code: def main():
      print("Torch version:", torch.__version__)
  
      # get command-line arguments
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=25,
          help="number of epochs to train",
      )
      parser.add_argument(
          "--output_dir", type=str, help="output directory"
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=0.001,
          help="learning rate",
      )
      parser.add_argument(
          "--momentum", type=float, default=0.9, help="momentum"
      )
      args = parser.parse_args()
  
      data_dir = download_data()
      print("data directory is: " + data_dir)
      model = fine_tune_model(
          args.num_epochs, data_dir, args.learning_rate, args.momentum
      )
      os.makedirs(args.output_dir, exist_ok=True)
      torch.save(model, os.path.join(args.output_dir, "model.pt"))
  
file_path: MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
imports: from __future__ import print_function, division
  import torch
  import torch.nn
  import torch.optim
  from torch.optim import lr_scheduler
  from torchvision import datasets, models, transforms
  import numpy
  import time
  import os
  import copy
  import argparse
  from azureml.core.run import Run
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types