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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
 """ data_transforms for mnist dataset """

    if mnist_mean is None:
        mnist_mean = [0.5]

    if mnist_std is None:
        mnist_std = [0.5]

    train_transform = transforms.Compose(
        [
            transforms.RandomCrop(28, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize(mnist_mean, mnist_std),
        ]
    )
    if args.cutout:
        train_transform.transforms.append(Cutout(args.cutout_length))

    valid_transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize(mnist_mean, mnist_std)]
    )
    return train_transform, valid_transform