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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_cifar10(args):
 """ data_transforms for cifar10 dataset """


    cifar_mean = [0.49139968, 0.48215827, 0.44653124]
    cifar_std = [0.24703233, 0.24348505, 0.26158768]

    train_transform = transforms.Compose(
        [
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize(cifar_mean, cifar_std),
        ]
    )
    if args.cutout:
        train_transform.transforms.append(Cutout(args.cutout_length))

    valid_transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std)]
    )
    return train_transform, valid_transform