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: Returns the config of the layer.
docstr_tok: ["Returns","the","config","of","the","layer","."]
code: def get_config(self):
      """Returns the config of the layer."""
      config = {"axis": self._axis, "epsilon": self._epsilon}
      base_config = super().get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
imports: import math
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, List
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the PixelNormalization obj's underlying member method code with example usage: obj.get_config() and returns: dict