code: def accumulate(model1, model2, decay=0.999):
      par1 = model1.collect_params()
      par2 = model2.collect_params()
      par1.reset_ctx(mx.cpu())
      par2.reset_ctx(mx.cpu())
  
      requires_grad(model1, False)
      key_dict = {
          "hybridsequential0": "hybridsequential5",
          "hybridsequential1": "hybridsequential6",
          "hybridsequential2": "hybridsequential7",
      }
  
      for k in par2.keys():
          k2 = k.split("_")[0]
          k1 = k.replace(k2, key_dict[k2], 1)
          par1[k1].set_data(
              par1[k1].data() * decay + ((1 - decay) * par2[k].data())
          )
  
      par1.reset_ctx(mx.gpu())
      par2.reset_ctx(context)
  
imports: import os
  import random
  import math
  import argparse
  import logging
  import os.path
  from io import BytesIO
  import lmdb
  import numpy
  from PIL import Image
  from tqdm import tqdm
  from mpl_toolkits.axes_grid1 import ImageGrid
  import matplotlib.pyplot
  import mxnet
  import mxnet.ndarray
  from mxnet import gluon, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.gluon.data import DataLoader
  from model import StyledGenerator, Discriminator
label_desc: Use image node_id 2 for calling a global function with example usage: accumulate(model1, model2, decay) without return types