code: def parse_args():
      """
      Parse arguments passed from the SageMaker API to the container.
      """
  
      parser = argparse.ArgumentParser()
  
      # Hyperparameters sent by the client are passed as command-line arguments to the script
      parser.add_argument("--epochs", type=int, default=1)
      parser.add_argument("--batch_size", type=int, default=1024)
  
      # Data directories
      parser.add_argument(
          "--train_dir",
          type=str,
          default=os.environ.get("SM_CHANNEL_TRAIN"),
      )
      parser.add_argument(
          "--valid_dir",
          type=str,
          default=os.environ.get("SM_CHANNEL_VALID"),
      )
  
      # Model directory: we will use the default set by SageMaker, /opt/ml/model
      parser.add_argument(
          "--model_dir",
          type=str,
          default=os.environ.get("SM_MODEL_DIR"),
      )
  
      return parser.parse_known_args()
  
file_path: Merlin/examples/sagemaker-tensorflow/train.py
imports: import argparse
  import json
  import logging
  import os
  import sys
  import tempfile
  import merlin.io
  import merlin.models.tf
  import nvtabular
  import tensorflow
  from merlin.schema.tags import Tags
  from merlin.systems.dag.ops.workflow import TransformWorkflow
  from merlin.systems.dag.ops.tensorflow import PredictTensorflow
  from merlin.systems.dag.ensemble import Ensemble
  import numpy
  from nvtabular.ops import *
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser