egg/zoo/mnist_autoenc/train.py [58:82]:
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def main(params):
    # initialize the egg lib
    opts = core.init(params=params)
    # get pre-defined common line arguments (batch/vocab size, etc).
    # See egg/core/util.py for a list

    # prepare the dataset
    kwargs = {"num_workers": 1, "pin_memory": True} if opts.cuda else {}
    transform = transforms.ToTensor()

    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST("./data", train=True, download=True, transform=transform),
        batch_size=opts.batch_size,
        shuffle=True,
        **kwargs
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST("./data", train=False, transform=transform),
        batch_size=opts.batch_size,
        shuffle=True,
        **kwargs
    )

    # initialize the agents and the game
    sender = Sender(opts.vocab_size)  # the "data" transform part of an agent
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egg/zoo/mnist_vae/train.py [114:132]:
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def main(params):
    opts = core.init(params=params)
    kwargs = {"num_workers": 1, "pin_memory": True} if opts.cuda else {}
    transform = transforms.ToTensor()

    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST("./data", train=True, download=True, transform=transform),
        batch_size=opts.batch_size,
        shuffle=True,
        **kwargs
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST("./data", train=False, transform=transform),
        batch_size=opts.batch_size,
        shuffle=True,
        **kwargs
    )

    sender = Sender(opts.vocab_size)
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