code: def test_train_script(dataset_name, custom_dataset):
      # we need to write some data for this test, so we use a temporary directory
      with tempfile.TemporaryDirectory() as temp_dir:
          temp_dir_path = Path(temp_dir)
          dataset = get_dataset(
              dataset_name, path=temp_dir_path, regenerate=True
          )  # exchange_rate, m4_yearly
  
          # either use provided dataset, in which case it must be present in the directory, or a built in one
          # for testing we will provide a built in dataset as a custom one too
          if custom_dataset:
              args = create_arguments(
                  str(temp_dir_path),
                  dataset_name,
                  s3_dataset_path=str(temp_dir_path / dataset_name),
              )
          else:
              args = create_arguments(str(temp_dir_path), dataset_name)
  
          # the test requires using a deserialized estimator, which we first need to create
          (
              estimator_cls,
              hyperparameters,
          ) = simple_feedforward_estimator()
          estimator = estimator_cls.from_hyperparameters(
              prediction_length=dataset.metadata.prediction_length,
              freq=dataset.metadata.freq,
              **hyperparameters,
          )
          serialized = serde.dump_json(estimator)
          with open(
              temp_dir_path / "estimator.json", "w"
          ) as estimator_file:
              estimator_file.write(serialized)
  
          # No assert necessary, the idea is just that the code needs to run through
          train(args)
  
file_path: gluonts/test/nursery/sagemaker_sdk/test_entry_point_scripts.py
imports: import argparse
  import platform
  import tempfile
  from pathlib import Path
  import pytest
  from gluonts.core import serde
  from gluonts.dataset.repository import get_dataset
  from gluonts.mx import SimpleFeedForwardEstimator
  from gluonts.nursery.sagemaker_sdk.defaults import NUM_SAMPLES, QUANTILES
  from gluonts.nursery.sagemaker_sdk.entry_point_scripts.train_entry_point import train
label_desc: Use image node_id 3 for calling a global function with example usage: test_train_script(dataset_name, custom_dataset) without return types