def random_normal(shape, mean=0.0, std=1.0, dtype=_FLOATX, seed=None):
    if seed is not None:
        seed = np.random.randint(10e6)
    return tf.random_normal(shape, mean=mean, stddev=std,
                            dtype=dtype, seed=seed)
dataset/ETHPy150Open fchollet/keras/keras/backend/tensorflow_backend.py/random_normal/operator `is` -> `is not`