code: def make_multiscale(
      image,
      resolutions,
      resize_method=tf.image.ResizeMethod.BICUBIC,
      num_channels=3,
  ):
      """Returns list of scaled images, one for each resolution.
  
      Args:
        image: Tensor of shape [height, height, num_channels].
        resolutions: List of heights that image's height is resized to.
        resize_method: tf.image.ResizeMethod.
        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].
      """
      scaled_images = []
      for height in resolutions:
          scaled_image = tf.image.resize_images(
              image,
              size=[height, height],  # assuming that height = width
              method=resize_method,
          )
          scaled_image = tf.to_int64(scaled_image)
          scaled_image.set_shape([height, height, num_channels])
          scaled_images.append(scaled_image)
  
      return scaled_images
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import io
  import os
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import metrics
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: make_multiscale(image, resolutions, resize_method, num_channels) and returns: scaled_images