code: def pad_image_to_factor(
      image: Tensor, factor: int, mode: str = "reflect"
  ) -> Tuple[Tensor, Tuple[int, int]]:
      """
      Pads an image if it is not divisible by factor.
  
      For many neural autoencoders, performance suffers if the input image is not
      divisible by the downsampling factor. This utility function can be used to
      pad the input image with reflection padding to avoid such cases.
  
      Args:
          image: A 4-D PyTorch tensor with dimensions (B, C, H, W)
          factor: A factor by which the output image should be divisible.
  
      Returns:
          The image padded so that its dimensions are disible by factor, as well
          as the height and width.
      """
      # pad image if it's not divisible by downsamples
      _, _, height, width = image.shape
      pad_height = (factor - (height % factor)) % factor
      pad_width = (factor - (width % factor)) % factor
      if pad_height != 0 or pad_width != 0:
          image = F.pad(image, (0, pad_width, 0, pad_height), mode=mode)
  
      return image, (height, width)
  
label_desc: Use image node_id 1 for calling a global function with example usage: pad_image_to_factor(image, factor, mode) and returns: image,  height,  width