code: def parse_rev_args(receive_msg):
      """parse reveive msgs to global variable"""
      global trainloader
      global testloader
      global net
  
      # Loading Data
      logger.debug("Preparing data..")
  
      (x_train, y_train), (x_test, y_test) = cifar10.load_data()
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255.0
      x_test /= 255.0
      trainloader = (x_train, y_train)
      testloader = (x_test, y_test)
  
      # Model
      logger.debug("Building model..")
      net = build_graph_from_json(receive_msg)
  
      # parallel model
      try:
          available_devices = os.environ["CUDA_VISIBLE_DEVICES"]
          gpus = len(available_devices.split(","))
          if gpus > 1:
              net = multi_gpu_model(net, gpus)
      except KeyError:
          logger.debug(
              "parallel model not support in this config settings"
          )
  
      if args.optimizer == "SGD":
          optimizer = SGD(
              lr=args.learning_rate,
              momentum=0.9,
              decay=args.weight_decay,
          )
      if args.optimizer == "Adadelta":
          optimizer = Adadelta(
              lr=args.learning_rate, decay=args.weight_decay
          )
      if args.optimizer == "Adagrad":
          optimizer = Adagrad(
              lr=args.learning_rate, decay=args.weight_decay
          )
      if args.optimizer == "Adam":
          optimizer = Adam(
              lr=args.learning_rate, decay=args.weight_decay
          )
      if args.optimizer == "Adamax":
          optimizer = Adamax(
              lr=args.learning_rate, decay=args.weight_decay
          )
      if args.optimizer == "RMSprop":
          optimizer = RMSprop(
              lr=args.learning_rate, decay=args.weight_decay
          )
  
      # Compile the model
      net.compile(
          loss="categorical_crossentropy",
          optimizer=optimizer,
          metrics=["accuracy"],
      )
      return 0
  
file_path: nni/examples/trials/network_morphism/cifar10/cifar10_keras.py
imports: import argparse
  import logging
  import os
  import tensorflow
  import keras
  from keras.callbacks import EarlyStopping, TensorBoard
  from keras.datasets import cifar10
  from keras.optimizers import SGD, Adadelta, Adagrad, Adam, Adamax, RMSprop
  from keras.utils import multi_gpu_model, to_categorical
  import keras.backend.tensorflow_backend
  import nni
  from nni.algorithms.hpo.networkmorphism_tuner.graph import json_to_graph
label_desc: Use image node_id 3 for calling a global function with example usage: parse_rev_args(receive_msg) and returns: int