Creates a random `seed_input` if None. Otherwise:
            - Ensures batch_size dim on provided `seed_input`.
            - Shuffle axis according to expected `image_data_format`.

def _get_seed_input(self, seed_input):
        """Creates a random `seed_input` if None. Otherwise:
            - Ensures batch_size dim on provided `seed_input`.
            - Shuffle axis according to expected `image_data_format`.
        """
        desired_shape = (1, ) + K.int_shape(self.input_tensor)[1:]
        if seed_input is None:
            return utils.random_array(desired_shape, mean=np.mean(self.input_range),
                                      std=0.05 * (self.input_range[1] - self.input_range[0]))

        # Add batch dim if needed.
        if len(seed_input.shape) != len(desired_shape):
            seed_input = np.expand_dims(seed_input, 0)

        # Only possible if channel idx is out of place.
        if seed_input.shape[-1] != desired_shape[-1] and \
           seed_input.shape[1] != desired_shape[1]:
            seed_input = np.moveaxis(seed_input, -1, 1)
        return seed_input.astype(K.floatx())