code: def _new_convolution(self, use_bias):
      
      def clean_dict(input_dict):
        if input_dict and not use_bias:
          cleaned_dict = input_dict.copy()
          cleaned_dict.pop("b", None)
          return cleaned_dict
        return input_dict
      return self._conv_class(
          output_channels=4*self._output_channels,
          kernel_shape=self._kernel_shape,
          stride=self._stride,
          rate=self._rate,
          padding=self._padding,
          use_bias=use_bias,
          initializers=clean_dict(self._initializers),
          partitioners=clean_dict(self._partitioners),
          regularizers=clean_dict(self._regularizers),
          name="conv")
docs: Returns new convolution.
  
      Args:
        use_bias: Use bias in convolutions. If False, clean_dict removes bias
          entries from initializers, partitioners and regularizers passed to
          the constructor of the convolution.