class_docstr: Demodulation layer for StyleGAN.
  
  This layer applies the Demodulated Convolution layer from StyleGANv2
  that is used in the paper "
  Analyzing and Improving the Image Quality of StyleGAN".
  https://arxiv.org/pdf/1912.04958.pdf
  
  It takes in the style input and applies demodulation on the features.
class_docstr_tok: ["Demodulation","layer","for","StyleGAN",".","This","layer","applies","the","Demodulated","Convolution","layer","from","StyleGANv2","that","is","used","in","the","paper","``","Analyzing","and","Improving","the","Image","Quality","of","StyleGAN","''",".","https",":","\/\/arxiv.org\/pdf\/1912.04958.pdf","It","takes","in","the","style","input","and","applies","demodulation","on","the","features","."]
code: def build(self, input_shape: Sequence[tf.TensorShape]) -> None:
      feature_map_shape, _ = input_shape
      self._dense_layer = FanInScaledDense(
          feature_map_shape[-1],
          multiplier=1.0,
          kernel_initializer=self._kernel_initializer,
          bias_initializer=self._bias_initializer,
      )
      self._bias = self.add_weight(
          shape=(self._filters,),
          name="bias",
          initializer=self._bias_initializer,
          trainable=True,
      )
  
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 DemodulatedConvolution obj's underlying member method code with example usage: obj.build(input_shape) without return types