code: def optimize_memory(model):
      """Save GPU memory through blob sharing."""
      for device in range(cfg.NUM_GPUS):
          namescope = "gpu_{}/".format(device)
          losses = [namescope + l for l in model.losses]
          model.net._net = memonger.share_grad_blobs(
              model.net,
              losses,
              set(model.param_to_grad.values()),
              namescope,
              share_activations=cfg.MEMONGER_SHARE_ACTIVATIONS,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import cv2
  import logging
  import numpy
  import os
  import re
  from caffe2.python import memonger
  from caffe2.python import workspace
  from detectron.core.config import cfg
  from detectron.core.config import get_output_dir
  from detectron.datasets.roidb import combined_roidb_for_training
  from detectron.modeling import model_builder
  from detectron.utils import lr_policy
  from detectron.utils.training_stats import TrainingStats
  import detectron.utils.env
  import detectron.utils.net
label_desc: Use image node_id 3 for calling a global function with example usage: optimize_memory(model) without return types