class_docstr: The :class:`LayerNorm` class is for layer normalization, see `tf.contrib.layers.layer_norm <https://www.tensorflow.org/api_docs/python/tf/contrib/layers/layer_norm>`__.
  
  Parameters
  ----------
  prev_layer : :class:`Layer`
      The previous layer.
  act : activation function
      The activation function of this layer.
  others : _
      `tf.contrib.layers.layer_norm <https://www.tensorflow.org/api_docs/python/tf/contrib/layers/layer_norm>`__.
class_docstr_tok: ["The",":","class",":","`","LayerNorm","`","class","is","for","layer","normalization",",","see","`","tf.contrib.layers.layer_norm","<","https",":","\/\/www.tensorflow.org\/api_docs\/python\/tf\/contrib\/layers\/layer_norm",">","`","__",".","Parameters","--","--","--","--","--","prev_layer",":",":","class",":","`","Layer","`","The","previous","layer",".","act",":","activation","function","The","activation","function","of","this","layer",".","others",":","_","`","tf.contrib.layers.layer_norm","<","https",":","\/\/www.tensorflow.org\/api_docs\/python\/tf\/contrib\/layers\/layer_norm",">","`","__","."]
code: def forward(self, inputs):
      mean, var = tf.nn.moments(inputs, self.norm_axes, keepdims=True)
      # compute layer normalization using batch_normalization function
      outputs = batch_normalization(
          inputs,
          mean,
          var,
          self.beta,
          self.gamma,
          self.epsilon,
          data_format=self.data_format,
      )
      if self.act:
          outputs = self.act(outputs)
      return outputs
  
imports: import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import moving_averages
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 3 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs