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.

def make_multiscale_dilated(image, resolutions, num_channels=3):
  """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.
  """
  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