class_docstr: The pixel normalization layer.
  
  This layer normalizes each feature vector of the feature map to 'unit' length
  as described in the paper:
  "Progressive growing of GANs for Improved Quality, Stability, and Variation"
  https://arxiv.org/pdf/1710.10196.pdf
  The per pixel length of the features is sqrt(C) with C the number of channels.
class_docstr_tok: ["The","pixel","normalization","layer",".","This","layer","normalizes","each","feature","vector","of","the","feature","map","to","'unit","'","length","as","described","in","the","paper",":","''","Progressive","growing","of","GANs","for","Improved","Quality",",","Stability",",","and","Variation","''","https",":","\/\/arxiv.org\/pdf\/1710.10196.pdf","The","per","pixel","length","of","the","features","is","sqrt","(","C",")","with","C","the","number","of","channels","."]
docstr: The call function that does the actual normalization.
docstr_tok: ["The","call","function","that","does","the","actual","normalization","."]
code: def call(self, inputs):
      """The call function that does the actual normalization."""
      return inputs * tf.math.rsqrt(
          tf.reduce_mean(
              tf.square(inputs), axis=self._axis, keepdims=True
          )
          + self._epsilon
      )
  
imports: import math
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, List
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling the PixelNormalization obj's underlying member method code with example usage: obj.call(inputs) and returns: unknown