class_docstr: Layer Normalization (LayerNorm) without output bias and gain.
class_docstr_tok: ["Layer","Normalization","(","LayerNorm",")","without","output","bias","and","gain","."]
docstr: Apply Layer Normalization without output bias and gain.
  
  Args:
    inputs: tensor to be normalized. Axis should be smaller than input tensor
      dimensions.
    **kwargs: more arguments (unused)
  
  Returns:
    tensor output.
docstr_tok: ["Apply","Layer","Normalization","without","output","bias","and","gain",".","Args",":","inputs",":","tensor","to","be","normalized",".","Axis","should","be","smaller","than","input","tensor","dimensions",".","*","*","kwargs",":","more","arguments","(","unused",")","Returns",":","tensor","output","."]
code: def call(self, inputs, **kwargs):
      """Apply Layer Normalization without output bias and gain.
  
      Args:
        inputs: tensor to be normalized. Axis should be smaller than input tensor
          dimensions.
        **kwargs: more arguments (unused)
  
      Returns:
        tensor output.
      """
      inputs -= tf.reduce_mean(inputs, axis=self.axis, keepdims=True)
      inputs += self.bias
      variance = tf.reduce_mean(
          tf.square(inputs), self.axis, keepdims=True
      )
      return inputs * tf.math.rsqrt(variance + self.epsilon)
  
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 3 for calling the LayerNormalization obj's underlying member method code with example usage: obj.call(inputs) and returns: unknown