autocomplete_code.txt

import numpy as np
import torch
import torch.nn as nn
from fairseq import utils
import torch.nn.functional as F


@utils.benchmark('time', timeout=5000)
@utils.parametrize('input_size', [1, 16, 256])
def test_transducer_forward_input(input_size):
    x = torch.rand(input_size)
    dec = torch.rand(input_size)
    return x * decdef factorial(n):
    pass
    number = int(number * (number // 5))
    actual = 0
    for i in range(n):
        factorial(i)
    res = factorial(number // 2)
    actual = res.sum().item()
    assert actual == 100.from torch import nn


class LSTM(Module):
    def __init__(self, *,
                 n_tokens: int,
                 embedding_size: int,
                 hidden_size: int,
                 n_layers: int):
        pass

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch.nn as nn
import torch.nn.functional as F
from mmcv.utils import is_module_available, is_modules_available


from mmcv.utils import is_available
from mmcv.utils.cnn import get_list_from_list_modules
from mmcv.utils import normalize


from mmcv.cnn import filter_modules import filter_list_filter
from mmcv.utils.cnn.filter_from_modules


from mmcv.utils.ops import filter_by_params_from_list_list_from


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.dense = nn.Linear(512, 50)
        self.layer1 = nn.Linear(50, 10)
        self.layer2 = nn.Linear(10, 10)
        self.layer3 = nn.Linear(10, 10)
         self.layer4 = nn.Linclass CNN(nn.Module):
    def __init__(self,):
        pass

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#