import tensorflow as tf

                         dtype=_x.dtype)
    input_shape = list(_x.get_shape())

    reduction_axes = list(range(len(input_shape)))
    del reduction_axes[axis]
    broadcast_shape = [1] * len(input_shape)
    broadcast_shape[axis] = input_shape[axis]

    # case: train mode (uses stats of the current batch)
    mean = tf.reduce_mean(_x, axis=reduction_axes)
    brodcast_mean = tf.reshape(mean, broadcast_shape)
    std = tf.reduce_mean(tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes)
    std = tf.sqrt(std)
    brodcast_std = tf.reshape(std, broadcast_shape)
    x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon)
    # x_normed = tf.layers.batch_normalization(_x, center=False, scale=False)
    x_p = tf.sigmoid(x_normed)

