def _get_seed_input(self, seed_input):
desired_shape = (<NUM_LIT:1>, ) + K.int_shape(self.input_tensor)[<NUM_LIT:1>:]<EOL>if seed_input is None:<EOL><INDENT>return utils.random_array(desired_shape, mean=np.mean(self.input_range),<EOL>std=<NUM_LIT> * (self.input_range[<NUM_LIT:1>] - self.input_range[<NUM_LIT:0>]))<EOL><DEDENT>if len(seed_input.shape) != len(desired_shape):<EOL><INDENT>seed_input = np.expand_dims(seed_input, <NUM_LIT:0>)<EOL><DEDENT>if seed_input.shape[-<NUM_LIT:1>] != desired_shape[-<NUM_LIT:1>] andseed_input.shape[<NUM_LIT:1>] != desired_shape[<NUM_LIT:1>]:<EOL><INDENT>seed_input = np.moveaxis(seed_input, -<NUM_LIT:1>, <NUM_LIT:1>)<EOL><DEDENT>return seed_input.astype(K.floatx())<EOL>
Creates a random `seed_input` if None. Otherwise:
            - Ensures batch_size dim on provided `seed_input`.
            - Shuffle axis according to expected `image_data_format`.