code: def execute_job_functions(step_args: _StepArguments):
      """Execute the job class functions during pipeline definition construction
  
      Executes the job functions such as run(), fit(), or transform() that have been
      delayed until the pipeline gets built, for steps built with a PipelineSession.
  
      Handles multiple functions in instances where job functions are chained
      together from the inheritance of different job classes (e.g. PySparkProcessor,
      ScriptProcessor, and Processor).
  
      Args:
          step_args (_StepArguments): A `_StepArguments` object to be used for composing
              a pipeline step, contains the necessary function information
      """
  
      chained_args = step_args.func(
          *step_args.func_args, **step_args.func_kwargs
      )
      if isinstance(chained_args, _StepArguments):
          execute_job_functions(chained_args)
  
imports: from __future__ import absolute_import
  import inspect
  import logging
  from functools import wraps
  from pathlib import Path
  from typing import List, Sequence, Union, Set, TYPE_CHECKING, Optional
  import hashlib
  from urllib.parse import unquote, urlparse
  from contextlib import contextmanager
  from _hashlib import HASH
  from sagemaker.utils import base_from_name
  from sagemaker.workflow.parameters import Parameter
  from sagemaker.workflow.pipeline_context import _StepArguments, _PipelineConfig
  from sagemaker.workflow.entities import Entity, RequestType
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
label_desc: Use image node_id 17 for calling a global function with example usage: execute_job_functions(step_args) without return types