code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--epochs", type=int, default=10)
      parser.add_argument(
          "--train", type=str, default=os.environ["SM_CHANNEL_TRAINING"]
      )
      parser.add_argument("--model_dir", type=str)
      parser.add_argument("--max-steps", type=int, default=200)
      parser.add_argument(
          "--save-checkpoint-steps", type=int, default=200
      )
      parser.add_argument("--throttle-secs", type=int, default=60)
      parser.add_argument(
          "--hosts",
          type=list,
          default=json.loads(os.environ["SM_HOSTS"]),
      )
      parser.add_argument(
          "--current-host",
          type=str,
          default=os.environ["SM_CURRENT_HOST"],
      )
      parser.add_argument("--batch-size", type=int, default=100)
      parser.add_argument(
          "--export-model-during-training", type=bool, default=False
      )
      return parser.parse_args()
  
file_path: sagemaker-python-sdk/tests/data/tensorflow_mnist/mnist_v2.py
imports: import argparse
  import numpy
  import os
  import json
  import tensorflow
  from tensorflow.keras.layers import Conv2D, BatchNormalization, Dense, Flatten
label_desc: Use image node_id 4 for calling a global function with example usage: parse_args() and returns: parser