code: def LayerNorm(
          x, epsilon=1e-5,
          use_bias=True, use_scale=True,
          gamma_init=None, data_format='channels_last'):
      
      data_format = get_data_format(data_format, keras_mode=False)
      shape = x.get_shape().as_list()
      ndims = len(shape)
      assert ndims in [2, 4]
  
      mean, var = tf.nn.moments(x, list(range(1, len(shape))), keep_dims=True)
  
      if data_format == 'NCHW':
          chan = shape[1]
          new_shape = [1, chan, 1, 1]
      else:
          chan = shape[-1]
          new_shape = [1, 1, 1, chan]
      if ndims == 2:
          new_shape = [1, chan]
  
      if use_bias:
          beta = tf.get_variable('beta', [chan], initializer=tf.constant_initializer())
          beta = tf.reshape(beta, new_shape)
      else:
          beta = tf.zeros([1] * ndims, name='beta')
      if use_scale:
          if gamma_init is None:
              gamma_init = tf.constant_initializer(1.0)
          gamma = tf.get_variable('gamma', [chan], initializer=gamma_init)
          gamma = tf.reshape(gamma, new_shape)
      else:
          gamma = tf.ones([1] * ndims, name='gamma')
  
      ret = tf.nn.batch_normalization(x, mean, var, beta, gamma, epsilon, name='output')
  
      vh = ret.variables = VariableHolder()
      if use_scale:
          vh.gamma = gamma
      if use_bias:
          vh.beta = beta
      return ret
docs: Layer Normalization layer, as described in the paper:
      `Layer Normalization <https://arxiv.org/abs/1607.06450>`_.
  
      Args:
          x (tf.Tensor): a 4D or 2D tensor. When 4D, the layout should match data_format.
          epsilon (float): epsilon to avoid divide-by-zero.
          use_scale, use_bias (bool): whether to use the extra affine transformation or not.