code: def make_multiscale_dilated(image, resolutions, num_channels=3):
    
    image_height = common_layers.shape_list(image)[0]
    scaled_images = []
    for height in resolutions:
      dilation_rate = image_height // height  # assuming height = width
      scaled_image = image[::dilation_rate, ::dilation_rate]
      scaled_image = tf.to_int64(scaled_image)
      scaled_image.set_shape([None, None, num_channels])
      scaled_images.append(scaled_image)
    return scaled_images
docs: Returns list of scaled images, one for each resolution.
  
    Resizes by skipping every nth pixel.
  
    Args:
      image: Tensor of shape [height, height, num_channels].
      resolutions: List of heights that image's height is resized to. The function
        assumes VALID padding, so the original image's height must be divisible
        by each resolution's height to return the exact resolution size.
      num_channels: Number of channels in image.
  
    Returns:
      List of Tensors, one for each resolution with shape given by
      [resolutions[i], resolutions[i], num_channels] if resolutions properly
      divide the original image's height; otherwise shape height and width is up
      to valid skips.