Adding a basic example model for experimentation and testing.

[drewoldag]

No description provided.

[Copilot]

Pull Request Overview

This PR replaces a basic example module with functions with a more comprehensive example PyTorch model for experimentation and testing purposes. The change shifts from simple greeting/meaning functions to a complete neural network model implementation.

• Removes the existing example module containing greeting and meaning functions along with their tests
• Adds a new ExampleModel class that implements a PyTorch neural network with hyrax model registry integration
• Includes a default configuration file for the model parameters

Reviewed Changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
tests/external_hyrax_example/test_example_module.py	Removes tests for the old example module functions
src/external_hyrax_example/example_module.py	Removes the old example module with greeting and meaning functions
src/external_hyrax_example/example_model.py	Adds new PyTorch model class with training logic and tensor conversion
src/external_hyrax_example/default_config.toml	Adds configuration file for model parameters
src/external_hyrax_example/init.py	Updates imports to expose ExampleModel instead of old functions

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[Copilot]

Missing input validation for data_sample parameter. The code assumes data_sample is not None and has the expected nested structure, but could fail if data_sample is None or missing the 'data'/'image' keys.

Suggested change

	super().__init__()
	super().__init__()
	# Input validation for data_sample
	if (
	data_sample is None
	or not isinstance(data_sample, dict)
	or "data" not in data_sample
	or not isinstance(data_sample["data"], dict)
	or "image" not in data_sample["data"]
	or not hasattr(data_sample["data"]["image"], "shape")
	):
	raise ValueError(
	"data_sample must be a dict with structure: {'data': {'image': <array with .shape>}}"
	)

[Copilot]

The forward method expects a flattened tensor but receives raw input. The tensor needs to be flattened before passing to the linear layer. Should be return self.linear(x.view(x.size(0), -1)).

Suggested change

	return self.linear(x)
	return self.linear(x.view(x.size(0), -1))

[Copilot]

Missing input validation and unclear indexing logic. The code assumes data_dict has the expected structure and that 'image' is indexable, but there's no validation. The [0] indexing is also unclear - consider adding a comment explaining why only the first element is taken.

Suggested change

	image = data_dict["data"]["image"][0]
	label = data_dict["data"]["label"]
	return (torch.tensor(image, dtype=torch.float32), torch.tensor(label, dtype=torch.float32))
	# Validate input structure
	if not isinstance(data_dict, dict):
	raise ValueError("Expected data_dict to be a dict.")
	if "data" not in data_dict or not isinstance(data_dict["data"], dict):
	raise ValueError("Expected data_dict to have a 'data' key containing a dict.")
	data = data_dict["data"]
	if "image" not in data or "label" not in data:
	raise ValueError("Expected 'data' dict to contain 'image' and 'label' keys.")
	image = data["image"]
	# Assume 'image' is a batch; take the first sample
	if not hasattr(image, "__getitem__") or len(image) == 0:
	raise ValueError("'image' must be a non-empty indexable object (e.g., list, array).")
	image_sample = image[0]
	label = data["label"]
	return (torch.tensor(image_sample, dtype=torch.float32), torch.tensor(label, dtype=torch.float32))

[Copilot]

Inconsistent tensor handling between train_step and forward methods. The train_step method passes x directly to forward(), but forward() expects a flattened tensor. This will cause a shape mismatch error.