code: def test_step_decorator_with_include_local_workdir(
      sagemaker_session,
      role,
      pipeline_name,
      region_name,
      monkeypatch,
      dummy_container_without_error,
  ):
      os.environ["AWS_DEFAULT_REGION"] = region_name
      source_dir_path = os.path.join(os.path.dirname(__file__))
      original_sagemaker_config = sagemaker_session.sagemaker_config
      with monkeypatch.context() as m:
          m.chdir(source_dir_path)
          sagemaker_config = load_sagemaker_config(
              [os.path.join(DATA_DIR, "workflow", "config.yaml")]
          )
          sagemaker_session.sagemaker_config = sagemaker_config
          dependencies_path = os.path.join(
              DATA_DIR, "workflow", "requirements.txt"
          )
  
          @step(
              role=role,
              instance_type=INSTANCE_TYPE,
              dependencies=dependencies_path,
              keep_alive_period_in_seconds=300,
              image_uri=dummy_container_without_error,
          )
          def train(x):
              from workdir_helpers import local_module
              from workdir_helpers.nested_helper import local_module2
  
              output = local_module.square(x) + local_module2.cube(x)
              print(output)
              return output
  
          step_result = train(2)
  
          pipeline = Pipeline(
              name=pipeline_name,
              steps=[step_result],
              sagemaker_session=sagemaker_session,
          )
  
          try:
              create_and_execute_pipeline(
                  pipeline=pipeline,
                  pipeline_name=pipeline_name,
                  region_name=region_name,
                  role=role,
                  no_of_steps=1,
                  last_step_name="train",
                  execution_parameters=dict(),
                  step_status="Succeeded",
                  step_result_type=int,
                  step_result_value=12,
              )
          finally:
              try:
                  pipeline.delete()
              except Exception:
                  pass
      sagemaker_session.sagemaker_config = original_sagemaker_config
  
imports: from __future__ import absolute_import
  import json
  import numpy
  import pytest
  import os
  import random
  from sagemaker import get_execution_role, utils
  from sagemaker.config import load_sagemaker_config
  from sagemaker.processing import ProcessingInput
  from sagemaker.remote_function.errors import RemoteFunctionError
  from sagemaker.sklearn import SKLearnProcessor
  from sagemaker.remote_function.core.serialization import CloudpickleSerializer
  from sagemaker.s3 import S3Uploader
  from sagemaker.s3_utils import s3_path_join
  from sagemaker.workflow.conditions import ConditionLessThan
  from sagemaker.workflow.fail_step import FailStep
  from sagemaker.workflow.function_step import step
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionEquals
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.parameters import ParameterString, ParameterInteger, ParameterFloat, ParameterBoolean
  from sagemaker.workflow.step_outputs import get_step
  from sagemaker.workflow.steps import ProcessingStep
  from tests.integ.sagemaker.workflow.helpers import create_and_execute_pipeline, wait_pipeline_execution
  from tests.integ import DATA_DIR
label_desc: Use image node_id 11 for calling a global function with example usage: test_step_decorator_with_include_local_workdir(sagemaker_session, role, pipeline_name, region_name, monkeypatch, dummy_container_without_error) and returns: output