7Hu7/DYHPjwR0WqR99du3BxoOfvfXvfuc7v9Or3MKyhd4c52g0dwe779xiV79KqlZbXF07HvJezzyX6vvmFyoni3NHtcbRQm37MA5OHFR3Ofptnr5q0D/kpNrB9j6Hfewf7KOLZW8mYWwhYKDaWl/FzzvbewwkraUV6mvteK561F/pLKjaH+tLnpV9jTHsPm0d9p+ff+yply7NHRxzw9aqVrbv3n/zEx1b9bXHn+fjCYdHWhy/c/0WN6irK+u8bE5z5RZzodmuNDnBt9vnvPZeZ+/dWxfPXW43l+Ywvb56vDn8zku//X+//kPkDJcZR+vVhaom+scsfuFbDCkqNQTyAGU3N4cB7MScf7D13Prld7b37+/e2z1p4KEjFscGw5WoMsVkn1yx9SMaEzXc1Zg6gzRichx1r+g/jQTjt3Ld35RIiQjiGRFtQalleqJkqQ4fer5RtJ0Z3AUqcflaz3fxoHZk6PgtkO4F6MjJh/xCKHp1UtE96uHJZkjTcmTRoossFx5w+yoUS7KcjBRE5mZ47CtW4jUoqAeGoAT4LfrkJGoaKIlL70xTGOMRNuS5V49imNvp9Db2uvs9npngiiiFkDRydykwKTKAEyaymQjgyGsCeG7ynHFiP//HEaXgVFtkHL0PxVrpsvO+y1lo0I/oEsMXAKS8YC0warWMwfxPfbc6oTKMdFM4yTZo6P3n49tfUFoC1YosQQMGUeETbV2bp8lwv3dSpaE1q5VvfvOl3/i1F3h9oVHlzXfyq5GIhkh7RCy9EIyw19n7FMGWjOwICMrSwsIoMKSATLG60hgEE2/ZQgsaiKGhOfDYDRraHRui2byX6PV2j/DCsNCFiouLrS89ceX9zV02moFha/fx0TEr7Br9OACfPRdLrUfOr/NiL7tTeMYx6B8xsvJHHsfrmCQ7WB9HzLWbc51DLb6zpY8d/pwlyVH3fJYEMnpPtHf2D289+Hh/h0d3xaDKpJ18t5fml1ZX1KER2KTGLDweDdIBpoK2u6zRseSWrU9k4Tq70QR2JnCOnLiEhtQJArOfEZtrmsCCiJPqCZoJRre7IuNR+jMtmeCy/FyL1Zk3ESNcdbK0INEkFYm+JNGvyYxJcALAJ3YTJyEAOVmeamklL4SiNCMtBcClrIRSCAD0bpgA5CUXntiT5NOAJBACywRALAJRJTiGIi6ArSInoyfgcwudbo9Tu7CCWkYqL4TtMZE66L7++puNxrtPPfsMe5mZRRC01qPZWLzG5b20kTu2XOr1Mm/C5Sb2eMA5lY+srzS4SWSfYvGkDNkakuwN7DkjJCPdfWguwHcR2GVbYerFtyoYapd4YXl/r3f9w9tXr12Bvlb3URLaM+swEkIfWNVyM4vGe3uHu/uHLLVzy6DOTG+mFa7TYBsdLGvF9do8uRzy6lu9/sy3fu3k3Pq7f/cqywRNTSjJIYQqYlzKHIWeFQenElTz16Vi3sRWY4eYziLKWUXBa29IkIzS1tN/LSelc8mYxA1RwpAvHD6sDeYa2iRBSJZAky7RRtAqeBYSpayKQPkydXQ/Dy+FxQIH3RtKSIce8cBsd2Bmwt5nzubGRXDxeIIasji/KjFMWuCJvgcOZTiyGuxF65ChaA2/8UVvzVdZF+fpxdHhzvYGJ4eyQcIa8zik0bsiaVRwiQB2qYgGBdKBLEjRpwXPf/APvfLxoNLhg0NqERr1CJr163C9YtVjJAzB/IVvdUgq7uWRCR+8q9bZ8Isp/Hn1o3dMHTheafHGkVZhsHO+zhsxSxzh3efL4fNLaMF2mhjOR36TZ0Hs6A9NXnNwkbMuRWhwqAV1nvOUCVRJqZWXY5TGmiK/8oY3UoaFdlqILKLSOUpmhGVoylMTTGmiLqRZsuzSuX3M+et7/9lLT7/z8f2f/OSD5jFfXVjSh6P15V6dLgUbryvTX1BDqDYyVm8OYLBeZo+bBn610FwK5zZCwsGw1sz9C0866Bz3uoy9vb2DPWRSJBB3D3U0B4l0sdRNfKAGhVV1Hh3xvTO+98tnRefXzy2vL+vwDo/yvv/2TDsMSFkUgHnEqaqPpc26wEjQ03Jm0X6xOCyx2RNq1BgIUfp50rTNzovWJaCOEQQakKY0kLgCkNBohbnguLsAEY/n+bXYDMhrpoTb5xAk+5NSkCkkOckGJ6lGqWvCGGcSwkIF+IkChTexJzLJifuRQtdUlnILtZilXI8cajlgAOKsPrVLYCoqh8DoPGiac1iU74Nm1ZW6vdnYZL9N77B/xHEzx12+TAgj6mg1KoY4u5ieAmSjNlhdaq6uzC8u8HBLc136qxazcJ3Sf9To99no9vSlK0v0QPTMNLYoeL2lkwLjlBylxqV72HAXsVD06GhkGIkXLfs0QfYg07AYAuNYuVqzwdxbCAi9kS4WqXEGxmKMXQesjdllO0p4m+BujQaq/JWhH4cODYbda0+s/w///X/OY++//NE/cJI77xvBxUHMURvlcC0iM8nQ6xKYEv53RAyR/qpkF98jWzfLUQPV7CG2gaNCK9VP/Sab8xS0c8lsgcUTcoEFFC5KIr85oWATT2LLa33BqYRn/BbMrl4xNEI006YZzCUqVwFsb1tIEpWshADYAQEAxAlZipz8NVnCJrEJMxNIXAlIZMYQuy4FUHQ90KTU3DDTJAkJmDCGy0IazHJ+zB1inpdYzgC4x6DIuU/moVAiA+k5bbq3oSrQnhPBTECGFS1llA5i2uBR8ixI1e7MWpQE2m/IMKbwQ/S5dB8WQlLCm97tk1QuFcJjmgFExZBF5agAqPQICUjaQSOKQKEqjp5iug2aET8IKOq8qPifi7KWLyKmt0yKbAyx+p2sIYDwDIb9ZTR+PQbnmUS3y63Rz9/7MbcT3BtwJDFIcZWZtLTCZuVOowK6eE54cHADIe+99x6LEV/52jc6O1odpppxNDPLnZWnvgLZ9q0fUffqC3UUkZRKhCSMIfB0HS5NkqKKoujwUOMN/RRbg7HnJz/5ye/93u+t1hpvv/32T3/0dwhZvXyeZW5EvfnmmxtbGxvbG60l3dTR1zN9B4/AnY0dKjZieebJEX1kSptMmNItLlJI3B9AVuQUKAI55de5Q9gx31XkOerREZaDVzNZX0X+Ue8I43nuqhbUr3zyySdH8ZIjb6aBaWifpcY2TpW6+tw17Ro41ouEA51Vt3dw/z4vu7EZAf9osicnVNiG8OyzzyJwZ3sbGyQ2HpPiCgCd4LbYRA7GkRXmi7xsuHrjpxQiBDiKnddodAEhEAmpnhdlx6L5zg6bBz9+8212VD377GUMGO4zFLIXvh000UyitiBHGP5HWXNpL1m+kApl5YgLaS/7+UCEhHEa41NcyhEiwchBV+Ql+reCWl6ChjixTwMWIrIyDTncNZRX+kUCnRcBLeQ9T5oJQ0l28+xD5ksJJzBCl4bZAMsp4PDVmKcyL4m77Bxy3lAqpiDQzLIsWztqLEdWl8fU1a2tHeoSdy7gLdmxd5uaGAzyQ4UQCXBqjjFvFIqkmRKAAJkBVxdbZnwuEAztSAeAaT/CP0HAhwS1lHyHTzQu5+s0m2iJMLqqAERmC1pyTw1C5klTE2UaJj1hpzJHL/rb3/4Gb4Q0+FhX6X8DSRpcBC5htANzA1DEJYY5Kdw8SgdpaY69+pyEJLcDpCIzJTQjdadUIgjQTkwHfrc3pL8Cpuzcq9OTWgg02tjILhK6nmrcG4wM/BQIR3ETjG087EM4Ky74je6RgBZ0HXXZItrDwGir+AEncszTwuq5dXi1dsGtSdzh40D6ahsM0p7J1RtJnCM/B5xLSIrsCqSBIVhscnLSkiqYixV82DmyNrEkIYn3NCAkKPE0FhPMTLU6eA1gFQB1SnbyLqvn2aE4ZwcWUWgEgNiXQTgZ5YyJJQFOzdmBJ1ggxpLUWk2cPJnrM6OTTAbGALGBaeG5hAQnRjBmwQZLzpNMj+Qk1gCVk76XdgEBd1m8RrDR3GKcZbGGxv7OO+9QXTm3NhgB+VKE+haezVudddlgMADQ8CoCcnh9l0vOGCN2T0sjSN4Al2V/ZJXtTLFYY+MYMQbTH2tq1NLx7sPBDrtlefmMVs/NOBkxlyUj3LzKZvQGO4dMgjpM25iewW7i5A0P+2QHDE2bVGBihDDJuVSrv/LKK7X+toR7bI2JZuiSnjDTIgW7YQGEGZKJNNGFWAQzldMVf1OtPLErKSSXzbSQ7x9kIpBAfSMwIbPB4KEHP0ZdXiT/m7hEy/+w0I8R6NDAg6GbKgkkkMAlugBIZYXQPncfSG1BpuVErkd9ixmJwUOAEChFPBe9dFcfRWRlliRUMz9ElFSE822AJSRjcqQFpiRTJiQapShyl5AmNjIxAoRRfFhbx4aQSnAqQpgF2KOz/aoqJlrqFfN8gPCSuYsYDNmyMaCwk5wuP3IRet/cQgAepTYDgHffZUNhhZJyiUhIZeGkREASdnJpdbDp+rME0yeBNgwR4Ck1YgJIYnYvcvYgzfBkW5+gRGMkqqyLjLDiFZRgaICMv3QiE7YkRQAYTSqiNMLW61QJGgHVchBvLqGb0uGgP8UKMgTHYA2s9FTcL3CDiJa1tRUW5tot1SvaYbhCptkYYpiJwRPPDDnxTAKQCDcZsKSXfk5AzmiNGVWe+PlhBCYzUnasa0JoqJYHCHLLZwxWNJMpl+ayMFmOT4zJ2oTB7OScxHKafZiRGAFMn9tmOAk0sbly3qQoScsxOWUimAkkrmjCauNuCB5HeKZrrkjV4yWqLkkMQ8AMYSxsbm/vksoeZ7XoWMYNYj1VohNmzOI+mlTd4/NYs+h+m7UWJ9XojE3GRGi4xebwSnLt9X8AKIsQ60hUeTR6cdYm2Y9QEni2CrEIyqEZGzxTJTvABJ6kQkmbEVm0KbNYGnGqfsC4JQm0TDsqlTVLG5DpY1DV6tLC0ne/+93F1SdZhr5+8z40IKXXuXCPWrQwG4n8pLZMCM8krM1W5zBWX1L6bIAMwojBtt8xl3RBFFyd7aNaxIttTOU6ngWBny2xxPINx+hrih2jxdpHmUo/X+yNAsNn4On49awg+nEQwQtOOmyTGXMrk6hJoNAoh7H4iEzt7hSggrfdjilcZVxpkzJ07X1haUQySfkAB0sIYaeYLRAMWkxYkEvB+KPwMjl5EABbFUchAxCSzJJ89OtqZ2JjM3oxqtaWJo3YMgy8rnMW4ljPTilqLe0d2h9qW/Vaj+9KsyYod+pBSWO+pfYVTs5tsKIck8OkImACk2xzjtJl6TFZwfNtZ8epdhRwAhLeQDzyHVOk0wfYCBU+gSa8y+QjapsuqGbqQ0QAjOdjsltl4V2loEdW3KcHnaYscoE1Ebu8tGuM51PsfWaSx65UHqJz0h+Vnz9oybQ2FyvvwYkoC5DMUlbIl22WmTR8DiBKSjV/IkT9UnvGJGJC5DBaRbQLujxovPyCz/udfQ6h+Mqvv8SpFw+2HrAWcPPGjScef/zj25vr57WrBRLtj7Bjicu8kI35apPziNeG7eV+886b7y0P5vb3D9nA+9477/7g3/xFZW94Z2GpstrQeUzthaVWnXNo2cIzqLB3ocP+5+7xUZ+NgsyYedOGxbLeUWt1/ccf/uypd5+/fdg5aDQ3Dlv87Rz3H/Qr13v3XvryS7fe6vz5T/72n//6b7avXDqpL9XbCy+89I2Vcxffvvnm37726rVfbf74+o+uvTDkI83Xth492tw42t7irI3FBX14Z29/q95Yr83zfs0u3/Po9jrN+QvUPLZLUP696P5OeN65tHgw1Eka7F2mxi6s62Md1XaT9vLgzt1hZ3j52Wtg7vUrbHRiCk1l6W5uMlzNcQjIvX02ebCjeWWpyfLuekNfmV9aXWXpfL7bYsvO0uoS/t/fe3Dhwrn641ceuXzxk3dv39/dbq8scSvH3UdncHxrZ2Pp8oUHW5t0WIxRfK+tfVJtVhtsXZzb66w9+vjqysVqZX595dKrb77+J3/yJ//2tZ92P/lF7XhB08cGb+fRCvAoRcapJiou6gD1jSquyjkcfnTrJke9E7hJrp+sLNZZLKvd2T68VR+w/3rY2RVx9JVqTowcWVNwZVNHpjDWB+o6rx5BoXY3qjGyRC2kHES917KwsOhyiwodfQWRxmbo4URedEe6Aox2FzoUQQNJYZblE/tbACbiaS4ecDulBStfqnpzvKfqMVUyQ7mVmgsaSY/WxJtCgfR4F3DYHKaiHeHKnxmnY5cCeAHx45w4HuGVNhacnRIlLUXeo3Pmzl1Jodb+tAm9wdzGzuHmLmdKsmYRyXKRsyAOYCxPGAAUEZSGguwWwkmJ0gTEYKyxhOUorEk6krQkgV3PfLxz0OMLyHq9VIqSuC8GsGr7B3vIFx23KgZTUg8Brk/QZc7BFvmidAJJ2gLE96+LdQTapcij1immG6B5EphccroxuzEuLbdeeOGFRx9drdY6c/oGtRwrHk2AtExAP0CDJUhRaUNBk7kCmXCJcTyohUZZkUBq1O1xisgCqMQrsmhNbL7QWcmaLBXzmKKJhxJaPVzFLetwyHeqr6wsVw8PmpVB7B/jkV6Xj2Mz3a8Oeg0qYfWYc/TqR1VtiuqLBr9ir8bRWYGGGGjaHcMn2dOrkX26406HJ5Z3N7dTXimiojrSBtkYWau0FrT3udWel6VsSgm7ab91/sUiPmjn1141HLfL0ulLAPxAXNb/YkomipLGJWLMzNgNBLIkcyZZjkyGJe15KrClzVQNMnEljabnMrGkJANiidSEt8acPsFOshboAfJOIJmXJEADAWRImBACTWI3jblyM8wCBoDYAGQAJs7jRGMyG2ZTEws0BLcWALO7dbhnBymA9hdpwIlsQheXKSkZlmfE8y7XcIkNmwE8lFDDWc/tHvG5YG0TJvXCxbXB8IijKrTnaVBt1FuHh12Ia43K6BDn6BnoP8D6rR06bsjZaMU3Ry4xqWi3Dzk1UQ+Z1cCO2VOl+7p5eyPZjDoCjswzFRghikEsMojBBL341eSulVFxntNcNzeHuzvdWx/fu3hpvdcZLC1r7Y9bWjjCAzy6YymzyqEGPNo85FPJc3Xu5Jn/6C4phhRpCZ8o75QDk/tGY3lxRWJ4a4I38IZH9Ub9oNNvL67yaEs34e7KtBvLlYH8qV+NHlp8KrUgot9l0tWqxVepdZvtd50qHBPMzFIvtY7lO3ijdKbRpFkLMf4JZxRVEcsJvEWkHWPaHxd9WgjLotHYRe8EO+sc8md0LFEogo1hXDCemyySgoqzs4tBUGS8VtmASg8YUE1cYwtQE8/Re4GijyyqmVNhAYitvSwsjsZriEhwlaAnp6p0Dw8O9/fwuyo/A4XvUkppyCGAI9YmQF0wK+DCvbQQclMWnCN7LENPgkMKk+ccNZ7K1rULWWss8hj/lSHt/ENycEn5eAgMXkIXowQr6XaL/BQ8LhHeh9k7GNQa2+eXdXY5g/JR5+hkqP5+eWUxSaSdMbjs7nDje8hmauoqT0NARrZwvBaweM0QZXxSD0XsP8dUfeennM6lzKrN1ihlPDltdFHWEJdGzqCRqih+C5DjdYlG1HElG+rV+tefvXz7y49/fO+txcrecaVJufUqdcZKspkkAMQsgg8tkoXRdjEumbtEm2WOrt3efNfJXOSL3ZecylJ0lPCrS3JditVnb3+GGrvmOKagvrq8sLLEwVsna6uL7H1e4vXR2N0puSo+PfmJLEtI8I0icuqkEUq1aaw65UmGJwgKd0ULzYknyPKkzw3PNBhp01mbqQKTbC2pCQhKXjEVDjmUtiznhiTzg/TGY3i188JpIoaGiiHK4Cplqt9DlSSrnSKt6ItKXqUkm0uuIFdUhNyAEqdft9MwVSoSWQ4kOGf8VNiWnMY7M5X6SYXDGA7R4aaVwVFnxvS04qzOIJoAMc2YD4g0mjXuvrF/a2sbF9Hqj/u6v9Mmf5pEnHlQqw95n2KxVV1u19pNjRvsXEAabxLwAgqn7XcPD9vD4cWlpRZaWSU7Zg8wp0TndRtvkwkFaZarZUl0ogwaKrX4uM2IxVkLSj2K490aAlkDJsZgHnyxxO5tyZzZKXERXBbllYomFStagMVrIIh4W4rLOstTfPapu4/el3/12tNPrf7gL1/nHfRDvTw1nG809QiKDodNh32spQ6pNoU7C1Xqk/Atj6mGg0a8YqI+j+xiAMv9pPLn1wiNhxrMVMsOLlXX6YCdegTWO24MKFOVEBjIHOfANG/CjD12s5fht6wk0bJm2ZDkjLSOUOQo2lmOifIYlauT0BhB3TFFApxYorTUOHNkSjWQzE54iI1MGIAkIYCRijJpBkvOnuAkPJmaMIlmGkjaI0dFHxHIs/06KQl2skagcVomDZNRjeMYaRKJmttL2i2XUBqZAUIUl5GYkoQ3dRZHkek6J0vpRlJiiYwk5zHRTANJlF2XYgsp8zhaXoEeGsKIQOVJIZKiJPDUkmAccVmviRi4RalFCcB4KK1fTUYKspAmmR4qAhsqpAne6Vz80jGhqBiKklX0D8C2JCEB7CWqAQA7cuiSDo4OaEGsvbJk/N71BwCQkV2tsjrojihuu8gNFSleNqfy4AfuRtjr9+0XrwG/+tOfcrlSW2Kzz6t33mVX9ZX5NvJXyt3N0CBWT+CrxYyqy+uofW0aunXr1p/+6Z/ubtXYg7P1/oN/+Id/aKzt0k0za2TBlE1Ar7zyym986VfYU/zHf/zHWL5ySUu6f/u3f8sevXb7cY77uHPnAPwTvTr7kfv372t3XpQUE/zVtbXKgTLYn9OpuHy4hI9z4xmM4SaKxWJ20XHm6bBzcO/evaPjYx6oPvHIY5rgt+o8CG3Xm+xBZgsA9nR2OQZkgFLk9Hc6mFc70imHVx67im3VE31Ol/d32PfknVAQ84rr7Ru32fXMAjQ7gB4/f45HrOQIP2g4qte98RzXAVBhYGGbPbrixra2MK/TnzlvRI95o5eDEq6XXnrpF7/4BeUDJaLwrcqKXKlCFsF5VFE2m5jE5wfJJjsslhZ0+nOjWufQj8rG9ve///2ioma8iEi1N6oK8vM6X6ggqdQmemtPQEoyYLzFQhmXSsnpDWMz+EJ41obG8CHU9LmEWQJDRdRq28so7EzJ2+O9hPRqcFX1kJGhJeXdOm08sOWQCqYgdKMr2UDmSaDNmyEz0iRiFpBYADDerpggZCsQdZXXrHGIfQIxwcSYaCRxYjSBMUls4jWZCwLKxJUAIyeSknzjcbJaPX9sBCgrSZLwSweSMc4F8m0PeDISqco+gMMZBnjnRco+rSYYVR1wBu7QOxfx6gkqaJXnz6/S73EmvO4Yx7fYuzpBTEAIlwTqmi0hTs4HzkMyPkcmmCwkGEoTGwPsVGKUcksfl8q7dZXEYxLMAgHDAX0XT9Tof+hewLPHhIk+7PR+eIYelVtgcsGUMtnwMADdppxwUkHyIQvK6unkLdomf2RI99EOHADdbNBhpu6UqS3GsOysAYil6AjQYrCwkWXikv+z/Trv4gmXWBpXucARTcjOkxIlSEJOCQwmSjzYxqMgL2wGHk+cvMrF5mkJL2njjw+nZdoe2CdqnS2UhDDDMWS58KQ0il6egswEYMyCWDBGJvqZADRJSyIwBmmWk+MTclq4RdkYYEJizIHT8NDMtCQ0FnkU76zygcZiaRe0Ds9b6BB4uDs/v8Acg5U4eo/8yB2ylqzK9SKHgPu58eYZNgJ5Zp8oSYKRVQpYzAWGVPEIKLKc4EALb+IkB4MxstdjaqBTCmhN7fluzKPuMNWpVFu0JvZnWzLtVNOnfv9gewuAO2mkYQY55UQdVOASWVNWA7iAUVHMTMrRihfsCPJPTAW1Oho1EAl+thfqiCLkfo4R2eUZxaE1O4i0fjc30dEW3DN/VHohVoBUs81c17YcM8iUg7uWmUISfdx3ao1JOY+8w0uhQ0AMLM/ENA4ng6TTwyF+agiejOD58LPqOdUDJIUCJUnEBCQYMAYyaBwjgFLjEhp2lAGglJgZJFqY4VNSXp40jSUYtsxfYozYQlrZb2AV1YYso5dUlxeWFz18ST5hQxKDW+RDbZVjbUWDQgpI5hLJVCQJ5zFoBHbs6iN9IQIKPKBjADnoT4+RY0Eq+1RAkgYgcRHgRR6quQp3jWYLxvBqPMTTtpsll2OBjm2SYFk3Ni5w6TkkvOSITdAvv/zyX7+9xVA71x69AkIWzO043GjbJDUF2ZYZh3CSiFOOqGRo4ZUHFwS+jTo86tIonfa8lvNWV5dQOj8/x30Hwy9CfCxArgt1Ks4yALuOJT+QAqzL0qycvuT7x/7m8v+xsoLffsNRZ0uT3pI+wTNZ7JYJgbBYUWLxJcT2ki6z0kxkBkgt9Rcpyf85pWUmMVY6Ya1p4AJvU1MxTVCaxvITV67uDDgMzprxFGkuMOmlK8MkqiJ12L1ovg+aW186TBagoWemiod3d/n8IF9d7UA814ojMXkmo85EQuLEjtEzGzTSyZBKfp13fWCpr76UgIEaXSKEQwJTmp0stK88QLlvdxJiaV8Yj1UEAJaYiQXEbT4ECBN7lHict5QmvqWaqV+EWyNCgB1bY2FJfN6cTpM7kW9+85sfffRRv6L5A4dt4it61FCqBWvLdtMsG6hwVsFGD8sHY7MZKukuzPWZYsmJjshc2KluPd7OAUMWcu0PI7k+9qQ09jPRQVlW4md6gVbnkyc8So0/HrjqIrgsx/rpgWVHuGW6nhY2xjOfYjeNnr1TGNqVZC0IBeAWpViAL4RJ2WmhVI1Cyr4wsASmmUSTdAVZQWM5xDIgugaAEi5yYwwMOWB+8g5gITng1FJsVNbY8V3mbLQqVGLEneXFl8R6BMdGit2D/T7e4bpZaywvcMRja3VJTzYI1eHC4jwTQZqkrqYqRY7JYYipoJKQhQmClKKZI5L92CiYonIqnbcS+OhcQTmqAblkGYb3XIFkoD76FxhsiNtW6hx1Airh9SwnpgvyOF2A+qlgiqGFRVX5nU1WLDlpSC6ER96VI8qRr4wCMBHnDnyuSfcMhgNyGMIh8FYt7cIug+TLrCJwiRAujB/lqSQ4+1fss8I0eprS2YGbJAfDyiu5Xmrd3NzcvrlzOOycVOu7e51b3ZNu+1z73NWdHmcXa9amTW3DYfNk54gDhip6sXSuD6baPuZtl71vX3m59+6tdnXu5V958esv/cb/+a/+1e7hwbVnn2k99zzzzoXtuyzmNj96AHyRI5x6B3ycmr6eo84Oq6qAve4RZdcaNFbqK6xHP/HCs6ydXXvmGXrqB937/9v/8X89cq1WX7nUbyxv9+bOX33+7sGb//u//te/8zu/89ylx1hcfmPnZ2+88cZfvvf/fPe73717/sZ2bXtz7zYW1vZuX799/cJhrdFvXOSjzevrjbX1O/WdRy7M37p3d+vBe9jw5c32CesZbIhg4Fqtn2vX73z4/ura4sny+qXVhRuvdy5deazX0ss+fAq72q5euHb53uEm53wwgB3vV1v1xfmdvd5ub715Ya219ti5iwx1qxfOcY4p02BqBZkddloXz19lflwZ9Dbubbz6N6+yLHjh0sqFC5eunb/UaDZWzy8xxWSPNEPCSaM2t9Te29hC43NfffG1115D7VCHN4EYNvRQtXq1VZ3bujtcbrbaAxrpzVsf/+qLv/GDP39ld6uL9hud9+jN+23Klt3xqo3DzjHfLOLobmzmEqv2D/Zub97/9V/5xs/feL3Z2F9abm7uHa2vXXz/jV+8/uEHv71+nlpBRYeYWs9bBVQcYIIaAk/jBaaxp0iKrU/FbEnp4i1aKK0AgWCIgd3cTZFmVSYQMlocqjJYoFRHcGXmkn0+gTfaqtWz6awu2R3qNG4yH9ANntt02OJumRZMHSbVEhRrNBHDqNlyS6kJie/xC8JRqkjL3I2kBBQ5KnItRMwVsDgyorzQNZmXODhEUwCf/hP+P+H1I00Tk084Mg1WnguxzHGTj7bFUbqR2o87P7tD0kdKM11GOg47lUZGjAGwosgaKZGLmJp4v55HX5dTzgU7AQyh0u8Me4cn/Z6PWURK5KQsbyn8ZQYMThnBBmW77ITZqKDpR1xCFoGcFtUh0LIEAveTNE1/P9ACmQ0zcoieP8pN4xg7+/TlaO0tOh5sb23cv/fJ45fX6Tz1mXkIFUl+qa5YsOagTqpiyCvybhWQcS2njYeo4NY6loqpcrFPeQMOZrMCx4GWqKYGFsOQyhU4Hv1iPpRchzrBtAS1A5bUT4aLtZOnrly8ef/Ohx/eb1+4SCrbIvTAr9lanG/v7neZj9JlMCXl1pmdKjo2DkOKJisT1BdMBEZUtqx0OvRKc4NhCzMafC3quBvZxbHMyTkklOCmz8rYAidQLrSZGzAe1xv6ECKBbYNM38ktsDXYe/KEyq4IcSkM612gSmJhXJNBRt5TUsn5uX6tPbGiJcdYkVNzfKIHsIXEBGhMRkytUH4js1wmSoBEY5hYIfxp0FzCzWJMLQUCUyb6XJ1FTccziRPSGs0F0vgcOS0wYUyMecnsECC3mCYB4IEJ0/JBQmx8knwGYHqzWGDCJC46kEKsel2hLZ8C0n1mdcgOVlKYpTAfIIkbY4pvZ08fSoKAGYiaDX88yWdGOldjSbLFq/Rz9epxDyw7k+gc5nuda+ceubI+Xzk55IBK+hiKn33QrHAyRQeCUi//RQaJDWCMAWIvUnONjezp9a0jVckErFXat1jIG0FtXkpuLHAc195ukzkbB3ZBxiSJ+QOdHpYzweBx1MlA841eRR9zbtIsF5e5a8Af+sohqul4sYCXEtn+Wp9jZRvdeoXLRXOio8b2dnaj2Wogdiq3wvzR6MeHZTlWAWw4GR8esz9YLyFq+INFlhOjkq4raMxBeYiL4JpCUsw5yooTxOqrROiAJAHRh5NTipIeQ/LHW5y8GoQcTIw/oQTDaKw7puh6kcF1kmkJyHINJQsEeiPwOJZcxNog3aTwSIQxiEuzompJgXrD6BZL+ZDp1Oj4hAAKe9pQIb0Uy7DT3d/du3XjI04iRayMKU0SnAU6/tFVvvd5hP2cEBnEHs6q0yzMrg1JxwMGycLtYdlEwalKxPpMldNLecC43+fTPWWgwgAiTfvfdWu319mba3I8TZ1T+B476q6rk8e9nhqd9Dp8dQZqGoLeiVfhyiGMfqo5dknthLOeONc8TkrhUz+1OrvsMQDfqlbRSNHGuwhMgVUEpwbnd2xaGbQuFLPRV0QjiZoSsxLj9dqtHnfIvObJ/jdeuPryl668+uqr1ZN1Zg9HA77XMMdDH9bKaPD8secERqgJzFJUtWUa7izGWyeJRs6cawy5z2my+kV2or5pakMqR73q1gKQZ2O0XTFU5ttz66tLayuLjdrJwnz9wuri6gIf7tGhtNqprdovZe5GXLtQB4ZAalHf4jKZEVezI/ttdtp/EmxuQMoImnN4piGfSgCXp5qJUn5S3cN5KmxUq+9UZ0CdVU/uEFeqw5QdMeULPtngS0qWACVJjk2Q+z+x5EBOnOMNp1RUuzvKaWxejvl8sJpS9EuoQ4JzZzgJ5NIGQMKpbDxTg4sQ3aPO6oSLzHIJzIcWtEu6XuVhCduZeWv5qMO7/X79Awf2ea2hMXdCPW7yDSpatk7/Z8uDXnZBJvfgy9VGr7t9jf0jw7mGloXUcCacSfO0BxTrTcJy9A9KpdHgYm7A1mEM49uyLLjR46kiRFeMqer2WYbmczB9Pr7EsBzl7rtC+yRmqskPE0AqIETZGzZSK6HsJeVNfTR5sZttIt1+rcIekX1Wr+RPvcmoA6ZEqk+CFR2wVbiKeXRjIKKITuY04rADE4E1ZtBxNPSEPWdfyiMKopJzIgSGe1aNd5Ic01r0pqydLdOpemPIEGwTDClpAn/G5edjUQWMYPYkBFzAY+12WnuyHOKAiUZhmt4Ya0GFL6kE8OTE6RLABTCWHO0tmZrLTEJsRLoEKC3McQU8ISppz0llSYzdlLcBMFhOUCOKph6wZj8TAnM5TrKKCZNyh+QsOWzGPNb0QN2ohzTVKJuBNJMF+4T/VFMDH26JjtsYm0fXzCVwCCm6dWNsM7FWQgnROrBBcEzyiC2ZGBYFf+nCLyNEWdO1RIKZXXPEZF6SJCu8Dc6XThaNoC8k5JYnWJZEjnCFQV8Sc3fERtrXXvspDm9farNrGLN6xz22DDCFzU1MLEJGSzmOJ4Ts4WXJmGVcnrNV4ht9rPb+/u//fvXqNfnnaJcttydvf/yjH/3oeHePeoUizKAGchvTW9Irb/2qBhVWzbDk6vGT2mp3p8tx0sPjBsu15x47x+4hCLDt1vU3ODG5XRn8xV/8xT/ML7NDZ6/6Mdqf+tJTDB6s5CK52+tym7TBm6S7u+dP1hiKllpLaEH4hx9+uDenM54ggwuA+NHLl7mc1zsrCmQOXQwPCEfg+aVFIXmTsdudby2xt2Lj/pZuupqX6Pqxmd3Kl9YeZQP1+eYaNvD9A9jtusGQjZDn5YSYkBGzasOg+NJL3+Hcao5iwqRBt8ZWoKPmHF+fZ4UFSzo7e2zZOLceLxXyRqFMcl1SVSSpd3DAUIB5vOPPodj//H/6n4F793uk8sVC7hL1jABBZdsRwKhbdG4MJDUmtf/Fl18iLzubO+DVG8RBk5hXPXcBD9CuQq+KWmLLGCS5EzYCSSVZAsq0kmDsetaF5aMmJUYhpCsBBU3clZUaxwiwimya0gS2TXFGmOtyXpj5mdf0uS7zKTU6iCTQeNgTyzSXaaZjG4nM3MiCbDrbUSWTEPOmSwMgbT+AS41zyWlx1GIIbKRzXRo8IaC4NE1KS3ZaCLExNjuRpVw4NRHnvkos0HCnTqOj9ibKJOqLAPBMLpZLZzNZ61QsjFDQUhMnigIuOxkyYNoLIZcMTBuiQyAUwqLDGQxWSLI3AJwkP8STfzDuDwHODjbbViVpsOSwL2cOLZB5dxUAlvfiBpKFIexhJo8xtBKSCAjJ8w4BGEZJhgk6YV5Pofjo5cDQzyzE11yhobvjlE9lKvbc1evaWhJuzBsfuLGAOrKPx8ACW5fWnctHz6n00Nhsz7P9mRNSYaE3tqnABODUd6QiznCFUlcGjOQarjFT4mKaZZrmDAz2W4IzMk1pvGkww/ZMk4GZsIRLeM0+wQUyKTUZ7KZ07PpAEsFJBqZh1IJEvl0EnIJFwVjITAkBTCAtHzlJ4wTBOPfDXiGNUrPfLDnntApSk1KngndSTjwNw3UaWQhQNT6NYEKaDSC2J2katBQqcKjQ1zJ4RMTMhNteCDyHFFDu7TI7ugBYJGMe8vWvf92zCCsygeEJk0hyauCLpkd75y4f+clOUtV/lYM+LEUj0i4wP6VWk2/U27IzvlrMRMWS4WIxPYmiSTJbc+68dGIyCJQFVQEJ93QocSETyZqoRICGSTr0BFpn3v8ULFRMcpMaeQxGEBsDOwIpej1b4l7a+VZq2Y8bGJeQjDEAOQIRlfCqS/ynncquU5npD82CDeSUmJIl8JtEWawJIidKoWRtOTEBTBiQmNQJpABvpKqWWguGQQoeORQWSJKMBA8jJYVQCotJLEkEkBAQA1uN5AeU8p2STHBGbNbT6EvBEkBBM2u1qNAoA6L6FWVamGOjRiUgDuipPGQTIaXVIUkWF7kgU5FaXNo/ECXbGGJAUjY4iv+yIWU4hE1EJkApgSR5NWTH8wLpTX6eZjQGArQjJ9mQUxbas/wYk9MjAZO/853v8CG1v/r5PW7H+Iq6pYHnMTcCMQ8yBy7FHmrOUC0/+BYs/EMDhQMJtoUr7cEY8spFlXsKzsjFY3yBUPugVzhmiydexdgclkgbLOi1JaFckQ0w4BgMQWllsG+5QrtxefaNSSwJKLm/8N/cmIcxNRlsRuLcxOnLz5SjKCB5b5xrzJ9JXfJnwsBlAyaKCQJbm4Bkp1msLtEkgSYbNyYlfn7gU8Wi0bkjI1RjKiQstH0zEtOr6D69TZ+hTyVBsDvcwyDwyruezSjL4AlIcCnZvTQL+tKTvg764NYYerGUldO5MqWFFAVcFLQMY8UJC1NAkSVgYXR3waG9wwOWxNUOYz7DpJkOiqOKEGve4sc9/GgThUyAQD8RnGv0IjDpIhnYRNZO2UOj5RR1/kXvB4yMeA6OTHgKmemn9Iw7h9FYBgGu6/WZUSB2ZExiHAFlou0My0dqSqTeBmMMlQeyw6lGQj4NmjgDeowcHdoNFC7LHOfMYEphDUm2JjE7AfszLiV6ODBj+chWK/5p7zP0ZuGRQpJmjC/9JColAaRTLIFTp4E9FF4iyyUYCQFIlyKA7TcZMCEhoTdMTAgtI152DUS1GKNPKpBjOMUhW0iJmpozJTIDZCHRJ1FkjNRet0NrNAHzFPYT8P4Y38PmmTSVipY0py+oad200JWJLuuVUDlckJQ22zmowNSkPRMDGHcLbixFkds/LnehylKQzQgsL8fFxJUal91SViw9G5SXyEyYiR3uTQKJPKXG6Td6uAMXb01AEDvQlaT6oLmknhbjSZ4qkw0oyRHreUz4tNcMnCh1UJzuisOUeFoes2NfC6nyKj2Tw8FwdjSqh9N0MQ8ZOwka60ym7KrrKS5BSm8WfLnZ2e9Wh0d8ROuot7py4bA7bM0tH2z3731yd311DSfpaZk6brVkcfsspGN1ZHO9WnPY+tZTX/7h9R82l1f7bN4ZDHc63Wd+9/d6jzzxwd0PIL+2tn756avz6xffuPHByc9elxvqeruQc5jubm+waQE588c1NiJ339nY39n5xSc36U9blSvdPTzbxeijTuNLzz1/+50POKW6162///rmk0+svvjii7e7H91nM9G5zSdffrL51N79+v2twW32QSwt6gMk3WpvaWnl8lC7kuc7LHP0V7p6YXyrpyUP9u4Nev3e0X7ncHewp5vDQW0R/PCwwx+uouRWFtscTLFyiaPlBjymmF9ssb+Rg1CPtvaWL1yod3rX33vv8Nb91XOXH1tab7faK029nsa50TgKFuoqp/ixs1jFwNyR2879w/2j7tNfep4PjGDVgK9Os42JvdK1Sufe5m6t1X7k3NL5NXZAMzJV2621Ry7e2/qAzftUQHVxbPkB3Tms7WzXOof7H3/0zSe+/Mb9zcbW9u+98NU3f/haf7fP0dMcqLTX3eT9TjY6wKgjU8qKh3maqTfr7358/Y233n7k0cc62x+x9YMnov3q8Vx7cfHcBZ740ma0jRMnqibzpxpQxgF6o0UMt9pGokIVDbtDotVP1ljaETRYUVQhy8hiDLORJtBlVl2nLyHzJhjL8P0BHZv7HBWfbYsdnWanWGgL0RWxG0Tdgh9b08LJKQzuW6CAJrUTJMMeY+3QlJ7EkFfwEoGiWIrkgj8wIc8N334bdQIpx9jJE2/n0ebl+U1kIUpX2FSOlrqUamHYC6NL5xrMoFKnuG8/2DnoRw1WmUVZRI8k4jJjymQwJ+0SFCElTXS2XFKrg0QyPW4q++hFs4QXjrSc5E9fsvno5Lh/0u1weqi2G0DrXDj5C455wEJOtdOMslTlKYLMIAPRYFVa4U/lgwJWPx/XAesyEgA4w96nEoEIWnkbdrywd9jprS609MTo+OBgrzK8SK20stClHVuFuhjOmMxRb+l5ijI1acQoUj0pvSQrwx6bYTyilYMw19UAPCETU4LlVg5S6eeINIaVK3rhnjlaPR5g2yUqWEwWp1ZTNH4y+D15fm3r4rkHLIoxVlQadBCcJY00bcdq1PvywEkNbJ/PzLY1lhbOtIdKM8pfZOI9Dvdk8wkNjtag14jIj3ZNjtagyQpdJQ8R+SgFAKMvA5FlNLkBYa+LW27M9/CY8y5kqSh+ZWcM0Jr3j6VMXUBpD+cp0xi87QIizimnYXht1USSJYCcIEjECZhgfJjLaYOTlgQkOVaU7FGOyrLLbbDMCXYjnQtge8NcjkESIJgWZQOcmoyZAFJqApJYV/gJyb6EmFRigukxLL/MtUBAUsLk8NnIlDoBIBB1DLx6Fe9kyMEWLFJSe1W5K5VF2kmYB0y/QQ/Alme+PkfNhKusn+o06Bj46NfXn39U8MkhOVpqze/3mRQxX+XVBMppZLakZX52hhjG+aN9pcKlHfDHyZh8/gcWRjWvhIJEO4ZjPNOM6Hiqtfry6lAfYtre2uXGHvmYx4ozNg99yNjqBdag5+r6ViFedFcEHJca09W/sckD6SLgv6bTTIa0Z/iIbz0VlIwmx0x0PHxGzxAyZkdkjf5c+6kpU0mDgXN/5Uz5RZ7LDYlLdItMBuS9Yw6LSbWFPwBVCe4i2Z6mcmlo1qt6oiTND1xniOEiZp8sxhBC2VhEaulhC5fkCUowjB6uz663KLVwZEEMHDSSjBISKYqQzMFyAkiF0QM0lyoKFf1gf2e7c7CHWdriqlscbIi2nWyMmZv8EsGiUmICJHOCMaWdBujGiTmKPpxwdMweP+6d8CKlQIlhLLaU1Te3qDQkTJKjePcI41nD4eaM2xF41TJkMLt1eXDBizJVDhmRaHDUh/pCdU4VEvkM4Xhm0NNBf2xqbvB8pcVnuvgGApvVcZHkxA1OpcUcho3PnLTM40nuBvgjiUHN94xF65ATcCE0VISJfFOZ+QsCdCrV8QQZBBMYUSIQvN69V9uGcY6v0pzMvfTMI4+vN+5v/+y1rZuH1TZ7CwYtjo1X90KwKPLHzlAOHsjFIsF6AqDe1hld2b5MmajDCXXJEu6Qca3qNiaop6qw8Zmjn+ebc3xv8MIaMB9v5w732BlUMygDZUiI0pzMMnioJuJU5qWAsV9MMn1izIGc1GQuHap2qHL6jDaYM34mOBlTiNacsFiULJ1fNKicskjyBEhNsQwx8Qyb42WAcI/TxK7U5FhgM5JiN1LBU41yHsf4Sx2f8ku1MQVm2NvJ5zbbqWckQWCTEuOnqPyMyTbDKqZZwfvmmmpMY2FLPzRgCEHMkZVD9kHjTPpkXhHmTXoCtx6ksl7DiNkGP19fajVacxW+vBsdpdzOpIe/NV7/WVq5tHyupSGFrkezYnU8MWpDhgFFCH2Yw28M+BxVqimH6yQw71VTZvqgNjbpnlp1G33EWItVTJspDmJguFQ0sYZOy9Zl3GOOakSoyyPk+BJ2nOaSxSnwYi5ZqpOOPXPV7Y3Nj977kD6Pb7+QiFat+OoVFLI1qnT0GKVICRbMUMvNDp2SpgSqMPSIdMcauGOwNssYY2GUTRvFzjXjGhXdRUyMCvC8RcSEZNgK+WSbHSbMbnJTRmImIfVlDxNC2UixLZhmNFmOlzPLAIyXsSwns6HEDiWtfktMatXRy+cU0RGPI4orVBDypHSJ2BwPPI1JxBOUOfE0l4mt2BIcJ0pf4oSyvU2LFwYyaEw8QQESacwmTUYMJYH7MsZoE0NDRcHbSa/xvswzn8MWCE2uNy8yCyGeoEn4HLCdjo3HSIBcuPG2ilR05amp9JIQ1f4oO2dN+GgMIAnhMDWJLMiHmrgR3B2YKGIIAz+j9JMExEKTx7nNwBL8Sw3oSiqA0mVSEphCKX6gI3JdAmCbGwuUzzzzDH0mzwDTVhfIYHec5OBMWFhKJkDPWcIQWBSbedlZ/IN/+wPwTyyvPPXUUy8urJPEJVwWTsZ5DfOgrZ01/VaVp477e3toPNndxSlH/Z3HHn/86eeffuWHP4SMwHHSfKiwcjy/u7Pz+saH7Al69LEFlq23ahsk7d/9iCf27Uc22KbHHRGD0/mTJaQ1tvUqCnUDyqX6PAb3jua4ocIMDlmjwvOY1Pmig3UFw04PEtwxsnGDlqKhTuWv9+QxHgJkcq4IemuNNk5Djqsf8lmqsRMQC5JUIaOVaQGlVvNubsR2dvfYB9Q70AHQR93+xx9/fHxpmVxjqiVwlvT2BzexwYVIjBmwsE/56nPPWiy7zjmBBIfDRb5QCrBLHYhqIEYsL294nEGE8OVsNoN/73vf27mzS0bQj0bKEauqK2sq4rFWIEQKYUZxhSjb5msuww+JdhKAwKInuKCLpILeVdgYKE2cCLhMciYU5Nqhidyr9ZqFVIpumsXehj7YqepF6zCXtZdJE9xuXzOacGiXrgRMcpbdoOWT6lyPk01am1IRaxjDACh3MLwdyd7nrS3Kn3JPtGcBzuNMCreL7F5jMi/w6iZSJQo4ZmrKdQKQRo3VmZQcUx79yQTLTBt+KUgpwo5yioYlSbXNA0NIusK1hfvExsQ0gusJIGsn0xUJdoRAQzOkC0IFLTcXa/nSVPaTZb+hTdNJewIgS7AJZpLJwKhLiXgm4JxiPGKJ6RtZOGYtSVPrclpFMSq/ZdUKdboEgIVtUPTkKydNegyWjukJ6YjIAgRISFyULw/iZtowjaTrU62I9+/0ncEyICC5hH4JO7lzJhFKK0IvfSlWsWIm+6ODtanJklKYCh/YMcTGi7fsvnKWmU5OokhNxElUnprDUJr4NJmyPCoXXDkNXOkSO63ISGAynrSY0VqMTIw5zWkwxOaNWJXQurjvSDJzADi/zMVO6DUZSIfExWUO5xISnAgCI1eYixhvgASgJhiZ/AM+ZySVIA9HQSfhIHMyw8TgLTlRTl+Gc3QDktPMhKWUj51m1RUyfT0JD2v5TDWZFkTvKsqaPumJWTa1KILILJUfJPSsHQKwC5IYA0TJalWZNTAEyFIo81hkqrwcc1EiBkiijOSSSY3xzCg4oZjejKaKFpoeszW+GwrcWljFcj7lASV5kBHlggsgFZUG7l4u7nQtWzYw73pQXI0a5lgOnBt3JGM5ExuUeClWOEWBLmLERoirh+iCkjoAyJHHbbakhyikUEZkAUUETf+mAjRoJINeBSYdDMESbI2ZgFXyESCQwPB5lioQjfSfCQllMBpRZJOiCauUX2ACZBQNMgF8KQfF9md2zoK3EOulRApxn/FnwpiH5EY7NYeqntNjLcEYWVp4vSQpMVEi5XmvqnuJIKCSi1fIk3w8Q4Ukv1Ggnp4UFYxCIoklGDyAW5GFbXJXeQ9oL1lHSAgpWe1COE6AQK4IOgCCWRKjMcQWmICczHBQltnIkmFEKJWBJx/sgOZ1SabrD/aLZxLYDG1uoesMeB0jmQVbkiGKZi7Dgp8kaAjSqKCnv2trq8vLC/iKb6hyf7Gy0sZ1zFksR0RZjscuMk0hraATy+nB2XE6JWIgsSTgdAFFysNTfqooE9gnidi25fPhlDQB2JJUrvJ2WYtySuMnMHkugFFaFo3KyPAES1yOTRjAuG4nf4LJJXNpt0MAflpsyBxjgcZCDEzDZnnIOElLZkwwJi0TeOeC3gMCzh1SHG+xA9Be3Cy41dCdLAc/N/TFUeIBZ/7R2HlUFUtbjF8gUZ28hFi3bvY6kMqEkzYFsdWJUvu4Rk5OVoEEdky7AMCeEKtJGgBWEUuO5rRF46Fcwes0uejMgXUZrRIuYBsDBl4wkBFPBFkVqcQ4xKKgEX68wbGRjgm/jXSO1P8WHTBWYfZIthvrOCYML0mQE5Zmc/QyafrX6ozHKEyNR6uS4CRikHTg8wM+AKkDCZSWdzHTQjMMK+rKKwv7JSdXeICXoZS/cT/gxCyjkmJM0elwrW00E0wiQ3gxIcZW1viTFFsPEiAItQ2Ny2AZVx7JJosc6hqgmC/FfrQgSZFs00q/DLKokZ2JKAEopdLENDF/QEi6ciQvldOOwBTeT+wJ0DPG8iK3E1wYHxWRZ2XKr6yK8kocJWf5myQEAktoQpq17B7ucuPJkw3w1XaT71zOtdivyc1kTLl01pWanJ6aZzc7pTR7o9TxcL8YX7KLQXmJvQsxG+da+fIjQyW7qY9ied5n2roETS9KhSL7elqVFX0MFegUr2BbHTGtnqkaeAotDLNBsoogkQ7QhNFETHZJoPvCfexYkVDsD7KoHkijgMMSF5ELJtqxM+64kFz+jKkrkeO/mT3jCenKJlv+tMCidajOYJkMt5cAKOV6lU29XW3z4CyO3uHyuZUXn75Md/Cly09w/EV9wDmylSP6brrFqA6xawCP1/oH3fbx4rPPPbM4aOzefFA9pO+obnUP+Grs/Fz19o2Pa/e7/9X3v//K//vnf/fOhzdPGiwiP7/YYhbJk/3li+fuV3v3Kkd8Q12thg/TsnmTsw1PWs88+iJLsb2toxd/+8X/+l98/6O3fvHxhx/8+1s/qlQ3H3vkkd//Z3/44x//+I13ju98uLuwfOHeg729tV361gf9jeNP7nY/eod186evLbC43L+9z2LcXGeBIzua210t9W7rxKgH63r15uBwh6MzrtUv84H5+f198jvXrnJf1aJasJjea7HSPNzdrx50BvudlfPnj/YPjg+Ouh0Gtt7gpHbj1t07724Pdw+vXD7/aHtl/rjC4YzdWnwIpVznwr1FuWv3TKW1tLJ+6TJHZ88vL3I7R1lwuB1nNeGB3vGA1RQGvK33bq3yId9mi9uGD996h8VrFrNGlUd186Ryf3eHF2NffHCue9z9xTucLvna9Xd+8zd/80tXXrp+dP2T/Q8Ycffr230+vjC3HzVWA22srpPvaMx0n436zes3L6xdOL+2tLLIEN0fsqmx3tjc3RtcvowSTIZXf2XXQlfBX9xpqgbxXy1SlTysimtqCRfay6jpqjmjMSBJDbBoGKLNWhlgahoAIXM00hsTHGoIXBIkQV2ZMARaqYyI7SrSwTaowm7Zqdc76Bb81Fb7X4t7ZOSEBGZ4UBf5VL+n0QRGWy75sTMzAF+JUTkdoQKCNxi5IBHPyD9chMHqdYFT0wuOUQQlatN1sBcmGZn6RkkbS5FM5enk5P7m/r2NvUGVJ98akEfSYj+oy8xlV/pw5PlEnAPKUeiy2c6OLWHjKmZoD9LID5HB4CfnVuFU4tqAg3aOqv1ubTC6F814c7W/HLjMb0gLI8kOVmmuEHWAakMaMBM+YmBcW3iNzp42wwRAWwLhE2MyK+iLK/hIiXqmfXkcaLa/d3hunXUa5o6x7YENVbG7KrHjTAKNMbYXs1dY9qTUBKAUMqURiulaZsMUi+mJk4QxQDugVEnCA3R7tZ39A5awVheWIOMVQfUOSiz2QfPehasxqXLGSWWh3nrm8WtL3Z3dT/YPBy2+Oba912EbdbPV6HQ4zZ8T7GvMKAa8TVEZ8tnxQY1lEW4JZtsjmZy5qQ5QqybSTKOTH6U3uYTerL20qJ0okRm6oJi060UkOIRjNz08UUDOHbfp8ljQU9tnOmQmsuA4/cfySU/A6bRnpdhy24ATLK3ITvCNzI/LaWsnCD7Vnk8lQM+0FpDUQPDJtiQnYcyV8LDksCW4tgPnATIuHef4JDkhobEZjie4MCCJAsjZoU9CDCQCyBJXzpLonZouPxXI6QvhzBl1W8SVRqbYB63DakQ5PO4N630+ixa+nWvU6VRrnAjJcmd1wO7kunqMk7X64KmLK7Qk8jg44bNqlflK/bh2jAi6IDbC0q/T52BbkYXoJQq4rFej1MgDvVq92mAbJZ0SljD54n5KJ+SWDUqjSzQePDRX104XgLWV9vJiWB66uHs/4nUWjrBrMGMi1iyO/8C8FUFspdxPEmix5C7vBbCQDQR32ws8wQqjpqIwYIQddXuoF5ptznQtnLBdZlZIYMzgZjz0CzMK7itICHauTCMEGY7LiLXcQ4hxnHzETCbeZuMpAfiRwFDnvDPOpyQwhpFhySVLkSUVZQTjMbgk0C+f1YpJFugC7zocJhWtADKrsJ8VRwOJuw9Sos6HpYcHuzvbG6qDZUNwa0WCeS1HistmG9JGZUVNTbpyYrPkMYwzCVjzOeyoqkCsSZhWBtSrsCwDiooxUdSSifG43qNqnDEC+xFDlEnHS4HcHg85zY6RQTcR9eZCrbF4Um0d147UpsIZrdjSUW822GzIS63YyRDFSD0/4Cb3hO+W0QqbNEbqD9/SlXvBxEAD5N21em+gKH3nxbFHK9msIGUmK1OVU5B2e5Ig2rIIoJzAO4nzaul2B8d71ISXnrt0672L93/2oFXv3471LEtNumIxiXaqOgOS8g5/6xInYHgqUVJ1z8e276CAYCIsL7YX2/MsBiw0G+x9XlvmPAPu7LxUoGnAdJjuZqEZ95IvsWKsBU2LApMylYTMJPv/GzI327bRkAF4uat0hdCpPgBESamum346hqbkhYZhziTyodTF/VSJdLmPRFHloCGODnzk9lCqKmfJ00qNOS3VMlOJn0Z2mth/PN6Wo5d79vjOK4/T1KuAIbbHqOEAbOqK1Sv8fELT5wFWTcNdlTcqWrwKQQdLEWiKzk0trUQrwoxr643W+bVzjM3xyrG96tYTXUmWAZWcgmssWnSwG0JAYySPXoF584PvOsTdKD2JJMAgH9Jns++iXIBmlKQJYzNJCir2AXcQIX2kMmXQQkgwBgBeJMAKhkvvg+aWV5Qnc0edbu+YVxyK/qHPMBOrz+4Z1BFHBUl1CQmGbTEDKu8PsyiI/HjvdkaXBctUSL1OkYJM5UgOIyAbOTKRnp3cYz/+dxqV10U5JXOEgJKL0dNUX4OyC5JrwIdWl9OIP0FmLNmVN+gdMAIy4BSbzBiQttLESeBnBWRq8JQ26MJmFCWTpaIrJ4uUIgozChebzIYJDhIA/xI7Ke+QRRY+JRUgwSbOL3O3hMCHjaQ0bn05081iiZkT4wEW32g/7PUknl/hIdC83sQrKzR8SYfz4stk1TQAgWtCYvyiAbsFLTbGvrTdyTzbkFxtCxOjeVNmyX2ShlBg6BGFTMdgEjEwIeEnNDrVBBEnxKcCub9nE+cUzlFhVWQeSwgxkQI9Kkdkufmwytlv9dlBzCote9x46M1Znysrazdv3rQ+uJAw8pKFHB+z2nv16lXqDzcSC/0lb92lH+G8IRZ/6VVZ/H3z/Hn2/D7dWPzrv/5rOnVr1C7d3V3Denpfq/DEst1eR8u//Bf/8keEf/MfYD9/nk8wr2IDXXl7ofkHf/AH3/3d77G4/Mb/8r++//77DzrvQz93dYNDqBGFXvLOovnh/vEbb3ywudPiYebcylPgOTz69u3bg080aH2yXGFhl09lscz91OWLCJ/b3MYDfLUQq3Y2N1ny4KgO7TtmL3algkAyyLo6y9wVnRlehxjPbG+rJrD1mE4T7YRY4SxqSPKb8OEuiJHvQCrS2CZJrgedfQ1avKNTqaD0xo0b65cuoHE+PtNL9vEt80nYISODSEMjK9TQA+Pn6nKdvdJXrlwhjydd3aXYJAhMk2LYCVJXr1+/fh11scFhpVo9wEt8fEgei1EcdWZWfEpwvkiEOIfBSEgx2EwymzixwJhgAEtDJEAuM8G5ONNPJwVmZJJlUgjQe2BDzzTXhGTXccgIuDTsKdpZ4EQOkHPlcGTKGlVkzhEMeAb3iNIeznnGBQbXaEQYJxxdlRWsylGP9+8/EJduOQhnmWd+EZ0ZJHwWibWcwZokA0RG1NVoyhinoTEPMhIJAIn4DIH/+CS0WJE9ZtgGgKFNqJDktLGSgYwBkVIzPTEG01gmSt6MVBNSCQgkQOxA+032g4edS3qSqAwjlkSTAEtTXKIAkVlejX5BkmSBI2wGkWSTLIGyAFAvfUEfbNHdPgaHmpjBpsm0zEMM8iFgVrA2r92L2zv+IjnHjBzQYdKHJ/l0L7ZwlpmZQQHCFcRFJwAL3sY/IIHpx7CNPtM2Q8yRHWaBLBglxRjTgLTB1uTLRGnkZ40t+TQu3EISNhCfochWzSSYiZxQhxZUOE6K8pwmeqTNxEMwkZGJyyThDCCZOsELPiVZETEYyOwfyzQXcbo0sXlzCSaYjpMHppPMnoRzaY+ZEjwBdmpO4gUzwZWSpoE8I9OpOQax6RJ12cCtSuLRmZtMWo2Xj03sagyMIliIz10+xwSDkZ8k2jZieR5GG6wd6DUsc007LWkniUbk/yYmdioCmWPkSJGNV2AwGA89dgInZ9JxOUlWxUgNAG9hUjH6iB52glOTLmthixmzRy9AJ4MjKScch7NuDwejDh8gHGOQQADGkMjHOGN+RcmEHMiyUhKFL5GDQAIYpm70RTyrI6DDyFwYMCpJNRJeAxYQ8WRjhN54KA0Qy+44K9l1zB4GwyUSlbfxdmRe451GbHkMY9ijdY/4zAmT1eK11qnmjxBxhTeCHcQoGDPhzBIpHmAsHzHMgqChKmt2nVUtkNQo778rHRbMCCv8p0v5JD7wruxwgl6cAc2kMkhNryLEBHKaPEnroH2ZJswTPRN+2BmnkNmvquE4I/Y2u4ZdKOZKvJaZ9PnSqcp7Tj0FWz7osGEquTRgRkKJkgpXwuGQWygOgv+7d/6GaT8DN/U+zzKUHs1nqkOIjAlzgcmpM2vhKXfBq7ZDw8RReJHmubKifdCcFmAW5NhjthEkl+ytK03Wr9VJY5ZHX5osh42ZiD+VYII+XRY5TdefF5gpJ8/4GYLtVROQkXQ5UyZkp+FJmukH0yexyZKZeJM5KVFaqWOrmCbIiYEp6GlMcshMOyfoP8fltFILSXq51ABQfs/DmYXL9higXehLg5EFarLOeWMOWa/TS7RaHF7U4DkU9ATYueSWnH1pTDj7WztQ0nRUw7NOylpM7zhplJpQxCqHYXotLDR7LiTBnuhiD10cSIiRj0CzozrUFbUIODFCYBik8WaHN7FbiFKHJ8zYfWlGFOG5mZ03eVVvkfVuxsDA5IOABGIMxjw8FANUKTvvTUpc/stM2ZLDeFHLvMg4/bO7aCRLTWZALiHBiaJYgC5mIlEAeYcEXSgb1WAk68+74Up5IW62+cFe0KEF9+ixRRlIzQkkJ6qLkXmSOSSBEBWOpx4ECgwu+PgPWqgsJAlKdS+uPQeFqfCm9gBSRRJLliYoyajB/EVnXTCOOqZxSuya4YRE47olpUUNFz3mh71qeMpGEQBHd2glXjTDObamHzNssIWN+2DRtesc68huB/Z2njTZ1zu3vLzKuwshQE9+EVlKCI0jLdogmZKseUYGSpvGLQSrciQHkR7TgpJSv5EVVMEFFQiXuvdBF3sSx2uR/UMpuGRhgZcN3wLiKY/wFliIRW7YS7fljiaUjmY5kVOOa0VCIVyb1pCBTEjDcpbasDCWCEFKIz+Zi0QflwU+FIJ8mAAjR5xBOSFwgtcLB0bGPgiBYQjKcJtcgrVYKb/FoyfjiXnSzzdSDyu9Lz32+P65jcHc8ULtZH/n7rnzl3qHHfZ4a5s3uSuqWaGZV0EXz1/63rf+GZKPDzrDzlGtt7I4t3Cr3lj72tdfvfEWCxOL3c5f/82/62zssaj9wtNPv/7aa43dbfYxYMe51bU7n9zr9Qds+1GZ0Dr6w/bK2vberatXVv7H/+6//NEPfsxD+Plh5eLiUmW389Qzz/w3/+23vv3tby2tD94+uVvZ6Q4W5jbf7+u4svv7h6t3F18+ubB+6d7gAQdg9AeH9GXDw8HlJx55dv7Rw9uHt97auXlzc3dDLXfj0iHz0aX1+d5hb293Q/m5f5futb6/sVep7G5urbHR+aiL/f29HXI3OHp046By0Osz+TtusU184ebW5ka305g/d36hfe3JqzhfTZLAo1UWlaIK2f/g7C/0ssTbXFr+xre/NajPHfZ7fIW3ubjIQvhhjWOsavX9I16TucjnZvcOD042GAWP22q/DE4qRw0JGpkQuNCtNo8bK0eVc4N6vUsR9Xdb/e3r719de+at4/eWh+3BwWBxYak2rHeb+/CybUQbhAfDGluRInjYOzzqvf6Lt3792eevczjJwjxDNUdSccojmyJpJtRy9VrqEZUfVT/VawEEhiIsCRzIgkB2ukVzsDetWjcG/EbrcEuJDgqUJERfFbArqv1U6rLGwEEJmbWoIpZmmIG6BIKHs9grs0QoOEwtJAcNVBJOhSMmgxpNy17Uos6ObQOxNIRPRvQej8q+yKkQFoDpsQxMfPN6xDgOhezkDfUtpYcnNbovMre59ruDe5t7nOROlfEgZvy4hlOvokTktE8L0ePpMCJ9RBkV1kLOMDbx5v5JNDpLjIO+ekfs6oESGs3uqG7Z3qIk4QsCCr8xFpQrwjbPuSja6rhuzIQrzxHpPC1KR7moYpVZp0JRAmUnK68wl4Ue+UmCZnIatQuZtCMuHduMcf2jK6Wq9hV7HgEsU+xhgMuP2n5aQGk0W1Ez297vss2j8smde5TM2spSFKKY6bTCUZOCUETX0ah2n7i4tnG0y+OrKj1G7+Cwp9N7OIlUHQveYMSKvQxuhqcZYzyK6JNYXlCnopl39D16/2CIzMWVZb5xpu2ZrNfXtF7v4vAkFWoSlEZf5DR1LIXZ9HzhH6UbSWyPwQ7SMAAhJflyOjaBWXLiHLYc8+bCc2k5PWVBEhtHsR08wZQAOTswmHBOMQmBzMTJnlyF4TwplzZNOYFBkQ0z3nqBEZLkJFNzXqeSlFKNsQQxR0ZIBYDRZCk2Mhf4kHBubVI9kzdRypSsqkwTz5TjjJRD+jTTDExokesQSEy1q9ZV9wjM4Zvzww4bkzSSsodWp2tARNsBwxNwaumjFxZX5k+aGsCOa7FhmZOX2VLMTfXgmEFP76DQmyBtpsHSy9hfL17Uc/+mVxRi7KYR2KrT2GVlzHvpK4IFeZqQcCm8GindnY840/2zJhrg/b4RG4YJnEwBsYu7aKuQKNC62QTN43YZGVkA0EccJnsd8uZGbr4iBseZ0VjFNTNInh5qUFEvoTc3imOgA+MJvkDMQNG4NPchSSfycrfwkAAePhKls+2rvFmUbSW18PGYLCQEsC8dO4/ETE2hcSszMnak4gRNRxJL8gkSTZwkO9cmKJAUc/jQkhFC6TCD3bj/QFN35Zr/GncsP4k6Aygpk2/OoB0ljVkV6ONKtdMrDhUxHeMju/H68bpLYXVyWwnYkZROXQde17RRQ+MmAsrhtuDkuqlS4tDnufmTSqvZWuEZzfCkcVw9wpvmabTrlSYfE+A1AjwQnW2hSDWWXdL8cS60/BO31m7pNDv+KJXwWxRNzPSm8+h8OcZvEHgURok2M8Zs2TJNA2wyLq3FI3guR5xseImDbprH+7/65Wtfe/7Dv/mbDznAjLm6n7PQo2izJ7tOyhOTNAGOFjMmqrxAqUPQoJk2rJ5nQGlw6z9XW1laWJrnawv1C+eW1lbbnPvMYe06czom4UxTSkn6dT3MMbNgeThaXlGRMCAnm3YmBOHwIh6jLjlNwJWbBkWqQqIfhbdcJyhpv9hfWzuhw5nSXRR28a+oQj4jYtSc4aV26WYkGqY8dUYo7i+iE1NlZOUlqEsH2RLhy2AYF9F2wBVJvlsxK+2GJVi9n1CUTsk6+zcJf0j62VL+0dhkBpKwhNy5QVkwqTYPgCT2vJEa3wrRmz6MSLw302AvNK8fNyihBq2JN5th4RPXnDdzYW2Vis3h0XTUvaF25tJIJDPWhUkCjkUgtGmw5oAPYt5b0bWan47yQC+jOGsdvGYRm1JkZxCodIARIoC3QJhtxXkDjJfOjHRFFnh0F7qKCUNiH5MQoiwc27CZnGIAd8NqGrZqePLIhYs68+rBA/oiTIol72NyJPdAE1UJi2jfmDldFwJfeFVitfSMFUVvIINlRslIzkIvFAqut2WtmxYOO3byLJ6ToLkT0UPtqKtWFCLOikY7oKGCx8ziKNdljLGPhC+DiOVqQoma9UsyvGbHel8mwpSUMDmQE1uCnH1mMAvxGVS52DPIUpKkRQ+ZMKUxCaHe3N4boaYguNhPkKNtp+WDt1gjTQacdAGjBbznQNQ6+mwumVCajF2ZRzpuXI+GaELcGoVGNYAkBAljvqELi0K3uk+NkZPMK2SWnuYyr7eJLJcJsux3jS6Yc1bsUZ+RhTw1hzMSgcg+LdV5FEGQuYEl+jBKlphdl7+MYDkPL60oiDiHBEtgxI4kZMy5pamYyTqCuto57fml0FkG7V99hJ0pW3Nbk5mI5mckYp944gnooRyuqVMGz+LsYbv9ve99r3HSQc7+e9fffffdyt7e3bt3dy9cQAvBGqlg9IZUN1dIlnnBa7Gmr6OQv/KVr7Azlx2dIB977LHv/NZvIfOb375AH3o07KNx8dIlTUlZmpfAzgE7eO/coVedW9RYS96JFxdX2Hndudt5++23H7x/wJnUjcoaKnjCj/DGQvHxXH0ifH+fOs8b4xiDE5DcPeJ1VxEQOC59c3OzH+c5HvBxg/AY9PV66/Lly9xHwYXnGYQI2ABBHkjFHgL7HoHZMM5RJDxoZdhjDzjyMQlpq/PL7DpHOz7kzgFR1ZUF7tCQDK/rvJ0MBnZUaMlY03JChc8zXvna7wNBYzOSJcwaQVJvnYouVdNwEYdH/+5Xv0ZZsM+aTeL0BPgWYlUkvhlDmUaDFBAhAVzlcEpFrOEUmyyhE8EEu/EpNbEDlBLGJENZGpXTjsFJWgLGkrMmMIE/7XKmnJlIJGB2NBd+5b1EFpen9jOJ7DQbwKtKZMkIZIfRgw1OUKfs9EI0yrP0hwWR8zDaLQ7KGNBkSK7RmQU5LY3KTw0kNktOaZlfUGxL0AtAsHbnVJc0zKKFjtzG7SFkQa62QJMxvZHORRTupMnyfQRoYEFCTPX09p/aVCSTBMmoeUIXoeCc+iFxClcgJpKcu/HxMWMNMZiBAc4aPQ8S+l311cPj83QCdDR4QxVMfU5xpyQ4Zg7FqFqv8HhsbV9y+NYiCujG6YsuLq3aADBK4j2ScoaaGTEJ4lsCUxKbFAUiddjgvlp+i9NvAUwDvYEkKyUljG1Ol18E8DlU5CzKl+sYKw3RKGYaCRlcE0kJk4AJgnQpR6WLzwLAZwthmvC2xaDa2iEzpS8DLYOtOcUW4svPYoho4bIKM3KJFjDJb0kpBDYjKUqMicYEFuU4YRKQpyZRpIbG0eJRTnY2bJuJsYfDJxHFMEwnMMf9bYToFhpgmBIAE3tchoUAhkovFXFXhRCQ1gg3AJcGjDwjTk5LNOZNAi3Nqci0LnPZmdimVN+J0N1gSRhDT0mr7/CRDczTgRw6ABp4bC4eckkC72lMslztmrekWS2ng3SVj2W2ovqnemwAAvkttBfUNpleK+yZajVKhjdusEuThRNhyAxpwjiEYVKC8wncDCV88OjKxuMcAIIJHMMIxh6DQMZG/cF78k+UF2JxkRnzGAkQm12vik8Fp06hxVXmRZ/5YYZppRhQPvgQk8geOkhXEOdcCCQXSopcOyknMEwqlIwOdHJyReFn2pFcqDIsfK8alPwrbaVkJBCQFsElGISlt0mFPA0KRZWLHhWNWv+JYnJtxOFolvwskOQri0opaAR2DBAqinKcoEwsE8BDkk1wpUupLusU2vnuOvcygxvqHFqN0esL0LuCAaDRBkcBFaWj1HTADhQRrAV3mQsctx585qHVrBF77zOC8SHbRqHBdWY5LUZC8hU0wHE56e3T2BM+uNLVWUCilCYVVumss5h++WkTGX9IBaqcUUsfnh1KhBMnQLrKTOMB8I5Ps0GMJT004bQx2rPZE6kNSJf/JICbLQZTM202ViXDUtaYnVLJ+c4v46kqczmfh0B3yg2dFw8N4fyFRd63Qyx7pTn1XFvQDjQvJXfEqf6rh6l5D7VOl2a9AMpBT2+cFL6NUQnhyQYAUm2wfeUkqixiPVaSChIMjHpqG6WjVB2YUwRz5RJI4JJMuS5BAMClC9l4kCx6eAEa4cjkwXW73eiy8j1kBQZ2iYzeTv3wRADvJOPRKPke8YT6bA08pBUVNVeE2baNLyWW2dVvnuVEDz7B/x/Fos27WicHPAAAAABJRU5ErkJggg==", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "import numpy as np\n", + "image = np.concatenate([dataset[0][k].permute(1, 2, 0).numpy() for \\\n", + " k in dataset.select_video_keys], axis=1) * 255\n", + "Image.fromarray(image.astype(np.uint8))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "66e32af3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2TFevXtXbb7+tqVOn5lp/amqq+vfvr/Dw8Czr/P39NXXqVK1bt04xMTFKSEjQr7/+qieeeMKp3Nq1a43p559/PscEJZJUt25dvf7667nGVFAKu+1ckZqaqgYNGmjlypUKCAhwWmcymfT8889r3bp1+uGHHyRJERERbk/ElZaWpurVq2vZsmUKDg52Wmc2m/XGG28oNTVV77zzjiTp3Xff1bPPPuuUuExKTwq3ePFiY37u3Ll66KGHsuyvVatWioyMVLNmzXTlyhXFxMTo888/19ixY53KZT7PPvvss2yTvknp7dq6dWu1bt06fwdeBOx2u1555RVj/rHHHtPUqVOzJB0JDAzUjBkzFB8fr59//lmS9NJLL6l3795OyYmuVxLPNwDuQ5+KPlXGPjOStNWrV0/r16/PNhGg2WzW008/LT8/Pw0dOlQ2m01vv/22072+KBRGvKNGjVJKSvrDQ61atVJUVFSW+2iGqlWrasyYMQV2POPHj9epU6ckpX8vIiMjVbNmzSzlBg4cKH9/fyMJ7C+//OJSAler1aqZM2dq4MCBWdblN0FuhrS0NDVr1kwLFy6Ur6+v0zo/Pz9NnDhRV69eNRLYvvbaa3riiSey9GFmzZqlXbt2SZK8vLy0ZMkSNW/ePMv+unbtqkWLFumee+6R1WrV9u3bFRERoUGDBjmVy/z9mTp1arZJ3yTJ09NTHTt2VMeOHfN/8IXs6tWr+ve//23MjxkzRu+//36WclWrVtXChQsVGhqqLVu2GEnbVq5cmWv9qampCg0N1cKFC+Xp6fwYgaenpz766COtXr1aW7ZskcPh0Ny5c/Xyyy8XzMHhTxkJY/yDJf8KuZcFgMLme+1vOVYeJAVckvFd8SnLfRyAM+6pQNHh39VA/vgGpf/kHoWSyLjmV+Caj9LHYZdsVslMWijgVpbzKEAAAAAAyMbe01e1YGf6oOOMpG+SFFKxjMZ0a6R1L9+v74a1Ua+7q8nH00PRZ+P1zv/2qf37kRo+fauW7olVmtWeU/UAAAAAAABAseZwOJSUZuWT6eNwONz9a8lRSEiIwsLCJElbtmzR1atXs5Q5deqU5s6da8x//fXXOSYocZdvv/1Wdnv631WbNWumZ555Jsey5cuX14cffmjMz549W1euXMm1fm9vb/3nP//Jcb2vr6/69+9vzG/evDlLmcxtW6lSpVz3V5QKu+1c9cEHH+SYPESSnnrqKWM6u/Z1h7fffjtL0rfM3njjDd12222SpMTEREVERGQpkzm5Tq9evbJN+pYhJCREr732mjH/9ddfZ7m+FNfzLD+WLVumo0ePSkr/7n3++edZkr5lMJlMmjhxory8vCSlJyxavnx5nvsoiecbSh/6VCWrX0Wf6tbuU2WO+dNPP802iVpmQ4YMUePG6WMFli5dqgsXLhRqfNcr6Hg3bdqk9evXS0q/906fPj3X+2hBcjgcmjRpkjH/r3/9K9ukbxn69OnjlAzwq6++ynMfbdq0yTbp28369NNPsyR9y+w///mPkVju2LFj2fZhMvcVn3766WyTvmVo3bq1RowYYcxnd+y3Ql9x9uzZSkhIkCRVqVJFb7/9do5lfXx8NGHCBGM+KipK0dHRee5j/PjxWZK+ZTCZTBo6dKgxT1+xkNiuPaxo5gWaAIqBjGuRjQQAgEtsaek/zd7ujQNA8cM9FSg63I+B/DHuUWnujQO4EVzzURplPt/59wVwyyO1IwAAAIB8+Wjpfjkc0sN33aamNYKyrDd7mBTaoJJCG1TSlWSLfv39tL7felI7Yy5rxb6zWrHvrCqU8Vaf5tX1zy4NFeDDP0sAAAAAAABQciRbbLp97FJ3h1Gs7H27m/y93fd3vhMnTmjz5s06cOCALl++rOTkZKekKRkJlhwOh3bt2qXQ0FCn7VesWCGr1SpJatCggbp37150wbto5cqVxvSQIUNyTBKVoU+fPqpQoYIuXryo1NRUbdiwIdfjuvfee1W1atVc68ychOHYsWNZ1mdOUDFjxgyNGDFC/v7+udZZFAq77Vzh6+urnj175lomr/Ytaj4+Pnr88cfzLNOvXz/997//lZSeaGLkyJFOZaKioozpzMnGcjJ06FC9+uqrstvtOnPmjKKjo41ELZLzefb111+7lOykuMl8Tj744IN5fveqV6+u7t2765dffpGU3qbdunXLsXxJPN9QOtGnyp47+1X0qbIqDX0qq9VqJOQKDAzUww8/7NJ2YWFh2r9/vxwOh9avX69evXoVWoyZFUa8S5YsMaYfeOAB3X777QUbdC727dun2NhYSZLZbNagQYPy3Gb48OFavHixJGnVqlV5lu/Xr99NxZidGjVqGMkgc1KpUiU9+OCD+umnnyRl7cPEx8dr69atxrwrfcXhw4cb/b8tW7YoMTFRZcqUMdbXrFlTBw8elJTeV3z55ZddP6hiIvN1qn///vLz88u1fJs2bdS0aVP98ccfktLbuVGjRjmWr1u3rlq0aJFrnfQVi4D12sOKnjysCKAYyLgWWUkAALjEeu2Ba08SuAK4DvdUoOhwPwbyJ+O7YiV5EEogrvkojTKf79ZUybtMzmUBlHhkWAAAAADgsg2HL2hV9Hl5epg0pmvOA0UzBPl5aWDb2hrYtrYOnYvX99tOav72Uzofn6op647qarJFHz92d75iSNq+Qyl796rc44/Jw5sBkAAAAAAAAEBptWHDBr3yyitau3atU1KS3MTFxWVZtnHjRmO6U6dOBRVegXE4HNq5c6cxf8899+S5jZeXl9q0aWMksdi+fXuuSUqaNm2aZ53BwcHG9NWrV7Osf/DBB1WmTBklJiZq+/btaty4sYYNG6aHHnpIzZs3l9lsznMfBa0o2s4VjRo1kpeXV65l8mrfota0aVMFBATkWa59+/ZG4rcdO3Y4rTt16pTOnTtnzLvS/pUqVVLDhg21f/9+STLOpQyPP/64pk6dKik9mce2bds0ePBgdevWTfXr18/7wIqBzO3kSptIUocOHYzEb9u3b8+1bEk83wC4F32qnJWGPtXvv/+uxMRESenH+/zzz7u03ZYtW4zpmJiYAo0pN4URb+ZzN69kZgUtc7+gUaNGTudHTjp06GBMx8bG6vTp06pWrVqO5Vu2bHlzQWajXbt2eSZOlNL7ihmJ367vK/7++++y2WySpICAAN1111151tesWTPj+2Gz2bRr1y6n7/Ljjz9uJE575ZVXtHz5cg0cOFBdunRRjRo1XD4+d7rRvmJG4re8+ooFcZ1CAbBde1jRzMOKAIqBjGuRjQQAgEuM+zjjlwFch3sqUHRs1xIscj8GXJPxXbGRnBQlENd8lEYenpJMkhxcu4FSgMRvAAAAAFzicDj0wZL0B636t6mlkIr5yxRfv3JZvdqjiV7s2kg/7zyt//t+lxbuOq03HrpdQf65P4CUIfXwYZ146ik5UlIUv2SJqn/xuTzLl8/3sQAAAAAAAAA3ys/LrL1vd3N3GMWKn1fRJ/SaOnWqhg8f7nJykgzx8fFZlp09e9aYrlu37k3HVtCuXLkii8VizNeuXdul7UJCQozp7JKzZBYUFJRnfZkTSWWOJ0NwcLAmT56sQYMGyWKxKCYmRuPGjdO4ceMUEBCgtm3bqmPHjurZs6eaNWvm0jHcrKJoO1fkt32tVutN7/Nm1apVK9/lzp8/77Qu87yfn58qVarkUp0hISFG4rfr279bt24aNWqUvvjiC0npSVwyErlUqVJF9957rzp16qTevXsX2+QemdulOHyfi8P5htKJPlX2irpfRZ8qb7d6n+r06dPG9IULFzRx4sR813Hp0qWbjsNVhRGvO8/dG+kXVKlSRb6+vkpJSZGUfl7mlvjN1T5YfhR0X7FmzZouJZLz8PBQzZo1c+wrDh8+XEuWLNGCBQskSZGRkYqMjDRiCQ0NVVhYmB555BFVrFjRpWMoasWtr5jddQoFwHrtQS1PHlYEUAxkXIusPEQKuMS4j5PAFcB1uKcCRcd6LcEi92PANRnfFSvJSVECcc1HaWQypZ/z1hSu3UAp4OHuAAAAAACUDEv3xGpXzGX5e5s16oH6N1yPp9lDf2lRXY2rllWq1a6fdpx0aTt7WppO/d8YOa4NXk7aulXHnuin1CNHbjgWAAAAAAAAIL9MJpP8vT35ZPq48oB+Qdq7d69GjhxpJCi544479Nlnn2nz5s06e/askpOT5XA4jM/gwYONbe12e5b6MicuCQgIKPwDyKeEhASn+TJlXHspR+Zy2SVnyaygfof9+vXT5s2b1adPH6dkAQkJCYqMjNTYsWPVvHlztWrVSmvXri2QfeamKNrOFUX9HSkI/v7+LpXLra0yt7+rbZ9XnZL0+eefa/78+WrTpo3T8rNnz+rHH3/UqFGjVKtWLT366KM6ceKEy/stKjfSLu74PgOFjT6V+/tV9KmK7zW4KPtUV65cudlwizSJaGHE685zt7D6S5n5+fnlP7A8FNe+otls1vz58zV58mTdfvvtTutOnDihWbNmafjw4apWrZqGDx+uixcvurzfokJfsZSwZTys6OveOABA+vNaZOMhUsAl3McB5IR7KlB0bBmJWLkfAy7JSJhlIzkpSiAbybdRSnHtBkoNEr8BAAAAyJPVZtdHS6MlScPvraPKZW/uP0hMJpMGtE1/s3PE5hiX3iB//j//Ver+/TKXL69a06bKq0YNWU6c0LF+/ZW4YcNNxQMAAAAAAACg5Bg/fryRrKFbt27avn27/vGPf6h169aqXLmyfH2d/36Z14PvZcuWNaavTwhSHFyffCIxMdGl7TKXy3yMha1Zs2aaP3++zp07p59//lkvvvii2rdv75S0ZNu2bQoLC9P3339fqLGUtLYrTpKSklwql1tbZW5/V9s+rzoz9OnTR5s2bdLx48c1ffp0jRw50im5h8Ph0I8//qgWLVrowIEDLu+7KNxIu3BOAigM9KmK9zW4qPpUmRNG3XXXXU7J/lz9jBs37mYO1e3xuvPcLcz+UmEqzn1Fk8mkYcOGac+ePYqOjtakSZM0ePBg1a1b1yhjsVg0ZcoUtWnTRufPn3d530WBvmIpYb32oJaZhxUBFANm7/SfVpLUAC4x7uPe7o0DQPHDPRUoOhnfM+7HgGsy/gbFPQolkXHN52+pKGW4dgOlBonfAAAAAOTp+20ndeR8oiqU8daI++rmvYELHmlWXb5eHoo+G6/tJy7lWjZh7TpdDA+XJN323rsq0769QubNlV/z5rJfvaoTI/6mS/PmFUhcAAAAAAAAAIq3yMhIY/qdd96Rt3fuA7qPHz+e6/oqVaoY00ePHr254ApBUFCQU4KPEydOuLTdsWPHjOmKFSsWdFh5KleunHr16qWPPvpIv/32m+Li4jRt2jTVqpX+UhCbzaZnnnlGycnJhRZDSW274sDVtoqJiTGmr2+rSpUqGdPJycmKi4tzqc78tH+tWrU0aNAgff3119qzZ49OnDiht956S/7+/pKkCxcu6IUXXnBpv0Ulc7twTgJwJ/pUJeMaXNh9qsy/t9jY2AKJuTAVRrzuPHdvpF9w7tw5paSkGPPuOC8Luq948uRJl14YaLfbc63zeg0bNtSIESMUHh6uw4cPKzo6Wi+88ILMZrMk6fDhw3rrrbdcOpaiQl+xlLBde1DLkwfUARQDntceIrWluTcOoKQw7uMkHQBwHe6pQNFwOLgfA/ll3KNIHoQSiGs+Siuu3UCpQeI3AAAAALlKTrPpv8sPSJJG3V9fZX298tjCNUF+Xup5VzVJ0qxNOQ9WtV64oNOvvipJKj9ggMqGhUmSPCtUUK3waQrs2VOyWhU79k2d/eBDOWy2AokPAAAAAAAAQPF0+vRpY7pp06a5lr1y5Yp+//33XMu0a9fOmI6Kirqp2Ewm001tn1OdzZo1M+Z/++23PLexWq3asmWLMd+iRYsCjyu/AgMDNWTIEK1cuVI+PumD0+Li4rRhw4ZC2+et0nbusHv3biUmJuZZLvPv7/q2ql69uipXrmzMu9L+cXFxOnDgQI515qVmzZoaO3asJk2aZCxbtmyZUlNvbCBkYXynmzdvbky70ibXlyut5ySAgkefqmT2Cwq6T9WsWTOjnnPnzunQoUMFFmthKIx4M5+7K1euvKm68nvuZu4X7N+/XxcvXsxzm/Xr1xvTVatWVbVq1fK1z4KwadMml8rl1le86667jARs8fHx+uOPP/Ksb9euXUYf1Ww26+6773Y1ZEnpieA+/fRTp2RvCxcuzFcdhY2+YingcEjWa/8+MfOwIoBiIONaZOUhUsAlxn2cBK4ArsM9FSgaNsuf09yPAddkfFesJCdFCZRx3nLNR2nDtRsoNUj8BgAAACBX0347qnPxqapR3k8D2tYq0Loz6vvf72d0JcmSZb3D4dCZ116XLS5OPg3qq/JLLzqt9/DxUbWPPlTFf4ySJF0MD9fJ50bJ7sLDaAAAAAAAAABKJg+PP4c6JCUl5Vp28uTJsliy/u0xsy5dusjT01OSdPDgQS1duvSGY/P19TWm89pvftx///3G9PTp0+VwOHItv2DBAl24cMGIqX379gUWy82qV6+e7rjjDmP+7Nmzhbq/W6ntpMI7x66XkpKi77//PtcyaWlpmjt3rjEfdu3FLZllXhYeHp7nfsPDw2W32yVJ1apVU6NGjVyM2FmvXr2MaYvF4lISlewURntnPicXLVqkc+fO5Vr+9OnTWrx4cbbbA8DNoE9VsvsFBdWn8vPzc2qXL7/88qZjK0yFEW+PHj2M6cjISO3bt++G68rvudukSRNVrVpVkmSz2TRz5sw8t5kyZYoxnV3/qyjExMRo1apVuZaJi4vTokWLjPnrYy1btqxatWplzLvSV8x87G3atFGZMmVcC/g6mfuKN/PvkcLuK86ZM0cpKSm5lt+6datTYk53nRPIB7tV0rX7jycPKwIoBjKuRTYeIgVckvFd8SSBK4DrcE8FioYtU3JF7seAazK+KzaSk6IEyjhvueajtOHaDZQaJH4DAAAAkKPLSWn6atVhSdKYro3k42ku0Pqb1SynxlXLKtVq1/wdJ7OsvzRrthJWr5bJ21vVPvlUHpkGzWYwmUyq9Mwzqv6fT2Xy9lZCVJSODXxSljNnCjRWALcuu92uEydO6Pjx44qLi1NycnKeD/oAAAAAAAD3qVu3rjG9cOHCHMsdPHhQb731Vp71VatWTU888YQxP3LkyBt++L9cuXJGEpXz588X2MP/I0aMMOrdvn27Jk2alGPZy5cv66WXXjLm+/fvr6CgoAKJIzdxcXEulbPZbDqT6e+3lStXLqyQJJWMtsuP4OBgY/rUqVOFuq+xY8fq0qVLOa5/7733jBjKlCmj/v37ZykzcuRIY/qnn37KNQnQ8ePH9e677zptazKZnMq4ep7FxMQY0x4eHk7tlh+F0d5du3ZVnTp1JEmpqakaPXp0jmUdDodGjRplXEvq1aunzp07F0gcAECfqnj2C9zRp3r55ZeN6S+++EIrVqxwedvY2Ngb3u+NKuh427Rpow4dOkhKv/cOGjRICQkJNxRbfvsOJpNJf/vb34z5t99+O9ftFi5cqP/973/G/N///vcbirMgjBkzRqmpOT9sMmbMGCNpWe3atdWlS5csZTL3FSdOnOiUwOx627Zt0zfffGPMZ3fsN9JXvJnvTmH0FQcMGKCAgABJ0pkzZ3K9/qalpWnUqFHGfFhY2A0nTkYRsmb63ph5WBFAMZBxLbLyECngkozvCvdxANfjngoUDWum5IrcjwHXmK8lJ7WSnBQljM0qOdJfHGicx0BpwbUbKDVI/IZSwZ6WpqvLlzt94les+PMTGen8Wbky0yfK+ROV6bNqldMnYfVq58+aNX9+1q697rPuz8+69c6f9X9+En/7zfmzYcOfn40br/tsSv9s2qykHTuUsnevUo8cUdrJU7KePy9bfLzsaWkkMAAAAC77ctVhxadY1eS2QPW6u1qB128ymTSwbS1JUsTmE079lJQDB3Tuo48kSZVffFG+jRrmWlfggw+q9ozpMgcHK3X/fh19/HEl//FHgccM4NYSFxen6dOna+rUqZo2bZomTJigDz/8UP/+97/16aef6uuvv9Z3332n+fPna+nSpVq3bp127NihAwcO6NSpU7py5YqsVqu7DwMAAAAAgFKlZ8+exvQLL7yQbSKpyMhIderUSfHx8SpTpkyedb7//vuqUKGCpPTkU+3bt88xQdXly5c1adIkp0QgGXx8fNSgQQNJksVi0YIFC1w5pDzVq1fPKSnCc889p4kTJ8putzuVO3TokLp27aqjR49KkgIDAzV27NgCiSEvL774ou677z7NmDFDly9fzrbMhQsXNGLECCNJSWBgoO65554s5cLDw2UymYzPsWPHbjiuktB2+XHnnXca099//71L24wbN86pPV3h7e2tmJgYde3aVYcPH3ZaZ7PZ9P777+vtt982lr366qsKDAzMUk9YWJh69OhhzD/66KPZxr1t2zZ17tzZOHdq1qypf/zjH1nKtW/fXgMGDNDixYuVlpb94MYDBw5o8ODBxvwDDzwgb+8bGwR8I+2dFw8PD33wwQfGfEREhEaMGJElwUx8fLyGDh2q+fPnG8s++ugjI2ERANws+lTFs1/gjj5Vx44djXun1WrVQw89pPfffz/H5GcpKSlasGCBHnnkEfXq1Sv/B3mTCiPezz//XD4+6Q+Kbt26Vffdd582bdqUbdnY2Fh98skn+vjjj7Osq1Onjvz9/SWlfwe2bNmS5/GMHj1a1atXl5T+u33ggQe0c+fOLOXmzJnjlGi3Z8+euu+++/KsPy9DhgwxzpGQkBCXtvH29ta2bdvUu3fvLAkeU1JS9I9//EPTp083lr377rvZ9mEGDhyou+++W1J6ErNu3bopKioqS7kVK1aoR48exv/LtmjRItukw7Vq1dLIkSO1evXqLN/rDFu3bnVKlpa5r5pfhdFXDAwM1L/+9S9j/oMPPtC//vWvLH3fs2fP6pFHHtHGjRslSZ6ennr//fcLJAYUMlum36UnD6gDKAY8r/3NxMZDpIBLMr4rniQdAHAd7qlA0bBdS67o4Snxf2aAazL+BmUjOSlKmMznLH9LRWnDtRsoNTzdHQBQFOyJiTo1KuuA4FLLZJLJx0cmHx95XPtp8vGWh49v+jJfH5m8fZynfX3l4ePtPJ1R3sdbJl9fmTw9JZNJMnnI5GFK/8NJLtMymYx5k9kseXrK5OUlk9mcXpenp0yenn/Oe3m5PAgdAIqC9cIFpUZHKyX6gFKjo2W7ckVlu3RRYI/u8vDzc3d4wE07dTlZ4b8dkyS93L2RPDwK5z78SPPqem/Rfh04m6Btxy+pVUgF2VNSdPr/xsiRlqYyHe9T+ScHulSXX7NmqjNvrmL+/rRSDx7U8b8OUrUPPlBg926FEjuAkstms+m3337TqlWrZLPZ5OXlpbJlyyoxMVGpqamy2+2Kj49XfHx8nnWZTCYFBweratWqqlKliqpWraqqVauqbNmyRXAkAAAAAACUPqNHj9bkyZN1/vx5Xbx4Ud27d1eLFi10++23y2Qyafv27dqzZ48kqVu3bqpcubK+++67XOusWbOm5s2bp969eyshIUFHjx5V9+7dVbt2bbVp00YVKlRQQkKCDhw4oJ07d8piseiRRx7Jtq6+ffvqvffek5SeyCA8PFz169eXl5eXUeaTTz7J93F/8skn2rp1q7Zs2SKr1arnnntOH3zwge69914FBATo8OHDWrNmjWw2m6T0B/+nTJnicvKGm+VwOLR27VqtXbtWZrNZjRs3VpMmTVS+fHklJyfr1KlTWr9+vVPCgk8++UR+RfB/KsW97fKjb9++RgKdl19+WYsXL9Ydd9xhJCqRpNdff13ly5e/qf08+uijOnTokDZv3qzGjRsrNDRU9erVU3x8vNasWWMkmpGk++67Ty+//HKOdU2bNk0dOnTQ4cOHlZCQoMcff1wNGjRQ27Zt5e3trb1792rTpk3Gy2HKlCmjiIgIlStXLktdFotFERERioiIkJ+fn+666y7VrVtXgYGBunTpko4cOaKtW7ca5f38/G7o+5ahb9+++uabbyRJX375pbZt26YWLVoYCV0k6emnn1a9evXyVe/jjz+uNWvWaOLEiZKkyZMna+7cuQoLC1OVKlV07tw5RUZGOiWwGT16tP7yl7/c8LEAwPXoUxXPfoG7+lTffPONzpw5o2XLliktLU2vvfaa3nnnHbVt21a1atWSj4+PLl++rMOHD2v37t1KTU1/yKBly5Y3td/iEm+LFi00ZcoUDRkyRFarVTt27FC7du3UqFEjNW/eXEFBQbpy5Yr27t2r3bt3y2636/nnn89Sj9lsVu/evTV79mxJUqdOndS9e3fVqlVLZrNZklShQgW99tprxjbly5fX7Nmz1aNHDyUlJSk6OlotWrRQ27ZtdfvttystLU0bN27UoUOHjG0aNGigKVOmFFh75tfTTz+tn3/+WUuWLFFISIg6deqkmjVr6sKFC4qKitKlS5eMsgMGDNDAgdmP+fD29lZERIQ6duyo8+fPKzY2Vvfff7/uvvtuNWvWTJK0c+dO7dq1y9imcuXKioiIcLoWZEhOTtakSZM0adIklS1bVs2aNVPt2rVVpkwZxcXFaf/+/cZ1TZIqVaqkcePG3XA7FFbffMyYMVq3bp1++eUXSdI777yjr776SmFhYSpfvrxiYmIUFRVlnNeS9PHHH6tt27Y3fCwoQtaU9J8ms+Rhdm8sACBJ5mv3rYzrE4DcZXxXzCQdAHAd7qlA0eBeDOSfcY8ieRBKmMznLNd9lDbma4mluXYDtzwSv6FUMHl6yq9Fiz8XXBssnGX6unmHrl+Xfbn81Jml3uuKulzP9esyVeSwO+SwWORISZEjNVX21FQ5UlP/3MbhSF+XkqLs32lYjHl4OCeF8/KSh6+vPPz9ZPL1k4df+sfklzHtmz7t63etjK88/Pzl4ecrmUxyWKzpbWW1yGG1StZr8xarHFZr+nKL5drya8tsVpk8zJKnWSZPr/Q4PK8lrjNnxOUpmbNZ7+klk7dXesI9b+9MH5/05Rnzmdfz5gHA7expaUo7cuTPJG/79yvlwAHZ4uKylE2IitLZDz5QUO9HVP6JJ+STz4c8gOJk/PIDSrPa1a5uBXVsWKnQ9hPo66Wed9+meVtPavbmE2oVUkHnPv5EqQcPylyxoqq9916+kr96Va+u2hGzdeqFF5S4Zq1OjR6ttH/+U8F/G0ESWQCSpNOnT2vhwoWKjY2VJNWrV089e/Y0HiS1WCxKSkpSYmJijp+EhARj2m63Ky4uTnFxcdq9e7exnzJlyhhJ4DKSwgUHBxsPdQAAAAAAgBtTuXJl/fzzz+rVq5firv2tfvv27dq+fbtTud69eys8PDzbhAzZeeCBB7Ru3ToNHjzYSCxw/PhxHT9+PNvyAQEB2S5/6aWXNH/+fO3fv18Wi0WLFi3KUuZGkpT4+/tr5cqVGjZsmObNmydJOnnypObMmZOl7G233aYpU6aoR48e+d7PjcqcBN9ms2nPnj1OSRWuL/vpp59qxIgRRRJbcW+7/BgyZIhmzpypNWvWyOFwKCoqSlFRUU5lnnvuuZtO/Obl5aWffvpJffv21caNG7PdjyQ99NBDmjNnjjw9cx6CVKVKFa1fv14DBgzQypUrJUkHDx7UwYMHs5StX7++Zs+erdatW2dbV+bzLDk5WZs2bdKmTZuyLVunTh3NnDlTd911V67HmpsuXbqof//+ioiIkKRs9/fwww/nO/GbJE2YMEFVq1bVO++8o9TUVMXHx2vhwoVZyvn6+mrs2LF69dVXb+wgACAH9KmKZ7/AXX0qHx8fLVq0SG+99ZY+/fRTJSUlKSkpKdv7fwYvLy+1a9fupvd9Iwoj3oEDB+q2227T8OHDdfToUUlSdHS0oqOjsy2f07n73nvvaeXKlYqNjVVSUpLmz5/vtL527dpOid+k9ES6kZGRGjhwoI4cOSKHw6GNGzdq48aNWerv3LmzZs+erUqVCm8cR17KlSunxYsXq3fv3oqOjtaSJUuyLffUU08ZSXRz0qRJE61bt079+vXTjh07JEm7du1ySvaWoUWLFpo3b16Ofa+AgAAjcW58fLyRRDE7d999t+bMmaNq1arlGl9uCqtv7uHhofnz5+uf//ynvvrqK9lsNl24cEE//PBDlrJBQUEaP368hgwZcsPHgSKW8ZCWJw8qAigmMq5H1rTcywFIl/Fd8fR2bxwAih/uqUDR4F4M5J8nyYNQQmWcsyYPyUxaHJQyniTtBEoL7nAoFcxlyypk9ix3h+FWDodDsliMJHD2lFQ50jKmU+RITZMjNeXa+kzT18o5Taekb5d52p6aIlltcjjskt0h2e2Swy5HxrTdnh5DxrQcRjmH3Z6eWM1qlcNmS0/AZrFkfyB2uxxpaVJampHqzlZkregmnp7pCeC8riWRM5udk9+ZzZJXpqRz183L0yyTyUMymSQPD8mkvOc9PHRtxbWEgY7035/DkZ5jMFMSQef1mdaZTDJ5mNLfSGj2SN+H2SO9bg+z5GFKT6Dn4SGT2ePaP7xcKOe0zJR+vKaMOq7FbDAZsVy/KH2x00z208qpjLJdnv86s9bjSh2mjPMi49wwpr1l8vaSyctbHt5ekpcXSY5y4HA4/rz2ZHwsFjnS0pR29KhTkrfUo0clqzVrJSaTvGvVkk+jRvJp3Egmk0mXf/hRllOndGnGd7o04zv5t2mj8v2eUNnOnWXy5o/KKDkOnI3Xj9tPSpJe6dGk0K8l/dvU0rytJ/W/38/o5aALujQrve9W7f335BkcnO/6zAEBqvnllzr70Ue6NOM7nf/vf5V29Kiqvv2WPPguAqVWWlqaVq1apQ0bNsjhcMjPz0/du3fXXXfd5XSd8/LyUlBQkIKCgvKs0+FwKCEhQbGxsYqNjdXZs2cVGxuruLg4JSYm6vDhwzp8+LBR3tPTU5UrVzaSwTVo0OCmH8IFAAAAAKA0at++vfbs2aPx48frl19+0ZEjRySlJ+do2bKlnnzySfXs2TPf9d59993asWOHFixYoAULFmjDhg06e/asEhMTFRgYqLp166pNmzbq2bOnunXrlm0dQUFB2rJli7788kv973//0759+3T58mVZcvo/0HwICAjQ3LlzNXr0aH333XdatWqVTp8+reTkZFWsWFF33nmnHn74YT311FMqU6bMTe8vP7744gs988wzWrFihTZu3Kg9e/boxIkTio+Pl6enp4KDg3XHHXeoa9eu+utf/6rKlSsXaXzFue3yw8vLSytWrNCUKVP0448/avfu3bp48aLS0gr+QaJq1app9erVmjlzpmbNmqX9+/crLi5OwcHBat26tYYOHarevXu7VFeVKlUUGRmpJUuWaO7cuVq3bp1iY2NlsVhUuXJlNW/eXL1799aTTz4pLy+vHOvZuXOnkYhu8+bNio6O1unTp5WUlCR/f39VrVpVzZo1U69evfT444/Lx+fmkynMmjVLDz/8sCIiIrRz507FxcUpJSXlpuuVpDfeeEN//etfNXnyZC1dulRHjx7V5cuXVa5cOdWtW1fdunXT8OHDVatWrQLZHwBcjz5V8esXuLNPZTab9fbbb2vUqFGaMWOGVqxYob179youLk4Wi0WBgYGqXbu2mjZtqrCwMD344INuTT5WGPHef//9io6O1pw5c/Trr79q69atOnfunFJTUxUUFKT69eurffv26tOnj0JDQ7Oto3bt2tq1a5cmTJigZcuW6cCBA4qPj5c1u7FHmbRr10779u3TzJkztWDBAu3cuVPnzp2Tl5eXqlatqnvvvVf9+/dX165db7jNClLjxo21ZcsWTZ06VfPmzdOhQ4d0+fJlValSRR06dNDf/vY3hYWFuVRXw4YNtXXrVv3www/68ccftXnzZp07d05SepLKtm3b6tFHH1Xfvn1zHb9y4cIFrVmzRqtXr9aWLVt08OBBnT17VikpKfL391eNGjXUsmVL9e3bV7169ZLHTb6UtzD75p6envriiy/097//XVOnTlVkZKRiYmIUHx+vChUqqGHDhnrwwQc1YsQIBd/AuBq4ke3a+WFm/BKAYiLjemTjIVLAJRnfFTNJXAFch3sqUDS4FwP5l/F9saWmP//N88YoKbjmozTLfO0GcEszORwZGXqQH1evXlVQUJCuXLmiwMBAd4cD3JIcNpscNptksRgJ4RxWa3qiJptNDotVDkuaHMnJsqekyJ6ULEdKsuzJybInp8ienJS+zphOSV+XkixHUnL6Trw8ZfK8llAtI7Gal2d6sizPa+syLTd5eaVnhbZf27/VKtmsxrTDdm2ZJVMSu8zrryWVyvyxXzfvSEv7M3kaUACcE8N5SWZz+gA483WJ9TIS9XlkJN7786fTsusT9XlkTtpnzlSPyViW/j2wXPveWv78PmSaltUiR5rzMofNlp5EMCMxYeZkhR4ef+7j2nqj3LWkhX8mdbOkfy8zJXnLNpFbLjwCA+XbsGF6krdGDeXbuLF86teXh7+/UzmH3a7E9et1ac5cJURFpSe8lGSuUEHl+vZVuccfk3fNmgX2+wUKy/DpW7Vi31n1uLOqvnqyZaHvz+Fw6MHP1yn26ElNXzdeXglXVWHwIFV59dWbrvtSRIRi33lXstnk16qlanzxhTxJsgSUOkePHtXChQt16dIlSdKdd96p7t27KyAgoFD2l5aWpnPnzmVJCHf9w0heXl4aNGiQatI/AAAAAAC3SElJ0dGjR1WnTh35+vq6OxwA+XTkyBHVq1dPktSgQQMdOHDAzRGVbOHh4Ro6dKgkafDgwQoPD3dvQCgRCuJeyjgs9yuI3wH9KgC4tY0bN05vvfWWJOnNN9/UuHHj3BsQSgT6B9k487v0TagUUEUaw79hARQD8bHSp40kmaQ3L5EAAMjLf5tKV05IwyOlGq3cHQ2A4oR7KlA0YrZIUzpL5WpJo/9wdzRAyZB8SfowJH36jfOSJy8kQAlx/oA0sbXkGyS9csLd0QBF6/sh0p6fpO4fSu3+7u5oAORTfsZheRZRTACQbyazWSazWfIuXf+IdDgcksUie5olPbFdWpocqanZJr6TLSOJlS09sZWRIC/TvMUqySGH3Z6eUM7uSP/psKfvK495k8l07Y/N136aTNcmr/0BOod1DrtDstvlsNvS67Tb0mOw2eVwpP+Uwy6HzZ53Obv92jJbHtvaM7fkn+3pvCijofM17VAR1eNqHRZreuJAS9q1hIJ/JhWUzabMMhKsKTFRyIPJJJOXl7xq1JBv40byafhnkjfPqlVzfWOsUYWHhwJCQxUQGipLbKwuf/+DLn//vaznzunCt9/qwuTJKnPvvSrf7wkFdOwokyfdMRQ/W45d1Ip9Z2X2MGlMt0ZFsk+TyaQBrWvI9OMn8kq4Kp/GjVTp//6vQOou37+/vGrU1Kl//lPJW7fp5NPPKGRORIHUDaD4S05O1vLly7V9+3ZJUtmyZfXwww+rUaPCvb55e3urRo0aqlGjhrHMbrfr0qVLRjK4gwcPKjY2VrNmzdLQoUNVpUqVQo0JAAAAAADgVhMfH29Mly1b1o2RAAAAAADgAlta+k+zj3vjAIAM5oxnNRyS3SqZvdwaDlDs2VLTf5pL13NOAFzAPRUoGsa9mH9XAy7L/H2xpZL4DSUH13yUZhnnfcb3AMAti0wjAFDMmEwmydtbZm9vSWXcHQ5KoPTEgH8mgssyfS3BnpFQz34tiZ7DLoftugR8DkfWZTltk2MiP0d6IkcvT5m8vGTy8pI8/5w2eXr9Oe2VebmnZPaUdC0Zod2eHrvD7jyd0zqTrtXtKZNn+sfYr2fmZZnKmM0F+rvwqlpVlUY9p4pP/10Jq1bp0py5Sly3Tolr1ypx7Vp5Vqmico89pnKPPSovEr2gmHA4HPpw8X5J0uOtaqpepYAi2/cDe6J05dwBpXp4KvGf/1LdAkz+GhB6r0IiZuvoX/oqeedOpezbJ98mTQqkbpvNpqSkJOOTnJzsNJ+UlCSr1SpPT095eXnJy8vLaTqvebvdLovFIovForS0NGM68ye75VarVT4+PvLz85Ovr6/xM/N05mUeHh4F0h5AcbJv3z7973//U0JCgiSpVatW6ty5s9veJO7h4aHg4GAFBwfrjjvuUGhoqGbMmKGTJ0/qu+++07Bhw1S+fHm3xAYAAAAAAFASHThwwJiuU6eOGyMBAAAAAMAF1msPafGALYDiwjPTw9PWVJLUAHkx7uUkHgBwHe6pQNHgXgzkn9M9Kk3i64OSwnrtJRpc81EaZfwfQsb3AMAti8RvAADcYkxmc3oCMzclNEFWJk9Ple3cWWU7d1baiRO6/P33uvzDj7KePau4CRMU99VXCurTW7e99VaBJ58D8mvFvnPaevySfL08NLpzgyLbb8revbry+XhJ0qSmveQba1arAt6HT4MGCnjgAcUvWaLLP/2kqvlI/Hby5Ent3btXCQkJWZK7pabeGlnzfXx8ckwQl1OyuIxpT0/+aYniJT4+XosXL9bevXslScHBwerZs6dCQkLcG9h1vL29NWDAAIWHh+vcuXP67rvv9NRTTykgoOiSbgIAAAAAAJRUVqtV3377rTHfrl07N0YDAAAAAIALbBkPqDO+E0AxYc708LSNB0mBPNlIPAAgB9xTgaLBvRjIPw+z5OEp2a1//m0KKAlsJPtEKZbxfwhct4FbHk/nAwAAFCHvWrVU+f/+TxVHjVL8suW6PGeOkrZu1ZUffpRX1dtU6bln3R0iSjGb3aGPluyXJD3VoY6qBBbNAEN7crJOjXlRslhku+c+LarUXt5/nNHYnrernH/Bvt22XJ/eil+yRFd/+VVVxoyRyTvn+m02m/bt26eNGzfq5MmTedbt7+8vf39/+fn5GdMZH09PT1mtVlksFuNnxieveQ8PD3l7e8vLyyvLJ7flHh4eSktLU3JyslJSUoyfmaeTk5NlsVgkSampqUpNTdWVK1fy3a5eXl5GIriQkBC1b99e5cuXz3c9wM1yOBzauXOnli5dqpSUFJlMJnXo0EEdO3aUl1fxfHOev7+/nnzySU2dOlUXL17Ud999pyFDhsjPz8/doQEAAAAAABRLr7/+uk6cOKH169fr6NGjkiQ/Pz/179/fzZEBAAAAAJAH67UH1M0FOyYKAG6Y2VMymSWHTbLyICmQp4zviZnEAwCuwz0VKBrci4EbY/ZJT/zGPQolCdd8lGYZ/4fAdRu45ZH4DQAAwA08vL0V9PBDCnr4IV3+aYHOvPqq4iZOlH+rlirTrp27w4MbOWw22a5elSMlRfaUFDlSU69Np8qRmvlnihwpqbKnpv90pKbInpYmD29vmXz95OHnK5Ovrzx8/WTy9ZGHn588fH2d12Us8/OTydtbP24/qYPnElTO30sjO9YrsmM++8GHSjtyRJ6VKqn+J+/r9u/2aO+Zq/px+ykNu7dOge6rTIcOMleqKNv5OCWsWaOynTtnKZOUlKTt27dr8+bNunr1qiTJbDbr9ttvV9WqVbNN7ubr6ysPD48CjbWoWK1Wpaam5pkgLrtlqanpfzjKSFIXHx+vc+fOacuWLWratKnuvfdeVa5c2c1HiNIiOTlZP/74ow4dOiRJuu2229SrVy/ddtttbo4sb4GBgfrrX/+qqVOn6uzZs4qIiNCTTz4p71ySUwIAAAAAAJRWs2bN0vHjx415k8mk8ePHq3r16m6MCgAAAAAAF9iuPaTlycOKAIoRTx/JkvTnNQpA9uy29IROEvdyANnjngoUPtu1hOqejLMH8sXTW7Ik/vkdAkoCrvkozTL+7sB1G7jlkfgNAADAzcr16a2krVt05cf5OvXii6r700/yrFjR3WHBDa4uXabYd/4t2/m4It+3uVIlzbr3GUll9Wyn+gry8yqS/V5dvlyX586VJFX78AN5VaigAW1r6Y0FuxWx+YSe6hAik8lUYPszeXoqqFcvXZwyVZcXLHBK/Hb+/Hlt2rRJO3fulNVqlSSVKVNGrVq1UqtWrVS2bNkCi6M48fT0lKenp8qUKZPvbe12u1PSuCtXrmjLli06cuSIfv/9d/3+++9q1KiR7r33XtWsWbMQogfSXbp0SbNmzVJcXJw8PT0VFhamdu3ayWw2uzs0lwUHB+uvf/2rpk2bphMnTuj7779Xv379StQxAAAAAAAAFBVPT09VqlRJ7du31+jRoxUaGurukAAAAAAAyJv12kNaZh5WBFCMmL3Tk9RYeZAUyJU1UyIn7uUAssM9FSh8GfdjM0lYgXzJ+M5YSU6KEoRrPkozrttAqUHiNwAAgGKg6htvKOX335V68JBOv/SSan77rUwkeik1rJcu6ey/39HVRYv+XOjpKQ9fX5l8feXh4+P809dHJh9fmXx95JH5p7e3HBaL7CnJciSnyJ6S4jTtSE6WPTU1/WdK+jJZLJIk2/nz6rd2ps4/OFp/bV+7SI7bcvasYt/4lySpwrCnVOaeeyRJjzSrpvcW7dOhcwnacuyS2tSpUKD7Lde7ty5OmaqEVatluXBBxy9d0saNG/+fvfsOb+u8z8Z/n4ENkCC496aGqS1Zki3LkldsJ/FIHNtR2iTO7ptf28Ru0mavtkmznPTN2za7aRo5dhLbsWPL2/KUJZnUILU5xb1AbGKc8fsDQ6RIbVLguD/Xda5zcM7BwRcEzvNgPTfR0tKS2ic/Px8bN25EfX09ZJlvm85EFEVYLBZYLBYAQFFREZYsWYKenh68/vrrOHLkCI4dO4Zjx46hvLwcmzZtQk1NzbSG+RF1d3dj+/btCIVCcDgc+MAHPoCCgoJ0l3VRCgoKsG3bNvz2t7/FiRMn8Pjjj+POO++EKIrpLo2IiIiIiIiIaNbo6OhIdwnz1oc//GF8+MMfTncZRERERDQLff3rX8fXv/71dJdBNPepiUFaMgcrEtEskmyTVA4kJTqr8ecI+3Iimgr7VKKZl3pfzRBWoguSPGdUhpPSHJJ8vrLNp4WI7TbRgsEEAyIiIqJZQLRYUPzgg2h/390IvrkLwz/9KXL/z/9Jd1l0GfhffBF9X/s61OFhQBSR/fGPI+dvPgXRbL4st6/HYhg53obuu9+HlcOt+IapHWbDDTN/u6qK3s//I1SvF+alS5H393+f2uYwG3DbiiL8fm8XHtpzctqD30y1tTDVX4FI8yE8/U//hH3FxaltixcvxoYNG1BeXs5wsktQXFyMe+65B8PDw3jjjTdw4MABdHZ2orOzEwUFBdi0aROWLl3KMCu6ZIcPH8ajjz4KRVFSoWkZGRnpLuuSlJeX4+6778bvf/97NDU1wWKx4JZbbmGbRERERERERERERERERERENNcpiQHqEgcrEtEsIiVCahQOJCU6q9Q5IgAih2QS0RTYpxLNvOT5JTGEleiCpPoohpPSHJL6LJVtPi1AbLeJFgyOsiciIiKaJUw1NSj42lcBAMM/+X8I7t6T5opoJqleL3r/8R/R/en/D+rwMIzV1ah4+PfI++xnLlvoGwAIBgN+2aXjt4vfAQAoe/gXUIaGZvx2R375K4R274ZgsaDo+9+HYJz4Y8b3X1kGAHiqqQ+jwen74tPr9eKFF17AXrsDAJDb1Ayj0YgNGzbg7/7u73DvvfeioqKCAUvTJCcnB7fffjv+/u//Hhs2bIDBYEB/fz/++Mc/4ic/+QkaGhqgKEq6y6Q5SNd1vPHGG3jkkUegKApqa2tx3333zfnQt6S6ujrccccdAIA9e/bglVdeSW9BREREREREREREREREREREdOnUxO+gZA5WJKJZRE78flPlQFKis0qeI7IJ4O+MiWgq7FOJZt74/piIzl/ynGEfRXMJ23xayPjegmjB4L+XICIiIppFnHfcgdDuPfA+9hh6/uEBVD32GOScnHSXRdPMv3Mn+r/6NSiDg4AoIvsj9yHnb/8WounyfwgViir4312dCFZfgw+OHYex7Tj6//VfUfLggzN2m2P792Poxz8GAOR/8QswVVVO2md5SSauKMrAoV4fHt3Xg49umrzPhWpqasKf//xnKIoCY2EB6kURWR4PPn3rO5G5csUlH5/OLDMzEzfffDM2b96MPXv2YPfu3XC73XjyySfx8ssvY+PGjVi7di1MaTgHaO5RVRVPP/00GhoaAADr1q3DzTffDEmS0lzZ9Fq+fDnC4TCefvpp7Ny5ExaLBevXr093WUREREQ0BU3T4PF4oCgKVFWdMD+fdU6nExUVFcjOzmYQOREREREREREREdF8poTjc4m/kSGiWSTZJiXbKCKampIYbM1+nIjOhH0q0cxL9cfG9NZBNNckzxmFAUI0h7DNp4Us9d6C7TbRfMfgNyIiIqJZpuArX8ZY00FEW1rR+/l/ROkvfg5BFNNdFk0D1e/HwLe/A++jjwIAjBUVKPz2v8K6alXaanryQC/8EQXluQ7Ufvdf0XnPPfDveAb+d98Gx3Vbp/32VL8fPQ/8A6CqcNxyM5x33TXlfoIg4P1XluHLjzdj++5OfOTqiose/K7rOl555RXs3LkTAFBSUoJNmzbBHhqD/9lnEd7xNIPfLhOr1YotW7bgqquuQkNDA3bt2gWfz4fnn38er776Kurr61FfX4/y8nKIbPdoCuFwGH/84x/R0tICAHjHO96BDRs2zNtwjCuvvBKhUAg7d+7Ejh07YLFYsHz58nSXRUREREQJuq7jyJEjeP755zE6OnrJx3M4HKioqEBlZSUqKyuRlZU1DVUSERERERERERER0ayhRONzmYMViWgWSbZJyTaKiKaWHGzNfpyIzoR9KtHMS/XHDGIluiAyA4RoDmKbTwsZ222iBYPBb0RERESzjGi1ouRHP0L7++5G8M03MfKznyHnU59Kd1l0iQKvv4G+L38ZSn8/IAhwfehDyP3M30M0m9Na1/bdJwEA264sg7W+Gtn3fRgjv/gl+r/5TVivXAfJbp+229J1Hf1f+xpiPT0wFBej8JvfPGtY0+0ri/CvTx9B61AQe9rdWF+VfcG3GYvF8Oc//xnNzc0AgI0bN+LGG2+EKIoIvOdO+J99Ft4nnkTeAw9AMPKHGJeL0WjExo0bsW7dOjQ1NeH111/HyMgIGhoa0NDQAIfDkQqBKyoqmrehXnRhvF4vtm/fjoGBAciyjPe+971YsmRJusuacddeey1CoRD27NmDxx9/HGazGXV1dekui4iIiGjB6+vrwzPPPIPOzk4AgCRJMBqNkGUZkiRNmJ9pWZIkiKKIwcFBdHV1we/3o6mpCU1NTQCAzMxMVFZWpsLgMjMz03mXiYiIiIiIiIiIiOhSqYlBWhIHKxLRLJJsk1QOJCU6K/bjRHQu7FOJZp6aCFaUOP6H6IIkzxmV4aQ0h7DNp4WM7TbRgsHgNyIiIqJZyFRTg4KvfAV9X/wihv79/8KyejVsV16Z7rLoIqiBIAa/+114HnkEAGAoK0PRv/4LrGvXprkyoLnHiwPdXhglEXetKQEA5Hz60/A9+xxiXV0YevBHKPjKl6ft9rx/+hN8T+8AJAnFP/g+JIfjrPs7zAbcvrIID+3pwkN7Tl5w8Jvf78fvf/979PT0QBRFvPOd78SaNWtS221XXw0pNwfq0DACr70Gx/XXX9T9oosnyzJWrVqFFStWoL29Hc3NzTh8+DD8fj927dqFXbt2weVyob6+HsuWLUNubm66S6Y06evrw/bt2+H3+2Gz2bBt2zYUFxenu6zLQhAE3HzzzQiHwzh48CAeeeQR/PVf/zXKy8vTXRoRERHRguT3+/HSSy9h3759AOLva6666ipcffXVMJku/gf+sVgMXV1d6OjoQHt7O3p6euD1erF//37s378fAJCVlYXKyspUGJzjHO+riejC6Lqe7hKIiIjmJPahdDo+J4iIiCiJrwumoCQCIGQOViSiWSTZJikcSEp0VslzhP04EZ0J+1SimZd6X80gVqILkjxnFIaT0hzCNp8WMrbbRAsGg9+IiIiIZinne+5EaM8eeB9/HL0P/AMqH38McvaFBV9RegV37ULfl76MWG8vACDrr/4Kefd/FqLVmubK4n63+yQA4B31Bci2xz8IEC0WFH7zGzh530cwun07Mt71TlhXrbrk24q0tqL/n/8FAJD7938Py8qV53W9919Zhof2dOHp5n58LRhFlu38fizR39+P7du3w+fzwWw245577kFlZeWEfQRZRuZtt8H9y1/B89hjDH5LI1EUUV1djerqarzzne9ES0sLmpqacOzYMbjdbrz66qt49dVXkZ+fj2XLlqG+vh5OpzPdZdNlcvz4cfzhD39ALBZDbm4uPvCBDyy4x18URdx+++0Ih8M4fvw4tm/fjvvuuw8FBQXpLo2IiIhowYjFYti9ezdeffVVRKPxH+jW19fjhhtumJbXpwaDAVVVVaiqqgIARCIRdHV1ob29HR0dHejt7cXo6ChGR0fR2NgIAMjJyUldp6KiAmaz+ZLrIFqIRFEEAGialuZKiIiI5qZkH5rsU2nh4usqIiIiOh1fK05BTQRASBysSESzSLJNUjmQlOiskucI+3EiOhP2qUQzj/0x0cWREuPx2EfRXJJq8xm+TQtQqt1mqDTRfMfgNyIiIqJZrOCrX8FYUxOira3o/fw/ovTnP4PAH8LNelowiMEf/ACj2x8CABiKi1H4r/8K2/or01zZKYGIgif29wAAtl1ZNmGbbeNGZN55J7yPPYa+r3wFVY8+CsF48R+QaZEIeu5/AHo4DNtVG5H9sY+e93WXlzhRX5yB5h4f/tTYjY9dU3XO6xw7dgx//OMfEYvFkJ2djW3btiH7DKGJzjvugPuXv0Jg5ytQ3G7ILtd510YzQ5ZlLF68GIsXL0YkEsGxY8fQ3NyMlpYWDAwMYGBgAC+88AJKS0uxbNkyLF26FHa7Pd1l0wzZs2cPduzYAV3XUVVVhbvvvnvBhllIkoT3ve99+O1vf4uTJ0/it7/9LT7ykY+csX0jIiIioumh6zqOHDmC5557Dh6PBwBQVFSEm2++GWVlZWe/8iUwmUyoqalBTU0NACAcDqOzsxMdHR1ob29Hf38/hoeHMTw8jD179kAQBJSUlKC6uhpVVVUoLi6GJEkzVh/RfCLLMgRBQDgchs1mS3c5REREc04kEoEgCJBl/gxtoUu+ropEInxdRURERAD4WnFKSmKwoswB6kQ0iyTbJIUBAERnpSQGW8sMHSCiM2CfSjTz2B8TXZxUH8UAIZpDUm0+P0ulBYjvLYgWDH6LSkRERDSLiVYrih/8ITruvgfBN97AyM9+jpxPfTLdZdEZ6LEYPH/6E4b/339AGRoCADjvvQf5n/scxFk2uOHP+3sQjKqoyrVhQ9XksLO8z38OgVdfRbSlFcM//zlyP/3pi76twX/7LiLHjkFyuVD4ne9ccHjh+68sw5cea8b2PSfx0U2VEARhyv10XceuXbvw3HPPAQAqKytx9913w2KxnPHYptpamJctQ7ipCb6//AWuD37wgmqjmWUymbB8+XIsX74coVAIhw8fRnNzMzo6OtDV1YWuri7s2LEDJSUlyM3NRXZ2NnJycpCdnY2srCyGHMxhmqbhueeew1tvvQUAWLVqFd71rnct+MfUYDBg27Zt+PWvf42BgQE89NBD+NjHPrZgw/CIiIiA+PuAvr4+jIyMQFEUxGIxxGKx1PK51qmqiqysLBQWFqKgoACFhYVwOp1nfN9BC0tfXx+eeeYZdHZ2AgAcDgduuOEGLFu2DOJlDuY3m81YtGgRFi1aBAAIhULo6OhAW1sb2tra4Ha7U++Tdu7cCZPJhIqKilQQXHZ2Np/XRGcgiiLsdjt8Ph/DtYmIiC5CMBiExWK57K+RafYRRREWiwXBYBAu/rMlIiIiAl8rTknlYEUimoWSbZLKAACis1KTAa78vR4RnQH7VKKZx/6Y6OIkzxmVAUI0h7DNp4WM7TbRgsHgNyIiIqJZzlxXh4KvfAV9X/oShv7932FduwbWtWvTXRaNo2safDt2YOjH/47YyZMAAENxMQq++Q3Yr746zdVNpus6tu+O17ntyrIpB37LWVnI/+IX0PvAP2Dkv36KjJtvhqm6+oJvy//iixjdvh0AUPSdb8OQl3fBx7h9ZTH+9akjaBsKYk+7G+urJg/AVRQFTz/9NBobGwEAa9aswa233npeIVGZd96BcFMTPI89zuC3WcxqtWLt2rVYu3YtfD4fDh06hKamJvT29qbCDcYTBAFZWVmpILjxk8PhYODBLBaNRvHoo4/i6NGjAIDrr78emzZt4mOWYDab8Vd/9Vf42c9+huHhYTz++OO4++67+UN5IiJacMLhMA4ePIiGhgYMDAxc0rGGh4dx4sSJ1GWz2YyCgoLUVFhYiJycnAUfQruQ+P1+vPTSS9i3bx8AQJZlXH311bj66qthNM6O/xZrtVqxdOlSLF26FAAwOjqKtrY2tLa2or29HWNjYzh27BiOHTsGAMjMzERVVRWqq6tRWVkJ2ywLqCdKt4yMDPT09CAYDPL8ICIiugDRaBTBYBC5ubnpLoVmCbvdjqGhIUSj0Vnz/omIiIjSg68Vz0BJDNKSGPxGRLNIsk1SOJCU6KxS/Tg/8yCiM2CfSjTzlESwIvtjoguTPGcUhpPSHMI2nxYytttECwaD34iIiIjmgMz33InQnt3w/vkJ9Nz/ACoffwwy/0t62um6juDrr2Pwhw8icuQIAEDKzkbO3/wNnHe/D+IsHcxwoNuLQ70+GGURd60pOeN+GbfeCt8TTyLwyivo+8pXUf6/v4VwAcFCsf5+9H3xSwAA14c/DPvmzRdVr90k47aVxXhoz0ls33NyUvBbKBTCI488go6ODgiCgJtuugkbNmw475CozFtvxeC3v4PIkSMIHzkC85IlF1UnXT4ZGRnYuHEjNm7cCLfbje7uboyMjGB4eBgjIyMYGRlBLBaD2+2G2+2edH2j0ZgKgSsrK8OiRYuQmZmZhntC42mahqamJrz00kvwer2QJAl33nkn6uvr013arONwOHDPPffg17/+NY4ePYrXX38dmy+yjSUiIppLdF1HT08PGhoa0NzcjFgsBiAeylVcXAyj0QhZlmEwGGAwGFLLZ1snCAKGh4fR39+Pvr4+DA4OIhwOo6OjAx0dHanbliQJ+fn5qSC4goIC5OfncxD7PBOLxfDWW2/htddeQzQa/6J82bJluP766+F0OtNb3DlkZWVhzZo1WLNmDTRNQ19fXyoIrqurC16vF/v27UuF2RUWFqK2tha1tbUoLi5mkDAteHa7HTabDV1dXSgtLWX4GxER0XlQVRXd3d2QZZmfsVNKZmYmRkdH0d3djfLycoaoExERLVB8rXgWamKQlszvF4hoFkm2SSoHkhKdVaofZ4ArEZ0B+1SimacmghXZHxNdmOQ5ozKclOYQtvm0kLHdJlowGPxGRERENAcIgoCCr34VY03NiLa1offz/4jSn/30gkK4aHqF9u3D0A8fRGjvXgCAaLMh+2MfheuDH4Q4yweGbt/dCQB457JCOK1n/hGhIAgo+NpX0faud2OssRGeRx5B1r33ntdt6KqK3n/4HFSvF+YrrkDe/Z+9pJq3XVmGh/acxI6mfnzt3VG4bPG6h4eHsX37drjdbhiNRtx1112oq6u7oGNLTifs110H/7PPwvv44wx+m2NcLhdcpwVh6roOv98/IQguuezxeBCNRtHX14e+vj40Nzfj6aefRmFhIRYvXoxFixYhPz//vIMDaXq0tbXh+eefR19fH4B4uN9dd92FsrKyNFc2e5WUlODWW2/Fk08+iZdeeikV3EFERDQfhcNhHDx4EA0NDRgYGEitz83NxZo1a7BixQpYLJaLPn51dXVqWVEUDA8Po6+vLxUG19/fj2g0it7eXvT29qb2FUURNTU1uOKKK7Bo0SKYzeaLroHSKxaL4cCBA3j99dfh8XgAAMXFxbj55ptRWlqa3uIugiiKKC4uRnFxMa655hpEo1F0dnamguAGBwdT74leffVVWK1W1NTUoLa2FtXV1bBarem+C0SXnSiKKCkpQXd3N06ePAmz2YyMjAyYzWaIosjPCYiIiBJ0XYeqqvD7/fD5fACAiooKyDJ/gkZxsiyjtLQUHR0daGlpQWZmJux2OyRJ4msqIiKieY6vFc+TkhikJXGwIhHNIsk2SeFAUqKzYj9OROfCPpVo5qX6YwaqE12Q5DnDPormErb5tJCl2m2GShPNd/wmlYiIiGiOEG02FP/oQXTcfQ+Cr7+OkV/8Ejmf+Hi6y1pwIidOYPBHP0bgxRcBAILRiKwPfADZn/g45KysNFd3br5wDE8eiAcrbVt/7kAlQ1ERcj/7WQz8y79g8Hvfh33rVhjy8895veH//C+E3n4botWK4h/+AILx0j5gW1aSiWXFmWjq8eLRxm587JoqtLW14ZFHHkE4HEZmZia2bduG/POobSqZd94RD3574knkPfDAJddL6SUIAjIyMpCRkYGqqqoJ2xRFwejoKIaHhzE4OIiWlhZ0dXWlQg9efvllOJ1OLFq0CIsXL0ZZWRkkSUrTPZn/BgYG8Pzzz6OlpQUAYDQacc0112DDhg0wGAxprm72W7NmDXp7e9HQ0IA//elP+MQnPjEpCJGIiGiu0nUdPT09aGhoQHNzM2KxGABAkiRcccUVWLt2LUpLS6d90LgsyygoKEBBQUFqnaZpGB0dRX9//4QwuEAggOPHj+P48eOQJAm1tbWor69HXV0djHxPMSf4/X7s3bsXe/fuxdjYGADA4XDghhtuwLJlyyDOk8B9o9GI2traVFCw3+9Ha2srjh8/jtbWVoRCIRw8eBAHDx6EIAgoKSlBXV0damtrGYxNs47f70coFEJmZua0B24mw98CgQB8Ph+Ghoag6/q03gYREdF8IcsysrKy4HQ6+f6HJjGZTKisrITH44HX68Xo6Gi6SyIiIqLLiK8Vz0FNDFaUGRhDRLOInGivVQYAEJ2VmhhsLfM1DhGdAftUopmX6o/5vprogiTPGZUBQjSHsM2nhSzVbvO9BdF8x+A3IiIiojnEXFeHgi9/CX1f/gqGfvxjWNeshnXNmnSXtSDEenow9H9/Au+f/wzoOiCKyHzPncj99KdhKCxMd3nn7fF9PRiLqajLt2Nt+fkF1WVtez+8f3kS4QMH0f/Nb6HkJ//3rIO+Q3v3Yvg//gMAUPD1r8FYXj4ttb//yjI0PdaE7XtOYoV5BDt27ICmaSgpKcG9994Lu91+0ce2b9oEKScH6vAwAq+9Bsf1109LzTT7yLKM3Nxc5ObmYsmSJbj22mtTgR1Hjx5FW1sbPB4Pdu/ejd27d8NsNqOurg6LFi1CTU0NTCZ+WDwdfD4fXn75Zezfvx+6rkMURaxduxbXXnstbDZbusubU2655RYMDAygu7sbDz/8MD760Y/yx/NERDSnhcNhNDU1oaGhAf39/an1OTk5WLt2LZYvXw6r1XpZaxJFEdnZ2cjOzsYVV1yRWj84OIhDhw6hubkZIyMjOHr0KI4ePQpZllFXV4f6+nrU1tYy0HYW6u/vx65du9Dc3AxVVQEATqcT69evx5o1a+b96ymHw4GVK1di5cqVUFUVXV1dOHHiBI4fP46hoSF0dXWhq6sLL774IhwOB2pra1FXV4fKykq+J6K0UFUVx48fR2NjI1paWlJhbCaTCU6nE5mZmVPObTbbBQcXiqKYCpPXNA2KokDTtJm4W0RERHOWJEmQZZkBwXRWRqMReXl5yM3NhaIoqfdeRERENL/xteJ5UBKDFaX5/Tk0Ec0xUuL7H4UBAERnpSQGW0v8zpSIzoB9KtHMY39MdHFSfRQDhGgOYZtPC1nyea8pgKYCopTeeohoxjD4jYiIiGiOyXzvexHcswe+J55Ez/0PoPLxxyBnnV+AF104ZWQEwz/9KTwP/R56LAYAcNx0E3L//u9gqq5Oc3UXRtd1/O6tkwCAbVeWnfePLAVJQuG3voX297wXgRdfhP+555Hxjpum3Ff1eNDzuc8DmobM229H5m23TVv9t60swr88dRhtQ0H88skGFIgali1bhttuu+2SgxQEWUbmbbfB/atfwfv44wx+W2DsdjtWr16N1atXIxqNorW1FceOHcPx48cRCoVw8OBBHDx4EJIkobKyEosXL8aiRYvgcDjSXfqcE4lE8MYbb+DNN9+EoigAgKVLl+L6669HdnZ2mqubm2RZxt13342f/vSnGBgYwBNPPIH3vve9/CE9ERHNKeFwGF1dXThy5AiampoQS7z3kiQJV1xxBdasWYOysvN/D3O55OXlIS8vD1u2bMHAwEAqBG50dBSHDx/G4cOHYTQasWjRIlxxxRWoqamBLPNrmXTRNA0tLS3YtWsX2tvbU+tLS0uxYcMGLF68GJK08L4UlyQJFRUVqKiowI033giPx4MTJ07gxIkTaGtrg9/vR2NjIxobGyFJEsrLy1FWVob8/Hzk5eUhKysLoiim+27QPDUyMoJ9+/Zh//79CAQCqfVmsxnhcBiRSAQDAwMYGBiY8vqSJE0KgyspKUFZWdl5fZYkiuK8D4IkIiIimmmCIMBgMDAUnYiIiChJCcfnMgcrEtEskmyTkm0UEU0tGTog8/sjIjoD9qlEM4/9MdHFSZ4zDH6juYRtPi1k45/3SgQwWtNXCxHNKI4wIiIiIppjBEFA4de+hnBTM6Lt7ej9x39E6X/9FwQOsp1WWjiMkZ//Au5f/xpaKAQAsG7YgLz7PwvL8uVpru7iNJ4cxbEBP8wGEXeuLrmg65rr6pD98Y9h5D//C/3//C3YNm6AlJExYR9d19H75S9D6e+HobwM+V/5ynSWD7tJxoYiGS92qDim5OL971iLzZs3T1v4Q+Ydt8P9q1/B//JOKG43ZJdrWo5Lc4vRaMSSJUuwZMkSaJqGrq4uHD16FMeOHYPb7UZLSwtaWlrwl7/8BYWFhaipqUFtbS2Ki4sXZEjE+VJVFQ0NDdi5cydCiTa1tLQUN910E0pLS9Nc3dyXkZGBu+++G7/5zW/Q3NyM4uJibNy4Md1lERERnVEwGMTJkyfR2dmJzs5O9Pf3Q9f11PacnBysWbMGK1asgNU6+7+kFAQBBQUFKCgowHXXXYe+vj40Nzfj0KFD8Hq9aGpqQlNTE0wmExYvXowrrrgCVVVVDIG7TKLRKA4ePIhdu3ZhZGQEQPwxW7p0KTZs2MDXo6dxOp1Yt24d1q1bh1gsho6ODpw4cQLHjx+Hx+NBW1sb2traUvvLsozc3NxUEGJyysjImHVhjTQ3xGIxHD16FA0NDejo6Eitt9lsWLlyJVavXo3s7GxEo1F4PB54vd4p536/H6qqwu12w+12T7gNWZZRXl6OmpoaVFdXIzc3l89XIiIiIiIiIiK6PNRofC5xsCIRzSLJNinZRhHR1NRE6IDEAFciOgP2qUQzj/0x0cVJnjMqg99oDmGbTwvZ+Oe9GgEw+8dUENHF4agiIiIiojlItNlQ/KMH0XH3PQi++hpGfvlL5Hz84+kua96ItLej5zOfReTYMQCA+YorkPfA/bBddVWaK7s0v9t9EgDwruVFyLQYLvj6OZ/6FPzPPItoezsGv/d9FH7rmxO2jz70EAIvvAgYDCj+wQ8h2W3TUnfSkSNHYO1tBHAFupCNZWs3TuugXHNdHcz19Qg3N8P3l6fg+uBfT9uxaW4SRRHl5eUoLy/HTTfdhKGhIRw7dgxHjx5FT08P+vr60NfXh9deew1msxnV1dWoqalBTU0NHA5HusufFXRdx9GjR/HCCy+kQjZcLhduuOEGLFmyhAPrp1F5eTne8Y53YMeOHXjuuedQUFCAysrKdJdFREQEAPB6vamQt87OTgwPD0/aJysrCxUVFVixYgXKy8vn7OsEQRBQVFSEoqIi3Hjjjejp6UmFwPn9fhw4cAAHDhyAKIrIzs5GXl7ehNCsrKwsBgpPE7/fjz179uDtt9/G2NgYAMBkMmH16tVYv349nE5negucAwwGA2pra1FbW4tbbrkFw8PDaGlpQX9/PwYHBzE0NARFUVLvjcYzmUyTwuDy8vJgs03vZwU0fwwODqKhoQEHDx5MnbMAUFNTg9WrV2PRokUT2kej0Zh6Xk1FURT4fL4JgXButxvt7e3w+/1obW1Fa2srAMDhcKC6uhrV1dWoqqri85SIiIiIiIiIiGaOkhisKHOwIhHNIsk2SWEAANFZsR8nonNhn0o085REsKLMQHWiC5I8ZxSGk9IcwjafFjJp3Phvtt1E8xqD34iIiIjmKPOiRcj/8pfQ/5WvYuhHP4Z1zRpYV69Od1lznm/HDvR9+SvQgkFI2dko+PKX4Lj55jkbOpDkDcXw1MH4IOxt68su6hiiyYTCb30TnX/11/D84Q/IePe7YLvySgBA+NgxDH7n3wAAeQ/cD0v9FdNTeEJ3dzf+9Kc/IVtUUGLT0R0U8Oyhfrz/you7L2eSeecdCDc3w/P4Ywx+owkEQUgNKL/mmmsQCATQ0tKClpYWtLa2YmxsDIcOHcKhQ4cAAAUFBaitrUVNTQ1KSkoWZHhHV1cXnn/+eZw8GQ+dtFqt2LJlC9asWbMg/x6Xw5VXXomenh4cPHgQf/jDH/DJT34SmZmZ6S6LiIgWGF3X4Xa7JwS9eTyeSfvl5uamQnbLy8uRkZFx+YudYYIgoKSkBCUlJbjpppvQ1dWFQ4cO4fDhwwgEAhgaGsLQ0NCE60iShJycnAlBWbm5uXA6nRBFMU33ZO7QdR29vb3Ys2cPmpqaoGkaAMDpdGL9+vVYtWoVzGZzmqucmwRBQG5uLnJzc1PrNE3D6OgoBgcHJ0wjIyOIRCLo6upCV1fXhONUVlZizZo1WLx4MWSZX1MudNFoFM3NzWhsbER3d3dqfUZGBlatWoVVq1ZddEijLMtwuVxwuVwT1uu6jqGhoVTwW0dHB/x+P/bv34/9+/cDAIqKilJBcCUlJXyuLnCxWAxerzcVIujxeBCJRGCz2eBwOGC321OTzWbjZx5EREREREREdHZqYoCWxMAYIppFpMQAapWDSInOKtWPM3SAiM6AfSrRzFMTwYp8X010YZLnjMpwUppD2ObTQiYI8ee+GmHbTTTP8VfqRERERHOY8667ENq9B76//AU99z+AyscehZyVle6y5iQtGsXgd/4No9u3AwCs69ah6PvfhyE/L82VTY8/NXYjomhYUpiBVaXOiz6Ode1aOO+5B56HH0b/V7+Gyj8/Dqgqeu5/AHo0Ctu1m+H60IemrW4AcLvd2L59OxRFQW1tLXILF+F7zx3H00190x78lnHrrRj8zr8hcvgIwkePwrx48bQen+YPu92OlStXYuXKldA0DT09PThx4gRaWlrQ29uL/v5+9Pf347XXXoPZbEZVVVUqCM7hcKS7/Bnj8XjQ3NyMpqYmDAwMAIgPtt+4cSOuvvpqhmzMMEEQ8O53vxuDg4Po7+/Hww8/jPvuuw8Gg+HcVyYiIrpEIyMjaGxsxMGDB+H3+ydsEwQBhYWFqZC3srIyWK3WNFWaHqIopu7/LbfcAp/PlwrJGhoaSs1jsRgGBgZSr6WSDAYDcnNzkZOTA1mWoes6NE2DrutnXD59nSRJcDqdyMrKmjBZLJY0/VWmRzAYRGtrayqUORgMpraVlpZi48aNWLRoEYN4ZoAoisjOzkZ2djaWLFmSWq8oCkZGRiYFwo2OjqK9vR3t7e2wWq1YuXIlVq9ejZycnDTeC0oHt9uNN954A01NTYhG4z94F0URdXV1WLNmDaqrq2cs7HJ8sPvGjRsRi8Vw8uTJVBDcwMAAent70dvbi9deew1GoxEVFRWoqalBdXU1XC7XnP8HETRROByGx+NJBbudPh/fr5wPm802IQzu9HC4nJwc2O32Gbo3RERERERERDTrKYkBWjIDY4hoFpETA6gVDiIlOqtUP87QASI6A/apRDNPSQQrsj8mujDso2guYptPC52cCH5TGCxNNJ8x+I2IiIhoDhMEAQVf/zrCzc2IdnSg75++gJL//A8IMzQwcr6KdnWh5zOfRfjQIQBA9ic/idy//f8gyPPj5bKu6/jd7k4AwLb1ZZc8ODXvHx5A4KWXEO3owPB//ifUETeira2Qc3NR9O1vT+vg11AohN/97ncIhUIoKCjAXXfdhV6/gu89dxxvto5gNBhFlm36fggpZ2XBft118D/7LLyPPQ7zF/5p2o5N85coiigtLUVpaSmuu+46BAIBtLS0pIInxsbGcPjwYRw+fBgA4HK5kJmZiczMTGRkZEyaz7VwtGAwiMOHD6OpqQknT55MrRdFEcuXL8fWrVuRmZmZxgoXFoPBgHvuuQc/+9nP0Nvbi6effhq33XYbgwmI5ohYLAafzwev1wtVVSEIwgVPkiQhMzMTRiMHi9DMi8ViOHLkCBoaGtDZ2ZlaL0kSiouLU0FnpaWlMJn4pXuSIAip14O1tbWp9ZqmwePxpILgktPw8DBisVgqiGi6mc3mSWFwySkzM3PWBaapqoqurq5U2FtfX9+E7QaDAXV1ddiwYQNKS0vTVOXCJssy8vPzkZ+fP2G9x+PBvn370NjYCL/fjzfffBNvvvkmKioqsGbNGixZsgTyPPkshqamaRrefvttPP/884jFYgCArKwsrF69GitXrkxLULrBYEB1dTWqq6sBAH6/PxUC19railAohOPHj+P48eMAAKfTmdq/srJyzodnzne6riMYDJ4x1M3r9SIcDp/zOAaDAU6nE5mZmXA6nTCbzQgGgwgEAvD7/QgEAggGg6nbCwaDk0Jcx8vKykp9llRSUoL8/PwZCzskIiIiIiIiollGTQyulfi9CRHNIsk2SWUAANFZqYmB1hJ/k0NEZ8A+lWjmpd5Xsz8muiDJc0ZleBDNIWzzaaFLtd18f0E0n3H0BBEREdEcJ9ltKP7xj9Bx9z0IvPIK3L/+NbI/+tF0lzVn+F94Ab1f+CI0vx+S04mi7/4b7Js3p7usabWn3Y3WoSCsRgl3rCy65ONJDgfyv/oV9Pzt32HkZz8HNA0QBBR977uQXa5pqDguFovh97//PUZGRpCRkYFt27bBZDKh0mTCksIMHOnz4fkjA7h77fQO5M+884548NuTTyLvHx6AYDBM6/Fp/rPb7Vi5ciVWrlwJTdPQ09ODEydOoKWlBb29vXC73XC73We8vslkmjIQLjMzE2azGQaDAUajEQaDAQaDAbIsX/ZQr0gkgmPHjqGpqQmtra3QNC21raKiAsuWLcPSpUs5AD5NsrKycNddd+F///d/sW/fPhQVFWHdunXpLotowdM0DYFAAF6vNxXudvoUCoWm7fYyMjKQk5OD7OzsCdNsDHGiuWdgYACNjY04cOBAKixFEATU1NRg9erVqKmpgYGvoy+YKIpwuVxwuVxYtGhRar2qqhgdHcXg4CBGRkag63oq7FEUxUnLU60TBAGxWAwejwejo6OpKRgMIhwOo6+vb1KAGnAqpC4jIwMOhyM12e32CZdNJtOMviZ1u92poLf29nZEoxN/fJSfn4+amhpUV1ejrKyM4WGzlNPpxNatW7F582a0tLTg7bffRktLCzo6OtDR0QGLxYKVK1dizZo1yMnJSXe5NM1GR0fxxBNPoL29HQBQXl6OLVu2oLy8fFYFXjkcjgnv6QcGBlLtz8mTJ+HxeNDQ0ICGhgYIgoCSkpJUEFxRURFfZ10muq5D13WoqopQKHTGUDePxwNFUc55PIvFkgp1Gx/wlpxbLJZz9nOapiEUCk0Igxs/+f1++P3+Cf3wwYMHAQBGoxHFxcUTwuD4mQoRERERERHRPKUkPt+WOViRiGaRZJukMACA6KyUxEBrmQGuRHQG7FOJZpausz8muljJc0ZheBDNIWzzaaFj2020IHDkCxEREdE8YF60CPlf/CL6v/Y1DP7wQVhWrYZ19ap0lzWr6dEoBn/wQ7h/8xsAgGXlShQ/+EMYCgvTXNn0277nJADgthVFcJinJ3wh48Yb4bvxBviffwEAkP2JT8C2YcO0HBuID5T885//jJMnT8JkMuEDH/gAMjIyUttvqS/AkT4fnmnun/bgN/umTZBycqAODyPw2mtwXHfdtB6fFhZRFFODdq+77joEAgEMDQ2lAn/GB//4fD6Ew2FEIhEMDQ1haGjovG5DEIRUCNz4QLjxyyaTCQ6HAxkZGRMms9l83gEdiqKgtbUVTU1NOHr06ISB04WFhVi2bBnq6+snnKuUPtXV1bj++uvxwgsvYMeOHSgoKEBp6fS2l0Q0maIocLvdqXbc7XZPaOfHB2WeicFgQEZGBgwGQyrQAjgVbnGuKRaLIRKJwOfzwefzoa2tbcLxk8FSpwfCZWdnw263X/YwUZo7IpEIDh06hMbGRnR3d6fWZ2ZmYtWqVVi1ahUyMzPTWOH8JUkScnJyZiQIKxqNTgigOT0YTlEUeDweeDyesx5HluUpg+HsdnsqqPhswXRTzX0+H1paWtDa2jopONlqtaaClqqrq+FwOKb9b0MzR5IkLFq0CIsWLYLH48G+ffvQ2NgIv9+PXbt2YdeuXSgvL8fatWuxePFiBknOcbquo7GxEc8++yyi0SgMBgNuuOEGrFu3blYFvk1FFEUUFhaisLAQmzZtQjQaRUdHB1pbW9Ha2orh4WF0dXWhq6sLO3fuhNlsRmVlZSqI0ul0pvsupJ2maQiHwwgGgxOmUCg04XI4HIamaec9JV8jny+HwzEpzG383GS69B8HiqIIu90Ou92OgoKCM+4XDofR3d2N7u5udHV1obu7G5FIBO3t7algRADIzc1NfaZUWlqK7OxsvlYnIiIiIiIimg/U5GBFc3rrICIaL9kmqRxESnRWKkMHiOgc2KcSzSxNAZD4vQD7Y6ILkzxnVIaT0hySfL6yzaeFim030YLA4DciIiKiecJ59/sQ2rMHvqeeQs8DD6Dy0T9BzspKd1mzUqy3Fz2fvR9jBw4AAFz33Ye8+z8LYR4OJHYHo9jR1A8A+MD68mk9dv6Xv4LwkaMwVlQg9//79LQe+6WXXkJzczNEUcQ999yD/Pz8CdtvXVaAHz5/HK+dGIIvHEPGNAXaAYAgy8i87Ta4f/UreB97jMFvNK2SA4DPJBnUMz4Ubvw8EokgFoshFotBVVUA8UH00WgU0WgUwWDwgupJhgudaXI4HBgeHkZTUxMOHTqEcDicuq7L5UqFveXm5l7cH4Rm1NVXX43e3l4cPnwYDz/8MD75yU8yFIVomsRiMYyMjKQC3pLTyMjIWUMoBEFItbGZmZkTpuQ6i8VyyYEOoVAIIyMjGB4exsjISGpyu91QFAXDw8MYHh6edD273Y4lS5Zg6dKlKCsrgyRJl1QHzX26rqO3txeNjY1oampCNBr/0lAURSxatAirV69GdXX1rA/uoTMzGo3Iz8+f9J4LiIf1BINBjI6Owufzwe/3w+/3IxAIpJb9fj8ikQgURUmFxc2EZKBydXU1ampqUFBQwOfdPOF0OrF161Zs3rwZLS0taGhowIkTJ9DZ2YnOzk5YLBasXLkSq1ev5vuOOcjr9eKJJ55Aa2srAKC0tBR33HEHsrOz01zZxTEajairq0NdXR0AwOPxpELg2traEA6HceTIERw5cgQAkJ2djerqahQWFsJkMsFoNE45n6uvuXRdh9/vT73udLvdCAQCk0LeLjSk7UKJooiMjIwzhrplZmZClmfPzyLMZjNqampQU1MDIN7fDg0NpUIEu7q6JoRJNzY2AgAsFgvKyspQXl6OsrIyFBYWztnnDhEREREREdGCpSqAnvhHUZIxvbUQEY2XbJMUDiIlOqvkOSIxdICIzoB9KtHMUsaFKrI/JrowyXNGYTgpzSHJ5yvbfFqo2HYTLQiz5xfORERERHRJBEFAwTe+gXBzM6Kdnej7whdR8p//ccmhEfONf+dO9P3jP0H1eiFmZKDo2/8Kx/XXp7usGfPHhi5EVQ3LijOxrCRzWo9tyM9DzQvPT+sxAeDtt9/G66+/DgC47bbbUFVVNWmfmjwHavPsODEYwItHBnDnqpJprSHzjtvh/tWv4H95JxS3G7LLNa3HJzoTk8mE3Nzc8wo0UFU1FQIXjUbPuhwOh+H3++Hz+VLT2NhYKrhoZGTkvOqz2+2or6/HsmXLUFRUxD5mlhMEAbfffntqsPgjjzyCD33oQ7NqwDvRbKdpGgYGBjA4ODgh4G10dPSMARbj2/Ls7OwJ4W52u/2yhDNYrVZYrVaUlpZOuj8+n29CGFxy8ng8CAQC2Lt3L/bu3Qur1ZoKgauoqGCoxAIzNjaGpqYmNDY2or+/P7Xe5XJh9erVWLFiBcNEFwBRFOFwOM75WEej0QlhcOOXg8EgVFWFpmnQdX3SfKp1ybnRaERlZSWqq6tRWVkJs9l8me45pYMkSVi0aBEWLVoEr9eLffv2obGxET6fD7t27cKuXbtQXFyMFStWoL6+HlarNd0l01nouo4DBw5gx44diEQikCQJ119/PTZs2DCvQhudTifWrFmDNWvWQNM09Pb2oqWlBa2treju7j7v99uSJE0ZCGcymWCxWFKv7axWK2w224TLhsvwjyzGf3aQDBBOLidDYc/FbDan6p9qMpvNkCQJoiiedRIEYdI6o9E4p59XoiimQljXrl0LAAgEAuju7k4FwfX29mJsbAzHjh3DsWPHAMTD/EtLS1NBcCUlJZfl+UBEREREREREl0AdNzhL5mBFIppFkm2SykGkRGeVPEfYjxPRmbBPJZpZ6rjfKLA/JrowciKcVGU4Kc0RmgroanyZbT4tVGy7iRYEjvQlIiIimkckuw3FP3oQHffci8DOnXD/+r+R/ZH70l3WrKArCoZ+/O8Y+fnPAQDm+noU/+hBGEumNzBsNtF1HQ/t6QIAbFtfluZqzs+JEyfw1FNPAQCuvfZarFy58oz73rKsECdePIEdTf3THvxmrquDub4e4eZm+P7yFFwf/OtpPT7RdJAkCZIkXXQARiwWmxAEN9UUDAZhMpmwdOlSLFu2DBUVFXN6MPVCZDKZcO+99+JnP/sZurq68Oyzz+Kd73xnussimvUGBgZw8OBBNDU1wefzTbmP2WxGbm4u8vLyUkFvubm5cDgcszYYUxRFOJ1OOJ1OVFdXT9gWi8XQ2dmJQ4cO4ejRowiFQmhoaEBDQwMsFgsWL16MK664ApWVlQyBm6dUVUVbWxsOHDiAo0ePQlEUAPHXHEuXLsXq1atRUVExa5/flD5GoxEulwsuBmbTNMnMzMSWLVuwefNmtLS0oKGhAcePH0dPTw96enrwzDPPoK6uDitWrEBtbS2DjWcZv9+PJ598EsePHwcAFBcX44477jivgPO5TBRFlJSUoKSkBFu2bEE4HEZ7eztaW1vh8XgQiUQQjUYnzFU1/sM0VVURCoUQCoUu+HYNBsOUgXBms3nKwLTzmQeDwQnhbh6P54y3LwgCsrKykJ2djezsbGRkZEwKdbNarTxPL5DdbsfixYuxePFiAICiKOjv70dnZyc6Oztx8uRJhMNhtLW1oa2tDUD8OVhcXJwKgisrK2NoKhEREREREdFso4wLf5A4WJGIZpFkm6RwECnRWSXPEcmY3jqIaPZin0o0s5LvqwUJEPk7VqILkuqjGE5Kc8SEz1L5HowWKLbdRAsCf2FNRERENM+YlyxB/he/iP6vfx2DP/whLKtWwrpqVbrLSqvYwAB6HngAY283AACy/uqvkPf5z0E0zu8PfXa1jqB9OAi7ScZtK4rSXc459fX14ZFHHoGu61ixYgW2bNly1v1vqS/Av794AjuPDyEQUWA3Te/bm8w770C4uRmexx9j8BvNSwaDITUw+0wURYEgCAz4meOys7Pxnve8Bw899BD27t2LoqIirFrgrw2IpuL1etHc3IyDBw9iYGAgtd5kMqGgoGBCuFtubi7sdvu8CsAyGAyoqalBTU0N3vWud6GjowOHDx/GkSNHEAqFsG/fPuzbtw9msxmLFy/G0qVLUVVVxRCPeWBgYAAHDhzAwYMHEQgEUutzc3OxZs0aLF++HFarNY0VEtFCJYoi6urqUFdXh0AggObmZhw4cAB9fX04evQojh49CovFgvr6eqxYsQLFxcXzqm+ea3RdR3NzM5566imEw2FIkoQtW7bgqquuWpDvKc1mM5YsWYIlS5accR9VVacMhEvOI5EIxsbGUqFwoVAIwWAwtaxpGmKxGLxeL7xe74zfn5ycHGRnZyMnJye17HK5+HrwMpBlORUsePXVV0PTNAwNDaWC4Do7OxEIBNDV1YWurq7U9QoKClBWVobS0tLU48UwOCIiIiIiIqI0UhPhD4IISPxMhYhmETnxW1qVg0iJzip5jsgMcCWiM2CfSjSz2BcTXbzkecM+iuaK8c9Vtvu0ULHtJloQ+I0hERER0TzkvOduhPbshu/pHeh54AFUPfooJKcz3WWlReCNN9D7uc9Ddbsh2mwo/Jd/RsbNN6e7rMvid3tOAgDuWFUE2zSHok03j8eD3/3ud4jFYqisrMS73/3ucw7WXlzgQGWODe3DQbx8dBDvnuZwu4xbb8Xgd/4NkcNHED56FObFi6f1+ERzAQdvzx+LFi3Cli1bsHPnTvzlL39Bbm4uSkpK0l0WUdqFw2EcOXIEBw8eRHt7e2p9Mmhm+fLlqK2thcFgSGOVl58kSaiurkZ1dTVuvfVWdHZ2pkLggsEg9u/fj/3798NkMmHRokVYunQpqqurF9zfaS4LBoNoampKBSglWSwWLFu2DCtXrkRhYSEDlIho1rDb7diwYQM2bNgwKbBy79692Lt3L7Kzs7FixQosX74czgX6OVi6BAIBPPXUUzhy5AgAoLCwEHfccQfy8/PTXNnsJkkSrFbrRQWs6rqOSCQyKQwuOYXDYWiaBk3ToOv6ec2TyxaLZVLAm81m4+uCWUQUReTn5yM/Px9XXnkldF3H6OhoKgTu5MmTcLvd6O/vR39/P/bs2ZO6rsVigcvlmjRlZWXxcSYiIiIiIiKaaUpicJbEgYpENMsk2yWFg0iJzkpJhLhK8/sfjxPRJWCfSjSz2BcTXbzkeZM8j4hmu9RzVQBEjm2jBYptN9GCwF6OiIiIaB4SBAEF3/wmxg4dQqzzJHq/8EWU/Mf/W1CDtnRNw8hPf4qhf/+/gK7DtHgxSn70IIwVFeku7bIY8kfwbHM/AGDbleVprubswuEwtm/fjkAggNzcXNx9993nFTYlCAJuqS/Af+xsxTPN/dMe/CZnZcF+3XXwP/ssvI89DvMX/mlaj09EdLlt3rwZvb29OH78OH7/+9/j4x//ODIzM9NdFtFlp6oqWlpacPDgQRw7dgyKoqS2lZWVYfny5Vi6dOlFBXDMR5IkoaqqClVVVbj11ltx8uRJHD58GIcPH0YgEMDBgwdx8OBBGAwGVFdXo66uDnV1dbDb7ekunU6jKApOnDiB/fv348SJE9A0DcCpoMMVK1agtraWwa9ENOvl5+fjpptuwg033IC2tjYcOHAAR44cwcjICF566SW89NJLqKiowIoVK7B06VKYTBxEOZMOHTqEp556CqFQCKIoYvPmzbjmmmsgSVK6S5vXBEGA2WyG2WyGy+VKdzmUZoIgpALcVq1aBQDw+/2pILi+vj6Mjo4iGAxibGwMPT096OnpmXQco9E4KRAuLy8PBQUFfI1IRERERERENB2S4Q8yB6gT0SyTbJcYUkN0dko4Ppf5/SMRnQH7VKKZxb6Y6OIlzxslDOg6sIDGmNIcNb7N5/OVFqrxbTcRzVv8dS4RERHRPCXZ7Sh58EF03Pt+BF5+Ge7//g2y7/twusu6LFSfD73/+E8IvPwyAMD5vruQ/6UvQTSb01zZ5fOHhi4omo6VpU4sLcpIdzlnpCgKHn74YQwODsJut+MDH/gALBbLeV//lvpC/MfOVrx0dBBjURUW4/QOKs6884548NuTTyLvHx6AYDBM6/GJiC4nURTxnve8B7/61a8wODiIhx56CPfddx+DMGhB0HUd3d3dOHjwIA4dOoRQKJTalp2djRUrVmDZsmXIyspKY5WznyiKqKioQEVFBW6++WZ0dXXh8OHDOHLkCHw+H44ePYqjR48CAEpKSrBo0SIsWrQIubm5CyqEejbRdR29vb04cOAAmpqaMDY2ltpWWFiIlStXor6+HjabLY1VEhFdHFEUUVNTg5qaGkQiERw+fBgHDhxAR0dHanrqqadQU1OD6upqVFVVweVysU+aJoODg3jllVdw6NAhAPFAvjvuuAOFhYVproyIAMDhcKC+vh719fWpdZFIBG63G6Ojo3C73RMmn8+HaDSK/v5+9Pf3TziWJEkoKChAcXExSkpKUFxczPaULgtd16GqKoB4wGHyOTd+mYiIiIiIaE5RE+EPEr+jJ6JZJtkuqQypITor9uVEdC7sU4lmlhqNz9kXE104KfmPCHRAUwCJ4+NolmObT3Sq7U6eD0Q0LzH4jYiIiGgeMy9divwv/BP6v/FNDP7gB7CuXgXLihXpLmtGhY8dQ/ff/h1iJ09CMBpR8LWvwvne96a7rMtK03T8fk8XAGDb+rI0V3Nmuq7jL3/5C9rb22EwGLBt2zY4nc4LOkZ9cQZKsizoHh3DK8cHcXP99A4utm/aBCknB+rwMAKvvQ7HdVun9fhERJeb2WzG+9//fvz85z9Hf38/Hn30Udxzzz0QRTHdpRHNCF3XcfjwYbz00ksYGRlJrbfZbFi2bBmWL1+OwsJCDhi/CKIoory8HOXl5bj55pvR19eH48eP49ixY+jr60N3dze6u7vx4osvwul0pkLgysvLIUnTG9ZLpwSDQQwODmJoaAhDQ0Po6OjA0NBQarvdbsfy5cuxcuVK5OXlpbFSIqLpZTKZsGrVKqxatQoejwcHDx7EgQMHMDIyMiGY1Ol0orq6GtXV1aisrLyg8HkCVFXF0aNHsXfvXnR0dACIh+9s2rQJ1157LWSZXz0TzWYmkwmFhYVTBjTGYjF4PJ5JgXC9vb0YGxtDT08Penp6sGfPHgCAxWKZEARXXFwMq9V6ue8SzVMjIyM4cOAADhw4AK/Xe8b9pgqCG79cVFSE++67b+YLJiIiIiIiOl9KYnCWzMGKRDTLJNslXQNUBZD4eT/RlFJ9ufHs+xHRwsU+lWhmKYlQRfbFRBdu/OdRSoTBbzT7sc0nOtV2KwyWJprP+MkBERER0TznvPdeBPfsgX/HM+j57P2ofPRPkC4wXGuu8D75JPq+8lXo4TAMRUUo/vd/h6X+inSXddm93jKMk+4QHGYZ715elO5yzuiVV17B/v37IQgC3ve+96Go6MJrFQQBty4rxM9ebcPTTf3THvwmyDIyb7sN7l/9Ct7HHmPwGxHNC1lZWbj33nvxm9/8BseOHcOLL76IG2+8Md1lEU27kydP4rnnnkN3dzcAwGAwYMmSJVi+fDkqKysZPjaNBEFAUVERioqKsGXLFni9Xhw/fhzHjx9HW1sbPB4Pdu/ejd27d8NkMqGmpgaLFi1CbW0tA3cugq7rCAaDGBoamhDyNjQ0hFAoNGl/WZaxePFirFy5ks99IloQnE4nNm/ejGuuuQZ9fX1oaWlBa2srurq64PF40NDQgIaGhlT/VV1djaqqKpSUlDC47Ax8Ph8aGxvR0NAAv98PIN7/L168GNdcc81FfaZDRLOLwWBAbm4ucnNzJ6zXdR2jo6Po7u5GT08Puru70d/fj7GxMbS0tKClpSW1r8vlSoXBlZSUoLCwkEHzdN7GxsZw6NAhHDhwAF1dXed1HV3XJ8xPF4vFpq0+IiIiIiKiaaEmBmdJHKxIRLPM+HZJjTCkhuhMUn05Q1yJ6AzYpxLNLPbFRBdv/HmjRtNXB9H5YptPdOr9hcrgN6L5jJ8cEBEREc1zgiCg8FvfQvjQYcROnkTvF7+Ekv/3EwiCkO7Spo0ejWLgu9/D6P/+LwDAtmkTir73XchZWWmuLD227z4JAHjv6hJYjLMz1OHtt9/Gzp07AQC33nor6urqLvpYN9cX4GevtuGlo4MIx1SYDdN7nzPvuB3uX/0K/p07oYyOLtjnFRHNL2VlZbj99tvx6KOP4o033kBOTg5WrVqV7rKIpsXw8DBeeOEFHD16FEA8wOCqq67Cxo0bYTab01zdwpCZmYl169Zh3bp1iEQiaGtrw7Fjx3D8+HGEQiEcOnQIhw4dgiAIKC0tRX5+PrKzs1OT0+lcsAERqqoiGo0iEomk5uFwGG63e0LQ29jY2BmPkZWVlQrtyM/PR11dHZ/7RLQgjQ8m3bx5MyKRCDo7O9Ha2orW1lYMDw+jp6cHPT09ePXVV2E0GlFRUYGqqipUV1cjJydnXn1+dqF0XUdHRwf27t2Lo0ePQtM0AIDNZsOaNWuwZs0aZGZmprlKIpppgiDA5XLB5XJh+fLlAABFUTAwMDAhDM7tdqempqYmAPH2YtGiRViyZAkqKysZrkmTqKqKtrY27N+/H0ePHoWqqgDiz7vq6mqsWLEC1dXVqf5Y1/UJYW+nL59+maHXNJe9+uqr+N73voeGhgb09fXhsccewx133HHW6+zcuRP3338/Dh06hNLSUnz5y1/Ghz/84ctSLxERERGdJyUxOEvmYEUimmXGt0tKBDDa0lcL0WymJAIyZIa4EtEZsE8lmlnsi4kuniQDggjo2qnPqIhmM7b5RKfeXygM7CSaz/jLWiIiIqIFQLLbUfzgD9F57/sReOkljP7P/8D1oQ+lu6xpERsYRM9nPoOxffsAADn/52+Q8+lPQ1igA3oGfGE8f2QAALBtfVmaq5nanj178PTTTwMArr76aqxbt+6SjreyxInCTDP6vGG8fmIYNyzNn44yU8x1dTDX1yPc3AzfX56C66//alqPT0SULsuXL8fw8DBeffVVPPnkk8jKykJFRUW6yyK6aMFgEDt37kRDQwM0TYMgCFi1ahW2bt0Kh8OR7vIWLJPJhCVLlmDJkiXQNA09PT04duwYjh07hqGhIZw8eRInT56ccB1JkpCVlTUhDC452e328wrh0XUdiqIgEolMOWmads7QgAtdPtd+yXpOD3YbP1cU5bz/ti6XKxXwlpxycnJgNPILbiKiqZhMJtTV1aXC571eL9ra2tDa2oq2tjaEQiEcP34cx48fBwBkZGSgrKwMpaWlKCsrQ15e3oIIkIlEIjhw4AD27t2LoaGh1PqysjKsW7cOS5YsYXgT0QInyzKKi4tRXFycWhcKhVJhmt3d3ejq6kIwGERjYyMaGxtTbfCSJUtQU1PD16wL3MDAAA4cOICDBw8iEAik1ufm5mLlypVYtmwZMjIy0lghUfoFg0GsWLECH/nIR/Ce97znnPu3t7fjne98Jz71qU/hd7/7HV588UV87GMfQ2FhId7xjndchoqJiIiI6LyoicFZEt8XE9EsI8oABAD6qbaKiCZTEwEZEkNciegM2KcSzSz2xUSXRjIBytipc4loNmObT3Tq+c92m2he46/yiYiIiBYIyxVXIO8L/4SBb34LA9//ASyrVsGyfHm6y7okwT170HP/A1CHhyE6HCj6t3+D47qt6S4rrR7Z2wVV07G2PAt1+bMv4OStt97CM888AwDYsGEDbrjhhks+pigKuLm+AL9+owNPN/dNe/AbAGS++10INzfD/+KLDH4jonlly5YtGB4exuHDh/Hwww/j4x//OFwuV7rLIrog0WgUb731Fl5//XVEo/EfS9XW1uLGG29EXl5emquj8URRRGlpKUpLS3HDDTfA7Xajs7MTIyMjqcntdkNRFAwPD2N4eHjSMYxGI7Kzs+FyuWCxWM4Y7BYOh6FpWhru5fSQJAkmkwlGoxEmkwlZWVmTAt4MBkO6yyQimtMyMzOxatUqrFq1CpqmYWBgIBUC19nZCZ/Ph+bmZjQ3NwOI90HFxcWpvqykpAQWiyXN92L6DA4OYu/evThw4EDqNZXBYMDy5cuxbt06FBQUpLlCIprNrFYramtrUVtbCwBQVRUdHR04cuQIjh49ikAggKamJjQ1NUGWZVRXV2PJkiWoq6uD1WpNc/V0OQSDQTQ1NeHAgQPo6+tLrbdYLFi2bBlWrlyJwsLC8wr6JloIbrnlFtxyyy3nvf9//dd/obKyEj/4wQ8AAEuWLMHrr7+OBx988IzBb8nPUJJ8Pt+lFU1ERERE56YkXn/JHKxIRLOMIMTbJiV8qq0iook0DdAS/8yPfTkRnQn7VKKZpSQCFWUGqhNdFNkYD35TGE5KcwDbfKJTz3+220TzGoPfiIiIiBaQrPe/H6Hde+B/9ln0fOazKH/oIRjy514Yhq7rcP/mNxj83vcBVYVp0SKU/PuPYSwvT3dpaaVqOn6/twsAsG19WZqrmezNN9/Ec889BwC4+uqrccMNN0zbIK5b6gvx6zc68MLhAUQVDUZZnJbjJtm3bsXAt7+D0NtvQ/X7ITlmX6geEdHFEEURd9xxBzweD3p7e7F9+3Z87GMfg9lsTndpROekaRoOHjyIl156KTUwtbCwEDfeeCOqqqrSXB2dD5fLNSlsUtM0+Hy+CWFwycnj8SAajaKvr29CUMC5JMPTTCYTzGYzjEYjJElKvRYVBOGSl89nv9PD3M42l2V+dE9EdDmJoojCwkIUFhZi06ZNiMVi6OrqmjBFIhG0t7ejvb09db28vLxUEFxpaSlcLtecCayJxWIYHBxEX18fmpqa0NnZmdqWk5ODdevWYcWKFXxvQEQXRZIkVFdXo7q6Grfeeiu6u7tTIXCjo6M4duwYjh07BlEUUVFRgSVLlmDx4sVw8HPXeSUWi+H48eNoamrC8ePHU+Hcoiiirq4OK1asQG1t7Vnf/+i6Dk1VocaiUGIxqMlJiUGJRhPz+GU1FoMSi6b2MTscqFt/9eW6u0RptWvXrkn/bOkd73gHPvOZz5zxOt/+9rfxjW98Y4YrIyIiIqIJ1ORgRYbFENEslAypUTmQlGhK6rgAJ/blRHQ27FOJZk6yP5b5WxaiiyKbAXgnvrYlmq3Y5hOdev6z3Saa1zh6jIiIiGgBEQQBhf/8LYQPH0asqwvtd9yBon/7DuybN6e7tPOmBYPo/fKX4d/xDAAg493vRuE3vwHRYklzZen36vEh9HjG4LQacOuywnSXM8Frr72GF198EQBwzTXX4LrrrpvWgdBryrOQ6zBhyB/Bm63D2LJoegMNjWVlMFZXI9raiuBrryHj1lun9fhEROlkNBpx77334uc//zmGh4fxhz/8Adu2bYMkSekujeiMWltb8dxzz2FgYAAAkJmZieuvvx719fUQxekNgKXLSxRFOJ1OOJ1OVFdXT9imKAo8Hg9GRkYwPDyMaDSaCnRLhrqNv5wMUeNzgoiILpTBYEBVVVUqTFbTNAwNDU0IgnO73RgcHMTg4CAaGhoAADabDaWlpSgoKIDD4YDdbofdbofD4YDNZkvba+xgMIiBgQH09/ejr68P/f39GB4ehq7rqX0EQcDixYuxbt06VFZWzpkAOzp/uq4Dug4dOqAnLkNPbMO4bXpydepy/Kmin3rO6OO36eNvJHV5wu0lr5O8zdRy8rinrp+6XZy2LrVq3PEnrpq8bdw+p3aZ4vrj/0anXW/ybZ1Woz7FPjit5rPcrwl/69NrPuP9Gn9Lp10vdVuniKIEk80Gs80Os90Bk9UK4TK+RhZFEWVlZSgrK8NNN92EgYEBHDlyBEeOHMHg4CDa2trQ1taGp556CiUlJairq0NhYSHy8/PhcDjYHs0BqhJDcHQUfvcIfMNDaO/oQGdfP4YCIWjj9jMoUVjGArCEA/D2tuCVl5/Cy5oWf24n5pqmQdc1QNOh6xpUVZ10Tp6vgpo6Br/RgtHf34/8/PwJ6/Lz8+Hz+TA2NgbLFN+jfuELX8D999+fuuzz+VBaWjrjtRIREREtaEpicJbEsBgimoWSbZPCgaREUxp/brAvJ6KzYZ9KNHNS76uN6a2DaK5K9VEMJ6U5gG0+0annP9ttonmNwW9EREREC4zkcKDsFz9H92c+i8iRI+j6xCfhuu8+5H32MxCMs/uDkEhbO7r/9m8RbW0FZBn5X/gnZG3bxoFfCb/b3QkAeO/qEpgNsyes55VXXsHLL78MANiyZQuuvfbaaX/MJFHAzVcU4LdvdWJHU/+0B78BgGPrFoy0tsL/8k4GvxHRvJORkYFt27bhV7/6FVpbW/HMM8/gne98Z7rLIpqkv78fzz//PFpbWwEAJpMJ11xzDdavXw+DwZDm6mimybKMnJwc5OTkYNGiRekuh4iIFhBRFJGfn4/8/HysXbsWABAIBCYEwfX29iIYDOLo0aM4evTolMexWq2pMLjxoXDjLxuNRsiyDEmSUvPz/RxF0zR4PB709/dPmHw+35T7WywWFBYWoqysDKtWrUJmZubF/YHmIF3ToMSiiEUiUKJRKNHIqeVIBLFoBEo0klpWo1GoigJNU6GpKjRFgaqq0FUVqqpCUxVoihrfrijQVBWqGp/rmhafdD0x16Br45YTQUcTQ490QNfiy4nwownHSC4n12v65MvJEDei0wkCTFYrzDY7TDZ7PBDOZofJbp+4zm6HbDRCEERAAARBhCAI8bkopJYhCqe2JZYBxM+fSDgxRRANjyEWji8LkTDqjDoKs2wYCoYxGlMwpgvo7u5Gd3d3qlSDJCErMwP5eXkoLitHSVkZcnNzYTJxUN3loCoxhHxehLxehLweBEZHEHCPn9wIjI4g6PVAtTqgZLgQc2QB8qmfIgnRCAw+N2TvCKRoGDqA0CXUJEoyJIMBksEAOTU3QpINkAxyfDmxPquw+JL/BkTzWTI0n4iIiIguIzUxWFHm6zAimoWSbZPKkBqiKanjBllL/I0SEZ0F+1SimZPsj/m+mujiyIlxo+yjaC5gm0/E9xZECwSD34iIiIgWIGN5OSp+/xAGv/d9jP7v/8L9618j9PbbKP7hD2Ccpf/J3ffcc+j7whehBYOQ8/JQ/KMfwbp6VbrLmhV0XcehXh9eOjoIAHj/lWVprihO13Xs3LkTr7zyCgDguuuuw+bNm2fs9m5ZFg9+e+5wP/5FrYcsidN6fPvWrRj5xS8RePVV6IoCQebbKSKaXwoLC/Ge97wHDz/8MPbu3Yvc3FxceeWV6S6LCEA88O3NN9/EwYMHAcTDV9atW4drr70WVqs1zdURERHRQmS327FkyRIsWbIEAKAoCvr6+nDy5EmMjIwgEAjA7/cjEAggGAxC0zSEQiGEQiEMDg5e0G2ND4E701zTNAwODiISmfoHHi6XCwUFBRMmh8ORtn+ooOs6YpEwwoEAIsEAwsEAIsFgYh5AOBgct37iZTUWix8kUbswfnnc/Uktj9tPicVSQW80gwQBAoREWJiAxKMUfyhS24Tk6lOXkZqlrhNfJ0xYJWDiYzt+WTj98hTP8ameG6cf59S2ibc98Zin1Tz++Xfa9U6/zuWsWVMUhENBhAN+KJEIoOuIBIOIBIMABibdVjrIAGyyAYrDCdXigGa2QDOaEVNVDLpHMegeRdPRY6n9jQLgMJvgcjqRX5CP0vIKlFRWwZqRyX8Ucxa6riM6NoaQz4OQxxOfez3xYLfUOi+CXg/GvB6Eg4EzHwuAZrYhluGCUlMG3XDqHwvJApDvsKOiqAAlpaWwZ7kgyfK4gEAxPgnCFJcTwYKiCEEQIUpSPNTNaIAsGyCI0/u5P9F8UVBQgIGBiW36wMAAMjIyYLFY0lQVEREREU2iJAYrSrP7n7MS0QKVbJuU6Nn3I1qolMR3S5Jp4vcTRESnY59KNHPG98dEdOGS547C303RHMA2n2jcewu220TzGZMKiIiIiBYo0WRCwZe/BNvGDej94pcQbmpC+53vQeE3v4GMW29Nd3kpeiyGwR/9CO5f/goAYF27FsUP/hBybm6aK0sfXdfROhTE7vYRvNXmxlttIxjyx9+8r690oSbPnuYK4zW++OKLeP311wEAN9xwAzZt2jSjt3llhQsumxHuYBS72924uiZnWo9vWbECUmYmVK8XY/v3w7p27bQen4hoNliyZAmuv/56vPjii9ixYwdcLhdqamrSXRYtULquo62tDW+++SZaW1tT65cuXYobbrgBLpcrjdURERERTSTLMkpLS1E6xT9V0DQNY2NjCAQCqSkZCnf6FI1GoWnahOurqgpVVc+rDlEUkWm3wWm3IcNqQYbFDLvRBAkaVFWFNtiDwd5O9KsKVCU+6ZoaT/ABoEMH9PiFxCy+Vk/tMGGdpqrQFAVq4niaEksdN35ZgZpYpykK1Fh8WYlFoZ92P9NFkmXIJhMMRhNkkwmycfyyEQajCZLRCEmWIYoSRFmCKMkQJSkxxcP3RHniunhYkTwp0EgURUA8tXwq+EiKhx0JpwKPxocfTQpHGr9P8riCkLosjA8ROz0Qb1wwW2odhNOC2VJXSAWzxQ8z/rgYt42DnOYCVYkhHBgfqhhAJHE5tS5wKoRRjcWg6xp0XYeu6fE2Qktc1nXoqeVx++gaoCN+XplMMJjMMJjNp5bHXzZbEutMiXVmaKoC39AgRgcHMTTQjxGPF/5wBFFBhGa2QJeNiOrAyFgEI2MDONE3AOw7CGgq5FgUDrMRednZKKusQvWSpcgtK4ckG9L9p58xmqpizO87FeDmHUXI50XI64kHuPm8CCZC3sa8XiixCxtkJIgirBmZsGZkwubKhmDPhA8SBgMhhMaFjZrNZixduhT19fWoqKiIt0lEdNls3LgRTz/99IR1zz//PDZu3JimioiIiIhoSmrifZTMwYpENAsl2yaVA0mJpqQmPltlP05E58I+lWjmpPpjBqoTXZTkuaMynJTmALb5ROPeW7DdJprPGPxGREREtMA5rr8eVY8tQc8/fA5jjY3ouf8BBHe9hfwvfgFimv8DfLS7B70PPICxAwcAAK777kPe/Z+FYJi/g7SmEg96C2BXIuRtd5sbw4GJXwQaZRGry5z40q1L01TlKbqu4/nnn8ebb74JAHjHO95xWQaWyJKId1yRj4f2dOHppr5pD34TZBm2azfD98ST8L/8MoPfiGje2rRpE4aHh3HgwAH84Q9/wMc+9jHkLuDAVbr8VFVFc3Mz3nzzTQwMDACIh0osXboUV111FYqLi9NcIREREVGcrmmIRcKIhsOIhcdS81j41LpT20/bJxJGNLGvFg7DEB6DPRyGGotBUZR4uJIgQhcEQIgHeuniuOXT5gAgRscgRiJQoGMYwHB6/zznRZRkmO12mKw2mG12mOz2+Nxqi6+3JS7bEtttdsiG8T/mGh9KlwisG798Kr0Ouq5DNhhhSAS6JYPdRFG6PHeWaBaQZANszizYnFnpLuWCxcJh+IaHMNh9Et1dXRgcHMCo1wd/OIqIIACiBMVkwagOjA57cGy4Ec/vfhtSZAxWWUROVhZKS0tRuWgJ8quqYc3ITPddOqNYOIyQLx7kFvR6EqFuntS60Lh1YwH/+KTO82IwW2DNzIQ10wlrhhPWzEzYMp2wZDhhczrjQW+ZTlgzndBFCb19fejq6sLhw4cxNDR06jgGAxYvXoz6+npUV1dDlvkTJKLpEggE0NLSkrrc3t6O/fv3w+VyoaysDF/4whfQ09OD//mf/wEAfOpTn8JPfvITfP7zn8dHPvIRvPTSS3jkkUfw1FNPpesuEBEREdFUlMTgLImDFYloFkq2TQoHkhJNSUn8bpv9OBGdC/tUopmT6o8ZxEp0UZLnjsJwUpoD2OYTsd0mWiD4q0siIiIigqGoCOX/8xsM/eQnGPnpz+D5wx8wtn8fin/4Q5hqa9NSk/+FF9D7xS9B8/kgZmSg8J+/hYybbkpLLZebrus4MRhIhbztbh/BcGDiF38mWcTqsixsqMrG+ioXVpY6YTakf5Cqrut45plnsHv3bgDALbfcgvXr11+227+lvhAP7enCs4f68c3b6yGJwrQe37F1K3xPPInAyzuR/7nPTeuxiYhmC0EQ8O53vxujo6M4efIktm/fjo997GOw2WzpLo3muXA4jMbGRrz11lvw+XwA4oPIV69ejQ0bNiAra+4FExAREdHsoSqx04LZ4vPxwW3j10+5b3gM0Ug4tU6JzNyPKQQA0FWkPtkQBMiyAaIsQJRlSJKUmMvxuSxDlHLic1mCJBsS6ySIsuHU/rIMUUrME5cFQYQgCEjemAABEIT4unG3H58J41YJk44VnwwTLouJWk7tZ4BkkGG22iGbTBNvh4joDAxmM7JLSpFdUoolGyZu0zQNg/19aDlyGJ3t7RgcGoY/EoEmilAtNvgB+ANhtB85gVcPHYMYCcGsqcjKzEBxcTHKq2pgy8qC2WqDyWaDKRFAKU1DkJmmqggHAwgHAggH/AgH/fFlvw8hny8R5jZ+8iIWCV/YjQgCLI4M2DKdiUC3rAnhbaeHvBlM5ikPE4vF0NfXh+6eHvQcOY7e3l643e4J+0iShNraWtTX16Ourg5GIwc5Es2Et99+G1u3bk1dvv/++wEAH/rQh/Df//3f6Ovrw8mTJ1PbKysr8dRTT+Gzn/0sfvzjH6OkpAS/+MUv8I53vOOy105EREREZ6Ek3u/JHKxIRLNQsm1SLvCzKaKFgv04EZ0v9qlEMyfVH/M7SqKLkjx3GCBEc0Hyeco2nxYytttECwKD34iIiIgIACDIMvI+8xnY1q9Hz+c/j8iJFrS/727kf/ELcL7vfZdtAKYWjWLwe9/H6G9/CwAwr1iO4h/8EMaS4sty++niDcXw8rFBPH9kAG+1jmAkODnobU15POhtQ1U2VpRmwiSnP+htPE3TsGPHDuzduxcA8K53vQtr1669rDVsrM5GpsWA4UAUb3e4sb4qe1qPb9u0CZBlRNvaEO3shLG8fFqPT0Q0W8iyjHvuuQc///nPMTo6iocffhgf/OAHIU/DgGei0/l8Prz11ltoaGhAJBGeYrPZsH79eqxduxZWqzXNFRIREVE66bqO6FgIkVAQkVB8Hg2dfnnc8mn7xsbGEA2HoanKjNUoCCIMZjOMZjMMZkti2QKDyTTx8rj5hHWmU+tkg3GKgDYJoji7PgciIppNRFFEQVExCoqKsSmxTtM0uN1utB07hvbWFvQP9MMbHIMmitAsdoQAhBSgp7MXe9q7IUbDEKORxDwMIRqBERosZjPMiSA4k82WmNthTiwLoohIMICxZLBbIBHsFvAhHAggEgpe1H2SDUZYnc7TAtxOhbclL9synTA7HBfcT6iqiqGhIfT09KCnpwe9vb0YGBiAruuT9nW5XCgqKkJ1dTUWL14Mi8VyUfeJiM7fli1bpjwfk/77v/97yuvs27dvBqsiIiIiokumJgZnSRysSESzULJtUqNn349ooUqeG+zHiehc2KcSzZxUf8wgVqKLkjx3VAYI0RyQ+iyVbT4tYGy3iRYEjtYlIiIioglsGzei6rHH0PtPX0Dw9dfR/9WvIfTWWyj4xjcgORwzetvRzk70fPZ+hA8fBgC4PvoR5H3mMxAMhhm93XTpHg3h+cMDeP7wAPa0u6FopwZwmA0i1pa7sKHKhfVV2VheMvuC3sbTNA1PPfUUGhoaAAC33XYbVq9efdnrMEgiblyajz82dGNHc/+0B79JDges69YitOst+F9+Gdkf/vC0Hp+IaDax2WzYtm0bfvnLX+LkyZP4y1/+gttvv/2yhcHS/DcwMIA333wTTU1N0DQNAJCTk4OrrroKy5Ytg2GevgYkIiJayHRdRyw8hjG/D2M+X3w+fppqnd8HPfFaYTpIsjxFGFsisM00PpjNklpvTISyTV5nSYW18XUyEdHsIooicnJykJOTgyuvvhrAqTC4rs4OtJ04gd7eXnj8AaiiCM1shWaeGDw+BsCrKvFAuEAYotubCoYToxEImnre9ZisNpjtdpjtjvhks8OSkQlrZiZsmVmwZGbCmhEPcrNmZsJgtkxL36KqKrxeLzweDzweDwYHB9HT04O+vj4oyuRQVLvdjuLiYhQXF6OoqAhFRUUMZCciIiIiIpouSmKAuszBikQ0CyXbJoUDSYmmlDw32I8T0bmwTyWaOeyPiS4N+yiaS9jmEwFyIlRaYag00XzG4DciIiIimkTOyUHpz34K969/jcEHfwTf0zsw1tSM4h/+AJZly2bkNr1PPYX+r34NWjAIyelE0b99B/Zrr52R20oXXddxqNeH5xJhb0f6fBO21+XbcePSfGxdlIflJU4YZTFNlV4YTdPw5JNPYt++fQCAO+64AytXrkxbPbcuK0gEv/Xhq+9aClGc3oHXji1bENr1FgIv72TwGxHNe3l5ebjrrruwfft27N+/H1lZWdi8eTNDLeii6bqO9vZ2vPHGG2htbU2tLy8vx1VXXYXa2lqI4tx4DUREREQTqYqC4KgbvpEhBEaG4R8Zht89DP/wMALuYQTcIxjz+6BOETJzPiRZhtFqg8lqhSkxN1psp5ZP35acm63jQt5MkGSGyxIRLVTjw+BWrVkLIP75tsfjwcjIyIRpeHgYPp8PkGRoFhmaxTbpeLIgwCQKMBhkmIxGmE0mWCxWWGxW2Ox22B0ZsGc6keF0wmK1wmw2w2KxQJan76c6qqrC5/Olgt3GT6Ojo/D7/dB1fcrrmkwmFBUVTQh6y8jI4Oc+REREREREM0VNDFaUOFiRiGahZNukMgCAaEpqYpC1ZExvHUQ0+7FPJZo57I+JLk3y3FEZIERzANt8Ir63IFogGPxGRERERFMSRBHZH/0orGvWoOeBf0Csqwsd79+GvPvvh+vDH4IwTYEcWjiMgX/9NjyPPAIAsKxdg+Lvfx+GgoJpOX66RRUNu9tH8PzhAbxweAC93nBqmygAaytcuGlpPm5cmo/y7MkDx2Y7TdPw+OOP4+DBgxAEAXfeeSeWL1+e1pqursmBwyRjwBfBvi4P1pRnTevx7Vu3YuDb30GooQGqzwcpI2Naj09ENNvU1tbi5ptvxo4dO/Dyyy+jv78f7373u2G1WtNdGs0hwWAQ+/fvR2NjI0ZGRgAAgiBg6dKl2LhxI0pKStJcIRER0eykqSoiYyFEQ0FEQiFEEvNoKIjo2BggCBBFEUJiSi6LkjR5nSBCEJPrxwW6nBYGMzkbRh+3TceYzwu/ewT+kSEERuJzv3sEQc/oVFeekmwwwpKRCYsjA5aMjPh8/HTaOpPdDoORgyGJiGj6iaIIl8sFl8uF2traCdtisRhGR0cnBMK53W6MjIwgEAhA0XUoqh7/sWk4CiAAYOSctynLMsxmM2RZhiiKkCQJYqLPPp9lXddTYW8+n++MwW7jb8/pdMLpdMLlcqVC3rKzsxm+TkREREREdDkpicGKMgcrEtEslGybFAYAEE1JSQyylvmdJRGdA/tUopnD/pjo0iTPHYUBQjQHsM0nYrtNtEAw+I2IiIiIzsqyciUqH3sUfV/+CvzPPYfB734Xvh074LhuK2wbN8JcXw9BvriXlZHWVvR85rOInDgBCAJy/uZTyPk//+eijzdbuINRvN4yjOcPD2DnsUH4w0pqm8UgYXNdDm5cWoDrFufBZZubP+TTdR39/f149dVXceTIEQiCgPe+972or69Pd2kwyRKuX5KHx/f3YkdT37QHvxnLymCsrka0tRXB119Hxq23TuvxiYhmoyuvvBKqquKFF17AkSNH0NPTgzvvvBOVlZXpLo1mMU3T0NbWhsbGRhw9ehSapgEADAYDVq1ahQ0bNsDlcqW5SiKiuUPXdSjRCJRYDGo0Gp/Hxs2jycvJdafWK7EoNEVJHScZ5HUqI0TH+BWnr7c5s5BfVYvcisoFH76l6zp0TYOmadA1NbWsqSqUaDQxRaDGYqnHS4lG4utj8e1qcr9YfN/o2Fgq1C2aDHcbi8+VyNz6sl6SZdizc+Bw5cDuyoYjJxcOVzYc2bmwu7JhzYyHvRlM5nSXSkREdE4GgwF5eXnIy8ubtC0cDsPtdsPn8yEcDp/3BACKoiAQCExbnZIkpYLdpprsdjsEQTj3gYiIiIiIiGhmqYnPe6WF/Tk7Ec1SybZJnVvfTRFdNuzHieh8sU8lmjlqIlBRmpvjsIjSLnnuqAwnpTmAbT4R222iBWJuJ2oQERER0WUhZWSg+Mc/gufhRzDw7W8j3NSEcFMThn787xDtdlivvBK2jRth27gBxurq8xpA5HnscfR/85vQx8Yg5eSg+HvfhW3jxstwb6ZXRFFxuNeHfSc92N8Vn066QxP2ybGbcMOSPNy4NB9X1+TAbJDSVO2lGxoaQnNzM5qbmzEyMgIAEEURd911F5YuXZrm6k65ZVlhPPituR9feueSaR/U5ti6BSOtrfC/vJPBb0S0IAiCgKuuugoVFRX405/+hJGREfzmN7/Bpk2bsGXLFshzPLSVppfX68X+/fvR2NgIr9ebWl9UVIQ1a9agvr4eJhN/BElE0yMWDiPgcSM6NgZdVaFpKjQlMU9eVrVx25RUSFdyu55YVlX11H6qduoYqjLxGIn9dV2HJEkQZRmiJEGUZEiyDFGW4+ulccuyfGq7JEHTVMTCYcTCYUTDYxPmsfAYooltsUhyOT4fl8iWFoIoIqe0HAXVtcivqkVBdS1yysohyYa01pUUi0YQCQQQCYUQDYcQDY3F52NjiI6Nm4fHEA0l5qn1Y4iGx+KPraaNC3c7taxpatoeA9lkgslqg8lihclqg9FqhdFsAYBJIXSnalYn3Qd93IRx75XP9b55/Haz3TEp1M2RnQNHdg4sjgwIojgzfwQiIqJZxGw2o6ioCEVFRed9HU3TEI1GEQ6HMTY2BjXxGlRL9NNTLU+1DgAcDgeysrLgdDphs9kgsv8lIiIiIiKa/ZRE8IPMwYpENAsl2yaFA0mJppQ8N9iPE9G5sE8lmjmp99X8DTLRRUmeOwrDSWkOYJtPxHabaIHgqFwiIiIiOi+CICDr3ntgv3YzAq+8guCbuxDcvRua14vASy8h8NJLAAA5Nxe2qzbCuiEeBGcoKJhwHC0YRP83vwXvn/8MALBdtRFF3/0u5Jycy36fLpSu6zjpDmF/lwf7Tnqwr8uDI70+RFVt0r51+XZctzgfNy7Nx6pSJ0RxeoPHLiePx5MKe+vv70+tlyQJdXV12LBhA8rLy9NY4WTX1uXCapTQ4xlDU48Xy0uc03p8+9atGPnFLxF49VXoigKBgUdEtEAUFRXhk5/8JJ555hk0Njbi9ddfR1tbG9773vciOzs73eVRGqmqiuPHj6OxsREtLS3QE6E4ZrMZy5cvx+rVq1Fw2utCIppeqcCzREiZpipQVWXcOhWqopwKPEus0xQlHiAx7rKmaRBFEQZLPFjKaLHAaLEm5hYYTOZpD1c+nRKNIuhxI+B2IzDqRnB0BIHRcctuN4KeUURCwRmtY7YSBBGy0QjJaIRsMEA2GCEZDJASy7LRAMlgnLBekuVxj5uQPND4WWp9aj8BgA54BvrQ33oCYz4vhjrbMdTZjqaXngMASLKMnLLKeBhcdQ0KqmqRXVIGUbqw0HNd16HGYohFI6kAvHAggHAwgEgwgHDAj3AwcTmxfsL2YABqLHbxf9RpIMkyZKMp/jgYTZCNxsTjEX9MZKMp/pgYjaltksEAo8USD3VLBLqZLDaYrKcC3kxWGyS+7yQiIprzRFGE2WyG2WyG0+lMdzlERERERER0uanJwBhzeusgIppKsm1SOZCUaErJc4P9OBGdC/tUopnD/pjo0iQDhNhH0VygMviN6FS7zVBpovmMo0SIiIiI6IIYCguRde+9yLr3XuiqivCRowjuehOhXW8h1NAAZWgI3j8/Ae+fnwAAGKuqYNuwAbarNkJyZaPvS19CtL0dEEXk/t3fIfsTH4cgimm+V1PzjsVwoMuTCHobxYFuL9zByW+SXTYjVpY6sarUiZVlTiwvcSLTYkhDxdPH7/fj8OHDaGpqQnd3d2q9KIqorq5GfX09Fi1aBLN5dn5hYjZI2Lo4D08d7MPTTf3THvxmWbECUmYmVK8XY/v2wbpu3bQen4hoNjMajbjttttQU1ODJ554Ar29vfiv//ov3HLLLVi1atWMBwHR7OJ2u9HY2Ij9+/cjEAik1peXl2P16tVYunQpDIa5/bqILp6u69A1DaoSiweOKckgMgVqYtJUFdD1eFigrkOHDuhIhAfqiM+S6+PbEN8CXdNSIWWnB5xNCDZLBJ7Fa4mv03UtfjwkDpmsIVE3xt8+cNbaUhUl6kvWGt906rjQ9VQ9uqbFg9o0DbqamCcua2piWY3vk7qfqhq/n+PvX+I+q4m/4+UiCCIMZvPEQLhkQJzZAkEU449/8nEdN2GqdYnHU4nFEBx1IzjqRjgYOGcdSbLJBLPVBlGWIYoSBEmCKIoQJWnilNyW2i6P2yae2ibJECURonjquoIoQZLGX1+CIOC0QL1EiJ4Sg3p64N74x1BRIEoSDGYLjGbzhLnBbIbBZJ5w2Th+bjJBNpouOFRtOui6Dv/IEAZaW9DfdgIDbS0YaD2BcDCAgbYTGGg7ATyfeEyMJuRVVCG3ogqCIEBJhrlFwonlCGKRcCrkLblO1ycHq18oQRTjgWnJ5+ek8MLxz9nJ6+KPtwhBFOPPCzHx/Bi/Tpq8ThSlWfv5BhERERERERERERHNAkpisKLEwYpENAtJxvhcYQAA0ZSUxO/Hk+cKEdGZsE8lmjnsj4kuTfIzKYUBQjQHpNp8fpZKC1iq3eZ7C6L5jMFvRERERHTRBEmCpf4KWOqvAD7+cWiRCMb27UNw11sI7tqFcHMzom1tiLa1YXT79tT15Px8FP/g+7CuXZvG6idzB6PY0z6Ct9rceKttBMcG/JOyE4ySiKVFGfGgtzInVpVmodRlmRchN2NjYzh8+DCam5vR0dFxKqACQEVFBerr67FkyRLYbLY0Vnn+bq0vxFMH+7CjuQ//ePOiaX2MBFmG7drN8D3xJPw7dzL4jYgWpKVLl6K4uBiPPfYYOjo68MQTT6ClpQXvete7YLVa010ezSBd13H48GG8/fbbaG9vT623Wq1YuXIlVq9ejZycnDRWSAAQi0YQCQYRCQYQDgQQCQURTi0HEuuDiIQCCAcDiAQCUGKnvsjXT38hPOniuBW6Dk2NB7ydCrmKh71Rep0KMRsXfibLkCatlyHKEqTkOlmGpiiIhscQHRtDdCwUn4fHEqFtWmJdCMDIjNUvG4ywuVywZ7lgy8qGPSu5PH6eDaNlfrwnm+0EQUBGTh4ycvJQu/4qAPG2wjs4gIG2E+hvPYGB1hMYaG9BdGwMvcePoPf4kYu6LUmWYTBbYLbZYbLZYbbbYU7MTbbksmPSdpPNzucDEREREREREREREc1OauK7OJkD1IloFpITA0lVBgAQTUlNDLKWGTpAROfAPpVo5rA/Jro0qT6KAUI0B7DNJzr1/NdigKYB/AflRPMSg9+IiIiIaNqIJhNsGzbAtmED8NnPQPV6EdyzB6FEEFy0owP2rVtR+C//DDkrK93lYjgQwe42N3a3j2B3mxvHBvyT9ilzWbGqzImVpfFpaVEGTLKUhmqnn6IoGBwcRE9PD06cOIGWlhZompbaXlxcjPr6elxxxRXIyMhIY6UXZ8uiXJgNIjpHQjjc58MVRZnTenzH1q3wPfEkAi/vRP7nPjetxyYimisyMzPxwQ9+EG+++SZeeuklHD58GN3d3bjzzjtRWVmZ7vJoBni9Xjz55JNoaWlJraupqcHq1atRV1cHWebHjTNF13WEgwGEPKMIekYR9HpOLSemkNeDkNeDcDAANRZLd8lTEiUZkpwIGZMNEEUREEUIEAABibAmAfGZMGl9fF2cIIqpEDNBGhdaNiHMTIYoivHbHLdeEBLHSmVDJS/HV6S2pzYLk2pLLWP8dZCo+dSxxi+nahCleP3SaXNRgiiKEKT4XBRFCKIEUY7Xfeq+xsPZkuFtE+/fqVC36Q6/0nUdSiSCaHgMkVAIsXAiFC48hmgoMQ+HoWsaBFGM/2UEAYIgJv5k4qnHNrkMAYIY/3tKsgx7VjZsWVmwZ2XDZLMxwGuWEwQBzvwCOPMLsGjjNQAAXdMw2t+LgdYTGO7qhChJkI0mGMxmGExmyCYTDCYzDCZTYhq/Lr5elObH+24iIiIiIiIiIiIiohQlMVhR4mBFIpqFkm2TwgAAoiml+nEGuBLRObBPJZo5SiJQkf0x0cVJnjsKw0lpDmCbTzTx+a9GAdGcvlqIaMZwJCYRERERzRgpMxMZN96IjBtvBADo0SgEY/o+bBn0h7G7zY232kawu92NlsHApH1q8+zYUJWN9VUuXFnpQp5jfrwZ1nUdbrcbPT09qamvrw+qqk7YLy8vD8uWLcMVV1wBl8uVpmqnh80k49q6XDx7aADPNPdPe/CbbdMmQJYRbWtDtLMTxvLyaT0+EdFcIYoiNm3ahKqqKvzpT3/CyMgIfvOb32DTpk3YunUrJAa3zAu6rmPfvn149tlnEYlEIEkSrrrqKqxZswZOpzPd5c15sUgY/pFh+IYG4Rsegn9kCMFRdyLQzYOgdxQhjweaqlzQcQVBhMlmg9lmh8lmTy2bE8umcctmmx2yyYRxUWfAaYFbk/O3Tq1IBY/JMiTZkLqcWpbHBa7RnCUIQjy8y2yGzZn+MG+anQRRhKuoBK6iknSXQkREREREREREREQ0e6iJwYoyBysS0SyUbJtUBgAQTSnVjzPAlYjOgX0q0cxRE4GK7I+JLk7y3FEZTkpzANt8oonPfzUCGObHWHcimojBb0RERER02Vzu0Ld+bxi720fwVpsbu9tG0DYcnLTP4gIH1le6sKEqG1dWupBtnx8fBgUCgQkhbz09PQiHw5P2M5vNKC4uRmlpKZYuXYq8vLw0VDtzbl1WiGcPDeCppj7cf2PdtAaNSA4HrOvWIrTrLfhffhnZH/7wtB2biGguKioqwic+8Qk8/dRfcOBgE15//XUcaW7ClYvrYNAUxKJRaIoCVYlBicWgxmJQlcQ8uawok9ePDynV9VOL4288tX7c9nH7YsLi+Av6VItTmtSFnB6IhdN2OP3i5AOc43jnuL1xlwVBgMFigclqS0zWeMiXJTG3WuMhX9bxyzYYTObz6hu9Xi+efPJJtLS0AABKSkpw++23Izc395zXpfhzccznjYe6jQzBPzw0IeDNNzSIMb/vvI9ntjtgzXTC5sxKTE5YMxPLmU5YMp2w2B0w2ewwWiwMWiMiIiIiIiIiIiIiIiKaDZTEYEVpfvw+jYjmmWTbpDAAgGhK7MeJ6HyxTyWaOUoiUJH9MdHFkRLjOtlH0VyQavP5TzRoARv//FcYLE00XzH4jYiIiIjmjV7PWDzordWN3e0j6BgJTdguCMCSggysr3JhfWU86M1lm9sf/iiKgtHRUbjdbgwPD6dC3rxe76R9JUlCYWEhiouLU5PL5ZrXYSDXLc6DURLRNhTEicEA6vId03p8x5YtCO16C4GXdzL4jYjmJV3TEPJ5EXCPIDA6gpDXizG/D+GAH2N+f2LZhzG/P7HOB13TYHY4ES6swIjHix1vvgVz/0nI3pHJYWaUVoIoIiMnF4uu2owrrr0erqKSCdt1Xce+ffvw7LPPIhKJQJIkXHfdddi4cSNEUUxT1bOLpqkIeRPniHsEfvdwfHlkGIHREfhHhuEfHoYSO/eXLEaLBRk5eXDk5CIjJxf2rGxYnacC3azOLFgznZANhstwz4iIiIiIiIiIiIiIiIhoWiUH1cocoE5Es5CcDACY/A+GiQjj+vG5/btzIroM2KcSzZzkecX+mOjiyAwnpTkk1ebzs1RawAQhHv6mRvn+gmgeY/AbEREREc1ZXe4Qdre7sbttBG+1j6DLPTZhuygAS4sysKEyG+ursnFlhQuZ1rkXlKGqKjweD0ZGRuB2uyfMvV4vdF2f8nq5ubkTQt7y8vIgywvrLYDDbMA1tTl48eggdjT1T3vwm33rVgx8+zsINTRA9fkgZWRM6/GJKH1URcGY34eQ14OQz4sxnzcReuZNrQv5vBjzehEZC0GSJIiyAZIsxyeDAWJyObU+vk4et81otsCSkQmLIwPWjExYMjLjc0cGDGbzjN0/XdcRCQUTgW5uBEfdqXC3gNsdnyfW65p2wce3RsPI9PTD68xHWDIgXFQJsagSZlmCxWCAxWiAzWyC3WKG3WqFw2aFwWiCZDCk/n6SbIjPJSn+YXWCMD4+bnySnCBAVVWEI1GEwxGMReJfSMqyBEmSYJAlyLIMWZIgSzJkWYYoihMDUM+UTHdaVzu579VP23765tO2Tz7g2W/vHPtrmoZ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+ "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", "\n", - "dataset[0].keys()" + "fig, axs = plt.subplots(1, len(dataset.select_action_keys), figsize=(50, 10))\n", + "\n", + "item = dataset[0]\n", + "for k, ax in zip(dataset.select_action_keys, axs):\n", + " actions = item[k][::10].reshape(150, -1)\n", + " for i in range(actions.shape[1]):\n", + " ax.plot(actions[:, i])\n", + " ax.legend([k], fontsize=24)\n", + " ax.set_xlabel(\"timestep\", fontsize=24)\n", + " ax.set_ylabel(\"action\", fontsize=24)\n", + " ax.set_title(k, fontsize=24)\n", + "plt.tight_layout()" ] }, { @@ -141,15 +232,119 @@ "id": "96c13e2f", "metadata": {}, "source": [ - "## 3. Multi-robot Dataset" + "## 2. Load MultiLeRobotDataset Dataset\n", + "We use [MultiLeRobotDataset ../eo/data/lerobot_dataset.py#329](../eo/data/lerobot_dataset.py#329) to load multi-robot dataset.\n", + "`MultiLeRobotDataset` supports parallel loading to quickly load data, which is very useful in large-scale training, e.g. training with 200+ robots datasets.\n", + "\n", + "Use [LerobotConfig ../eo/data/schema.py#12](../eo/data/schema.py#12) to specify the dataset config for each robot, where:\n", + "\n", + "```python\n", + "@dataclass\n", + "class LerobotConfig:\n", + " repo_id: str # the repo id of the robot dataset\n", + " root: str # the root path of the robot dataset\n", + " episodes: list[int] | None = None # Optional, `the episodes to load\n", + " delta_action: bool = False # Optional, whether to load delta actions\n", + " state_mode: str = \"MEAN_STD\" # Optional, the state normalization mode\n", + "\n", + " train_subtask: str | bool | None = False # Optional[true, false, mix:0.5, cumulate] # the tasks prompts to hybridize with the overall instructions\n", + " select_video_keys: list[str] = None # Optional, the video keys to load\n", + " select_action_keys: list[str] = None # Optional, the action keys to load\n", + " select_state_keys: list[str] = None # Optional, the state keys to load\n", + " effector_indices: list[int] = None # Optional, the effector indices to load\n", + " weight: float | None = None # Optional, the weight of the dataset for sampling\n", + "```\n", + "\n", + "In the following example, we load two robot datasets, `demos25` and `libero_spatial_no_noops_1.0.0_lerobot`. All the features keys will be loaded from the metadata, which is different from the `LeRobotDataset` that only computes the intersection of features." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "30b20ce3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* load 2 lerobot datasets with 8 processes ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:\n", + "The dataset you requested (libero_spatial_no_noops_1.0.0_lerobot) is in 2.0 format.\n", + "While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global\n", + "stats instead of per-episode stats. Update your dataset stats to the new format using this command:\n", + "```\n", + "python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 --repo-id=libero_spatial_no_noops_1.0.0_lerobot\n", + "```\n", + "\n", + "If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)\n", + "or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose).\n", + "\n", + "Loading lerobot datasets: 0%| | 0/2 [00:00<|image_pad|><|vision_end|>Locate bowl of carrots and annotate its bounding box, providing the output in json format.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"```json\\n[\\n {\\\"bbox_2d\\\": [86, 82, 345, 249], \\\"label\\\": \\\"bowl of carrots\\\"}\\n]\\n```\"\n", + " }\n", + " ],\n", + " \"seq_length\": 198,\n", + " \"vision_base_idx\": 0\n", + "}\n" ] - }, + } + ], + "source": [ + "import pprint\n", + "from eo.data.multim_dataset import MultimodaDataset\n", + "from eo.data.schema import MMDatasetConfig\n", + "import json\n", + "\n", + "multim_dataset = MultimodaDataset(\n", + " data_configs = [\n", + " MMDatasetConfig(\n", + " json_path=\"../demo_data/refcoco/refcoco.jsonl\",\n", + " vision_base_path=\"../demo_data/refcoco\"\n", + " ),\n", + " ]\n", + ")\n", + "\n", + "print(json.dumps(multim_dataset[0], indent=2, ensure_ascii=False))" + ] + }, + { + "cell_type": "markdown", + "id": "a94528e9", + "metadata": {}, + "source": [ + "Plotting Util" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "767338f0", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import random\n", + "import io\n", + "import ast\n", + "from PIL import Image, ImageDraw\n", + "from PIL import ImageColor\n", + "import xml.etree.ElementTree as ET\n", + "\n", + "additional_colors = [colorname for (colorname, colorcode) in ImageColor.colormap.items()]\n", + "\n", + "def decode_xml_points(text):\n", + " try:\n", + " root = ET.fromstring(text)\n", + " num_points = (len(root.attrib) - 1) // 2\n", + " points = []\n", + " for i in range(num_points):\n", + " x = root.attrib.get(f'x{i+1}')\n", + " y = root.attrib.get(f'y{i+1}')\n", + " points.append([x, y])\n", + " alt = root.attrib.get('alt')\n", + " phrase = root.text.strip() if root.text else None\n", + " return {\n", + " \"points\": points,\n", + " \"alt\": alt,\n", + " \"phrase\": phrase\n", + " }\n", + " except Exception as e:\n", + " print(e)\n", + " return None\n", + "\n", + "def plot_bounding_boxes(im, bounding_boxes, input_width, input_height):\n", + " \"\"\"\n", + " Plots bounding boxes on an image with markers for each a name, using PIL, normalized coordinates, and different colors.\n", + "\n", + " Args:\n", + " img_path: The path to the image file.\n", + " bounding_boxes: A list of bounding boxes containing the name of the object\n", + " and their positions in normalized [y1 x1 y2 x2] format.\n", + " \"\"\"\n", + "\n", + " # Load the image\n", + " img = im\n", + " width, height = img.size\n", + " print(img.size)\n", + " # Create a drawing object\n", + " draw = ImageDraw.Draw(img)\n", + "\n", + " # Define a list of colors\n", + " colors = [\n", + " 'red',\n", + " 'green',\n", + " 'blue',\n", + " 'yellow',\n", + " 'orange',\n", + " 'pink',\n", + " 'purple',\n", + " 'brown',\n", + " 'gray',\n", + " 'beige',\n", + " 'turquoise',\n", + " 'cyan',\n", + " 'magenta',\n", + " 'lime',\n", + " 'navy',\n", + " 'maroon',\n", + " 'teal',\n", + " 'olive',\n", + " 'coral',\n", + " 'lavender',\n", + " 'violet',\n", + " 'gold',\n", + " 'silver',\n", + " ] + additional_colors\n", + "\n", + " # Parsing out the markdown fencing\n", + " bounding_boxes = parse_json(bounding_boxes)\n", + "\n", + " try:\n", + " json_output = ast.literal_eval(bounding_boxes)\n", + " except Exception as e:\n", + " end_idx = bounding_boxes.rfind('\"}') + len('\"}')\n", + " truncated_text = bounding_boxes[:end_idx] + \"]\"\n", + " json_output = ast.literal_eval(truncated_text)\n", + "\n", + " # Iterate over the bounding boxes\n", + " for i, bounding_box in enumerate(json_output):\n", + " # Select a color from the list\n", + " color = colors[i % len(colors)]\n", + "\n", + " # Convert normalized coordinates to absolute coordinates\n", + " abs_y1 = int(bounding_box[\"bbox_2d\"][1]/input_height * height)\n", + " abs_x1 = int(bounding_box[\"bbox_2d\"][0]/input_width * width)\n", + " abs_y2 = int(bounding_box[\"bbox_2d\"][3]/input_height * height)\n", + " abs_x2 = int(bounding_box[\"bbox_2d\"][2]/input_width * width)\n", + "\n", + " if abs_x1 > abs_x2:\n", + " abs_x1, abs_x2 = abs_x2, abs_x1\n", + "\n", + " if abs_y1 > abs_y2:\n", + " abs_y1, abs_y2 = abs_y2, abs_y1\n", + "\n", + " # Draw the bounding box\n", + " draw.rectangle(\n", + " ((abs_x1, abs_y1), (abs_x2, abs_y2)), outline=color, width=4\n", + " )\n", + "\n", + " # Draw the text\n", + " if \"label\" in bounding_box:\n", + " draw.text((abs_x1 + 8, abs_y1 + 6), bounding_box[\"label\"], fill=color)\n", + "\n", + " # Display the image\n", + " return img\n", + "\n", + "\n", + "def plot_points(im, text, input_width, input_height):\n", + " img = im\n", + " width, height = img.size\n", + " draw = ImageDraw.Draw(img)\n", + " colors = [\n", + " 'red', 'green', 'blue', 'yellow', 'orange', 'pink', 'purple', 'brown', 'gray',\n", + " 'beige', 'turquoise', 'cyan', 'magenta', 'lime', 'navy', 'maroon', 'teal',\n", + " 'olive', 'coral', 'lavender', 'violet', 'gold', 'silver',\n", + " ] + additional_colors\n", + " xml_text = text.replace('```xml', '')\n", + " xml_text = xml_text.replace('```', '')\n", + " data = decode_xml_points(xml_text)\n", + " if data is None:\n", + " img.show()\n", + " return\n", + " points = data['points']\n", + " description = data['phrase']\n", + "\n", + " for i, point in enumerate(points):\n", + " color = colors[i % len(colors)]\n", + " abs_x1 = int(point[0])/input_width * width\n", + " abs_y1 = int(point[1])/input_height * height\n", + " radius = 2\n", + " draw.ellipse([(abs_x1 - radius, abs_y1 - radius), (abs_x1 + radius, abs_y1 + radius)], fill=color)\n", + " draw.text((abs_x1 + 8, abs_y1 + 6), description, fill=color)\n", + " \n", + " img.show()\n", + " \n", + "\n", + "# @title Parsing JSON output\n", + "def parse_json(json_output):\n", + " # Parsing out the markdown fencing\n", + " lines = json_output.splitlines()\n", + " for i, line in enumerate(lines):\n", + " if line == \"```json\":\n", + " json_output = \"\\n\".join(lines[i+1:]) # Remove everything before \"```json\"\n", + " json_output = json_output.split(\"```\")[0] # Remove everything after the closing \"```\"\n", + " break # Exit the loop once \"```json\" is found\n", + " return json_output" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "74357e3a", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loaded 9 samples from ../demo_data/refcoco/refcoco.jsonl\n" + "{'question_id': '710706', 'image': 'images/COCO_train2014_000000580957_2.jpg', 'conversations': [{'from': 'human', 'value': 'Locate bowl of carrots and annotate its bounding box, providing the output in json format.'}, {'from': 'gpt', 'value': '```json\\n[\\n {\"bbox_2d\": [86, 82, 345, 249], \"label\": \"bowl of carrots\"}\\n]\\n```'}], 'seq_length': 198, 'vision_base_idx': 0}\n", + "(364, 252)\n", + "{'question_id': '480908', 'image': 'images/COCO_train2014_000000580905_2.jpg', 'conversations': [{'from': 'human', 'value': 'Identify and mark the bounding box for little girl.'}, {'from': 'gpt', 'value': '```json\\n[\\n {\"bbox_2d\": [67, 157, 167, 252], \"label\": \"little girl\"}\\n]\\n```'}], 'seq_length': 190, 'vision_base_idx': 0}\n", + "(364, 252)\n", + "{'question_id': '98434', 'image': 'images/COCO_train2014_000000168643_2.jpg', 'conversations': [{'from': 'human', 'value': 'In json format, please annotate the bounding box for 3 cushion couch.'}, {'from': 'gpt', 'value': '```json\\n[\\n {\"bbox_2d\": [17, 138, 163, 240], \"label\": \"3 cushion couch\"}\\n]\\n```'}], 'seq_length': 195, 'vision_base_idx': 0}\n", + "(364, 252)\n", + "{'question_id': '1071411', 'image': 'images/COCO_train2014_000000579299_4.jpg', 'conversations': [{'from': 'human', 'value': 'Format the bounding box coordinates for front pizza.'}, {'from': 'gpt', 'value': '```json\\n[\\n {\"bbox_2d\": [7, 118, 298, 299], \"label\": \"front pizza\"}\\n]\\n```'}], 'vision_base_idx': 0}\n", + "(308, 308)\n", + "{'question_id': '2191497', 'image': 'images/COCO_train2014_000000567396_13.jpg', 'conversations': [{'from': 'human', 'value': 'please annotate the bounding box for horse far left.'}, {'from': 'gpt', 'value': '```json\\n[\\n {\"bbox_2d\": [18, 88, 70, 237], \"label\": \"horse far left\"}\\n]\\n```'}], 'vision_base_idx': 0}\n", + "(364, 252)\n" ] }, { "data": { + "image/jpeg": 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", "text/plain": [ - "9" + "" ] }, - "execution_count": 1, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from eo.data.multim_dataset import MultimodaDataset\n", - "from eo.data.schema import MMDatasetConfig\n", + "from qwen_vl_utils import smart_resize\n", + "images = []\n", + "for i in range(5):\n", + " item = multim_dataset[i]\n", + " bounding_boxes = item['conversations'][1]['content']\n", + " image = Image.open(f\"../demo_data/refcoco/{item['image']}\")\n", + " w, h = image.size\n", + " h_bar, w_bar = smart_resize(\n", + " h, w, min_pixels=64 * 28 * 28, max_pixels=128 * 28 * 28\n", + " )\n", + " image = image.resize((w_bar, h_bar))\n", + " images.append(plot_bounding_boxes(image,bounding_boxes,w_bar,h_bar))\n", "\n", - "multim_dataset = MultimodaDataset(\n", - " data_configs = [\n", - " MMDatasetConfig(\n", - " json_path=\"../demo_data/refcoco/refcoco.jsonl\",\n", - " vision_base_path=\"../demo_data/refcoco\"\n", - " ),\n", - " ]\n", - ")\n", "\n", - "len(multim_dataset)" + "total_width = sum(img.width for img in images)\n", + "max_height = max(img.height for img in images)\n", + "combined_image = Image.new('RGB', (total_width, max_height))\n", + "x_offset = 0\n", + "for img in images:\n", + " combined_image.paste(img, (x_offset, 0))\n", + " x_offset += img.width\n", + "combined_image" + ] + }, + { + "cell_type": "markdown", + "id": "fd5b7923", + "metadata": {}, + "source": [ + "### Load MultiModal Interleaved Datasets\n", + "\n", + "For Multimodal data, e.g., image, video, text, points and bounding boxes, we follow the [Qwen2.5-VL](https://colab.research.google.com/github/QwenLM/Qwen2.5-VL/blob/main/cookbooks/spatial_understanding.ipynb) and [Qwen2-VL-Finetune](https://github.com/2U1/Qwen2-VL-Finetune) recipes. In interleaved pretraining, we integrate the EO-Data1.5M dataset — a large-scale, high-quality embodied dataset designed to unify reasoning and control. Data are organized in a standardized format as shown below:\n", + "\n", + "

    \n", + "\n", + "Here, the `lerobot` and `view` fields connect actions with multimodal conversations, enabling the model to capture the rich temporal dynamics and causal dependencies among vision, language, and action modalities — a core requirement for robust performance in open-world embodied interactions. For more details, please refer to [getting_started/1_load_dataset](getting_started/1_load_dataset.ipynb)\n", + "\n", + "To combine robot control data and multimodal data, we support a [flexible YAML-based configuration](eo/data/schema.py), where each dataset can be assigned weights and sampling strategies. This makes it easy to balance embodied control trajectories with multimodal reasoning data for interleaved training. For example:\n", + "\n", + "```yaml\n", + "# @multimodal data config\n", + "mm_datasets:\n", + " # classical multimodal data\n", + " - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file\n", + " vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL\n", + " sampling_strategy: random:10% # sampling strategy\n", + "\n", + " # interleaved data jsonl, rely on `lerobot_datasets` to load robot control data\n", + " - json_path: demo_data/interleaved_demo.jsonl\n", + "\n", + "# @robot control config\n", + "lerobot_datasets:\n", + " - repo_id: demo25\n", + " root: ./demo_data\n", + " # Optional fields:\n", + " episodes: [1, 2, 3] # specific episodes to load (None = all)\n", + " train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task\n", + " delta_action: false # train with delta actions\n", + " state_mode: \"MEAN_STD\" # state normalization mode\n", + " select_video_keys: # which camera streams to load\n", + " [\n", + " observation.images.head,\n", + " observation.images.hand_left,\n", + " observation.images.hand_right,\n", + " ]\n", + " select_state_keys: # proprioceptive states\n", + " [observation.states.joint.position, observation.states.effector.position]\n", + " select_action_keys: # action targets\n", + " [actions.joint.position, actions.effector.position]\n", + " effector_indices: [14, 15] # indices of effector channels in the flattened action vector\n", + " weight: 1.0 # dataset weight for sampling\n", + "```" ] }, { "cell_type": "code", "execution_count": 2, - "id": "0dff93ce", + "id": "5105f7a0", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/shared-storage-user/eorobotics-shared/miniconda3/envs/eo/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -254,97 +767,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[warn] read dataset demos25 failed, skipped!\n", - "One or several timestamps unexpectedly violate the tolerance inside episode range.\n", - " This might be due to synchronization issues during data collection.\n", - " \n", - "[{'diff': np.float32(-59.433334),\n", - " 'episode_index': 0,\n", - " 'timestamps': [np.float32(59.433334), np.float32(0.0)]},\n", - " {'diff': np.float32(-66.96667),\n", - " 'episode_index': 1,\n", - " 'timestamps': [np.float32(66.96667), np.float32(0.0)]},\n", - " {'diff': np.float32(-59.033333),\n", - " 'episode_index': 2,\n", - " 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n", - " {'diff': np.float32(-58.533333),\n", - " 'episode_index': 3,\n", - " 'timestamps': [np.float32(58.533333), np.float32(0.0)]},\n", - " {'diff': np.float32(-61.2),\n", - " 'episode_index': 4,\n", - " 'timestamps': [np.float32(61.2), np.float32(0.0)]},\n", - " {'diff': np.float32(-66.0),\n", - " 'episode_index': 5,\n", - " 'timestamps': [np.float32(66.0), np.float32(0.0)]},\n", - " {'diff': np.float32(-60.166668),\n", - " 'episode_index': 6,\n", - " 'timestamps': [np.float32(60.166668), np.float32(0.0)]},\n", - " {'diff': np.float32(-63.366665),\n", - " 'episode_index': 7,\n", - " 'timestamps': [np.float32(63.366665), np.float32(0.0)]},\n", - " {'diff': np.float32(-61.0),\n", - " 'episode_index': 8,\n", - " 'timestamps': [np.float32(61.0), np.float32(0.0)]},\n", - " {'diff': np.float32(-57.1),\n", - " 'episode_index': 9,\n", - " 'timestamps': [np.float32(57.1), np.float32(0.0)]},\n", - " {'diff': np.float32(-59.166668),\n", - " 'episode_index': 10,\n", - " 'timestamps': [np.float32(59.166668), np.float32(0.0)]},\n", - " {'diff': np.float32(-59.033333),\n", - " 'episode_index': 11,\n", - " 'timestamps': [np.float32(59.033333), np.float32(0.0)]},\n", - " {'diff': np.float32(-57.266666),\n", - " 'episode_index': 12,\n", - " 'timestamps': [np.float32(57.266666), np.float32(0.0)]},\n", - " {'diff': np.float32(-52.933334),\n", - " 'episode_index': 13,\n", - " 'timestamps': [np.float32(52.933334), np.float32(0.0)]},\n", - " {'diff': np.float32(-58.933334),\n", - " 'episode_index': 14,\n", - " 'timestamps': [np.float32(58.933334), np.float32(0.0)]},\n", - " {'diff': np.float32(-57.8),\n", - " 'episode_index': 15,\n", - " 'timestamps': [np.float32(57.8), np.float32(0.0)]},\n", - " {'diff': np.float32(-55.7),\n", - " 'episode_index': 16,\n", - " 'timestamps': [np.float32(55.7), np.float32(0.0)]},\n", - " {'diff': np.float32(-61.933334),\n", - " 'episode_index': 17,\n", - " 'timestamps': [np.float32(61.933334), np.float32(0.0)]},\n", - " {'diff': np.float32(-55.433334),\n", - " 'episode_index': 18,\n", - " 'timestamps': [np.float32(55.433334), np.float32(0.0)]},\n", - " {'diff': np.float32(-57.0),\n", - " 'episode_index': 19,\n", - " 'timestamps': [np.float32(57.0), np.float32(0.0)]},\n", - " {'diff': np.float32(-63.066666),\n", - " 'episode_index': 20,\n", - " 'timestamps': [np.float32(63.066666), np.float32(0.0)]},\n", - " {'diff': np.float32(-61.833332),\n", - " 'episode_index': 21,\n", - " 'timestamps': [np.float32(61.833332), np.float32(0.0)]},\n", - " {'diff': np.float32(-54.033333),\n", - " 'episode_index': 22,\n", - " 'timestamps': [np.float32(54.033333), np.float32(0.0)]},\n", - " {'diff': np.float32(-54.833332),\n", - " 'episode_index': 23,\n", - " 'timestamps': [np.float32(54.833332), np.float32(0.0)]}]\n" + "[warn] demos25 failed to calculate episode subtask cumulate: 'action_config'\n", + "* set train_subtask None for demos25\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading lerobot datasets: 100%|██████████| 1/1 [00:25<00:00, 25.90s/it]" + "Loading lerobot datasets: 100%|██████████| 1/1 [00:25<00:00, 25.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "successfully load dataset 0/1:\n", - "[] \n" + "successfully load dataset 1/1:\n", + "['demos25'] \n" ] }, { @@ -358,21 +797,25 @@ "source": [ "from eo.data.lerobot_dataset import MultiLeRobotDataset\n", "from eo.data.multim_dataset import MultimodaDataset\n", - "from eo.data.schema import LerobotConfig, MMDatasetConfig\n", + "from eo.data.schema import MMDatasetConfig, LerobotConfig\n", "\n", "lerobot_dataset = MultiLeRobotDataset(\n", - " data_configs = [\n", + " data_configs=[\n", " LerobotConfig(\n", " repo_id=\"demos25\",\n", " root=\"../demo_data\",\n", + " select_video_keys=[\"observation.images.head\", 'observation.images.hand_left', 'observation.images.hand_right'],\n", + " select_state_keys=[\"observation.states.joint.position\", \"observation.states.effector.position\"],\n", + " select_action_keys=[\"actions.joint.position\", \"actions.effector.position\"],\n", " )\n", - " ]\n", + " ],\n", + " chunk_size=16 # automatically construct delta_timestamps from the `metadata` and `chunk_size`\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "d6a92945", "metadata": {}, "outputs": [ @@ -380,35 +823,104 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loaded 1132 samples from ../demo_data/interleaved_demo.jsonl\n" - ] - }, - { - "ename": "ValueError", - "evalue": "invalid dataset: demos25. available dataset: []", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m 1\u001b[0m multim_dataset \u001b[38;5;241m=\u001b[39m MultimodaDataset(\n\u001b[1;32m 2\u001b[0m data_configs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 3\u001b[0m MMDatasetConfig(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m meta_dataset \u001b[38;5;241m=\u001b[39m lerobot_dataset\n\u001b[1;32m 8\u001b[0m )\n\u001b[0;32m---> 10\u001b[0m \u001b[43mmultim_dataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/EO-1/eo/data/multim_dataset.py:124\u001b[0m, in \u001b[0;36mMultimodaDataset.__getitem__\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m le \u001b[38;5;129;01min\u001b[39;00m sources[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlerobot\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 123\u001b[0m repo_id, idx, chunk_size \u001b[38;5;241m=\u001b[39m le\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 124\u001b[0m items \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m 125\u001b[0m sources \u001b[38;5;241m=\u001b[39m build_interleaved_prompt(\n\u001b[1;32m 126\u001b[0m items,\n\u001b[1;32m 127\u001b[0m sources,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 130\u001b[0m sample_actions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_actions,\n\u001b[1;32m 131\u001b[0m )\n\u001b[1;32m 132\u001b[0m transformed_source \u001b[38;5;241m=\u001b[39m sources\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/EO-1/eo/data/multim_dataset.py:143\u001b[0m, in \u001b[0;36mMultimodaDataset.__get_metadata\u001b[0;34m(self, repo_id, idx)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get the metadata from lerobot dataset.\"\"\"\u001b[39;00m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;66;03m# raise NotImplementedError(\"__get_metadata is not implemented\")\u001b[39;00m\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmeta_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetitem_by_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/mnt/shared-storage-user/eorobotics-shared/EO-1/eo/data/lerobot_dataset.py:411\u001b[0m, in \u001b[0;36mMultiLeRobotDataset.getitem_by_id\u001b[0;34m(self, repo_id, idx, chunk_size)\u001b[0m\n\u001b[1;32m 409\u001b[0m dataset_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_repo_ids_index\u001b[38;5;241m.\u001b[39mget(repo_id)\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dataset_idx \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 411\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minvalid dataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. available dataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepo_ids\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 412\u001b[0m lerobot_dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_datasets[dataset_idx]\n\u001b[1;32m 414\u001b[0m delta_indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: invalid dataset: demos25. available dataset: []" + "Loaded 1132 samples from ../demo_data/demos25/interleaved_demo.jsonl\n", + "len(interleaved_dataset)=1132, len(interleaved_dataset.data)=1132, len(interleaved_dataset.meta_dataset)=44492\n" ] } ], "source": [ - "multim_dataset = MultimodaDataset(\n", + "interleaved_dataset = MultimodaDataset(\n", " data_configs = [\n", " MMDatasetConfig(\n", - " json_path=\"../demo_data/interleaved_demo.jsonl\"\n", + " json_path=\"../demo_data/demos25/interleaved_demo.jsonl\"\n", " ),\n", " ],\n", " meta_dataset = lerobot_dataset\n", ")\n", "\n", - "multim_dataset[0]" + "print(f\"{len(interleaved_dataset)=}, {len(interleaved_dataset.data)=}, {len(interleaved_dataset.meta_dataset)=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "217253b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"lerobot\": [\n", + " \"demos25 0 0\",\n", + " \"demos25 299 0\",\n", + " \"demos25 414 0\",\n", + " \"demos25 619 0\"\n", + " ],\n", + " \"conversations\": [\n", + " {\n", + " \"from\": \"human\",\n", + " \"value\": \"Can retrieve the sunflower seeds from the table with the right arm be done now?\"\n", + " },\n", + " {\n", + " \"from\": \"gpt\",\n", + " \"value\": \"no\"\n", + " },\n", + " {\n", + " \"from\": \"human\",\n", + " \"value\": \"What should be the next step to move toward packing in the supermarket?\"\n", + " },\n", + " {\n", + " \"from\": \"gpt\",\n", + " \"value\": \"grab the packaged ham sausage on the table with the right arm\"\n", + " },\n", + " {\n", + " \"from\": \"human\",\n", + " \"value\": \"Can you outline the contents of the current scene?\"\n", + " },\n", + " {\n", + " \"from\": \"gpt\",\n", + " \"value\": \"The robotic arm holds a green snack bag above a red shopping bag. Various items, including a yellow box and a white tube, are arranged on the table nearby.\"\n", + " },\n", + " {\n", + " \"from\": \"human\",\n", + " \"value\": \"With these actions completed: 0-grasp the left arm with red canvas tote bag, 1-grab the packaged ham sausage on the table with the right arm, 2-place the grasped packaged ham sausage into red canvas tote bag, and your target being packing in the supermarket, what are the subsequent activities to complete the task?\"\n", + " },\n", + " {\n", + " \"from\": \"gpt\",\n", + " \"value\": \"3-grasp the hair conditioner on the table with the right arm, 4-place the grasped hair conditioner into red canvas tote bag, 5-retrieve the sunflower seeds from the table with the right arm, 6-place the grasped sunflower seeds into red canvas tote bag, 7-grab the sweet bean sauce on the table with the right arm, 8-place the grasped sweet bean sauce into red canvas tote bag with the right arm\"\n", + " }\n", + " ],\n", + " \"view\": [\n", + " [\n", + " \"observation.images.head\"\n", + " ],\n", + " [\n", + " \"observation.images.head\"\n", + " ],\n", + " [\n", + " \"observation.images.head\"\n", + " ],\n", + " [\n", + " \"observation.images.head\"\n", + " ]\n", + " ],\n", + " \"vision_base_idx\": 0\n", + "}\n", + "multimodal data dict_keys(['lerobot', 'conversations', 'view', 'vision_base_idx'])\n", + "interleaved data dict_keys(['conversations', 'action', 'state', 'image', 'action_is_pad'])\n" + ] + } + ], + "source": [ + "import pprint\n", + "import json\n", + "\n", + "print(json.dumps(interleaved_dataset.data[0], indent=2, ensure_ascii=False))\n", + "\n", + "print(\"multimodal data\", interleaved_dataset.data[0].keys())\n", + "print(\"interleaved data\", interleaved_dataset[0].keys())" ] } ], diff --git a/getting_started/2_train_finetune.ipynb b/getting_started/2_train_finetune.ipynb index 6185061..5162637 100644 --- a/getting_started/2_train_finetune.ipynb +++ b/getting_started/2_train_finetune.ipynb @@ -1,12 +1,260 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "id": "b6b00319", + "cell_type": "markdown", + "id": "119d23fd", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "# Train and Finetune\n", + "This section shows how to train and finetune EO-1 on libero and custom dataset. Detailed scripts can be found in [../experiments/2_libero](../experiments/2_libero/train.sh)." + ] + }, + { + "cell_type": "markdown", + "id": "70246600", + "metadata": {}, + "source": [ + "## 1. Download Libero Dataset and Qwen2.5-VL-3B-Instruct\n", + "\n", + "Before running the following code, please download the libero dataset from the [libero-benchmark-dataset](https://huggingface.co/collections/IPEC-COMMUNITY/libero-benchmark-dataset-684837af28d465aa8b043950) and Qwen2.5-VL-3B-Instruct model from the [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) with huggingface-cli.\n", + "\n", + "```bash\n", + "# Install Hugging Face CLI if not already installed\n", + "pip install huggingface-cli\n", + "huggingface-cli login\n", + "\n", + "# Download libero dataset\n", + "datasets=(\n", + " libero_spatial_no_noops_1.0.0_lerobot\n", + " libero_object_no_noops_1.0.0_lerobot\n", + " libero_90_no_noops_lerobot\n", + " libero_10_no_noops_1.0.0_lerobot\n", + ")\n", + "\n", + "HF_LEROBOT_HOME=YOUR_PATH_TO_DATASET\n", + "\n", + "for dataset in ${datasets[@]};\n", + "do\n", + " echo \"Downloading ${dataset}...\"\n", + " huggingface-cli download \\\n", + " --repo-type dataset --resume-download --local-dir-use-symlinks False \\\n", + " IPEC-COMMUNITY/${dataset} \\\n", + " --local-dir ${HF_LEROBOT_HOME}/${dataset}\n", + "done\n", + "```\n", + "\n", + "Download the Qwen2.5-VL-3B-Instruct model from the [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) with huggingface-cli.\n", + "\n", + "```bash\n", + "huggingface-cli download \\\n", + " --resume-download --local-dir-use-symlinks False \\\n", + " Qwen/Qwen2.5-VL-3B-Instruct \\\n", + " --local-dir ../pretrained/Qwen2.5-VL-3B-Instruct\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "c387a2df", + "metadata": {}, + "source": [ + "## 2. Finetune on Libero Dataset\n", + "\n", + "Set the dataset config in `experiments/2_libero/data-libero.yaml` according to the metadata `info.json` in the dataset:\n", + "\n", + "```yaml\n", + "lerobot_datasets:\n", + " - repo_id: libero_spatial_no_noops_1.0.0_lerobot\n", + " root: HF_LEROBOT_HOME\n", + " select_video_keys: [observation.images.image, observation.images.wrist_image]\n", + " select_state_keys: [observation.state]\n", + " select_action_keys: [action]\n", + "\n", + " - repo_id: libero_90_no_noops_lerobot\n", + " root: HF_LEROBOT_HOME\n", + " select_video_keys: [observation.images.image, observation.images.wrist_image]\n", + " select_state_keys: [observation.state]\n", + " select_action_keys: [action]\n", + "\n", + " - repo_id: libero_object_no_noops_1.0.0_lerobot\n", + " root: HF_LEROBOT_HOME\n", + " select_video_keys: [observation.images.image, observation.images.wrist_image]\n", + " select_state_keys: [observation.state]\n", + " select_action_keys: [action]\n", + "\n", + " - repo_id: libero_10_no_noops_1.0.0_lerobot\n", + " root: HF_LEROBOT_HOME\n", + " # automatically load all features if not specified\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6d886bd0", + "metadata": {}, + "source": [ + "Start training with the following command, and the model will be saved in `./outputs/libero_train`.\n", + "\n", + "```bash\n", + "accelerate launch $ACCELERATE_ARGS scripts/train.py \\\n", + " --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \\\n", + " --data-path experiments/2_libero/data-libero.yaml \\\n", + " --chunk-size 8 \\\n", + " --dataloader-num-workers 8 \\\n", + " --bf16 True \\\n", + " --tf32 True \\\n", + " --fp16 False \\\n", + " --num-train-epochs 50 \\\n", + " --per-device-train-batch-size 256 \\\n", + " --learning-rate 1e-4 \\\n", + " --merger-lr 1e-4 \\\n", + " --vision-lr 2e-5 \\\n", + " --weight-decay 0.1 \\\n", + " --warmup-ratio 0.03 \\\n", + " --lr-scheduler-type cosine \\\n", + " --gradient-checkpointing True \\\n", + " --save-strategy steps \\\n", + " --logging-steps 100 \\\n", + " --save-steps 5000 \\\n", + " --save-total-limit 3 \\\n", + " --report-to none \\\n", + " --run-name libero_train \\\n", + " --attn-implementation flash_attention_2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a8ab8bfe", + "metadata": {}, + "source": [ + "## 3 Visualize the Trained Model\n", + "\n", + "Use the following command to visualize the trained model, where [../tools/openloop.py](../tools/openloop.py) read a lerobot dataset and visualize the inference action trajectory with the trained model.\n", + "\n", + "```bash\n", + "python tools/openloop.py \\\n", + " --repo-id libero_spatial_no_noops_1.0.0_lerobot \\\n", + " --root HF_LEROBOT_HOME \\\n", + " --model_path ./outputs/libero_train/path/to/checkpoint\n", + "```\n", + "\n", + "The script will visualize the inference action trajectory. With the following result:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "69841ee7", + "metadata": {}, + "source": [ + "## 4 Finetune on Custom Dataset\n", + "\n", + "To fine-tune **EO-1** on your own embodiment, you only need to adapt the configuration file. Specifically, convert your dataset into the LeRobot format, then define the fields that describe where your videos, states, and actions are located.\n", + "\n", + "### 4.1 Dataset Conversion with Any4LeRobot\n", + "\n", + "[Any4LeRobot](https://github.com/Tavish9/any4lerobot) is a comprehensive tool collection for LeRobot that provides data conversion scripts, preprocessing tools, and training workflow helpers. Supported Input Formats\n", + "\n", + "- **Custom Video + State + Action**: Convert from custom data structures\n", + "- **RLDS**: Convert from RLDS (Reinforcement Learning Datasets) format\n", + "- **RoboSet**: Convert from RoboSet format\n", + "- **Custom JSON**: Convert from custom JSON configurations\n", + "\n", + "Please refer to the [Any4LeRobot](https://github.com/Tavish9/any4lerobot), clone the repo, and select the corresponding format to convert your dataset.\n", + "\n", + "### 4.2 Dataset Configuration\n", + "\n", + "Once your dataset is converted to LeRobot format, create a configuration file (e.g., `custom_dataset.yaml`):\n", + "\n", + "```yaml\n", + "# @multimodal data config\n", + "# leave empty if only robot control data\n", + "mm_datasets:\n", + "\n", + "lerobot_datasets:\n", + " - repo_id: your_custom_dataset_name # replace with your dataset name\n", + " root: ./your_dataset_path/ # replace with your dataset root path\n", + " select_video_keys: [\n", + " observation.images.image, # replace with your video feature keys\n", + " observation.images.wrist_image,\n", + " ]\n", + " select_state_keys: [observation.state] # replace with your state keys\n", + " select_action_keys: [action] # replace with your action keys\n", + " # Optional fields:\n", + " episodes: [1, 2, 3] # specific episodes to load (None = all)\n", + " train_subtask: mix:0.9 # mix sub-task instructions and overall instructions\n", + " delta_action: false # train with delta actions\n", + " state_mode: \"MEAN_STD\" # state normalization mode\n", + " effector_indices: [14, 15] # indices of effector channels\n", + " weight: 1.0 # dataset weight for sampling\n", + "\n", + " # Add more datasets if needed\n", + " - repo_id: another_dataset\n", + " root: ./another_dataset_path/\n", + " # If not specified, uses all keys by default\n", + "```\n", + "\n", + "### 4.3 Training Configuration\n", + "\n", + "Create a training script (e.g., `train_custom.sh`) based on the Libero training script:\n", + "\n", + "```bash\n", + "#!/bin/bash\n", + "\n", + "# Set your custom dataset path\n", + "CUSTOM_DATA_PATH=\"experiments/custom/data-custom.yaml\"\n", + "OUTPUT_DIR=\"./outputs/custom_train\"\n", + "\n", + "# Training hyperparameters\n", + "ACCELERATE_ARGS=\"--config_file accelerate_config.yaml\"\n", + "VLM_PATH=\"../pretrained/Qwen2.5-VL-3B-Instruct\"\n", + "\n", + "# Launch training\n", + "accelerate launch $ACCELERATE_ARGS scripts/train.py \\\n", + " --vlm-name-or-path $VLM_PATH \\\n", + " --data-path $CUSTOM_DATA_PATH \\\n", + " --chunk-size 8 \\\n", + " --dataloader-num-workers 8 \\\n", + " --bf16 True \\\n", + " --tf32 True \\\n", + " --fp16 False \\\n", + " --num-train-epochs 50 \\\n", + " --per-device-train-batch-size 256 \\\n", + " --learning-rate 1e-4 \\\n", + " --merger-lr 1e-4 \\\n", + " --vision-lr 2e-5 \\\n", + " --weight-decay 0.1 \\\n", + " --warmup-ratio 0.03 \\\n", + " --lr-scheduler-type cosine \\\n", + " --gradient-checkpointing True \\\n", + " --save-strategy steps \\\n", + " --logging-steps 100 \\\n", + " --save-steps 5000 \\\n", + " --save-total-limit 3 \\\n", + " --report-to none \\\n", + " --run-name custom_train \\\n", + " --attn-implementation flash_attention_2 \\\n", + " --output-dir $OUTPUT_DIR\n", + "```\n", + "\n", + "### 4.6 Tips for Custom Datasets\n", + "\n", + "1. **Data Quality**: Ensure your dataset has consistent video frame rates and action frequencies\n", + "2. **Feature Keys**: Verify that your `select_video_keys`, `select_state_keys`, and `select_action_keys` match your dataset's metadata\n", + "3. **Episode Selection**: Use the `episodes` field to select specific episodes for training/testing\n", + "4. **State Normalization**: Choose appropriate `state_mode` (MEAN_STD, MIN_MAX, or NONE) based on your data distribution\n", + "5. **Memory Management**: Adjust `chunk_size` and `per-device-train-batch-size` based on your GPU memory\n", + "\n", + "### 4.7 Troubleshooting\n", + "\n", + "- **Data Loading Issues**: Check that your dataset follows LeRobot format and paths are correct\n", + "- **Memory Errors**: Reduce batch size or chunk size\n", + "- **Training Instability**: Adjust learning rates or add gradient clipping\n", + "- **Poor Performance**: Verify data quality and feature selection\n" + ] } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index b549f60..7d52c72 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "datasets>=2.19.0,<=3.6.0", "huggingface-hub[hf-transfer,cli]>=0.34.2", "lerobot>=0.3.3", + "transformers==4.56.0", # Core dependencies "cmake>=3.29.0.1", diff --git a/scripts/eval_policy.py b/scripts/eval_policy.py index 3a2aca1..01192d5 100644 --- a/scripts/eval_policy.py +++ b/scripts/eval_policy.py @@ -8,7 +8,7 @@ argparser.add_argument( "--model_path", type=str, - default="experiments/outputs/your_path", + default="outputs/your_path", help="Path to the pretrained model", ) argparser.add_argument( @@ -36,12 +36,10 @@ def eval_policy(): "task": ["put the object in the box."], "repo_id": [args.repo_id], } - ov_output = processor.select_action( model, batch, ) - print(ov_output) diff --git a/tools/test_hf_model.py b/scripts/test_vlm.py similarity index 84% rename from tools/test_hf_model.py rename to scripts/test_vlm.py index d6bb120..f92f6a8 100644 --- a/tools/test_hf_model.py +++ b/scripts/test_vlm.py @@ -1,17 +1,14 @@ -from transformers import AutoProcessor - -from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration +from transformers import AutoProcessor, AutoModel """set model name or path""" model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B -model = Qwen2_5_VLForConditionalGeneration.from_pretrained( +model = AutoModel.from_pretrained( model_name_or_path, - device_map="auto", - trust_remote_code=True, + device_map="auto" # attn_implementation="flash_attention_2", ) -processor = AutoProcessor.from_pretrained(model_name_or_path, trust_remote_code=True) +processor = AutoProcessor.from_pretrained(model_name_or_path) messages = [ { diff --git a/scripts/train.py b/scripts/train.py index 544e62d..9b40164 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -14,7 +14,7 @@ from eo.data.dataset import make_supervised_data_module from eo.model.modeling_eo1 import EO1VisionFlowMatchingModel -from eo.model.processor import OneVisionProcessor +from eo.model.processing_eo1 import OneVisionProcessor from eo.train.pipeline_config import TrainPipelineConfig from eo.train.train_utils import ( aggregate_dataset_length, @@ -148,7 +148,9 @@ def train(): model.config.use_cache = True trainer.save_state() - safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir) + safe_save_model_for_hf_trainer( + trainer=trainer, output_dir=f"{training_args.output_dir}/checkpoint-final-{trainer.state.global_step}" + ) if __name__ == "__main__": diff --git a/tools/openloop.py b/tools/openloop.py index e69de29..1b90dfa 100644 --- a/tools/openloop.py +++ b/tools/openloop.py @@ -0,0 +1,119 @@ +import argparse + +import matplotlib.pyplot as plt +import numpy as np +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata +from lerobot.policies.normalize import Unnormalize +from PIL import Image +from tqdm import tqdm +from transformers import AutoModel, AutoProcessor + +from eo.data.lerobot_dataset import LeRobotDataset + +argparser = argparse.ArgumentParser() +argparser.add_argument("--repo_id", type=str, default="libero_spatial_no_noops_1.0.0_lerobot", help="repo id") +argparser.add_argument("--root", type=str, default="./demo_data", help="root path") +argparser.add_argument( + "--model_path", + type=str, + default="outputs/2025-09-13/17-50-05-data-libero_ck8_gpu8_lr1e-4_vlr2e-5_mlr1e-4_bs256", + help="model path", +) +argparser.add_argument("--num_step", type=int, default=10, help="model path") +argparser.add_argument("--train_subtask", type=bool, default=False, help="model path") +argparser.add_argument("--delta_action", type=bool, default=False, help="delta action") +args = argparser.parse_args() + +num_step = args.num_step + +# load models and set keys +processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True) +model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True, dtype=torch.bfloat16).eval().cuda() +action_horizon = processor.robot_config.get("action_chunk_size", 50) + +select_video_keys = processor.select_video_keys[args.repo_id] +select_state_keys = processor.select_state_keys[args.repo_id] +select_action_keys = processor.select_action_keys[args.repo_id] +state_mode = processor.robot_config["state_mode"] + +# load dataset +meta = LeRobotDatasetMetadata(args.repo_id, root=f"{args.root}/{args.repo_id}") +dataset = LeRobotDataset( + args.repo_id, + root=f"{args.root}/{args.repo_id}", + delta_timestamps={ + k: [i / meta.fps for i in range(action_horizon)] + for k in map(lambda x: x, select_action_keys) # noqa: C417 + }, + state_mode=state_mode, + train_subtask=args.train_subtask, + select_action_keys=select_action_keys, + delta_action=args.delta_action, + effector_indices=[14, 15], +) + +# helper functions +fn = lambda x: Image.fromarray((x.permute(1, 2, 0) * 255).numpy().astype(np.uint8)) # noqa: E731 +unnormalizer = Unnormalize(dataset.normalizer.features, dataset.normalizer.norm_map, dataset.normalizer.stats) +actions = [] +actions_data = [] + +for i in tqdm(range(num_step)): + data = dataset[action_horizon * i] + # model + data = unnormalizer(data) + batch = { + **{k: [fn(data[k])] for k in select_video_keys}, + **{k: [data[k]] for k in select_state_keys}, + "task": [data["task"]], + "repo_id": [args.repo_id], + } + selected_actions = processor.select_action(model, batch).action.squeeze(0).cpu().numpy() + # raw + actions_data += [ + torch.cat( + [data[k].unsqueeze(-1) if data[k].ndim == 1 else data[k] for k in select_action_keys], dim=1 + ).numpy() + ] + + if args.delta_action: + selected_states = [] + for k in select_action_keys: + state_key = k.replace("action", "observation.state") + selected_states.append( + data[state_key].unsqueeze(0) if data[state_key].ndim == 1 else data[state_key] + ) + selected_states = torch.cat(selected_states, dim=1).numpy() + + accumulated_actions = np.cumsum(selected_actions, axis=0) + exec_actions = selected_states + accumulated_actions + exec_actions[..., -2:] = selected_actions[..., -2:] + else: + exec_actions = selected_actions + + actions += [exec_actions] + + +actions = np.concatenate(actions, axis=0) +actions_data = np.concatenate(actions_data, axis=0) + +# plot actions +fig, axs = plt.subplots(actions.shape[-1], 1, figsize=(12 * 4, 3 * 4 * actions.shape[-1])) +for i in range(actions.shape[-1]): + axs[i].plot(range(num_step * action_horizon), actions_data[:, i], color="tab:green") # , linestyle="--") + axs[i].plot(range(num_step * action_horizon), actions[:, i], color="tab:red") # , linestyle=":") + +fig.suptitle(f"{args.model_path}", fontsize=16) +fig.legend( + labels=["Dataset Action", "Model Action"], + loc="center", + ncol=2, + bbox_to_anchor=(0.5, -0.05), + frameon=False, +) +step = args.model_path.split("/")[-1].split("-")[-1] + +# save visualization +plt.savefig(f"{args.model_path}/openloop_{step}.png", dpi=100, bbox_inches="tight") +print(f"save to {args.model_path}/openloop_{step}.png") From b241ddd6798c357bf847a3d6dd0b78c451414b64 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sun, 14 Sep 2025 12:35:11 +0800 Subject: [PATCH 06/13] Refactor import order in test_vlm.py for improved readability and consistency. --- scripts/test_vlm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/test_vlm.py b/scripts/test_vlm.py index f92f6a8..6d30448 100644 --- a/scripts/test_vlm.py +++ b/scripts/test_vlm.py @@ -1,10 +1,10 @@ -from transformers import AutoProcessor, AutoModel +from transformers import AutoModel, AutoProcessor """set model name or path""" model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B model = AutoModel.from_pretrained( model_name_or_path, - device_map="auto" + device_map="auto", # attn_implementation="flash_attention_2", ) From 27d1222ef34e41282288742390ba6aa4ac0aca57 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sun, 14 Sep 2025 12:43:37 +0800 Subject: [PATCH 07/13] Update pre-commit configuration to exclude processing_eo1.py from bandit checks for improved security analysis. --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3c42fb4..1656b9e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -87,7 +87,7 @@ repos: - id: bandit args: ["-c", "pyproject.toml"] additional_dependencies: ["bandit[toml]"] - exclude: ^eo/constants\.py$|eo/model/processor\.py$ + exclude: ^eo/constants\.py$|eo/model/processing_eo1\.py$ # TODO(Steven): Uncomment when ready to use ##### Static Analysis & Typing ##### From 7e46c9019e42861254f157cfb6ab77e94ec8f6bf Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sat, 20 Sep 2025 12:46:02 +0800 Subject: [PATCH 08/13] Refactor EO1 configuration and processing classes for improved structure and functionality. Updated EO1VisionFlowMatchingConfig to inherit from PretrainedConfig, streamlined initialization, and added keys_to_ignore_at_inference. Enhanced EO1VisionProcessor to support new text processing capabilities and improved handling of robot inputs and outputs. Adjusted class names for consistency and clarity. --- eo/model/configuration_eo1.py | 65 +++++++------- eo/model/modeling_qwen2_5_vl.py | 30 +++---- eo/model/processing_eo1.py | 151 ++++++++++++++++++-------------- 3 files changed, 128 insertions(+), 118 deletions(-) diff --git a/eo/model/configuration_eo1.py b/eo/model/configuration_eo1.py index a59825e..ad09082 100644 --- a/eo/model/configuration_eo1.py +++ b/eo/model/configuration_eo1.py @@ -12,39 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. +from transformers.configuration_utils import PretrainedConfig from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import ( - Qwen2_5_VLConfig, Qwen2_5_VLTextConfig, Qwen2_5_VLVisionConfig, ) -class EO1VisionVLTextConfig(Qwen2_5_VLTextConfig): - def __init__( - self, - state_token_id=None, - action_token_start_id=None, - action_token_id=None, - action_pass_id=None, - vision_token_start_id=None, - image_token_id=None, - video_token_id=None, - **kwargs, - ): - super().__init__(**kwargs) - self.state_token_id = state_token_id - self.action_token_start_id = action_token_start_id - self.action_token_id = action_token_id - self.action_pass_id = action_pass_id - - self.vision_token_start_id = vision_token_start_id - self.image_token_id = image_token_id - self.video_token_id = video_token_id - - -class EO1VisionFlowMatchingConfig(Qwen2_5_VLConfig): - model_type = "onevision_fm" - sub_configs = {"vision_config": Qwen2_5_VLVisionConfig, "text_config": EO1VisionVLTextConfig} +class EO1VisionFlowMatchingConfig(PretrainedConfig): + model_type = "eo1" + sub_configs = {"vision_config": Qwen2_5_VLVisionConfig, "text_config": Qwen2_5_VLTextConfig} + keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, @@ -52,26 +30,43 @@ def __init__( vision_config=None, image_token_id=151655, video_token_id=151656, - # flow matching specific action_chunk_size=50, max_action_dim=32, num_denoise_steps=10, action_act="linear", num_action_layers=2, + state_token_id=151670, + action_token_id=151666, + action_pass_id=151667, **kwargs, ): - super().__init__( - text_config=text_config, - vision_config=vision_config, - image_token_id=image_token_id, - video_token_id=video_token_id, - **kwargs, - ) + if isinstance(vision_config, dict): + self.vision_config = self.sub_configs["vision_config"](**vision_config) + elif vision_config is None: + self.vision_config = self.sub_configs["vision_config"]( + hidden_size=1280, + out_hidden_size=2048, + tokens_per_second=2, + ) + + if isinstance(text_config, dict): + self.text_config = self.sub_configs["text_config"](**text_config) + elif text_config is None: + self.text_config = self.sub_configs["text_config"](**kwargs) + + self.image_token_id = image_token_id + self.video_token_id = video_token_id + self.state_token_id = state_token_id + self.action_token_id = action_token_id + self.action_pass_id = action_pass_id + self.action_chunk_size = action_chunk_size self.max_action_dim = max_action_dim self.num_denoise_steps = num_denoise_steps self.action_act = action_act self.num_action_layers = num_action_layers + super().__init__(**kwargs) + EO1VisionFlowMatchingConfig.register_for_auto_class() diff --git a/eo/model/modeling_qwen2_5_vl.py b/eo/model/modeling_qwen2_5_vl.py index c3928b5..9223b72 100644 --- a/eo/model/modeling_qwen2_5_vl.py +++ b/eo/model/modeling_qwen2_5_vl.py @@ -24,14 +24,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -from dataclasses import dataclass -from typing import Any, Optional, Union from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional import torch import torch.nn as nn import torch.nn.functional as F # noqa: N812 -from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLTextConfig, Qwen2_5_VLVisionConfig from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin @@ -47,8 +46,13 @@ from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel -from transformers.processing_utils import Unpack from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm +from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import ( + Qwen2_5_VLConfig, + Qwen2_5_VLTextConfig, + Qwen2_5_VLVisionConfig, +) +from transformers.processing_utils import Unpack from transformers.utils import ( TransformersKwargs, auto_docstring, @@ -665,9 +669,8 @@ def forward( output_attentions: bool = False, use_cache: bool = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None | ( - tuple[torch.Tensor, torch.Tensor] - ) = None, # necessary, but kept here for BC + position_embeddings: None + | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: bsz, q_len, _ = hidden_states.size() @@ -744,9 +747,8 @@ def forward( output_attentions: bool | None = False, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None | ( - tuple[torch.Tensor, torch.Tensor] - ) = None, # necessary, but kept here for BC + position_embeddings: None + | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: """ @@ -1340,7 +1342,6 @@ def forward( second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. ```""" - output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) @@ -1452,7 +1453,6 @@ def prepare_inputs_for_generation( **kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model - model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, @@ -1475,7 +1475,7 @@ def prepare_inputs_for_generation( # When compiling, we can't check tensor values thus we check only input length # It is safe to assume that `length!=1` means we're in pre-fill because compiled # models currently cannot do asssisted decoding - if cache_position[0] == 0 or self.model.rope_deltas is None: + if cache_position[0] == 0 or self.rope_deltas is None: vision_positions, rope_deltas = self.get_rope_index( model_inputs.get("input_ids", None), image_grid_thw=image_grid_thw, @@ -1483,11 +1483,11 @@ def prepare_inputs_for_generation( second_per_grid_ts=second_per_grid_ts, attention_mask=attention_mask, ) - self.model.rope_deltas = rope_deltas + self.rope_deltas = rope_deltas # then use the prev pre-calculated rope-deltas to get the correct position ids elif "position_ids" in model_inputs: position_ids = model_inputs["position_ids"][None, ...] - delta = self.model.rope_deltas + delta = self.rope_deltas delta = delta.repeat_interleave(position_ids.shape[1] // delta.shape[0], dim=0) vision_positions = position_ids + delta.expand_as(position_ids) vision_positions = vision_positions.expand(3, vision_positions.shape[1], -1) diff --git a/eo/model/processing_eo1.py b/eo/model/processing_eo1.py index f10c07a..185cef7 100644 --- a/eo/model/processing_eo1.py +++ b/eo/model/processing_eo1.py @@ -12,16 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import TypedDict, Union +from typing import Union import numpy as np import torch from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.constants import OBS_STATE from lerobot.datasets.utils import cast_stats_to_numpy from lerobot.policies.normalize import Normalize, Unnormalize from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput -from transformers.processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs +from transformers.processing_utils import ( + ImagesKwargs, + ProcessingKwargs, + ProcessorMixin, + TextKwargs, + Unpack, + VideosKwargs, +) from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from transformers.video_utils import VideoInput @@ -42,14 +50,13 @@ TASK_VLA_TOKEN = "<|vla|>" RobotInput = Union[np.ndarray, "torch.Tensor", list[np.ndarray], list["torch.Tensor"]] -RobotIDInput = Union[str, list[str]] -class OneVisionVideosProcessorKwargs(VideosKwargs, total=False): +class EO1VisionVideosProcessorKwargs(VideosKwargs, total=False): fps: list[float] | float -class OneVisionImagesKwargs(ImagesKwargs): +class EO1VisionImagesKwargs(ImagesKwargs): min_pixels: int | None max_pixels: int | None patch_size: int | None @@ -57,25 +64,25 @@ class OneVisionImagesKwargs(ImagesKwargs): merge_size: int | None -class OneVisionRobotKwargs(TypedDict, total=False): - repo_id: str | None +class EO1VisionTextKwargs(TextKwargs): + noise_token_num: int | None + noise_prompt: str | None -class OneVisionProcessorKwargs(ProcessingKwargs, total=False): - images_kwargs: OneVisionImagesKwargs - videos_kwargs: OneVisionVideosProcessorKwargs - robot_kwargs: OneVisionRobotKwargs +class EO1VisionProcessorKwargs(ProcessingKwargs, total=False): + text_kwargs: EO1VisionTextKwargs + images_kwargs: EO1VisionImagesKwargs + videos_kwargs: EO1VisionVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, - "robot_kwargs": {"repo_id": None}, } -class OneVisionProcessor(ProcessorMixin): - """EOneVision Processor for Image, Text, Video, and Robotic Action Processing""" +class EO1VisionProcessor(ProcessorMixin): + """EEO1Vision Processor for Image, Text, Video, and Robotic Action Processing""" attributes = ["image_processor", "tokenizer", "video_processor"] valid_kwargs = ["chat_template"] @@ -92,22 +99,14 @@ def __init__( robot_config=None, **kwargs, ): - self.image_token = ( - DEFAULT_IMAGE_TOKEN if not hasattr(tokenizer, "image_token") else tokenizer.image_token - ) - self.video_token = ( - DEFAULT_VIDEO_TOKEN if not hasattr(tokenizer, "video_token") else tokenizer.video_token - ) - self.action_token = ( - DEFAULT_ACTION_TOKEN if not hasattr(tokenizer, "action_token") else tokenizer.action_token - ) - self.state_token = ( - DEFAULT_STATE_TOKEN if not hasattr(tokenizer, "state_token") else tokenizer.state_token - ) + self.image_token = getattr(tokenizer, "image_token", DEFAULT_IMAGE_TOKEN) + self.video_token = getattr(tokenizer, "video_token", DEFAULT_VIDEO_TOKEN) + self.action_token = getattr(tokenizer, "action_token", DEFAULT_ACTION_TOKEN) + self.state_token = getattr(tokenizer, "state_token", DEFAULT_STATE_TOKEN) # robot policy self.action_token_id = tokenizer.convert_tokens_to_ids(DEFAULT_ACTION_TOKEN) or 151666 - self.action_pass_id = tokenizer.convert_tokens_to_ids(PASS_ACTION_TOKEN) or 151672 + self.action_pass_id = tokenizer.convert_tokens_to_ids(PASS_ACTION_TOKEN) or 151667 self.robot_config = robot_config or {} self.set_normalization(self.robot_config) @@ -119,15 +118,14 @@ def set_normalization(self, robot_config: dict): robot_config.get("stats"), robot_config.get("state_mode"), ) - if features is None or stats is None or state_mode is None: + if None in [features, stats, state_mode]: return else: normalization_mapping = { "STATE": NormalizationMode(state_mode), "ACTION": NormalizationMode(state_mode), } - self.robot_config = dict(robot_config) - self.normalize_inputs, self.unnormalize_outputs = {}, {} + normalize_inputs, unnormalize_outputs = {}, {} for repo_id, fea in features.items(): stat = cast_stats_to_numpy(stats[repo_id]) fea = dataset_to_policy_features(fea) @@ -135,12 +133,11 @@ def set_normalization(self, robot_config: dict): input_features = {k: v for k, v in fea.items() if v.type == FeatureType.STATE} output_features = {k: v for k, v in fea.items() if v.type == FeatureType.ACTION} - self.normalize_inputs[repo_id] = Normalize(input_features, normalization_mapping, stat) - self.unnormalize_outputs[repo_id] = Unnormalize(output_features, normalization_mapping, stat) + normalize_inputs[repo_id] = Normalize(input_features, normalization_mapping, stat) + unnormalize_outputs[repo_id] = Unnormalize(output_features, normalization_mapping, stat) - self.select_video_keys = robot_config.get("select_video_keys") - self.select_state_keys = robot_config.get("select_state_keys") - self.select_action_keys = robot_config.get("select_action_keys") + self.robot_config = dict(robot_config) + self.normalize_inputs, self.unnormalize_outputs = normalize_inputs, unnormalize_outputs def __call__( self, @@ -149,14 +146,17 @@ def __call__( videos: VideoInput = None, states: RobotInput = None, actions: RobotInput = None, - **kwargs: Unpack[OneVisionProcessorKwargs], + **kwargs: Unpack[EO1VisionProcessorKwargs], ) -> BatchFeature: output_kwargs = self._merge_kwargs( - OneVisionProcessorKwargs, + EO1VisionProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) + noise_token_num = output_kwargs["text_kwargs"].pop("noise_token_num", None) + output_kwargs["text_kwargs"].pop("noise_prompt", None) + image_inputs = videos_inputs = {} if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) @@ -213,17 +213,16 @@ def __call__( text[i] = text[i].replace("<|placeholder|>", self.video_token) # noise tokens - denoise_length = self.robot_config.get("action_chunk_size", 50) + noise_token_num = noise_token_num or self.robot_config.get("action_chunk_size") for i in range(len(text)): while self.action_token in text[i]: text[i] = text[i].replace( self.action_token, - "<|placeholder|>" * denoise_length, + "<|placeholder|>" * noise_token_num, 1, ) text[i] = text[i].replace("<|placeholder|>", self.action_token) - # state tokens return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) if return_mm_token_type_ids: @@ -234,10 +233,11 @@ def __call__( # robot inputs robot_inputs = {} + if states is not None: if isinstance(states, list): states = torch.stack(states, dim=0) - if states.ndim == 2: + if states.ndim == 1: states = states.unsqueeze(0) robot_inputs.update({"states": states}) @@ -257,22 +257,31 @@ def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) - return names_from_processor + ["second_per_grid_ts"] + ["actions"] + return names_from_processor + ["second_per_grid_ts"] + ["states", "actions"] @torch.no_grad - def select_action(self, model, batch: dict, **kwargs): - # normalize batch + def _prepare_robot_inputs(self, batch: dict): + """Prepare model inputs from raw robot batch""" batch_messages = [] batch_states = [] max_state_dim = self.robot_config.get("max_state_dim", 32) - # normalize robot inputs - for i, repo_id in enumerate(batch["repo_id"]): + state_keys = [x for x in batch.keys() if x.startswith(OBS_STATE)] + batch_size = len(batch[state_keys[0]]) + + if "repo_id" in batch: + repo_ids = batch.pop("repo_id") + else: + print("no repo_id found, use the first one in normalize_inputs") + repo_ids = list(self.normalize_inputs.keys())[0] + repo_ids = [repo_ids] * batch_size if isinstance(repo_ids, str) else repo_ids + + for i, repo_id in enumerate(repo_ids): mini_batch = {k: v[i] for k, v in batch.items()} normalize_inputs = self.normalize_inputs[repo_id] - select_video_keys = self.select_video_keys[repo_id] - select_state_keys = self.select_state_keys[repo_id] + select_video_keys = self.robot_config["select_video_keys"][repo_id] + select_state_keys = self.robot_config["select_state_keys"][repo_id] for k in normalize_inputs.features: if not isinstance(mini_batch[k], torch.Tensor): @@ -286,32 +295,20 @@ def select_action(self, model, batch: dict, **kwargs): "role": "user", "content": [ *({"type": "image", "image": mini_batch[k]} for k in select_video_keys), - {"type": "state", "state": states}, - {"type": "text", "text": f"{mini_batch['task']}{TASK_VLA_TOKEN}"}, # add task token + {"type": "state", "state": []}, # chat template state token + {"type": "text", "text": f"{mini_batch['task']}{TASK_VLA_TOKEN}"}, ], } ] batch_messages += [messages] + return repo_ids, batch_messages, batch_states - noise_prompt = f"{ACTION_START_TOKEN}{DEFAULT_ACTION_TOKEN}{ACTION_END_TOKEN}" - inputs = self.apply_chat_template( - batch_messages, - states=batch_states, - add_generation_prompt=True, - add_noise_prompt=noise_prompt, - tokenize=True, - return_dict=True, - return_tensors="pt", - ).to(model.device) - - outputs = model.generate(**inputs, max_new_tokens=128, return_dict_in_generate=True) - actions = outputs.actions.cpu() - - # unnormalize actions + def _process_robot_outputs(self, repo_ids: list[str], actions: torch.Tensor): + """Process model outputs back to robot format""" output_actions = [] - for i, repo_id in enumerate(batch["repo_id"]): + for i, repo_id in enumerate(repo_ids): unnormalize_outputs = self.unnormalize_outputs[repo_id] - select_action_keys = self.select_action_keys[repo_id] + select_action_keys = self.robot_config["select_action_keys"][repo_id] features = unnormalize_outputs.features cum_dims = [0] + np.cumsum([features[k].shape[0] for k in select_action_keys]).tolist() origin_action = torch.tensor(actions[i], dtype=torch.float32)[..., : cum_dims[-1]] @@ -323,6 +320,24 @@ def select_action(self, model, batch: dict, **kwargs): output_actions.append(unnorm_actions) output_actions = torch.stack(output_actions, dim=0) + @torch.no_grad + def select_action(self, model, batch: dict, **kwargs): + repo_ids, batch_messages, batch_states = self._prepare_robot_inputs(batch) + + noise_prompt = f"{ACTION_START_TOKEN}{DEFAULT_ACTION_TOKEN}{ACTION_END_TOKEN}" + inputs = self.apply_chat_template( + batch_messages, + states=batch_states, + add_generation_prompt=True, + noise_prompt=noise_prompt, + tokenize=True, + return_dict=True, + return_tensors="pt", + ).to(model.device) + + outputs = model.generate(**inputs, max_new_tokens=128, return_dict_in_generate=True) + actions = outputs.actions.cpu() + output_actions = self._process_robot_outputs(repo_ids, actions) return BatchFeature({"action": output_actions}) @@ -367,4 +382,4 @@ def pad_vector(vector, new_dim=32): return new_vector -OneVisionProcessor.register_for_auto_class() +EO1VisionProcessor.register_for_auto_class() From d2ef83bf15d88cdeea8e3b505a3a01fe695171c4 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sat, 20 Sep 2025 12:46:56 +0800 Subject: [PATCH 09/13] Update .gitignore to exclude hf_save_pretrained.py and enhance README with integration details for EO-1 with LERobot. Refactor dataset handling in MultimodaLeRobotDataset and adjust model architecture in EO1VisionFlowMatchingModel for improved functionality. Update training utilities for better configuration management and streamline processor methods for action selection. --- .gitignore | 2 + README.md | 3 +- eo/data/dataset.py | 5 +- eo/data/lerobot_dataset.py | 1 - eo/model/modeling_eo1.py | 439 +++++++++--------- eo/model/processing_eo1.py | 12 +- eo/train/train_utils.py | 56 +-- eo/train/trainer.py | 6 +- .../3_simpler/simpler_env/eo/eo_model.py | 2 +- experiments/8_vllmeval/README.md | 2 +- experiments/8_vllmeval/vlm/model.py | 2 +- scripts/chat_template.json | 2 +- scripts/eval_policy.py | 2 +- scripts/test_vlm.py | 7 +- scripts/train.py | 37 +- tools/openloop.py | 15 +- 16 files changed, 304 insertions(+), 289 deletions(-) diff --git a/.gitignore b/.gitignore index ae1b171..7384385 100644 --- a/.gitignore +++ b/.gitignore @@ -237,3 +237,5 @@ demo_data/demos25 demo_data/libero_spatial_no_noops_1.0.0_lerobot experiments/test + +tools/hf_save_pretrained.py diff --git a/README.md b/README.md index 012facc..d6a8eb5 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ pip install --upgrade setuptools # install flash-attn 2 MAX_JOBS=4 pip install flash-attn==2.8.3 --no-build-isolation -# [recommended] install from source with H100 / H800 GPU, CUDA 12.8 for best performance +# [recommended] ⭐️ install flash-attn 3 from source with H100 / H800 GPU, CUDA 12.8 for best performance # git clone https://github.com/Dao-AILab/flash-attn.git -b v2.8.3 --recursive --depth 1 # cd hopper && python setup.py install @@ -308,6 +308,7 @@ Robot Control Benchmark Results ## 📅 Roadmap - [x] 🤖 Release [EO-1](https://huggingface.co/IPEC-COMMUNITY/EO-1-3B) pretraining, finetune scripts, and documentations. +- [x] Integrate into [LERobot](https://github.com/huggingface/lerobot). We have merged the [PR](https://github.com/huggingface/lerobot/pull/1971) into the main branch. You can now use EO-1 with LERobot without any modifications. - [ ] 🤗 Release [pre-training models](https://huggingface.co/collections/IPEC-COMMUNITY/eo-robotics-68ac4ff30e1f746cac28ca14), Interleaved Dataset `EO-Data1.5M` and benchmark `EO-Bench`. - [ ] ⚡️ Efficient LLM Inference over Long Sequences, Efficient KV-cache, etc. - [ ] 🤖 Integrate with human feedback fine-tuning. diff --git a/eo/data/dataset.py b/eo/data/dataset.py index 9ab3951..a750c64 100644 --- a/eo/data/dataset.py +++ b/eo/data/dataset.py @@ -88,7 +88,7 @@ def __init__( if len(data_configs.mm_datasets) > 0: mm_dataset = MultimodaDataset( data_configs=data_configs.mm_datasets, - max_packed_length=args.max_packed_length, + # max_packed_length=args.max_packed_length, max_action_dim=args.max_action_dim, meta_dataset=lerobot_dataset, chunk_size=args.chunk_size, @@ -327,6 +327,9 @@ def __getitem__(self, i) -> dict[str, torch.Tensor]: def info_qwen_vision_fetch(self): from qwen_vl_utils import smart_resize + if not self.lerobot_dataset: + return + print(f"qwen2.5 vl min pixel {self.args.image_min_pixels}, max pixel {self.args.image_max_pixels}") for dataset in self.lerobot_dataset._datasets: meta_features, video_key = dataset.meta.features, dataset.select_video_keys diff --git a/eo/data/lerobot_dataset.py b/eo/data/lerobot_dataset.py index d59bb69..a9fe541 100644 --- a/eo/data/lerobot_dataset.py +++ b/eo/data/lerobot_dataset.py @@ -77,7 +77,6 @@ def __init__( delta_action: bool = False, effector_indices: list[int] | None = None, weight: float | None = None, - chunk_size: int = 32, ): super().__init__( repo_id=repo_id, diff --git a/eo/model/modeling_eo1.py b/eo/model/modeling_eo1.py index 4fc12d5..062d144 100644 --- a/eo/model/modeling_eo1.py +++ b/eo/model/modeling_eo1.py @@ -21,13 +21,10 @@ import torch.nn.functional as F # noqa: N812 from torch import Tensor from transformers.activations import ACT2FN +from transformers.generation import GenerationMixin from transformers.modeling_outputs import ModelOutput -from transformers.processing_utils import Unpack -from transformers.utils import ( - TransformersKwargs, - is_torchdynamo_compiling, - logging, -) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging from .configuration_eo1 import EO1VisionFlowMatchingConfig from .modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration @@ -58,42 +55,19 @@ def create_sinusoidal_pos_embedding( return pos_emb -def sample_beta(alpha, beta, bsize, device): - gamma1 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / alpha) - gamma2 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / beta) - return gamma1 / (gamma1 + gamma2) - - -def sample_noise(shape, device): - noise = torch.normal( - mean=0.0, - std=1.0, - size=shape, - dtype=torch.float32, - device=device, - ) - return noise - - -def sample_time(bsize, device): - time_beta = sample_beta(1.5, 1.0, bsize, device) - time = time_beta * 0.999 + 0.001 - return time.to(dtype=torch.float32, device=device) - - @dataclass class EO1VisionFlowMatchingOutputWithPast(ModelOutput): loss: torch.FloatTensor | None = None fm_loss: torch.FloatTensor | None = None ar_loss: torch.FloatTensor | None = None - actions: torch.FloatTensor | None = None + actions: torch.FloatTensor | None = None logits: torch.FloatTensor | None = None + past_key_values: list[torch.FloatTensor] | None = None hidden_states: tuple[torch.FloatTensor] | None = None attentions: tuple[torch.FloatTensor] | None = None rope_deltas: torch.LongTensor | None = None - meta_states: torch.FloatTensor | None = None class EO1VisionActionProjector(torch.nn.Sequential): @@ -124,46 +98,65 @@ def dtype(self): return self[0].weight.dtype -class EO1VisionFlowMatchingModel(Qwen2_5_VLForConditionalGeneration): +class EO1VisionFlowMatchingModel(PreTrainedModel, GenerationMixin): config_class = EO1VisionFlowMatchingConfig + supports_gradient_checkpointing = True + + _supports_flash_attn = True + _supports_sdpa = True + _supports_attention_backend = True + _can_compile_fullgraph = True + _skip_keys_device_placement = "past_key_values" def __init__( self, config: EO1VisionFlowMatchingConfig, - build_projector: bool = True, + vlm_backbone: Qwen2_5_VLForConditionalGeneration = None, ): super().__init__(config) - if build_projector: - self.build_projector() - def build_projector(self, dtype=None, device=None): - device = device or self.device - dtype = dtype or self.dtype hidden_size = self.config.text_config.hidden_size max_action_dim = self.config.max_action_dim - self.state_proj = nn.Linear(max_action_dim, hidden_size).to(dtype=dtype, device=device) - self.action_in_proj = nn.Linear(max_action_dim, hidden_size).to(dtype=dtype, device=device) + self.vlm_backbone = vlm_backbone or Qwen2_5_VLForConditionalGeneration(self.config) + self.state_proj = nn.Linear(max_action_dim, hidden_size) + self.action_in_proj = nn.Linear(max_action_dim, hidden_size) self.action_out_proj = EO1VisionActionProjector( hidden_size, max_action_dim, self.config.num_action_layers, self.config.action_act, - dtype=dtype, + ) + self.action_time_mlp_in = nn.Linear(hidden_size * 2, hidden_size) + self.action_time_mlp_out = nn.Linear(hidden_size, hidden_size) + + self.post_init() + self.to_float32_flow_matching_head() + + def get_input_embeddings(self): + return self.vlm_backbone.get_input_embeddings() + + def to_float32_flow_matching_head(self): + self.action_out_proj = self.action_out_proj.to(dtype=torch.float32) + self.action_time_mlp_in = self.action_time_mlp_in.to(dtype=torch.float32) + self.action_time_mlp_out = self.action_time_mlp_out.to(dtype=torch.float32) + self.state_proj = self.state_proj.to(dtype=torch.float32) + self.action_in_proj = self.action_in_proj.to(dtype=torch.float32) + + def sample_noise(self, shape, device): + noise = torch.normal( + mean=0.0, + std=1.0, + size=shape, + dtype=torch.float32, device=device, ) - self.action_time_mlp_in = nn.Linear(hidden_size * 2, hidden_size).to(dtype=dtype, device=device) - self.action_time_mlp_out = nn.Linear(hidden_size, hidden_size).to(dtype=dtype, device=device) + return noise - def _has_action_gen_seq( - self, - input_ids: torch.LongTensor = None, - ): - """Check if the input_ids has action generation sequence.""" - if input_ids is None: - return False, None - action_token_id = self.config.text_config.action_token_id - mask = input_ids == action_token_id - return mask.any() + def sample_time(self, bsize, device): + beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0) + time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32) + time = time_beta * 0.999 + 0.001 + return time def replace_special_embeddings( self, @@ -188,51 +181,32 @@ def replace_special_embeddings( inputs_embeds = inputs_embeds.masked_scatter(special_mask, special_features) return inputs_embeds, None - def forward( + def embed_prefix( self, - input_ids: torch.LongTensor | None = None, - attention_mask: torch.Tensor | None = None, - position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | None = None, + input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor | None = None, - labels: torch.LongTensor | None = None, - use_cache: bool | None = None, - output_attentions: bool | None = None, - output_hidden_states: bool | None = None, pixel_values: torch.Tensor | None = None, pixel_values_videos: torch.FloatTensor | None = None, image_grid_thw: torch.LongTensor | None = None, video_grid_thw: torch.LongTensor | None = None, - rope_deltas: torch.LongTensor | None = None, - cache_position: torch.LongTensor | None = None, - second_per_grid_ts: torch.Tensor | None = None, - logits_to_keep: int | torch.Tensor = 0, states: torch.Tensor | None = None, - actions: torch.Tensor | None = None, - action_is_pad: torch.Tensor | None = None, - **kwargs: Unpack[TransformersKwargs], - ) -> tuple | EO1VisionFlowMatchingOutputWithPast: - output_attentions = ( - output_attentions if output_attentions is not None else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) + ) -> tuple[torch.FloatTensor, torch.Tensor, torch.Tensor]: + """Embed the suffix""" if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) if pixel_values is not None: - image_embeds = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = self.vlm_backbone.get_image_features(pixel_values, image_grid_thw) image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) - image_mask, _ = self.get_placeholder_mask( + image_mask, _ = self.vlm_backbone.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds ) inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) if pixel_values_videos is not None: - video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) + video_embeds = self.vlm_backbone.get_video_features(pixel_values_videos, video_grid_thw) video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) - _, video_mask = self.get_placeholder_mask( + _, video_mask = self.vlm_backbone.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds ) inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) @@ -241,47 +215,85 @@ def forward( states = states.type(self.state_proj.weight.dtype) state_embs = self.state_proj(states) inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, state_embs, self.config.text_config.state_token_id + input_ids, inputs_embeds, state_embs, self.config.state_token_id ) + return inputs_embeds + + def embed_suffix( + self, + timestep: torch.Tensor, + noisy_actions: torch.Tensor, + ) -> torch.FloatTensor: + """Embed the suffix""" + time_embs = create_sinusoidal_pos_embedding( + timestep, + self.config.text_config.hidden_size, + device=noisy_actions.device, + ) + time_embs = time_embs.type(noisy_actions.dtype) + noisy_actions = noisy_actions.type(self.action_in_proj.weight.dtype) + action_embs = self.action_in_proj(noisy_actions) + time_embs = time_embs[:, None, :].expand_as(action_embs) + + action_time_embs = torch.cat([action_embs, time_embs], dim=2) + action_time_embs = self.action_time_mlp_in(action_time_embs) + action_time_embs = F.silu(action_time_embs) + action_time_embs = self.action_time_mlp_out(action_time_embs) + return action_time_embs + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + pixel_values: torch.Tensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + cache_position: torch.LongTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + states: torch.Tensor | None = None, + actions: torch.Tensor | None = None, + action_is_pad: torch.Tensor | None = None, + **kwargs, + ) -> EO1VisionFlowMatchingOutputWithPast: + """multi-modal forward pass, including image, video, state, action, and language.""" + + inputs_embeds = self.embed_prefix( + input_ids, + inputs_embeds, + pixel_values, + pixel_values_videos, + image_grid_thw, + video_grid_thw, + states, + ) if actions is not None: - noise_mask = input_ids == self.config.text_config.action_token_id - pass_mask = input_ids == self.config.text_config.action_pass_id + noise_mask = input_ids == self.config.action_token_id + pass_mask = input_ids == self.config.action_pass_id mask = noise_mask | pass_mask # (b s) pass_mask_in_action = pass_mask[mask] # (n, ) pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1) - time = sample_time(actions.shape[0], inputs_embeds.device) # (n,) + time = self.sample_time(actions.shape[0], inputs_embeds.device) # (n,) time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1) time_expanded[pass_mask_in_action] = 0.0 - noise = sample_noise(actions.shape, inputs_embeds.device) + noise = self.sample_noise(actions.shape, inputs_embeds.device) x_t = time_expanded * noise + (1 - time_expanded) * actions u_t = noise - actions - time_embs = create_sinusoidal_pos_embedding( - time, - self.config.text_config.hidden_size, - device=inputs_embeds.device, - ) - time_embs = time_embs.type(inputs_embeds.dtype) - - x_t = x_t.type(self.action_in_proj.weight.dtype) - action_embs = self.action_in_proj(x_t) - time_embs = time_embs[:, None, :].expand_as(action_embs) - - action_time_embs = torch.cat([action_embs, time_embs], dim=2) - action_time_embs = self.action_time_mlp_in(action_time_embs) - action_time_embs = F.silu(action_time_embs) - action_time_embs = self.action_time_mlp_out(action_time_embs) - - num_actions = mask.sum().item() - num_action_features = action_time_embs.shape[0] * action_time_embs.shape[1] - assert num_actions == num_action_features, ( - f"action features and tokens do not match: {num_actions=}, {num_action_features=}" - ) - + action_time_embs = self.embed_suffix(time, x_t) mask_unsqueezed = mask.unsqueeze(-1) mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) action_mask = mask_expanded.to(inputs_embeds.device) @@ -289,67 +301,62 @@ def forward( action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs) - if attention_mask is not None: - attention_mask = attention_mask.to(inputs_embeds.device) + if attention_mask is not None: + attention_mask = attention_mask.to(inputs_embeds.device) if position_ids is None: - prefill_compiled_stage = is_torchdynamo_compiling() and ( - (input_ids is not None and input_ids.shape[1] != 1) - or (inputs_embeds is not None and inputs_embeds.shape[1] != 1) + prefill_noncompiled_stage = (cache_position is not None and cache_position[0] == 0) or ( + past_key_values is None or past_key_values.get_seq_length() == 0 ) - prefill_noncompiled_stage = not is_torchdynamo_compiling() and ( - (cache_position is not None and cache_position[0] == 0) - or (past_key_values is None or past_key_values.get_seq_length() == 0) - ) - if (prefill_compiled_stage or prefill_noncompiled_stage) or self.rope_deltas is None: - position_ids, rope_deltas = self.get_rope_index( + if prefill_noncompiled_stage or self.vlm_backbone.rope_deltas is None: + position_ids, rope_deltas = self.vlm_backbone.get_rope_index( input_ids, image_grid_thw, video_grid_thw, second_per_grid_ts=second_per_grid_ts, attention_mask=attention_mask, ) - self.rope_deltas = rope_deltas + self.vlm_backbone.rope_deltas = rope_deltas else: batch_size, seq_length, _ = inputs_embeds.shape position_ids = torch.arange(seq_length, device=inputs_embeds.device) position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1) if cache_position is not None: - delta = (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) + delta = (cache_position[0] + self.vlm_backbone.rope_deltas).to(inputs_embeds.device) else: delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device) delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1) position_ids += delta.to(position_ids.device) - model_kwargs = { - "position_ids": position_ids, - "attention_mask": attention_mask, - "past_key_values": past_key_values, - "inputs_embeds": inputs_embeds, - "use_cache": False if self.training else use_cache, - "output_attentions": output_attentions, - "output_hidden_states": output_hidden_states, - "return_dict": True, - "cache_position": cache_position, - } - - _actions = None - if not (self.training or states is None) and actions is None and self._has_action_gen_seq(input_ids): - # action sampling - _actions, outputs = self._sample_actions(input_ids=input_ids, **model_kwargs) - logits = torch.zeros( - inputs_embeds.shape[0], 1, self.config.text_config.vocab_size, device=inputs_embeds.device + # generation + output_actions = None + if not (self.training or states is None): + output_actions, outputs = self.sample_actions( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + cache_position=cache_position, ) - logits[..., self.config.text_config.eos_token_id] = 33.8125 # <|im_end|> else: - # text generation - outputs = self.model(**model_kwargs) - hidden_states = outputs[0] - # only compute necessary logits, do not upcast to float if not computing loss - slice_indices = ( - slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + outputs = self.vlm_backbone.model( + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + cache_position=cache_position, ) - logits = self.lm_head(hidden_states[:, slice_indices, :]) + + hidden_states = outputs[0] + + # only compute necessary logits, do not upcast to float if not computing loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.vlm_backbone.lm_head(hidden_states[:, slice_indices, :]) loss = None fm_loss = None @@ -369,12 +376,13 @@ def forward( in_denoise_bound = (~pass_mask_in_action).reshape(-1, 1) losses = losses * in_denoise_bound + fm_loss = losses.mean() loss = fm_loss ar_loss = None if labels is not None: - ar_loss = self.loss_function( + ar_loss = self.vlm_backbone.loss_function( logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs ) loss = loss + ar_loss if loss is not None else ar_loss @@ -383,108 +391,105 @@ def forward( loss=loss, fm_loss=fm_loss, ar_loss=ar_loss, - actions=_actions, + actions=output_actions, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, - rope_deltas=self.rope_deltas, + rope_deltas=self.vlm_backbone.rope_deltas, ) @torch.no_grad() - def _sample_actions( + def sample_actions( self, input_ids: torch.LongTensor | None = None, position_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: torch.FloatTensor | None = None, - use_cache: bool | None = None, - output_attentions: bool | None = None, - output_hidden_states: bool | None = None, - return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, + pixel_values: torch.Tensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + states: torch.Tensor | None = None, + **kwargs, ) -> Tensor: - """Sample actions from the model, break down into 3 steps to make a unified generation interface: - input_ids: - <|im_start|>user<|vision_start|><|image_pad|>...<|vision_end|><|state_start|><|state_pad|><|state_end|>task...<|vla|><|im_end|> -> AR kvcache - <|im_start|>assistant<|action_start|><|action_pad|>...<|action_end|> -> FM denoising - <|im_end|> -> AR - """ - chunksz_eoa = self.config.action_chunk_size + 1 - - # 1. pass the mm prefix to the model, and update kvcache - mm_outputs = self.model( - position_ids=position_ids[..., :-chunksz_eoa], - attention_mask=attention_mask[:, :-chunksz_eoa], + """Sample actions from the model.""" + + # prepare position_ids and kv_cache + if position_ids is None: + position_ids, _ = self.vlm_backbone.get_rope_index( + input_ids, + image_grid_thw=image_grid_thw, + attention_mask=attention_mask, + ) + + # embed prefix + if inputs_embeds is None: + inputs_embeds = self.embed_prefix( + input_ids, + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + states=states, + ) + + # pass prefix, update kvcache + seq_len = input_ids.shape[-1] + suffix_len = -1 # exclude <|action_end|> + prefix_len = seq_len - self.config.action_chunk_size - 1 + + outputs = self.vlm_backbone.model( + position_ids=position_ids[..., :prefix_len], + attention_mask=attention_mask[:, :prefix_len], past_key_values=past_key_values, - inputs_embeds=inputs_embeds[:, :-chunksz_eoa], - use_cache=use_cache, - cache_position=cache_position[:-chunksz_eoa], + inputs_embeds=inputs_embeds[:, :prefix_len], + use_cache=True, + cache_position=cache_position[:-prefix_len] if cache_position is not None else None, ) - # 2. perform denoising steps, with noise q and mm kvcache - device = inputs_embeds.device - x_t = sample_noise( - [ - inputs_embeds.shape[0], - self.config.action_chunk_size, - self.config.max_action_dim, - ], - device, - ) - x_t = x_t.type(self.action_in_proj.weight.dtype) + # denoising + device = states.device + actions_shape = (states.shape[0], self.config.action_chunk_size, self.config.max_action_dim) + noise = self.sample_noise(actions_shape, device) + x_t = noise.type(self.action_in_proj.weight.dtype) dt = torch.tensor(-1.0 / self.config.num_denoise_steps, device=device) time = torch.ones(inputs_embeds.shape[0], device=device) - pass_seq_length = past_key_values.get_seq_length() + past_key_values, past_hidden_state = outputs.past_key_values, outputs.last_hidden_state - action_mask = input_ids == self.config.text_config.action_token_id + action_mask = input_ids == self.config.action_token_id while time >= -dt / 2: - time_embs = create_sinusoidal_pos_embedding( - time, - self.config.text_config.hidden_size, - device=device, - ) - time_embs = time_embs.type(inputs_embeds.dtype) - action_embs = self.action_in_proj(x_t) - time_embs = time_embs[:, None, :].expand_as(action_embs) - - action_time_embs = torch.cat([action_embs, time_embs], dim=2) - action_time_embs = self.action_time_mlp_in(action_time_embs) - action_time_embs = F.silu(action_time_embs) - action_time_embs = self.action_time_mlp_out(action_time_embs) - action_time_embs = action_time_embs.to(device, inputs_embeds.dtype) - inputs_embeds[action_mask] = action_time_embs - - past_key_values.crop(pass_seq_length) - outputs = self.model( - position_ids=position_ids[..., -chunksz_eoa:], - attention_mask=attention_mask, + action_time_embs = self.embed_suffix(time, x_t) + inputs_embeds[action_mask] = action_time_embs.to(inputs_embeds.dtype) + + past_key_values.crop(prefix_len) + + outputs = self.vlm_backbone.model( + position_ids=position_ids[..., prefix_len:suffix_len], + attention_mask=attention_mask[:, :suffix_len], past_key_values=past_key_values, - inputs_embeds=inputs_embeds[:, -chunksz_eoa:], - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - cache_position=cache_position[-chunksz_eoa:], + inputs_embeds=inputs_embeds[:, prefix_len:suffix_len], + use_cache=True, + cache_position=cache_position[prefix_len:suffix_len] if cache_position is not None else None, ) - - hidden_states = outputs[0] - action_time_embs = hidden_states[:, :-1] # exclude + action_time_embs = outputs.last_hidden_state[:, : self.config.action_chunk_size] action_time_embs = action_time_embs.type(self.action_out_proj.dtype) v_t = self.action_out_proj(action_time_embs) - # euler step x_t += dt * v_t.reshape(x_t.shape) time += dt - # 3. get the final EOS token logits + # last step + if time < -dt * 3 / 2: + suffix_len = seq_len - outputs.last_hidden_state = torch.cat( - [mm_outputs.last_hidden_state, outputs.last_hidden_state], dim=1 - ) - return (x_t, outputs) + outputs.last_hidden_state = torch.cat([past_hidden_state, outputs.last_hidden_state], dim=1) + return x_t, outputs + + def prepare_inputs_for_generation(self, *args, **kwargs): + return self.vlm_backbone.prepare_inputs_for_generation(*args, **kwargs) + + def _expand_inputs_for_generation(self, *args, **kwargs): + return self.vlm_backbone._expand_inputs_for_generation(*args, **kwargs) EO1VisionFlowMatchingModel.register_for_auto_class() diff --git a/eo/model/processing_eo1.py b/eo/model/processing_eo1.py index 185cef7..36b41e3 100644 --- a/eo/model/processing_eo1.py +++ b/eo/model/processing_eo1.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os from typing import Union import numpy as np @@ -33,6 +34,8 @@ from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from transformers.video_utils import VideoInput +os.environ["TOKENIZERS_PARALLELISM"] = "0" + """constants""" DEFAULT_IMAGE_TOKEN = "<|image_pad|>" DEFAULT_VIDEO_TOKEN = "<|video_pad|>" @@ -49,6 +52,7 @@ STATE_END_TOKEN = "<|state_end|>" TASK_VLA_TOKEN = "<|vla|>" + RobotInput = Union[np.ndarray, "torch.Tensor", list[np.ndarray], list["torch.Tensor"]] @@ -301,7 +305,7 @@ def _prepare_robot_inputs(self, batch: dict): } ] batch_messages += [messages] - return repo_ids, batch_messages, batch_states + return batch_messages, batch_states, repo_ids def _process_robot_outputs(self, repo_ids: list[str], actions: torch.Tensor): """Process model outputs back to robot format""" @@ -319,10 +323,11 @@ def _process_robot_outputs(self, repo_ids: list[str], actions: torch.Tensor): unnorm_actions = torch.concat([unnorm_actions[k] for k in select_action_keys], -1) output_actions.append(unnorm_actions) output_actions = torch.stack(output_actions, dim=0) + return output_actions @torch.no_grad def select_action(self, model, batch: dict, **kwargs): - repo_ids, batch_messages, batch_states = self._prepare_robot_inputs(batch) + batch_messages, batch_states, repo_ids = self._prepare_robot_inputs(batch) noise_prompt = f"{ACTION_START_TOKEN}{DEFAULT_ACTION_TOKEN}{ACTION_END_TOKEN}" inputs = self.apply_chat_template( @@ -335,8 +340,7 @@ def select_action(self, model, batch: dict, **kwargs): return_tensors="pt", ).to(model.device) - outputs = model.generate(**inputs, max_new_tokens=128, return_dict_in_generate=True) - actions = outputs.actions.cpu() + actions = model.sample_actions(**inputs)[0].cpu() output_actions = self._process_robot_outputs(repo_ids, actions) return BatchFeature({"action": output_actions}) diff --git a/eo/train/train_utils.py b/eo/train/train_utils.py index 07b4d7a..848c57d 100644 --- a/eo/train/train_utils.py +++ b/eo/train/train_utils.py @@ -8,30 +8,34 @@ def set_requires_grad(parameters, requires_grad): + """Set the requires_grad attribute for the parameters.""" for p in parameters: p.requires_grad = requires_grad -def configure_vision_tower(model, training_args, compute_dtype, device): - vision_tower = model.visual +def configure_vision_tower(vlm, training_args, compute_dtype, device): + """Configure the vision tower.""" + vision_tower = vlm.visual vision_tower.to(dtype=compute_dtype, device=device) - vision_model_params = model.visual.parameters() + vision_model_params = vlm.visual.parameters() set_requires_grad(vision_model_params, not training_args.freeze_vision_tower) - merger_params = model.visual.merger.parameters() + merger_params = vlm.visual.merger.parameters() set_requires_grad(merger_params, not training_args.freeze_merger) -def configure_llm(model, training_args): - lm_head = model.lm_head.parameters() +def configure_llm(vlm, training_args): + """Configure the LLM.""" + lm_head = vlm.lm_head.parameters() set_requires_grad(lm_head, not training_args.freeze_llm) - llm_params = model.model.parameters() + llm_params = vlm.model.parameters() set_requires_grad(llm_params, not training_args.freeze_llm) def configure_processor(processor, dataset, training_args): + """Configure the processor.""" if training_args.chat_template: import json @@ -53,8 +57,9 @@ def configure_processor(processor, dataset, training_args): def smart_tokenizer_and_embedding_resize( processor: transformers.ProcessorMixin, - model: transformers.PreTrainedModel, + vlm: transformers.PreTrainedModel, ): + """Smart tokenizer and embedding resize.""" from eo.constants import ( ACTION_END_TOKEN, ACTION_START_TOKEN, @@ -82,28 +87,7 @@ def smart_tokenizer_and_embedding_resize( ] num_new_tokens = tokenizer.add_tokens(eo1_special_tokens, special_tokens=True) - if num_new_tokens > 0: - model.resize_token_embeddings(len(tokenizer)) - input_embeddings = model.get_input_embeddings().weight.data - output_embeddings = model.get_output_embeddings().weight.data - - input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - - input_embeddings[-num_new_tokens:] = input_embeddings_avg - output_embeddings[-num_new_tokens:] = output_embeddings_avg - new_token_ids = tokenizer.convert_tokens_to_ids(eo1_special_tokens) - logger.warning( - f"New tokens {list(zip(eo1_special_tokens, new_token_ids, strict=False))}", - main_process_only=True, - ) - - def set_token_ids(model, tokenizer, token_dict): - for key, token in token_dict.items(): - token_id = tokenizer.convert_tokens_to_ids(token) - setattr(model.model.config, key, token_id) - setattr(model.config.text_config, key, token_id) - + # NOTE: qwen2.5 vl vocab 151936 > tokenizer 151664 + 8, we don't need to resize embeddings token_dict = { "state_token_id": DEFAULT_STATE_TOKEN, "action_token_start_id": ACTION_START_TOKEN, @@ -114,13 +98,18 @@ def set_token_ids(model, tokenizer, token_dict): "video_token_id": DEFAULT_VIDEO_TOKEN, } - set_token_ids(model, tokenizer, token_dict) - processor.action_token_id = model.model.config.action_token_id - processor.action_pass_id = model.model.config.action_pass_id + for key, token in token_dict.items(): + token_id = tokenizer.convert_tokens_to_ids(token) + setattr(vlm.model.config, key, token_id) + setattr(vlm.config.text_config, key, token_id) + + processor.action_token_id = vlm.model.config.action_token_id + processor.action_pass_id = vlm.model.config.action_pass_id return num_new_tokens def find_target_linear_names(model, num_lora_modules=-1, lora_namespan_exclude=None, verbose=True): + """Find the target linear names for LoRA.""" if lora_namespan_exclude is None: lora_namespan_exclude = [] linear_cls = torch.nn.modules.Linear @@ -156,6 +145,7 @@ def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: st def aggregate_dataset_length(dataset): + """Aggregate the lengths of the dataset, used for dataset packing.""" import bisect from torch.utils.data import DataLoader diff --git a/eo/train/trainer.py b/eo/train/trainer.py index 0a087df..d23a515 100644 --- a/eo/train/trainer.py +++ b/eo/train/trainer.py @@ -36,10 +36,10 @@ def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: return control -class OneVisionTrainer(Trainer): - """Custom Trainer for EOneVision model. +class EO1VisionTrainer(Trainer): + """Custom Trainer for EO1Vision model. This class extends the Trainer class from the transformers library to provide - additional functionality specific to the EOneVision model. + additional functionality specific to the EO1Vision model. It includes methods for creating an optimizer with different learning rates for different parts of the model, and for handling mixed precision training. Args: diff --git a/experiments/3_simpler/simpler_env/eo/eo_model.py b/experiments/3_simpler/simpler_env/eo/eo_model.py index 7b8e314..c42f746 100644 --- a/experiments/3_simpler/simpler_env/eo/eo_model.py +++ b/experiments/3_simpler/simpler_env/eo/eo_model.py @@ -59,7 +59,7 @@ def __init__( saved_model_path, trust_remote_code=True, local_files_only=True, - torch_dtype=torch.bfloat16, + dtype=torch.bfloat16, ) .eval() .cuda() diff --git a/experiments/8_vllmeval/README.md b/experiments/8_vllmeval/README.md index 4f0d2cb..84a2e13 100644 --- a/experiments/8_vllmeval/README.md +++ b/experiments/8_vllmeval/README.md @@ -25,7 +25,7 @@ These benchmarks test the model's ability to: # 1. Download model and datasets bash experiments/8_vllmeval/download_all.sh -# 2. Install VLMEvalKit +# 2. Install VLMEvalKit, TODO: pr to VLMEvalKit to add EO-1-3B model @ Xianqiang Gao git clone https://github.com/DelinQu/VLMEvalKit cd VLMEvalKit pip install -e . diff --git a/experiments/8_vllmeval/vlm/model.py b/experiments/8_vllmeval/vlm/model.py index 9b579ae..e7138a3 100644 --- a/experiments/8_vllmeval/vlm/model.py +++ b/experiments/8_vllmeval/vlm/model.py @@ -303,7 +303,7 @@ def __init__( else: self.model = MODEL_CLS.from_pretrained( model_path, - torch_dtype=torch.bfloat16, + dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, # attn_implementation='flash_attention_2' diff --git a/scripts/chat_template.json b/scripts/chat_template.json index ddbd22e..fbeaeb3 100644 --- a/scripts/chat_template.json +++ b/scripts/chat_template.json @@ -1,3 +1,3 @@ { - "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% set state_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'state' or 'state' in content %}{% set state_count.value = state_count.value + 1 %}<|state_start|><|state_pad|><|state_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}{{ add_noise_prompt }}" + "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% set state_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'state' or 'state' in content %}{% set state_count.value = state_count.value + 1 %}<|state_start|><|state_pad|><|state_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}{{ noise_prompt }}" } diff --git a/scripts/eval_policy.py b/scripts/eval_policy.py index e494895..eb278f1 100644 --- a/scripts/eval_policy.py +++ b/scripts/eval_policy.py @@ -38,7 +38,7 @@ def eval_policy(): "observation.images.wrist_image": [image1], "observation.state": [torch.rand(8)], "task": ["put the object in the box."], - "repo_id": [args.repo_id], + # "repo_id": [args.repo_id], } ov_output = processor.select_action( model, diff --git a/scripts/test_vlm.py b/scripts/test_vlm.py index 6d30448..69b843d 100644 --- a/scripts/test_vlm.py +++ b/scripts/test_vlm.py @@ -1,8 +1,10 @@ -from transformers import AutoModel, AutoProcessor +from transformers import AutoProcessor + +from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration """set model name or path""" model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B -model = AutoModel.from_pretrained( +model = Qwen2_5_VLForConditionalGeneration.from_pretrained( model_name_or_path, device_map="auto", # attn_implementation="flash_attention_2", @@ -32,6 +34,7 @@ if prompt == "q": exit(0) messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]}) + inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to("cuda") diff --git a/scripts/train.py b/scripts/train.py index 9b40164..e550dc1 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -10,11 +10,13 @@ from peft import LoraConfig, get_peft_model except ImportError: pass + from transformers import HfArgumentParser from eo.data.dataset import make_supervised_data_module -from eo.model.modeling_eo1 import EO1VisionFlowMatchingModel -from eo.model.processing_eo1 import OneVisionProcessor +from eo.model.modeling_eo1 import EO1VisionFlowMatchingConfig, EO1VisionFlowMatchingModel +from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration +from eo.model.processing_eo1 import EO1VisionProcessor from eo.train.pipeline_config import TrainPipelineConfig from eo.train.train_utils import ( aggregate_dataset_length, @@ -25,51 +27,54 @@ safe_save_model_for_hf_trainer, smart_tokenizer_and_embedding_resize, ) -from eo.train.trainer import OneVisionTrainer +from eo.train.trainer import EO1VisionTrainer logger = get_logger(__name__, log_level="INFO") def train(): parser = HfArgumentParser(TrainPipelineConfig) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): (training_args,) = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: (training_args,) = parser.parse_args_into_dataclasses() + training_args.output_dir = broadcast_object_list([training_args.output_dir])[0] logger.info(f"set output-dir to {training_args.output_dir}") # configure model - compute_dtype = ( - torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32) - ) + compute_dtype = torch.bfloat16 if training_args.bf16 else torch.float32 + if training_args.model_name_or_path is None: - model = EO1VisionFlowMatchingModel.from_pretrained( + config = EO1VisionFlowMatchingConfig.from_pretrained( training_args.vlm_name_or_path, - build_projector=False, - torch_dtype=compute_dtype, + dtype=compute_dtype, attn_implementation=training_args.attn_implementation, action_act=training_args.action_act, ) - model.build_projector(dtype=torch.float32) + vlm_backbone = Qwen2_5_VLForConditionalGeneration.from_pretrained( + training_args.vlm_name_or_path, dtype=compute_dtype + ) + model = EO1VisionFlowMatchingModel(config, vlm_backbone=vlm_backbone) else: model = EO1VisionFlowMatchingModel.from_pretrained( training_args.model_name_or_path, - torch_dtype=compute_dtype, + dtype=compute_dtype, attn_implementation=training_args.attn_implementation, ) # load processor and resize embeddings - processor = OneVisionProcessor.from_pretrained( + processor = EO1VisionProcessor.from_pretrained( training_args.processor_name_or_path, padding_side="right", use_fast=True, ) - smart_tokenizer_and_embedding_resize(processor, model) + smart_tokenizer_and_embedding_resize(processor, model.vlm_backbone) # configure model - configure_llm(model, training_args) - configure_vision_tower(model, training_args, compute_dtype, training_args.device) + configure_llm(model.vlm_backbone, training_args) + configure_vision_tower(model.vlm_backbone, training_args, compute_dtype, training_args.device) model.config.action_chunk_size = training_args.chunk_size # lora peft tuning @@ -111,7 +116,7 @@ def train(): main_process_only=True, ) - trainer = OneVisionTrainer(model=model, processing_class=processor, args=training_args, **data_module) + trainer = EO1VisionTrainer(model=model, processing_class=processor, args=training_args, **data_module) # aggregate data lengths for packing if training_args.pack_dataset: diff --git a/tools/openloop.py b/tools/openloop.py index 1b90dfa..1ca7561 100644 --- a/tools/openloop.py +++ b/tools/openloop.py @@ -7,9 +7,10 @@ from lerobot.policies.normalize import Unnormalize from PIL import Image from tqdm import tqdm -from transformers import AutoModel, AutoProcessor +from transformers import AutoModel from eo.data.lerobot_dataset import LeRobotDataset +from eo.model.processing_eo1 import EO1VisionProcessor argparser = argparse.ArgumentParser() argparser.add_argument("--repo_id", type=str, default="libero_spatial_no_noops_1.0.0_lerobot", help="repo id") @@ -17,7 +18,7 @@ argparser.add_argument( "--model_path", type=str, - default="outputs/2025-09-13/17-50-05-data-libero_ck8_gpu8_lr1e-4_vlr2e-5_mlr1e-4_bs256", + default="path/to/your/model", help="model path", ) argparser.add_argument("--num_step", type=int, default=10, help="model path") @@ -28,13 +29,15 @@ num_step = args.num_step # load models and set keys -processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True) +# processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True) + +processor = EO1VisionProcessor.from_pretrained(args.model_path, trust_remote_code=True) model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True, dtype=torch.bfloat16).eval().cuda() action_horizon = processor.robot_config.get("action_chunk_size", 50) -select_video_keys = processor.select_video_keys[args.repo_id] -select_state_keys = processor.select_state_keys[args.repo_id] -select_action_keys = processor.select_action_keys[args.repo_id] +select_video_keys = processor.robot_config["select_video_keys"][args.repo_id] +select_state_keys = processor.robot_config["select_state_keys"][args.repo_id] +select_action_keys = processor.robot_config["select_action_keys"][args.repo_id] state_mode = processor.robot_config["state_mode"] # load dataset From 440eb925fd0744e94477a8bd5b254944f7d5701f Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Mon, 22 Sep 2025 09:20:41 +0800 Subject: [PATCH 10/13] Refactor training scripts to remove env.sh sourcing and activate conda environment directly. --- experiments/1_demo/train.sh | 2 +- experiments/2_libero/train.sh | 2 +- .../3_simpler/simpler_env/eval_simpler.sh | 2 +- experiments/3_simpler/train_bridge.sh | 2 +- experiments/3_simpler/train_fractal.sh | 2 +- {scripts => tests}/test_vlm.py | 0 tools/test_hf_model.py | 54 ------------------- 7 files changed, 5 insertions(+), 59 deletions(-) rename {scripts => tests}/test_vlm.py (100%) delete mode 100644 tools/test_hf_model.py diff --git a/experiments/1_demo/train.sh b/experiments/1_demo/train.sh index 86e71b4..162f43f 100644 --- a/experiments/1_demo/train.sh +++ b/experiments/1_demo/train.sh @@ -18,7 +18,7 @@ epoch=50 model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} -. scripts/env.sh + conda activate eo accelerate launch $ACCELERATE_ARGS scripts/train.py \ diff --git a/experiments/2_libero/train.sh b/experiments/2_libero/train.sh index 9df9f3f..96af8b8 100644 --- a/experiments/2_libero/train.sh +++ b/experiments/2_libero/train.sh @@ -18,7 +18,7 @@ epoch=50 model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} -. scripts/env.sh + conda activate eo accelerate launch $ACCELERATE_ARGS scripts/train.py \ diff --git a/experiments/3_simpler/simpler_env/eval_simpler.sh b/experiments/3_simpler/simpler_env/eval_simpler.sh index 5408ed6..778390b 100644 --- a/experiments/3_simpler/simpler_env/eval_simpler.sh +++ b/experiments/3_simpler/simpler_env/eval_simpler.sh @@ -1,4 +1,4 @@ -. scripts/env.sh + dist_tasks=( bridge.sh diff --git a/experiments/3_simpler/train_bridge.sh b/experiments/3_simpler/train_bridge.sh index 8e97e15..9644f1e 100644 --- a/experiments/3_simpler/train_bridge.sh +++ b/experiments/3_simpler/train_bridge.sh @@ -17,7 +17,7 @@ epoch=20 model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} -. scripts/env.sh + conda activate eo accelerate launch $ACCELERATE_ARGS scripts/train.py \ diff --git a/experiments/3_simpler/train_fractal.sh b/experiments/3_simpler/train_fractal.sh index 09b1a84..20963cc 100644 --- a/experiments/3_simpler/train_fractal.sh +++ b/experiments/3_simpler/train_fractal.sh @@ -17,7 +17,7 @@ epoch=10 model_name_or_path= run_name=${dataset_name}_ck${chunk_size}_gpu${GPUS}_lr${lr}_vlr${vlr}_mlr${mlr}_bs${PER_DEVICE_BATCH_SIZE} -. scripts/env.sh + conda activate eo accelerate launch $ACCELERATE_ARGS scripts/train.py \ diff --git a/scripts/test_vlm.py b/tests/test_vlm.py similarity index 100% rename from scripts/test_vlm.py rename to tests/test_vlm.py diff --git a/tools/test_hf_model.py b/tools/test_hf_model.py deleted file mode 100644 index d6bb120..0000000 --- a/tools/test_hf_model.py +++ /dev/null @@ -1,54 +0,0 @@ -from transformers import AutoProcessor - -from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration - -"""set model name or path""" -model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B -model = Qwen2_5_VLForConditionalGeneration.from_pretrained( - model_name_or_path, - device_map="auto", - trust_remote_code=True, - # attn_implementation="flash_attention_2", -) - -processor = AutoProcessor.from_pretrained(model_name_or_path, trust_remote_code=True) - -messages = [ - { - "role": "user", - "content": [ - {"type": "image", "image": "demo_data/refcoco/images/COCO_train2014_000000168643_2.jpg"}, - { - "type": "text", - "text": "If the yellow robot gripper follows the yellow trajectory, what will happen? Choices: A. Robot puts the soda on the wooden steps. B. Robot moves the soda in front of the wooden steps. C. Robot moves the soda to the very top of the wooden steps. D. Robot picks up the soda can and moves it up. Please answer directly with only the letter of the correct option and nothing else.", - }, - ], - }, -] - -times = 0 -past_key_values = None - -while True: - if times > 0: - prompt = input("Enter your prompt: ") - if prompt == "q": - exit(0) - messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]}) - inputs = processor.apply_chat_template( - messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" - ).to("cuda") - - input_length = inputs["input_ids"].shape[1] - outputs = model.generate( - **inputs, max_new_tokens=1024, past_key_values=past_key_values, return_dict_in_generate=True - ) - - past_key_values = outputs.past_key_values - generated_ids = outputs.sequences - - completion = processor.decode(generated_ids[0, input_length:], skip_special_tokens=False) - print(completion) - - messages.append({"role": "assistant", "content": [{"type": "text", "text": completion}]}) - times += 1 From 82096b5f274c31a788d82586cd024d20fcbd7792 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Wed, 24 Sep 2025 12:12:02 +0800 Subject: [PATCH 11/13] Fix unified generation in modeling_eo1.py. Update 'freeze_lm_head' option in TrainPipelineConfig for enhanced training flexibility. Refactor training utilities to align with new configuration settings. --- .gitignore | 1 + eo/model/modeling_eo1.py | 20 ++-- eo/model/modeling_qwen2_5_vl.py | 165 +------------------------------- eo/train/pipeline_config.py | 3 +- eo/train/train_utils.py | 2 +- tests/test_vlm.py | 20 +++- 6 files changed, 37 insertions(+), 174 deletions(-) diff --git a/.gitignore b/.gitignore index 6053e87..e5f472a 100644 --- a/.gitignore +++ b/.gitignore @@ -241,3 +241,4 @@ experiments/test tools/hf_save_pretrained.py tools/hf_save_pretrained.py dev/ +eo/model_dev \ No newline at end of file diff --git a/eo/model/modeling_eo1.py b/eo/model/modeling_eo1.py index 062d144..3ed810b 100644 --- a/eo/model/modeling_eo1.py +++ b/eo/model/modeling_eo1.py @@ -338,6 +338,7 @@ def forward( past_key_values=past_key_values, inputs_embeds=inputs_embeds, cache_position=cache_position, + states=states, ) else: outputs = self.vlm_backbone.model( @@ -434,21 +435,25 @@ def sample_actions( # pass prefix, update kvcache seq_len = input_ids.shape[-1] + chunk_size = self.config.action_chunk_size suffix_len = -1 # exclude <|action_end|> - prefix_len = seq_len - self.config.action_chunk_size - 1 + prefix_len = seq_len - chunk_size - 1 + + cache_seq_len = attention_mask.shape[-1] + cache_prefix_len = cache_seq_len - chunk_size - 1 outputs = self.vlm_backbone.model( position_ids=position_ids[..., :prefix_len], - attention_mask=attention_mask[:, :prefix_len], + attention_mask=attention_mask[:, :cache_prefix_len], past_key_values=past_key_values, inputs_embeds=inputs_embeds[:, :prefix_len], use_cache=True, - cache_position=cache_position[:-prefix_len] if cache_position is not None else None, + cache_position=cache_position[:prefix_len] if cache_position is not None else None, ) # denoising device = states.device - actions_shape = (states.shape[0], self.config.action_chunk_size, self.config.max_action_dim) + actions_shape = (states.shape[0], chunk_size, self.config.max_action_dim) noise = self.sample_noise(actions_shape, device) x_t = noise.type(self.action_in_proj.weight.dtype) @@ -461,7 +466,7 @@ def sample_actions( action_time_embs = self.embed_suffix(time, x_t) inputs_embeds[action_mask] = action_time_embs.to(inputs_embeds.dtype) - past_key_values.crop(prefix_len) + past_key_values.crop(cache_prefix_len) outputs = self.vlm_backbone.model( position_ids=position_ids[..., prefix_len:suffix_len], @@ -471,7 +476,7 @@ def sample_actions( use_cache=True, cache_position=cache_position[prefix_len:suffix_len] if cache_position is not None else None, ) - action_time_embs = outputs.last_hidden_state[:, : self.config.action_chunk_size] + action_time_embs = outputs.last_hidden_state[:, :chunk_size] action_time_embs = action_time_embs.type(self.action_out_proj.dtype) v_t = self.action_out_proj(action_time_embs) @@ -480,8 +485,7 @@ def sample_actions( # last step if time < -dt * 3 / 2: - suffix_len = seq_len - + suffix_len = cache_seq_len outputs.last_hidden_state = torch.cat([past_hidden_state, outputs.last_hidden_state], dim=1) return x_t, outputs diff --git a/eo/model/modeling_qwen2_5_vl.py b/eo/model/modeling_qwen2_5_vl.py index 9223b72..73febd4 100644 --- a/eo/model/modeling_qwen2_5_vl.py +++ b/eo/model/modeling_qwen2_5_vl.py @@ -26,7 +26,7 @@ from collections.abc import Callable from dataclasses import dataclass -from typing import Any, Optional +from typing import Any import torch import torch.nn as nn @@ -34,12 +34,6 @@ from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin -from transformers.generation.utils import ( - GenerateNonBeamOutput, - GenerationConfig, - LogitsProcessorList, - StoppingCriteriaList, -) from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer @@ -1499,9 +1493,10 @@ def prepare_inputs_for_generation( text_positions = model_inputs["position_ids"][None, ...] model_inputs["position_ids"] = torch.cat([text_positions, vision_positions], dim=0) - if cache_position[0] != 0: + if cache_position[0] != 0 and cache_position.shape[-1] == 1: model_inputs["pixel_values"] = None model_inputs["pixel_values_videos"] = None + model_inputs["states"] = None return model_inputs @@ -1651,160 +1646,6 @@ def _expand_dict_for_generation(dict_to_expand): return input_ids, model_kwargs - def _sample( - self, - input_ids: torch.LongTensor, - logits_processor: LogitsProcessorList, - stopping_criteria: StoppingCriteriaList, - generation_config: GenerationConfig, - synced_gpus: bool, - streamer: Optional["BaseStreamer"], # noqa: F821 - **model_kwargs, - ) -> GenerateNonBeamOutput | torch.LongTensor: - pad_token_id = generation_config._pad_token_tensor - output_attentions = generation_config.output_attentions - output_hidden_states = generation_config.output_hidden_states - output_scores = generation_config.output_scores - output_logits = generation_config.output_logits - return_dict_in_generate = generation_config.return_dict_in_generate - has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria) - do_sample = generation_config.do_sample - - # init attention / hidden states / scores tuples - scores = () if (return_dict_in_generate and output_scores) else None - raw_logits = () if (return_dict_in_generate and output_logits) else None - decoder_attentions = () if (return_dict_in_generate and output_attentions) else None - decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None - - # keep track of which sequences are already finished - batch_size, cur_len = input_ids.shape[:2] - this_peer_finished = False - unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device) - model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs) - - model_forward = self.__call__ - compile_forward = self._valid_auto_compile_criteria(model_kwargs, generation_config) - if compile_forward: - import os - - os.environ["TOKENIZERS_PARALLELISM"] = "0" - # If we use FA2 and a static cache, we cannot compile with fullgraph - if self.config._attn_implementation == "flash_attention_2": - # only raise warning if the user passed an explicit compile-config - if ( - generation_config.compile_config is not None - and generation_config.compile_config.fullgraph - ): - logger.warning_once( - "When using Flash Attention 2 and a static cache, you cannot use the option `CompileConfig(fullgraph=True)` as " - "FA2 introduces graph breaks. We overrode the option with `fullgraph=False`." - ) - generation_config.compile_config.fullgraph = False - model_forward = self.get_compiled_call(generation_config.compile_config) - - if generation_config.prefill_chunk_size is not None: - model_kwargs = self._prefill_chunking(input_ids, generation_config, **model_kwargs) - is_prefill = False - else: - is_prefill = True - - while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device): - # prepare model inputs - model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) - - # prepare variable output controls (note: some models won't accept all output controls) - model_inputs.update({"output_attentions": output_attentions} if output_attentions else {}) - model_inputs.update( - {"output_hidden_states": output_hidden_states} if output_hidden_states else {} - ) - - if is_prefill: - outputs = self(**model_inputs, return_dict=True) - is_prefill = False - else: - outputs = model_forward(**model_inputs, return_dict=True) - - # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping - model_kwargs = self._update_model_kwargs_for_generation( - outputs, - model_kwargs, - is_encoder_decoder=self.config.is_encoder_decoder, - ) - if synced_gpus and this_peer_finished: - continue - - # Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration - # (the clone itself is always small) - next_token_logits = outputs.logits[:, -1, :].to( - copy=True, dtype=torch.float32, device=input_ids.device - ) - - # pre-process distribution - next_token_scores = logits_processor(input_ids, next_token_logits) - - # Store scores, attentions and hidden_states when required - if return_dict_in_generate: - if output_scores: - scores += (next_token_scores,) - if output_logits: - raw_logits += (next_token_logits,) - if output_attentions: - decoder_attentions += (outputs.attentions,) - if output_hidden_states: - decoder_hidden_states += (outputs.hidden_states,) - actions = outputs.get("actions", None) - - # token selection - if do_sample: - probs = nn.functional.softmax(next_token_scores, dim=-1) - # TODO (joao): this OP throws "skipping cudagraphs due to ['incompatible ops']", find solution - next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) - else: - next_tokens = torch.argmax(next_token_scores, dim=-1) - - # finished sentences should have their next token be a padding token - if has_eos_stopping_criteria: - next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences) - - # update generated ids, model inputs, and length for next step - input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) - if streamer is not None: - streamer.put(next_tokens.cpu()) - - unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores) - this_peer_finished = unfinished_sequences.max() == 0 - cur_len += 1 - - del outputs - - if streamer is not None: - streamer.end() - - if return_dict_in_generate: - return GenerateDecoderOnlyOutput( - sequences=input_ids, - scores=scores, - logits=raw_logits, - attentions=decoder_attentions, - hidden_states=decoder_hidden_states, - past_key_values=model_kwargs.get("past_key_values"), - actions=actions, - ) - else: - return input_ids - - -# custom model output -@dataclass -class GenerateDecoderOnlyOutput(ModelOutput): - sequences: torch.LongTensor - scores: tuple[torch.FloatTensor] | None = None - logits: tuple[torch.FloatTensor] | None = None - attentions: tuple[tuple[torch.FloatTensor]] | None = None - hidden_states: tuple[tuple[torch.FloatTensor]] | None = None - past_key_values: tuple[tuple[tuple[torch.FloatTensor]]] | None = None - actions: torch.FloatTensor | None = None - __all__ = [ "Qwen2_5_VLForConditionalGeneration", diff --git a/eo/train/pipeline_config.py b/eo/train/pipeline_config.py index badccc2..4fad576 100644 --- a/eo/train/pipeline_config.py +++ b/eo/train/pipeline_config.py @@ -64,6 +64,7 @@ class TrainPipelineConfig(TrainingArguments): freeze_vision_tower: bool = field(default=False) freeze_llm: bool = field(default=False) freeze_merger: bool = field(default=False) + freeze_lm_head: bool = field(default=False) attn_implementation: str = field(default="sdpa") # sdpa, flash_attention_2, flash_attention_3 lora_enable: bool = False @@ -97,7 +98,7 @@ def __post_init__(self): self.freeze_llm = True warnings.warn("`freeze_llm` is set to True when `lora_enable`.", stacklevel=2) - if not self.lora_enable: + if not self.lora_enable and self.vision_lora: self.vision_lora = False warnings.warn("`vision_lora` is set to False when `lora_enable` is False.", stacklevel=2) diff --git a/eo/train/train_utils.py b/eo/train/train_utils.py index 848c57d..dc31e2e 100644 --- a/eo/train/train_utils.py +++ b/eo/train/train_utils.py @@ -28,7 +28,7 @@ def configure_vision_tower(vlm, training_args, compute_dtype, device): def configure_llm(vlm, training_args): """Configure the LLM.""" lm_head = vlm.lm_head.parameters() - set_requires_grad(lm_head, not training_args.freeze_llm) + set_requires_grad(lm_head, not training_args.freeze_lm_head) llm_params = vlm.model.parameters() set_requires_grad(llm_params, not training_args.freeze_llm) diff --git a/tests/test_vlm.py b/tests/test_vlm.py index 69b843d..df43ede 100644 --- a/tests/test_vlm.py +++ b/tests/test_vlm.py @@ -27,18 +27,34 @@ times = 0 past_key_values = None +past_pixel_values_n = 0 +past_grid_thw_n = 0 while True: if times > 0: prompt = input("Enter your prompt: ") if prompt == "q": exit(0) - messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]}) - + messages.append( + { + "role": "user", + "content": [ + {"type": "image", "image": "demo_data/refcoco/images/COCO_train2014_000000580957_2.jpg"}, + {"type": "text", "text": prompt}, + ], + } + ) inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to("cuda") + if "pixel_values" in inputs: + inputs["pixel_values"] = inputs["pixel_values"][past_pixel_values_n:] + inputs["image_grid_thw"] = inputs["image_grid_thw"][past_grid_thw_n:] + + past_pixel_values_n += inputs["pixel_values"].shape[0] + past_grid_thw_n += inputs["image_grid_thw"].shape[0] + input_length = inputs["input_ids"].shape[1] outputs = model.generate( **inputs, max_new_tokens=1024, past_key_values=past_key_values, return_dict_in_generate=True From 3594979224f5522e1aaa06164e9c2cf8f10d9a3b Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Wed, 24 Sep 2025 12:13:36 +0800 Subject: [PATCH 12/13] Update .gitignore to include 'eo/model_dev' directory, ensuring proper exclusion of model development files. --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index e5f472a..7ce5191 100644 --- a/.gitignore +++ b/.gitignore @@ -241,4 +241,4 @@ experiments/test tools/hf_save_pretrained.py tools/hf_save_pretrained.py dev/ -eo/model_dev \ No newline at end of file +eo/model_dev From c3fd94f022ae9d720334c55a57ca235d1e9e210e Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Sat, 4 Oct 2025 19:16:37 +0800 Subject: [PATCH 13/13] fix dependence and redirect url. --- README.md | 15 +++++++++------ eo/data/schema.py | 2 +- pyproject.toml | 11 +++++++---- 3 files changed, 17 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index d6a8eb5..5164390 100644 --- a/README.md +++ b/README.md @@ -67,8 +67,8 @@ We introduce **EO-1** model, an open-source unified embodied foundation model co Clone the repository: ```bash -git clone https://github.com/EO-Robotics/EO-1.git -cd EO-1 +git clone https://github.com/EO-Robotics/EO1.git +cd EO1 ``` Create a conda environment and install dependencies: @@ -79,14 +79,13 @@ conda create -n eo python=3.10 conda activate eo pip install --upgrade setuptools -# install flash-attn 2 -MAX_JOBS=4 pip install flash-attn==2.8.3 --no-build-isolation - # [recommended] ⭐️ install flash-attn 3 from source with H100 / H800 GPU, CUDA 12.8 for best performance # git clone https://github.com/Dao-AILab/flash-attn.git -b v2.8.3 --recursive --depth 1 # cd hopper && python setup.py install - pip install -e . + +# install flash-attn 2 +MAX_JOBS=4 pip install flash-attn==2.8.3 --no-build-isolation ``` ## Examples @@ -313,6 +312,10 @@ Robot Control Benchmark Results - [ ] ⚡️ Efficient LLM Inference over Long Sequences, Efficient KV-cache, etc. - [ ] 🤖 Integrate with human feedback fine-tuning. +## Troubleshooting + +1. If you encounter the error `FFmpeg is not properly installed in your environment. We support`, you can install it with `conda install ffmpeg`. + ## 🤝 Contributing We welcome contributions! Please check out CONTRIBUTING.md. Join our community on Discord. diff --git a/eo/data/schema.py b/eo/data/schema.py index f93fc57..2f55ea5 100644 --- a/eo/data/schema.py +++ b/eo/data/schema.py @@ -29,7 +29,7 @@ class MMDatasetConfig: @dataclass class LerobotConfig: repo_id: str - root: str + root: str | None = None episodes: list[int] | None = None delta_action: bool = False state_mode: str = "MEAN_STD" diff --git a/pyproject.toml b/pyproject.toml index 7d52c72..79c18e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,9 +4,9 @@ build-backend = "setuptools.build_meta" [project.urls] homepage = "https://eo-robotics.ai" -documentation = "https://github.com/EO-Robotics/EO-1/getting_started" -source = "https://github.com/EO-Robotics/EO-1" -issues = "https://github.com/EO-Robotics/EO-1/issues" +documentation = "https://github.com/EO-Robotics/EO1/getting_started" +source = "https://github.com/EO-Robotics/EO1" +issues = "https://github.com/EO-Robotics/EO1/issues" discord = "https://discord.com/invite/JqfDs6va" [project] @@ -36,8 +36,9 @@ dependencies = [ # Hugging Face dependencies "datasets>=2.19.0,<=3.6.0", "huggingface-hub[hf-transfer,cli]>=0.34.2", - "lerobot>=0.3.3", + "lerobot>=0.3.3,<=0.3.4", "transformers==4.56.0", + "accelerate>=1.10.1", # Core dependencies "cmake>=3.29.0.1", @@ -56,6 +57,8 @@ dependencies = [ # Binpacking dependency "binpacking>=1.5.0", + "qwen_vl_utils>=0.0.11", + "ujson>=5.11.0", ] # Optional dependencies [project.optional-dependencies]

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EO1VisionFlowMatchingOutputWithPast: output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions @@ -217,74 +218,76 @@ def forward( output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) - if inputs_embeds is None: - inputs_embeds = self.model.embed_tokens(input_ids) - if pixel_values is not None: - pixel_values = pixel_values.type(self.visual.dtype) - image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, image_embeds, self.config.image_token_id - ) + inputs_embeds = self.get_input_embeddings()(input_ids) - if pixel_values_videos is not None: - pixel_values_videos = pixel_values_videos.type(self.visual.dtype) - video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, video_embeds, self.config.video_token_id - ) + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - if states is not None: - states = states.type(self.state_proj.weight.dtype) - state_embs = self.state_proj(states) - inputs_embeds, _ = self.replace_special_embeddings( - input_ids, inputs_embeds, state_embs, self.config.text_config.state_token_id - ) + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) - if actions is not None: - noise_mask = input_ids == self.config.text_config.action_token_id - pass_mask = input_ids == self.config.text_config.action_pass_id - mask = noise_mask | pass_mask # (b s) + if states is not None: + states = states.type(self.state_proj.weight.dtype) + state_embs = self.state_proj(states) + inputs_embeds, _ = self.replace_special_embeddings( + input_ids, inputs_embeds, state_embs, self.config.text_config.state_token_id + ) - pass_mask_in_action = pass_mask[mask] # (n, ) - pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1) + if actions is not None: + noise_mask = input_ids == self.config.text_config.action_token_id + pass_mask = input_ids == self.config.text_config.action_pass_id + mask = noise_mask | pass_mask # (b s) - time = sample_time(actions.shape[0], inputs_embeds.device) # (n,) - time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1) - time_expanded[pass_mask_in_action] = 0.0 + pass_mask_in_action = pass_mask[mask] # (n, ) + pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1) - noise = sample_noise(actions.shape, inputs_embeds.device) - x_t = time_expanded * noise + (1 - time_expanded) * actions - u_t = noise - actions + time = sample_time(actions.shape[0], inputs_embeds.device) # (n,) + time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1) + time_expanded[pass_mask_in_action] = 0.0 - time_embs = create_sinusoidal_pos_embedding( - time, - self.config.text_config.hidden_size, - device=inputs_embeds.device, - ) - time_embs = time_embs.type(inputs_embeds.dtype) + noise = sample_noise(actions.shape, inputs_embeds.device) + x_t = time_expanded * noise + (1 - time_expanded) * actions + u_t = noise - actions + + time_embs = create_sinusoidal_pos_embedding( + time, + self.config.text_config.hidden_size, + device=inputs_embeds.device, + ) + time_embs = time_embs.type(inputs_embeds.dtype) - x_t = x_t.type(self.action_in_proj.weight.dtype) - action_embs = self.action_in_proj(x_t) - time_embs = time_embs[:, None, :].expand_as(action_embs) + x_t = x_t.type(self.action_in_proj.weight.dtype) + action_embs = self.action_in_proj(x_t) + time_embs = time_embs[:, None, :].expand_as(action_embs) - action_time_embs = torch.cat([action_embs, time_embs], dim=2) - action_time_embs = self.action_time_mlp_in(action_time_embs) - action_time_embs = F.silu(action_time_embs) - action_time_embs = self.action_time_mlp_out(action_time_embs) + action_time_embs = torch.cat([action_embs, time_embs], dim=2) + action_time_embs = self.action_time_mlp_in(action_time_embs) + action_time_embs = F.silu(action_time_embs) + action_time_embs = self.action_time_mlp_out(action_time_embs) - num_actions = mask.sum().item() - num_action_features = action_time_embs.shape[0] * action_time_embs.shape[1] - assert num_actions == num_action_features, ( - f"action features and tokens do not match: {num_actions=}, {num_action_features=}" - ) + num_actions = mask.sum().item() + num_action_features = action_time_embs.shape[0] * action_time_embs.shape[1] + assert num_actions == num_action_features, ( + f"action features and tokens do not match: {num_actions=}, {num_action_features=}" + ) - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - action_mask = mask_expanded.to(inputs_embeds.device) + mask_unsqueezed = mask.unsqueeze(-1) + mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) + action_mask = mask_expanded.to(inputs_embeds.device) - action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs) + action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype) + inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs) if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.device) @@ -332,13 +335,21 @@ def forward( _actions = None if not (self.training or states is None) and actions is None and self._has_action_gen_seq(input_ids): + # action sampling _actions, outputs = self._sample_actions(input_ids=input_ids, **model_kwargs) - hidden_states = outputs[0] - logits = self.lm_head(hidden_states[:, -1]) + logits = torch.zeros( + inputs_embeds.shape[0], 1, self.config.text_config.vocab_size, device=inputs_embeds.device + ) + logits[..., self.config.text_config.eos_token_id] = 33.8125 # <|im_end|> else: + # text generation outputs = self.model(**model_kwargs) hidden_states = outputs[0] - logits = self.lm_head(hidden_states) + # only compute necessary logits, do not upcast to float if not computing loss + slice_indices = ( + slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + ) + logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None fm_loss = None @@ -346,9 +357,11 @@ def forward( if actions is not None: action_time_embs = hidden_states[action_mask[..., 0]] action_time_embs = action_time_embs.type(self.action_out_proj.dtype) + v_t = self.action_out_proj(action_time_embs) u_t = u_t.reshape(v_t.shape) v_t = v_t.type(u_t.dtype) + losses = F.mse_loss(u_t, v_t, reduction="none") if action_is_pad is not None: in_episode_bound = (~action_is_pad).reshape(-1, 1) @@ -361,15 +374,9 @@ def forward( ar_loss = None if labels is not None: - logits = logits.float() - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.vocab_size) - shift_labels = shift_labels.view(-1) - # enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - ar_loss = loss_fct(shift_logits, shift_labels) + ar_loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) loss = loss + ar_loss if loss is not None else ar_loss return EO1VisionFlowMatchingOutputWithPast( @@ -398,15 +405,15 @@ def _sample_actions( return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, ) -> Tensor: - """Sample actions from the model, break down into 2 steps to make a unified generation interface: - 1. pass the mm prefix to the model, and update kvcache - 2. perform denoising steps, with noise q and mm kvcache + """Sample actions from the model, break down into 3 steps to make a unified generation interface: input_ids: <|im_start|>user<|vision_start|><|image_pad|>...<|vision_end|><|state_start|><|state_pad|><|state_end|>task...<|vla|><|im_end|> -> AR kvcache <|im_start|>assistant<|action_start|><|action_pad|>...<|action_end|> -> FM denoising <|im_end|> -> AR """ chunksz_eoa = self.config.action_chunk_size + 1 + + # 1. pass the mm prefix to the model, and update kvcache mm_outputs = self.model( position_ids=position_ids[..., :-chunksz_eoa], attention_mask=attention_mask[:, :-chunksz_eoa], @@ -415,6 +422,8 @@ def _sample_actions( use_cache=use_cache, cache_position=cache_position[:-chunksz_eoa], ) + + # 2. perform denoising steps, with noise q and mm kvcache device = inputs_embeds.device x_t = sample_noise( [ @@ -469,6 +478,9 @@ def _sample_actions( # euler step x_t += dt * v_t.reshape(x_t.shape) time += dt + + # 3. get the final EOS token logits + outputs.last_hidden_state = torch.cat( [mm_outputs.last_hidden_state, outputs.last_hidden_state], dim=1 ) diff --git a/eo/model/modeling_qwen2_5_vl.py b/eo/model/modeling_qwen2_5_vl.py index d7a735b..2d9f979 100644 --- a/eo/model/modeling_qwen2_5_vl.py +++ b/eo/model/modeling_qwen2_5_vl.py @@ -24,17 +24,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -import math -from collections.abc import Callable from dataclasses import dataclass from typing import Any, Optional, Union +from collections.abc import Callable import torch import torch.nn as nn import torch.nn.functional as F # noqa: N812 -from torch.nn import CrossEntropyLoss +from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLTextConfig, Qwen2_5_VLVisionConfig from transformers.activations import ACT2FN -from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache +from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.generation.utils import ( GenerateNonBeamOutput, @@ -42,46 +41,25 @@ LogitsProcessorList, StoppingCriteriaList, ) -from transformers.modeling_attn_mask_utils import AttentionMaskConverter -from transformers.modeling_flash_attention_utils import ( - is_flash_attn_available, -) +from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask +from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel -from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import ( - Qwen2_5_VLConfig, - Qwen2_5_VLTextConfig, - Qwen2_5_VLVisionConfig, -) from transformers.processing_utils import Unpack +from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm from transformers.utils import ( - add_start_docstrings, - add_start_docstrings_to_model_forward, - is_torch_flex_attn_available, + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_torchdynamo_compiling, logging, - replace_return_docstrings, ) from transformers.utils.deprecation import deprecate_kwarg -if is_flash_attn_available(): - from flash_attn import flash_attn_varlen_func - from flash_attn.layers.rotary import apply_rotary_emb - from transformers.modeling_flash_attention_utils import FlashAttentionKwargs - - -if is_flash_attn_available(): - pass - -if is_torch_flex_attn_available(): - from torch.nn.attention.flex_attention import BlockMask - from transformers.integrations.flex_attention import make_flex_block_causal_mask - logger = logging.get_logger(__name__) -_CONFIG_FOR_DOC = "Qwen2_5_VLConfig" - class Qwen2_5_VLMLP(nn.Module): def __init__(self, config, bias: bool = False): @@ -124,6 +102,8 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class Qwen2_5_VisionRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) @@ -135,26 +115,6 @@ def forward(self, seqlen: int) -> torch.Tensor: return freqs -class Qwen2RMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - Qwen2RMSNorm is equivalent to T5LayerNorm - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - input_dtype = hidden_states.dtype - hidden_states = hidden_states.to(torch.float32) - variance = hidden_states.pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - return self.weight * hidden_states.to(input_dtype) - - def extra_repr(self): - return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" - - class Qwen2_5_VLPatchMerger(nn.Module): def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: super().__init__() @@ -171,58 +131,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x -def apply_rotary_pos_emb_flashatt( - q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor -) -> tuple[torch.Tensor, torch.Tensor]: - cos = cos.chunk(2, dim=-1)[0].contiguous() - sin = sin.chunk(2, dim=-1)[0].contiguous() - q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q) - k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k) - return q_embed, k_embed - - -class Qwen2_5_VLVisionFlashAttention2(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: - super().__init__() - self.num_heads = num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) - - def forward( - self, - hidden_states: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb: torch.Tensor | None = None, - position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - seq_length = hidden_states.shape[0] - q, k, v = ( - self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) - ) - if position_embeddings is None: - logger.warning_once( - "The attention layers in this model are transitioning from computing the RoPE embeddings internally " - "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " - "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " - "removed and `position_embeddings` will be mandatory." - ) - emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - cos = emb.cos() - sin = emb.sin() - else: - cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_flashatt(q.unsqueeze(0), k.unsqueeze(0), cos, sin) - q = q.squeeze(0) - k = k.squeeze(0) - - max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() - attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape( - seq_length, -1 - ) - attn_output = self.proj(attn_output) - return attn_output - - def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] @@ -244,6 +152,18 @@ def apply_rotary_pos_emb_vision( return q_embed, k_embed +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + def eager_attention_forward( module: nn.Module, query: torch.Tensor, @@ -271,12 +191,18 @@ def eager_attention_forward( class Qwen2_5_VLVisionAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: + def __init__(self, config: Qwen2_5_VLVisionConfig) -> None: super().__init__() - self.num_heads = num_heads - self.head_dim = dim // num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) + self.dim = config.hidden_size + self.num_heads = config.num_heads + self.head_dim = self.dim // self.num_heads + self.num_key_value_groups = 1 # needed for eager attention + self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + self.scaling = self.head_dim**-0.5 + self.config = config + self.attention_dropout = 0.0 + self.is_causal = False def forward( self, @@ -284,9 +210,10 @@ def forward( cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, ) -> torch.Tensor: seq_length = hidden_states.shape[0] - q, k, v = ( + query_states, key_states, value_states = ( self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) ) if position_embeddings is None: @@ -301,89 +228,69 @@ def forward( sin = emb.sin() else: cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) - - attention_mask = torch.full( - [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype - ) - for i in range(1, len(cu_seqlens)): - attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 - - q = q.transpose(0, 1) - k = k.transpose(0, 1) - v = v.transpose(0, 1) - attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) - attn_weights = attn_weights + attention_mask - attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) - attn_output = torch.matmul(attn_weights, v) - attn_output = attn_output.transpose(0, 1) - attn_output = attn_output.reshape(seq_length, -1) - attn_output = self.proj(attn_output) - return attn_output + query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) -class Qwen2_5_VLVisionSdpaAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 16) -> None: - super().__init__() - self.num_heads = num_heads - self.qkv = nn.Linear(dim, dim * 3, bias=True) - self.proj = nn.Linear(dim, dim) + attention_interface: Callable = eager_attention_forward + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] - def forward( - self, - hidden_states: torch.Tensor, - cu_seqlens: torch.Tensor, - rotary_pos_emb: torch.Tensor | None = None, - position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, - ) -> torch.Tensor: - seq_length = hidden_states.shape[0] - q, k, v = ( - self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) - ) - if position_embeddings is None: - logger.warning_once( - "The attention layers in this model are transitioning from computing the RoPE embeddings internally " - "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " - "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " - "removed and `position_embeddings` will be mandatory." + if self.config._attn_implementation == "flash_attention_2": + # Flash Attention 2: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, ) - emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) - cos = emb.cos() - sin = emb.sin() else: - cos, sin = position_embeddings - q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) - - attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool) - for i in range(1, len(cu_seqlens)): - attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True - q = q.transpose(0, 1) - k = k.transpose(0, 1) - v = v.transpose(0, 1) - attn_output = F.scaled_dot_product_attention( - q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attention_mask, dropout_p=0.0 - ) - attn_output = attn_output.squeeze(0).transpose(0, 1) - attn_output = attn_output.reshape(seq_length, -1) + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) + for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + )[0] + for q, k, v in zip(*splits, strict=False) + ] + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() attn_output = self.proj(attn_output) return attn_output -QWEN2_5_VL_VISION_ATTENTION_CLASSES = { - "eager": Qwen2_5_VLVisionAttention, - "flash_attention_2": Qwen2_5_VLVisionFlashAttention2, - "sdpa": Qwen2_5_VLVisionSdpaAttention, -} - - -class Qwen2_5_VLVisionBlock(nn.Module): +class Qwen2_5_VLVisionBlock(GradientCheckpointingLayer): def __init__(self, config, attn_implementation: str = "sdpa") -> None: super().__init__() self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6) - self.attn = QWEN2_5_VL_VISION_ATTENTION_CLASSES[attn_implementation]( - config.hidden_size, num_heads=config.num_heads - ) + self.attn = Qwen2_5_VLVisionAttention(config=config) self.mlp = Qwen2_5_VLMLP(config, bias=True) def forward( @@ -392,67 +299,35 @@ def forward( cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, ) -> torch.Tensor: hidden_states = hidden_states + self.attn( self.norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb, position_embeddings=position_embeddings, + **kwargs, ) hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) return hidden_states -Qwen2_5_VL_START_DOCSTRING = r""" - This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. - Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage - and behavior. - - Parameters: - config ([`Qwen2_5_VLConfig`]): - Model configuration class with all the parameters of the model. Initializing with a config file does not - load the weights associated with the model, only the configuration. Check out the - [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - - -@add_start_docstrings( - "The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top.", - Qwen2_5_VL_START_DOCSTRING, -) +@auto_docstring class Qwen2_5_VLPreTrainedModel(PreTrainedModel): - config_class = Qwen2_5_VLConfig + config: Qwen2_5_VLConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] _skip_keys_device_placement = "past_key_values" - _supports_flash_attn_2 = True + _supports_flash_attn = True _supports_sdpa = True - _supports_cache_class = True - _supports_static_cache = ( - False # TODO (joao): fix. torch.compile failing probably due to `cache_positions` - ) - def _init_weights(self, module): - std = self.config.get_text_config().initializer_range - if isinstance(module, (nn.Linear, nn.Conv3d)): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - elif isinstance(module, Qwen2RMSNorm): - module.weight.data.fill_(1.0) + _can_compile_fullgraph = True + _supports_attention_backend = True class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel): - config_class = Qwen2_5_VLVisionConfig + config: Qwen2_5_VLVisionConfig _no_split_modules = ["Qwen2_5_VLVisionBlock"] def __init__(self, config, *inputs, **kwargs) -> None: @@ -473,9 +348,7 @@ def __init__(self, config, *inputs, **kwargs) -> None: head_dim = config.hidden_size // config.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) - self.blocks = nn.ModuleList( - [Qwen2_5_VLVisionBlock(config, config._attn_implementation) for _ in range(config.depth)] - ) + self.blocks = nn.ModuleList([Qwen2_5_VLVisionBlock(config) for _ in range(config.depth)]) self.merger = Qwen2_5_VLPatchMerger( dim=config.out_hidden_size, context_dim=config.hidden_size, @@ -553,7 +426,7 @@ def get_window_index(self, grid_thw): return window_index, cu_window_seqlens - def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor: + def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor: """ Args: hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): @@ -601,14 +474,13 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch. cu_seqlens_now = cu_seqlens else: cu_seqlens_now = cu_window_seqlens - if self.gradient_checkpointing and self.training: - hidden_states = self._gradient_checkpointing_func( - blk.__call__, hidden_states, cu_seqlens_now, None, position_embeddings - ) - else: - hidden_states = blk( - hidden_states, cu_seqlens=cu_seqlens_now, position_embeddings=position_embeddings - ) + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + position_embeddings=position_embeddings, + **kwargs, + ) hidden_states = self.merger(hidden_states) reverse_indices = torch.argsort(window_index) @@ -617,7 +489,34 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch. return hidden_states +@dataclass +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +class Qwen2_5_VLModelOutputWithPast(ModelOutput): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + last_hidden_state: torch.FloatTensor = None + past_key_values: list[torch.FloatTensor] | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + class Qwen2_5_VLRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + def __init__(self, config: Qwen2_5_VLTextConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" @@ -714,18 +613,6 @@ def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim return q_embed, k_embed -def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: - """ - This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, - num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) - """ - batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) - return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) - - class Qwen2_5_VLAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer @@ -778,8 +665,9 @@ def forward( output_attentions: bool = False, use_cache: bool = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None - | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC + position_embeddings: None | ( + tuple[torch.Tensor, torch.Tensor] + ) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: bsz, q_len, _ = hidden_states.size() @@ -856,8 +744,9 @@ def forward( output_attentions: bool | None = False, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, - position_embeddings: None - | (tuple[torch.Tensor, torch.Tensor]) = None, # necessary, but kept here for BC + position_embeddings: None | ( + tuple[torch.Tensor, torch.Tensor] + ) = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: """ @@ -914,12 +803,9 @@ def forward( return outputs -@add_start_docstrings( - "The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top.", - Qwen2_5_VL_START_DOCSTRING, -) +@auto_docstring class Qwen2_5_VLModel(Qwen2_5_VLPreTrainedModel): - config_class = Qwen2_5_VLTextConfig + config: Qwen2_5_VLTextConfig def __init__(self, config: Qwen2_5_VLTextConfig): super().__init__(config) @@ -933,6 +819,7 @@ def __init__(self, config: Qwen2_5_VLTextConfig): self._attn_implementation = config._attn_implementation self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types self.gradient_checkpointing = False # Initialize weights and apply final processing @@ -944,18 +831,20 @@ def get_input_embeddings(self): def set_input_embeddings(self, value): self.embed_tokens = value + @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, cache_position: torch.LongTensor | None = None, + **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple | BaseModelOutputWithPast: output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions @@ -979,7 +868,7 @@ def forward( # torch.jit.trace() doesn't support cache objects in the output if use_cache and past_key_values is None and not torch.jit.is_tracing(): - past_key_values = DynamicCache() + past_key_values = DynamicCache(config=self.config) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) @@ -993,12 +882,43 @@ def forward( # the hard coded `3` is for temporal, height and width. if position_ids is None: position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) - elif position_ids.dim() == 2: + elif position_ids.ndim == 2: position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) - causal_mask = self._update_causal_mask( - attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions - ) + # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions + # where each dim indicates visual spatial positions for temporal/height/width grids. + # There are two scenarios when FA2-like packed masking might be activated. + # 1. User specifically passed packed `position_ids` and no attention mask. + # In this case we expect the useer to create correct position ids for all 3 grids + # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len] + # 2. User runs forward with no attention mask and no position ids. In this case, position ids + # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are + # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass + # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation` + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + text_position_ids = position_ids[0] + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "input_embeds": inputs_embeds, + "attention_mask": attention_mask, + "cache_position": cache_position, + "past_key_values": past_key_values, + "position_ids": text_position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) hidden_states = inputs_embeds @@ -1008,41 +928,25 @@ def forward( # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None - next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) - if self.gradient_checkpointing and self.training: - layer_outputs = self._gradient_checkpointing_func( - decoder_layer.__call__, - hidden_states, - causal_mask, - position_ids, - past_key_values, - output_attentions, - use_cache, - cache_position, - position_embeddings, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=causal_mask, - position_ids=position_ids, - past_key_value=past_key_values, - output_attentions=output_attentions, - use_cache=use_cache, - cache_position=cache_position, - position_embeddings=position_embeddings, - ) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[decoder_layer.attention_type], + position_ids=text_position_ids, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache = layer_outputs[2 if output_attentions else 1] - if output_attentions: all_self_attns += (layer_outputs[1],) @@ -1052,206 +956,40 @@ def forward( if output_hidden_states: all_hidden_states += (hidden_states,) - next_cache = next_decoder_cache if use_cache else None - if not return_dict: return tuple( - v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None + v + for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] + if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, - past_key_values=next_cache, + past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, ) - def _update_causal_mask( - self, - attention_mask: Union[torch.Tensor, "BlockMask"], - input_tensor: torch.Tensor, - cache_position: torch.Tensor, - past_key_values: Cache, - output_attentions: bool = False, - ): - if self.config._attn_implementation == "flash_attention_2": - if attention_mask is not None and past_key_values is not None: - is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] - if is_padding_right: - raise ValueError( - "You are attempting to perform batched generation with padding_side='right'" - " this may lead to unexpected behaviour for Flash Attention version of Qwen2_5_VL. Make sure to " - " call `tokenizer.padding_side = 'left'` before tokenizing the input. " - ) - if attention_mask is not None and 0.0 in attention_mask: - return attention_mask - return None - if self.config._attn_implementation == "flex_attention": - if isinstance(attention_mask, torch.Tensor): - attention_mask = make_flex_block_causal_mask(attention_mask) - return attention_mask - - # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in - # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail - # to infer the attention mask. - past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 - using_static_cache = isinstance(past_key_values, StaticCache) - using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) - - # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward - if ( - self.config._attn_implementation == "sdpa" - and not (using_static_cache or using_sliding_window_cache) - and not output_attentions - ): - if AttentionMaskConverter._ignore_causal_mask_sdpa( - attention_mask, - inputs_embeds=input_tensor, - past_key_values_length=past_seen_tokens, - sliding_window=self.config.sliding_window, - is_training=self.training, - ): - return None - - dtype = input_tensor.dtype - min_dtype = torch.finfo(dtype).min - sequence_length = input_tensor.shape[1] - # SlidingWindowCache or StaticCache - if using_sliding_window_cache or using_static_cache: - target_length = past_key_values.get_max_cache_shape() - # DynamicCache or no cache - else: - target_length = ( - attention_mask.shape[-1] - if isinstance(attention_mask, torch.Tensor) - else past_seen_tokens + sequence_length + 1 - ) - - # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). - causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( - attention_mask, - sequence_length=sequence_length, - target_length=target_length, - dtype=dtype, - cache_position=cache_position, - batch_size=input_tensor.shape[0], - config=self.config, - past_key_values=past_key_values, - ) - - if ( - self.config._attn_implementation == "sdpa" - and attention_mask is not None - and attention_mask.device.type in ["cuda", "xpu", "npu"] - and not output_attentions - ): - # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when - # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. - # Details: https://github.com/pytorch/pytorch/issues/110213 - causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) - - return causal_mask - - @staticmethod - def _prepare_4d_causal_attention_mask_with_cache_position( - attention_mask: torch.Tensor, - sequence_length: int, - target_length: int, - dtype: torch.dtype, - cache_position: torch.Tensor, - batch_size: int, - config: Qwen2_5_VLConfig, - past_key_values: Cache, - ): - """ - Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape - `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. - - Args: - attention_mask (`torch.Tensor`): - A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. - sequence_length (`int`): - The sequence length being processed. - target_length (`int`): - The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. - dtype (`torch.dtype`): - The dtype to use for the 4D attention mask. - cache_position (`torch.Tensor`): - Indices depicting the position of the input sequence tokens in the sequence. - batch_size (`torch.Tensor`): - Batch size. - config (`Qwen2_5_VLConfig`): - The model's configuration class - past_key_values (`Cache`): - The cache class that is being used currently to generate - """ - if attention_mask is not None and attention_mask.dim() == 4: - # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. - causal_mask = attention_mask - else: - min_dtype = torch.finfo(dtype).min - causal_mask = torch.full( - (sequence_length, target_length), - fill_value=min_dtype, - dtype=dtype, - device=cache_position.device, - ) - diagonal_attend_mask = torch.arange( - target_length, device=cache_position.device - ) > cache_position.reshape(-1, 1) - if config.get_text_config().sliding_window is not None: - # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also - # the check is needed to verify is current checkpoint was trained with sliding window or not - if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: - sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= ( - cache_position.reshape(-1, 1) - config.get_text_config().sliding_window - ) - diagonal_attend_mask.bitwise_or_(sliding_attend_mask) - causal_mask *= diagonal_attend_mask - causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) - if attention_mask is not None: - causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit - if attention_mask.shape[-1] > target_length: - attention_mask = attention_mask[:, :target_length] - mask_length = attention_mask.shape[-1] - padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( - causal_mask.device - ) - padding_mask = padding_mask == 0 - causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( - padding_mask, min_dtype - ) - return causal_mask - @dataclass -class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): - """ +@auto_docstring( + custom_intro=""" Base class for Qwen2_5_VL causal language model (or autoregressive) outputs. - - Args: - loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): - Language modeling loss (for next-token prediction). - logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): - Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) - - Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see - `past_key_values` input) to speed up sequential decoding. - hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): - Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + - one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. - - Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): - Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, - sequence_length)`. - - Attentions weights after the attention softmax, used to compute the weighted average in the self-attention - heads. - rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): - The rope index difference between sequence length and multimodal rope. + """ +) +class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. """ loss: torch.FloatTensor | None = None @@ -1262,87 +1000,13 @@ class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): rope_deltas: torch.LongTensor | None = None -QWEN2_5_VL_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide - it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see - `past_key_values`). - - If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] - and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more - information on the default strategy. - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, - config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape - `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention - blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - pixel_values (`torch.FloatTensor` of shape `(seq_length, num_channels * image_size * image_size)): - The tensors corresponding to the input images. Pixel values can be obtained using - [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses - [`Qwen2_5_VLImageProcessor`] for processing images. - pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)): - The tensors corresponding to the input videos. Pixel values can be obtained using - [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses - [`Qwen2_5_VLImageProcessor`] for processing videos. - image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): - The temporal, height and width of feature shape of each image in LLM. - video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): - The temporal, height and width of feature shape of each video in LLM. - rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): - The rope index difference between sequence length and multimodal rope. -""" - - class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] - config_class = Qwen2_5_VLConfig _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] + config = Qwen2_5_VLConfig + accepts_loss_kwargs = False + def __init__(self, config): super().__init__(config) self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config) @@ -1353,7 +1017,6 @@ def __init__(self, config): self.lm_head = nn.Linear(text_config.hidden_size, text_config.vocab_size, bias=False) self.rope_deltas = None # cache rope_deltas here - # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): @@ -1374,6 +1037,86 @@ def set_decoder(self, decoder): def get_decoder(self): return self.model + def get_video_features( + self, pixel_values_videos: torch.FloatTensor, video_grid_thw: torch.LongTensor | None = None + ): + """ + Encodes videos into continuous embeddings that can be forwarded to the language model. + + Args: + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + pixel_values_videos = pixel_values_videos.type(self.visual.dtype) + video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) + split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + video_embeds = torch.split(video_embeds, split_sizes) + return video_embeds + + def get_image_features( + self, pixel_values: torch.FloatTensor, image_grid_thw: torch.LongTensor | None = None + ): + """ + Encodes images into continuous embeddings that can be forwarded to the language model. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype) + image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) + split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + image_embeds = torch.split(image_embeds, split_sizes) + return image_embeds + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor = None, + video_features: torch.FloatTensor = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = ( + special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + ) + if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel(): + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}" + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = ( + special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + ) + if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel(): + raise ValueError( + f"Videos features and video tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}" + ) + + return special_image_mask, special_video_mask + def get_rope_index( self, input_ids: torch.LongTensor | None = None, @@ -1562,20 +1305,18 @@ def get_rope_index( return position_ids, mrope_position_deltas - @add_start_docstrings_to_model_forward(QWEN2_5_VL_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=Qwen2_5_VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + @can_return_tuple def forward( self, - input_ids: torch.LongTensor | None = None, + input_ids: torch.LongTensor = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, - return_dict: bool | None = None, pixel_values: torch.Tensor | None = None, pixel_values_videos: torch.FloatTensor | None = None, image_grid_thw: torch.LongTensor | None = None, @@ -1583,124 +1324,82 @@ def forward( rope_deltas: torch.LongTensor | None = None, cache_position: torch.LongTensor | None = None, second_per_grid_ts: torch.Tensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], ) -> tuple | Qwen2_5_VLCausalLMOutputWithPast: r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - - Returns: - - Example: - - ```python - >>> from PIL import Image - >>> import requests - >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration - - >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") - >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") - - >>> messages = [ - { - "role": "user", - "content": [ - {"type": "image"}, - {"type": "text", "text": "What is shown in this image?"}, - ], - }, - ] - >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" - >>> image = Image.open(requests.get(url, stream=True).raw) - - >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) - >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos]) - - >>> # Generate - >>> generate_ids = model.generate(inputs.input_ids, max_length=30) - >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..." + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. ```""" + output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is None: - inputs_embeds = self.model.embed_tokens(input_ids) - if pixel_values is not None: - pixel_values = pixel_values.type(self.visual.dtype) - image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) - n_image_tokens = (input_ids == self.config.image_token_id).sum().item() - n_image_features = image_embeds.shape[0] - if n_image_tokens != n_image_features: - raise ValueError( - f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" - ) + inputs_embeds = self.get_input_embeddings()(input_ids) - mask = input_ids == self.config.image_token_id - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - image_mask = mask_expanded.to(inputs_embeds.device) - - image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - - if pixel_values_videos is not None: - pixel_values_videos = pixel_values_videos.type(self.visual.dtype) - video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw) - n_video_tokens = (input_ids == self.config.video_token_id).sum().item() - n_video_features = video_embeds.shape[0] - if n_video_tokens != n_video_features: - raise ValueError( - f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" - ) - - mask = input_ids == self.config.video_token_id - mask_unsqueezed = mask.unsqueeze(-1) - mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) - video_mask = mask_expanded.to(inputs_embeds.device) - - video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) - inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw) + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) - if attention_mask is not None: - attention_mask = attention_mask.to(inputs_embeds.device) + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw) + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) - # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme - if position_ids is None and (attention_mask is None or attention_mask.ndim == 2): - # calculate RoPE index once per generation in the pre-fill stage only - if ( + if position_ids is None: + # Calculate RoPE index once per generation in the pre-fill stage only. + # When compiling, we can't check tensor values thus we check only input length + # It is safe to assume that `length!=1` means we're in pre-fill because compiled + # models currently cannot do asssisted decoding + prefill_compiled_stage = is_torchdynamo_compiling() and ( + (input_ids is not None and input_ids.shape[1] != 1) + or (inputs_embeds is not None and inputs_embeds.shape[1] != 1) + ) + prefill_noncompiled_stage = not is_torchdynamo_compiling() and ( (cache_position is not None and cache_position[0] == 0) - or self.rope_deltas is None or (past_key_values is None or past_key_values.get_seq_length() == 0) - ): + ) + if (prefill_compiled_stage or prefill_noncompiled_stage) or self.rope_deltas is None: position_ids, rope_deltas = self.get_rope_index( input_ids, image_grid_thw, video_grid_thw, - second_per_grid_ts, - attention_mask, + second_per_grid_ts=second_per_grid_ts, + attention_mask=attention_mask, ) self.rope_deltas = rope_deltas - # then use the prev pre-calculated rope-deltas to get the correct position ids else: batch_size, seq_length, _ = inputs_embeds.shape - delta = ( - (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) - if cache_position is not None - else 0 - ) position_ids = torch.arange(seq_length, device=inputs_embeds.device) - position_ids = position_ids.view(1, -1).expand(batch_size, -1) - if cache_position is not None: # otherwise `deltas` is an int `0` - delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0) - position_ids = position_ids.add(delta) - position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1) + if cache_position is not None: + delta = (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) + else: + delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device) + delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1) + position_ids += delta.to(position_ids.device) outputs = self.model( input_ids=None, @@ -1711,31 +1410,22 @@ def forward( use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, - return_dict=return_dict, + return_dict=True, cache_position=cache_position, + **kwargs, ) hidden_states = outputs[0] - logits = self.lm_head(hidden_states) + + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: - # Upcast to float if we need to compute the loss to avoid potential precision issues - logits = logits.float() - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.vocab_size) - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) return Qwen2_5_VLCausalLMOutputWithPast( loss=loss, @@ -1780,8 +1470,35 @@ def prepare_inputs_for_generation( **kwargs, ) - # Qwen2-5-VL position_ids are prepareed with rope_deltas in forward - model_inputs["position_ids"] = None + # Qwen2-5-VL position_ids are prepared with rope_deltas + if position_ids is None: + # Calculate RoPE index once per generation in the pre-fill stage only. + # When compiling, we can't check tensor values thus we check only input length + # It is safe to assume that `length!=1` means we're in pre-fill because compiled + # models currently cannot do asssisted decoding + if cache_position[0] == 0 or self.model.rope_deltas is None: + vision_positions, rope_deltas = self.get_rope_index( + model_inputs.get("input_ids", None), + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + attention_mask=attention_mask, + ) + self.model.rope_deltas = rope_deltas + # then use the prev pre-calculated rope-deltas to get the correct position ids + elif "position_ids" in model_inputs: + position_ids = model_inputs["position_ids"][None, ...] + delta = self.model.rope_deltas + delta = delta.repeat_interleave(position_ids.shape[1] // delta.shape[0], dim=0) + vision_positions = position_ids + delta.expand_as(position_ids) + vision_positions = vision_positions.expand(3, vision_positions.shape[1], -1) + + # Concatenate "text + vision" positions into [4, bs, seq-len] + if "position_ids" not in model_inputs: + text_positions = torch.arange(input_ids, device=input_ids.device)[None, None, :] + else: + text_positions = model_inputs["position_ids"][None, ...] + model_inputs["position_ids"] = torch.cat([text_positions, vision_positions], dim=0) if cache_position[0] != 0: model_inputs["pixel_values"] = None @@ -1792,6 +1509,7 @@ def prepare_inputs_for_generation( def _get_image_nums_and_video_nums( self, input_ids: torch.LongTensor | None, + inputs_embeds: torch.Tensor | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Get the number of images and videos for each sample to calculate the separation length of the sample tensor. @@ -1809,10 +1527,31 @@ def _get_image_nums_and_video_nums( video_token_id = self.config.video_token_id vision_start_token_id = self.config.vision_start_token_id - vision_start_mask = input_ids == vision_start_token_id + if inputs_embeds is not None: + vision_start_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(vision_start_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + image_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + video_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + else: + vision_start_mask = input_ids == vision_start_token_id + image_mask = input_ids == image_token_id + video_mask = input_ids == video_token_id + vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1) - image_mask = input_ids == image_token_id - video_mask = input_ids == video_token_id image_nums = torch.sum(vision_first_mask & image_mask, dim=1) video_nums = torch.sum(vision_first_mask & video_mask, dim=1) @@ -1844,7 +1583,9 @@ def _expand_inputs_for_generation( def _expand_dict_for_generation_visual(dict_to_expand): image_grid_thw = model_kwargs.get("image_grid_thw", None) video_grid_thw = model_kwargs.get("video_grid_thw", None) - image_nums, video_nums = self._get_image_nums_and_video_nums(input_ids) + image_nums, video_nums = self._get_image_nums_and_video_nums( + input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None) + ) def _repeat_interleave_samples(x, lengths, repeat_times): samples = torch.split(x, lengths) @@ -1879,14 +1620,9 @@ def _repeat_interleave_samples(x, lengths, repeat_times): dict_to_expand[key], lengths=lengths, repeat_times=expand_size ) elif key == "second_per_grid_ts": - if not isinstance(dict_to_expand[key], list): - raise TypeError( - f"Expected value for key '{key}' to be a list, but got {type(dict_to_expand[key])} instead." - ) - tensor = torch.tensor(dict_to_expand[key]) - lengths = list(video_nums) - tensor = _repeat_interleave_samples(tensor, lengths=lengths, repeat_times=expand_size) - dict_to_expand[key] = tensor.tolist() + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size + ) return dict_to_expand def _expand_dict_for_generation(dict_to_expand): @@ -1900,10 +1636,7 @@ def _expand_dict_for_generation(dict_to_expand): dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) return dict_to_expand - # input_ids is required for expanding visual inputs - # If input_ids is unavailable, visual inputs will not be used; therefore, there is no need to expand visual inputs. - if input_ids is not None and input_ids.numel() != 0: - model_kwargs = _expand_dict_for_generation_visual(model_kwargs) + model_kwargs = _expand_dict_for_generation_visual(model_kwargs) if input_ids is not None: input_ids = input_ids.repeat_interleave(expand_size, dim=0) @@ -2074,4 +1807,9 @@ class GenerateDecoderOnlyOutput(ModelOutput): actions: torch.FloatTensor | None = None -__all__ = ["Qwen2_5_VLForConditionalGeneration", "Qwen2_5_VLModel", "Qwen2_5_VLPreTrainedModel"] +__all__ = [ + "Qwen2_5_VLForConditionalGeneration", + "Qwen2_5_VLModel", + "Qwen2_5_VLPreTrainedModel", + "Qwen2_5_VLTextModel", +] diff --git a/experiments/1_demo/data-demo.yaml b/experiments/1_demo/data-demo.yaml index 76067a0..1027e97 100644 --- a/experiments/1_demo/data-demo.yaml +++ b/experiments/1_demo/data-demo.yaml @@ -1,8 +1,24 @@ mm_datasets: - - json_path: demo_data/libero_spatial_mmu.jsonl - # - json_path: demo_data/refcoco/refcoco.jsonl - # vision_base_path: demo_data/refcoco + - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file + vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL + sampling_strategy: random:10% # sampling strategy + - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl + +# @robot control config lerobot_datasets: - - repo_id: libero_spatial_no_noops_1.0.0_lerobot + - repo_id: demo25 root: ./demo_data + # Optional fields: + # episodes: [1, 2, 3] # specific episodes to load (None = all) + train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task + delta_action: false # train with delta actions + state_mode: "MEAN_STD" # state normalization mode + # which camera streams to load + select_video_keys: [observation.images.head, observation.images.hand_left, observation.images.hand_right] + # proprioceptive states + select_state_keys: [observation.states.joint.position, observation.states.effector.position] + # action targets + select_action_keys: [actions.joint.position, actions.effector.position] + effector_indices: [14, 15] # indices of effector channels in the flattened action vector + weight: 1.0 # dataset weight for sampling diff --git a/experiments/2_libero/data-libero.yaml b/experiments/2_libero/data-libero.yaml index fb08f31..ccafef4 100644 --- a/experiments/2_libero/data-libero.yaml +++ b/experiments/2_libero/data-libero.yaml @@ -3,7 +3,7 @@ mm_datasets: lerobot_datasets: - repo_id: libero_spatial_no_noops_1.0.0_lerobot - root: HF_LEROBOT_HOME + root: ./demo_data/ select_video_keys: [observation.images.image, observation.images.wrist_image] select_state_keys: [observation.state] select_action_keys: [action] diff --git a/getting_started/1_customize_data.ipynb b/getting_started/1_customize_data.ipynb deleted file mode 100644 index e69de29..0000000 diff --git a/scripts/eval_policy.py b/scripts/eval_policy.py index 3b2da18..3a2aca1 100644 --- a/scripts/eval_policy.py +++ b/scripts/eval_policy.py @@ -8,7 +8,7 @@ argparser.add_argument( "--model_path", type=str, - default="outputs/", + default="experiments/outputs/your_path", help="Path to the pretrained model", ) argparser.add_argument( @@ -22,19 +22,15 @@ def eval_policy(): # set the observation (image, state, etc.) - image0 = "test/demo/example1.png" - image1 = Image.open("test/demo/example1.png") + image0 = "demo_data/example.png" + image1 = Image.open("demo_data/example.png") - model = ( - AutoModel.from_pretrained(args.model_path, trust_remote_code=True, torch_dtype=torch.bfloat16) - .eval() - .cuda() - ) + model = AutoModel.from_pretrained(args.model_path, dtype=torch.bfloat16).eval().cuda() - processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True) + processor = AutoProcessor.from_pretrained(args.model_path) batch = { - "observation.images.image_0": [image0], + "observation.images.image": [image0], "observation.images.wrist_image": [image1], "observation.state": [torch.rand(8)], "task": ["put the object in the box."], diff --git a/tools/test_hf_model.py b/tools/test_hf_model.py new file mode 100644 index 0000000..d6bb120 --- /dev/null +++ b/tools/test_hf_model.py @@ -0,0 +1,54 @@ +from transformers import AutoProcessor + +from eo.model.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration + +"""set model name or path""" +model_name_or_path = "../pretrained/Qwen2.5-VL-3B-Instruct" # or EO-3B +model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + model_name_or_path, + device_map="auto", + trust_remote_code=True, + # attn_implementation="flash_attention_2", +) + +processor = AutoProcessor.from_pretrained(model_name_or_path, trust_remote_code=True) + +messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": "demo_data/refcoco/images/COCO_train2014_000000168643_2.jpg"}, + { + "type": "text", + "text": "If the yellow robot gripper follows the yellow trajectory, what will happen? Choices: A. Robot puts the soda on the wooden steps. B. Robot moves the soda in front of the wooden steps. C. Robot moves the soda to the very top of the wooden steps. D. Robot picks up the soda can and moves it up. Please answer directly with only the letter of the correct option and nothing else.", + }, + ], + }, +] + +times = 0 +past_key_values = None + +while True: + if times > 0: + prompt = input("Enter your prompt: ") + if prompt == "q": + exit(0) + messages.append({"role": "user", "content": [{"type": "text", "text": prompt}]}) + inputs = processor.apply_chat_template( + messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" + ).to("cuda") + + input_length = inputs["input_ids"].shape[1] + outputs = model.generate( + **inputs, max_new_tokens=1024, past_key_values=past_key_values, return_dict_in_generate=True + ) + + past_key_values = outputs.past_key_values + generated_ids = outputs.sequences + + completion = processor.decode(generated_ids[0, input_length:], skip_special_tokens=False) + print(completion) + + messages.append({"role": "assistant", "content": [{"type": "text", "text": completion}]}) + times += 1 From 1390b23363ae95b67cc930cba1e67457fce98c88 Mon Sep 17 00:00:00 2001 From: Delin Qu Date: Fri, 12 Sep 2025 23:47:50 +0800 Subject: [PATCH 03/13] Refactor model input handling for multimodal data, including image and video features --- .github/settings.yml | 127 --- .gitignore | 4 +- README.md | 179 ++-- demo_data/example1.jpg | Bin 0 -> 117078 bytes demo_data/example2.png | Bin 0 -> 412328 bytes eo/model/modeling_eo1.py | 166 ++-- eo/model/modeling_qwen2_5_vl.py | 1112 +++++++++--------------- experiments/1_demo/data-demo.yaml | 24 +- experiments/1_demo/train.sh | 45 +- experiments/2_libero/data-libero.yaml | 2 +- getting_started/1_customize_data.ipynb | 0 scripts/eval_policy.py | 16 +- tools/test_hf_model.py | 54 ++ 13 files changed, 718 insertions(+), 1011 deletions(-) delete mode 100644 .github/settings.yml create mode 100644 demo_data/example1.jpg create mode 100644 demo_data/example2.png delete mode 100644 getting_started/1_customize_data.ipynb create mode 100644 tools/test_hf_model.py diff --git a/.github/settings.yml b/.github/settings.yml deleted file mode 100644 index 0163e46..0000000 --- a/.github/settings.yml +++ /dev/null @@ -1,127 +0,0 @@ -repository: - # See https://docs.github.com/en/rest/reference/repos#update-a-repository for all available settings. - - # The name of the repository. Changing this will rename the repository - # name: EO-1 - - # A short description of the repository that will show up on GitHub - description: "EO: Open-source Unified Embodied Foundation Model Series" - - # A URL with more information about the repository - homepage: https://eo-robotics.ai - - # A comma-separated list of topics to set on the repository - topics: robot-control, generalist-robot-policy, vla, embodied-ai, large-embodied-model, unified-model - - # Either `true` to make the repository private, or `false` to make it public. - private: false - - # Either `true` to enable issues for this repository, `false` to disable them. - has_issues: true - - # Either `true` to enable projects for this repository, or `false` to disable them. - # If projects are disabled for the organization, passing `true` will cause an API error. - has_projects: false - - # Either `true` to enable the wiki for this repository, `false` to disable it. - has_wiki: false - - # Either `true` to enable downloads for this repository, `false` to disable them. - has_downloads: true - - # Updates the default branch for this repository. - default_branch: main - - # Either `true` to allow squash-merging pull requests, or `false` to prevent - # squash-merging. - allow_squash_merge: true - - # Either `true` to allow merging pull requests with a merge commit, or `false` - # to prevent merging pull requests with merge commits. - allow_merge_commit: false - - # Either `true` to allow rebase-merging pull requests, or `false` to prevent - # rebase-merging. - allow_rebase_merge: false - - # Either `true` to enable automatic deletion of branches on merge, or `false` to disable - delete_branch_on_merge: true - - # Either `true` to enable automated security fixes, or `false` to disable - # automated security fixes. - enable_automated_security_fixes: true - - # Either `true` to enable vulnerability alerts, or `false` to disable - # vulnerability alerts. - enable_vulnerability_alerts: true - -# Milestones: define milestones for Issues and Pull Requests -# milestones: -# - title: milestone-title -# description: milestone-description -# # The state of the milestone. Either `open` or `closed` -# state: open - -# Collaborators: give specific users access to this repository. -# See https://docs.github.com/en/rest/reference/repos#add-a-repository-collaborator for available options -# collaborators: - # - username: bkeepers - # permission: push - # - username: hubot - # permission: pull - - # Note: `permission` is only valid on organization-owned repositories. - # The permission to grant the collaborator. Can be one of: - # * `pull` - can pull, but not push to or administer this repository. - # * `push` - can pull and push, but not administer this repository. - # * `admin` - can pull, push and administer this repository. - # * `maintain` - Recommended for project managers who need to manage the repository without access to sensitive or destructive actions. - # * `triage` - Recommended for contributors who need to proactively manage issues and pull requests without write access. - -# See https://docs.github.com/en/rest/reference/teams#add-or-update-team-repository-permissions for available options -teams: - - name: core - # The permission to grant the team. Can be one of: - # * `pull` - can pull, but not push to or administer this repository. - # * `push` - can pull and push, but not administer this repository. - # * `admin` - can pull, push and administer this repository. - # * `maintain` - Recommended for project managers who need to manage the repository without access to sensitive or destructive actions. - # * `triage` - Recommended for contributors who need to proactively manage issues and pull requests without write access. - permission: admin - - name: docs - permission: push - -branches: - - name: main - # https://docs.github.com/en/rest/reference/repos#update-branch-protection - # Branch Protection settings. Set to null to disable - protection: - # Required. Require at least one approving review on a pull request, before merging. Set to null to disable. - required_pull_request_reviews: - # The number of approvals required. (1-6) - required_approving_review_count: 1 - # Dismiss approved reviews automatically when a new commit is pushed. - dismiss_stale_reviews: true - # Blocks merge until code owners have reviewed. - require_code_owner_reviews: true - - # Required. Require status checks to pass before merging. Set to null to disable - required_status_checks: - # Required. Require branches to be up to date before merging. - strict: true - # Required. The list of status checks to require in order to merge into this branch - contexts: - # - "test" - - "quality" - - "security" - - "release" - - # Required. Enforce all configured restrictions for administrators. Set to true to enforce required status checks for repository administrators. Set to null to disable. - enforce_admins: true - # Prevent merge commits from being pushed to matching branches - required_linear_history: true - # Required. Restrict who can push to this branch. Team and user restrictions are only available for organization-owned repositories. Set to null to disable. - restrictions: - apps: [] - users: [] - teams: [] diff --git a/.gitignore b/.gitignore index c8b1e51..c2cee81 100644 --- a/.gitignore +++ b/.gitignore @@ -226,9 +226,11 @@ source experiments/outputs scripts/env.sh -scripts/pre-commit.sh +pre-commit.sh tools/caption_video.html experiments/2_libero/logs experiments/2_libero/logs/videos demo_data/demos25 + +demo_data/libero_spatial_no_noops_1.0.0_lerobot diff --git a/README.md b/README.md index a6bf213..4691a8c 100644 --- a/README.md +++ b/README.md @@ -67,8 +67,8 @@ We introduce **EO-1** model, an open-source unified embodied foundation model co Clone the repository: ```bash -git clone https://github.com/EO-Robotics/EO.git -cd EO +git clone https://github.com/EO-Robotics/EO-1.git +cd EO-1 ``` Create a conda environment and install dependencies: @@ -101,8 +101,8 @@ pip install -e . ### Experiment Examples - [Demo Training](experiments/1_demo/) - Quick start with demo data and debug mode -- [Libero Benchmark](experiments/2_libero/) - Spatial reasoning tasks and evaluation -- [SimplerEnv Benchmark](experiments/3_simpler/) - Real-world deployment on WidowX and Google Robot +- [Libero Benchmark](experiments/2_libero/) - Tuning on Libero benchmark tasks +- [SimplerEnv Benchmark](experiments/3_simpler/) - Tuning on SimplerEnv benchmark, including WidowX and Google Robot - [SO101 Tasks](experiments/4_so101/) - SO100 collection manipulation tasks - [WidowX Platform](experiments/5_widowx/) - WidowX robot specific training and evaluation - [AgiBot Platform](experiments/6_agibot/) - AgiBot robot training and deployment @@ -116,27 +116,51 @@ pip install -e . ```python from transformers import AutoModel, AutoProcessor -# load the model and processor + +# load model and processor processor = AutoProcessor.from_pretrained("IPEC-COMMUNITY/EO-1-3B", trust_remote_code=True) model = AutoModel.from_pretrained( "IPEC-COMMUNITY/EO-1-3B", trust_remote_code=True, - torch_dtype=torch.bfloat16 + dtype=torch.bfloat16 ).eval().cuda() # prepare the model input batch = { - "observation.images.image": [img], # PIL.Image + "observation.images.image": [img], "observation.images.wrist_image": [wrist_img], "observation.state": [state], - "task": ["You are a helpful physical agent equipped with both reasoning and robotic control. \ - You see the Tic-Tac-Toe board, think strategically, act logically, and block threats."] + "task": ["Pick up a red piece and place it at (0, 2)."] } -# generate multimodal outputs -output = processor.generate(model, batch) -text = output.text -actions = output.action.numpy() +# 1. action sampling [robot control] +output = processor.select_action(model, batch) +print(output.action) + +# prepare conversation +messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": "demo_data/example2.png"}, + {"type": "text", "text": "You are a helpful physical agent equipped with both reasoning and robotic control. \ + You see the Tic-Tac-Toe board, think strategically, act logically, and block threats."}, + ], + }, +] +# 2. text generation [multimodal reasoning] +input_length = inputs["input_ids"].shape[1] +inputs = processor.apply_chat_template( + messages, + tokenize=True, + return_dict=True, + return_tensors="pt" +).to("cuda") + +outputs = model.generate(**inputs, max_new_tokens=1024, return_dict_in_generate=True) +generated_ids = outputs.sequences +text = processor.decode(generated_ids[0, input_length:]) +print(text) ``` ### Datasets @@ -145,22 +169,40 @@ We use [LeRobot](https://github.com/huggingface/lerobot) as the primary source f For Multimodal data, e.g., image, video, text, points and bounding boxes, we follow the [Qwen2.5-VL](https://colab.research.google.com/github/QwenLM/Qwen2.5-VL/blob/main/cookbooks/spatial_understanding.ipynb) and [Qwen2-VL-Finetune](https://github.com/2U1/Qwen2-VL-Finetune) recipes. In interleaved pretraining, we integrate the EO-Data1.5M dataset — a large-scale, high-quality embodied dataset designed to unify reasoning and control. Data are organized in a standardized format as shown below:

-Here, the `lerobot` and `view` fields connect actions with multimodal conversations, enabling the model to capture the rich temporal dynamics and causal dependencies among vision, language, and action modalities — a core requirement for robust performance in open-world embodied interactions. +Here, the `lerobot` and `view` fields connect actions with multimodal conversations, enabling the model to capture the rich temporal dynamics and causal dependencies among vision, language, and action modalities — a core requirement for robust performance in open-world embodied interactions. For more details, please refer to [getting_started/1_load_dataset](getting_started/1_load_dataset.ipynb). -To combine robot control data and multimodal data, we support a flexible YAML-based configuration, where each dataset can be assigned weights and sampling strategies. This makes it easy to balance embodied control trajectories with multimodal reasoning data for interleaved training. For example: +To combine robot control data and multimodal data, we support a [flexible YAML-based configuration](eo/data/schema.py), where each dataset can be assigned weights and sampling strategies. This makes it easy to balance embodied control trajectories with multimodal reasoning data for interleaved training. For example: ```yaml -# configs/example.yaml -mm_datasets: # optional - - json_path: LEROBOT_DATASET/bridge_interleaved_data.jsonl - sampling_strategy: random:5% +# @multimodal data config +mm_datasets: + - json_path: demo_data/refcoco/refcoco.jsonl # jsonl file + vision_base_path: demo_data/refcoco # base path for vision data files referenced in the JSONL + sampling_strategy: random:10% # sampling strategy - - json_path: RefCOCO/refcoco.jsonl - sampling_strategy: random:10% + - json_path: demo_data/interleaved_demo.jsonl # interleaved data jsonl +# @robot control config lerobot_datasets: - - repo_id: bridge - select_video_keys: [observation.images.image_0] + - repo_id: demo25 + root: ./demo_data + # Optional fields: + episodes: [1, 2, 3] # specific episodes to load (None = all) + train_subtask: mix:0.9 # mix sub-task instructions and overall instructions with 90% sub-task + delta_action: false # train with delta actions + state_mode: "MEAN_STD" # state normalization mode + select_video_keys: # which camera streams to load + [ + observation.images.head, + observation.images.hand_left, + observation.images.hand_right, + ] + select_state_keys: # proprioceptive states + [observation.states.joint.position, observation.states.effector.position] + select_action_keys: # action targets + [actions.joint.position, actions.effector.position] + effector_indices: [14, 15] # indices of effector channels in the flattened action vector + weight: 1.0 # dataset weight for sampling ``` ### 2. Fine-tuning on your dataset @@ -174,62 +216,64 @@ lerobot_datasets: To fine-tune **EO-1** on your own embodiment, you only need to adapt the configuration file. Specifically, convert your dataset into the LeRobot format, then define the fields that describe where your videos, states, and actions are located. The following YAML snippet shows a typical setup: ```yaml -# @multimodal corpora +# @multimodal data config +# leave empty if only robot control data mm_datasets: -# @robot control episodes lerobot_datasets: - - repo_id: AgiBotWorld-Beta/example001 # dataset identifier - root: /oss/vla_next/DATA # path to the dataset root directory - - # Optional fields: - train_subtask: mixture:0.9 # mix sub-task instructions and overall instructions with 90% sub-task - delta_action: false # train with delta actions + - repo_id: libero_spatial_no_noops_1.0.0_lerobot # replace with your dataset name + root: ./demo_data/ # replace with your dataset root path select_video_keys: [ - observation.images.head, - observation.images.hand_left, - observation.images.hand_right, - ] # which camera streams to load - select_state_keys: [ - observation.states.joint.position, - observation.states.effector.position, - ] # proprioceptive states - select_action_keys: [actions.joint.position, actions.effector.position] # the action targets to supervise during training - select_effector_keys: [actions.effector.position] # effector control channels - effector_indices: [14, 15] # indices of effector channels in the flattened action vector + observation.images.image, + observation.images.wrist_image, + ] # replace with your feature keys + select_state_keys: [observation.state] + select_action_keys: [action] + + - repo_id: libero_90_no_noops_lerobot + root: HF_LEROBOT_HOME + # If not specified, uses all keys by default ``` -Once your dataset is prepared and the configuration file (e.g., example.yaml) is set up, you can launch fine-tuning with the following command. We use torchrun to support distributed or multi-GPU training, while the arguments control training mode, optimization, and which model components to freeze or update. +Once your dataset is prepared and the configuration file (e.g., example.yaml) is set up, you can launch fine-tuning with the following command. We use torchrun to support distributed or multi-GPU training, while the arguments control training mode, optimization, and which model components to freeze or update. Please launch scripts to [experiments/1_demo](experiments/1_demo) and [experiments/2_libero](experiments/2_libero)to start a demo training. ```bash -torchrun $TORCH_RUN_ARGS onvisfm/train.py \ - ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ # load pre-trained model - --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ # load vlm backbone from Qwen2.5-VL-3B-Instruct - --train-lerobot-only True \ # w/o multimodal data - --data-path configs/example.yaml \ - --chunk-size 16 \ - --dataloader-num-workers 8 \ - --freeze-vision-tower False \ - --freeze-llm False \ - --freeze-merger False \ - --bf16 True \ - --tf32 True \ - --num-train-epochs 25 \ - --per-device-train-batch-size 64 \ - --learning-rate 5e-5 \ - --merger-lr 5e-5 \ - --vision-lr 1e-5 \ - --warmup-ratio 0.03 \ - --gradient-checkpointing True \ - --save-steps 2000 \ - --report-to wandb \ - --run-name bridge \ - --state-mode MAEN_STD +accelerate launch $ACCELERATE_ARGS scripts/train.py \ + ${model_name_or_path:+--model-name-or-path $model_name_or_path} \ + ${deepspeed:+--deepspeed configs/${deepspeed}.json} \ + --vlm-name-or-path ../pretrained/Qwen2.5-VL-3B-Instruct \ + --train-lerobot-only ${lerobot_only} \ + --data-path ${dataset} \ + --chunk-size ${chunk_size} \ + --dataloader-num-workers ${data_num_workers} \ + --freeze-vision-tower False \ + --freeze-llm False \ + --freeze-merger False \ + --bf16 True \ + --tf32 True \ + --fp16 False \ + --num-train-epochs ${epoch} \ + --per-device-train-batch-size ${PER_DEVICE_BATCH_SIZE} \ + --gradient-accumulation-steps 1 \ + --learning-rate ${lr} \ + --merger-lr ${mlr} \ + --vision-lr ${vlr} \ + --weight-decay 0.1 \ + --warmup-ratio 0.03 \ + --lr-scheduler-type cosine \ + --logging-steps ${logging_steps} \ + --gradient-checkpointing True \ + --save-strategy steps \ + --save-steps ${save_steps} \ + --save-total-limit 3 \ + --report-to ${report} \ + --run-name ${run_name} \ + --attn-implementation flash_attention_2 ``` ## Benchmark -Mastering Diverse Manipulations on Multiple Embodiments +Mastering Diverse Manipulations on Multiple Embodiments. More details can be found in [experiments/2_libero](experiments/2_libero/), [experiments/3_simpler](experiments/3_simpler/), and [experiments/8_vllmeval](experiments/8_vllmeval/). | Model | Franka Pick-and-Place (7 Tasks) | AgiBot Long-horizon Dexterity (4 Tasks) | WidowX Out-of-Box (13 Tasks) | Reasoning Control (4 Tasks) | | ------------ | ------------------------------- | --------------------------------------- | ---------------------------- | --------------------------- | @@ -260,6 +304,7 @@ Robot Control Benchmark Results ## 📅 Roadmap +- [x] 🤖 Release [EO-1](https://huggingface.co/IPEC-COMMUNITY/EO-1-3B) pretraining, finetune scripts, and documentations. - [ ] 🤗 Release [pre-training models](https://huggingface.co/collections/IPEC-COMMUNITY/eo-robotics-68ac4ff30e1f746cac28ca14) and experiment finetune scripts. - [ ] 🔥 Release Interleaved Dataset `EO-Data1.5M`, benchmark `EO-Bench` and all detailed pre-training code. - [ ] ⚡️ Efficient LLM Inference over Long Sequences, Efficient 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