class_docstr: Layer Normalization (LayerNorm) without output bias and gain.
class_docstr_tok: ["Layer","Normalization","(","LayerNorm",")","without","output","bias","and","gain","."]
docstr: Initialize Layer Normalization layer.
  
  Args:
    axis: Tuple or number of axis for calculating mean and variance
    epsilon: Small epsilon to avoid division by zero
    **kwargs: keyword args passed to super.
docstr_tok: ["Initialize","Layer","Normalization","layer",".","Args",":","axis",":","Tuple","or","number","of","axis","for","calculating","mean","and","variance","epsilon",":","Small","epsilon","to","avoid","division","by","zero","*","*","kwargs",":","keyword","args","passed","to","super","."]
code: def __init__(self, axis=1, epsilon=1e-10, **kwargs):
      """Initialize Layer Normalization layer.
  
      Args:
        axis: Tuple or number of axis for calculating mean and variance
        epsilon: Small epsilon to avoid division by zero
        **kwargs: keyword args passed to super.
      """
      self.axis = axis
      self.epsilon = epsilon
      self.bias = None
      super(LayerNormalization, self).__init__(**kwargs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.layers.common_layers import gelu
  from tensor2tensor.models.research.shuffle_network import reverse_shuffle_layer
  from tensor2tensor.models.research.shuffle_network import shuffle_layer
  from tensor2tensor.models.research.shuffle_network import ShuffleNetwork
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 to create a new LayerNormalization object from inherited base classes: tf.keras.layers with example: obj = LayerNormalization(axis, epsilon)