code: def parse_rev_args(receive_msg):
      """parse reveive msgs to global variable"""
      global trainloader
      global testloader
      global net
      global criterion
      global optimizer
  
      # Loading Data
      logger.debug("Preparing data..")
  
      raw_train_data = torchvision.datasets.FashionMNIST(
          root="./data", train=True, download=True
      )
  
      dataset_mean, dataset_std = (
          [raw_train_data.train_data.float().mean() / 255],
          [raw_train_data.train_data.float().std() / 255],
      )
  
      transform_train, transform_test = utils.data_transforms_mnist(
          args, dataset_mean, dataset_std
      )
  
      trainset = torchvision.datasets.FashionMNIST(
          root="./data",
          train=True,
          download=True,
          transform=transform_train,
      )
      trainloader = torch.utils.data.DataLoader(
          trainset,
          batch_size=args.batch_size,
          shuffle=True,
          num_workers=2,
      )
  
      testset = torchvision.datasets.FashionMNIST(
          root="./data",
          train=False,
          download=True,
          transform=transform_test,
      )
      testloader = torch.utils.data.DataLoader(
          testset,
          batch_size=args.batch_size,
          shuffle=False,
          num_workers=2,
      )
  
      # Model
      logger.debug("Building model..")
      net = build_graph_from_json(receive_msg)
  
      net = net.to(device)
      criterion = nn.CrossEntropyLoss()
  
      if args.optimizer == "SGD":
          optimizer = optim.SGD(
              net.parameters(),
              lr=args.learning_rate,
              momentum=0.9,
              weight_decay=5e-4,
          )
      if args.optimizer == "Adadelta":
          optimizer = optim.Adadelta(
              net.parameters(), lr=args.learning_rate
          )
      if args.optimizer == "Adagrad":
          optimizer = optim.Adagrad(
              net.parameters(), lr=args.learning_rate
          )
      if args.optimizer == "Adam":
          optimizer = optim.Adam(
              net.parameters(), lr=args.learning_rate
          )
      if args.optimizer == "Adamax":
          optimizer = optim.Adamax(
              net.parameters(), lr=args.learning_rate
          )
      if args.optimizer == "RMSprop":
          optimizer = optim.RMSprop(
              net.parameters(), lr=args.learning_rate
          )
  
      return 0
  
file_path: nni/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py
imports: import argparse
  import logging
  import sys
  import nni
  from nni.networkmorphism_tuner.graph import json_to_graph
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  import utils
label_desc: Use image node_id 3 for calling a global function with example usage: parse_rev_args(receive_msg) and returns: int