human
I wrote the below code

    n_validation = 1345
    def reshape_data(data):
        return data.reshape(((-1), 28, 28)).reshape(((-1), (28 * 28)), order='fortran')
    omni_raw = scipy.io.loadmat(os.path.join(config.DATA_DIR, config.OMNIGLOT))
    train_data = reshape_data(omni_raw['data'].T.astype('float32'))
    test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))
    if binarize:
        np.random.seed(5)
        train_data = (np.random.rand(*train_data.shape) < train_data).astype(float)
        test_data = (np.random.rand(*test_data.shape) < test_data).astype(float)
    shuffle_seed = 123
    permutation = np.random.RandomState(seed=shuffle_seed).permutation(train_data.shape[0])
    train_data = train_data[permutation]
    x_train = train_data[:(- n_validation)]
    x_valid = train_data[(- n_validation):]
    x_test = test_data
    return (x_train, x_valid, x_test)

What's a good function header?

Here's a good function header?
assistant
#Here's a good function header.

def read_omniglot(binarize=False):