docs: def normalize(input_tensor, output_tensor):
      """Normalizes the `output_tensor` with respect to `input_tensor` dimensions.
      This makes regularizer weight factor more or less uniform across various input image dimensions.
  
      Args:
          input_tensor: An tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=
                  channels_first` or `(samples, image_dims..., channels)` if `image_data_format=channels_last`.
          output_tensor: The tensor to normalize.
  
      Returns:
          The normalized tensor.
      """
      image_dims = utils.get_img_shape(input_tensor)[1:]
      return output_tensor / np.prod(image_dims)