def METHOD_NAME(rng, x_shape, batch_axis, no_scale, no_bias):
    x = rng.randn(*x_shape).astype(np.float32)
    stat_shape = list(x_shape)
    for baxis in _force_list(batch_axis):
        stat_shape[baxis+len(x_shape)*(baxis < 0)] = 1
    beta = None if no_bias else rng.randn(*stat_shape).astype(np.float32)
    gamma = None if no_scale else rng.randn(*stat_shape).astype(np.float32)
    return x, beta, gamma