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) = fashion_mnist.load_data()
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
      x_train = x_train.reshape(x_train.shape + (1,)).astype("float32")
      x_test = x_test.reshape(x_test.shape + (1,)).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/FashionMNIST/FashionMNIST_keras.py
imports: import argparse
  import logging
  import os
  import tensorflow
  import keras
  from keras.callbacks import EarlyStopping, TensorBoard
  from keras.datasets import fashion_mnist
  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.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