class_docstr: Layer Normalization (LayerNorm) without output bias and gain.
class_docstr_tok: ["Layer","Normalization","(","LayerNorm",")","without","output","bias","and","gain","."]
docstr: Initialize bias weights for layer normalization.
  
  Args:
    input_shape: shape of input tensor
docstr_tok: ["Initialize","bias","weights","for","layer","normalization",".","Args",":","input_shape",":","shape","of","input","tensor"]
code: def build(self, input_shape):
      """Initialize bias weights for layer normalization.
  
      Args:
        input_shape: shape of input tensor
      """
      num_units = input_shape.as_list()[-1]
      self.bias = self.add_weight(
          "bias", [1, 1, num_units], initializer=tf.zeros_initializer
      )
      super(LayerNormalization, self).build(input_shape)
  
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 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.build(input_shape) without return types