code: def retrieve_caller_name(job_instance):
      """Convenience method for runnable_by_pipeline decorator
  
      This function takes an instance of a job class and maps it
      to the pipeline session function that creates the job request.
  
      Args:
          job_instance: A job class instance, one of the following
              imported types
      """
  
      from sagemaker.processing import Processor
      from sagemaker.estimator import EstimatorBase
      from sagemaker.transformer import Transformer
      from sagemaker.tuner import HyperparameterTuner
      from sagemaker.automl.automl import AutoML
  
      if isinstance(job_instance, Processor):
          return "process"
      if isinstance(job_instance, EstimatorBase):
          return "train"
      if isinstance(job_instance, Transformer):
          return "transform"
      if isinstance(job_instance, HyperparameterTuner):
          return "create_tuning_job"
      if isinstance(job_instance, AutoML):
          return "auto_ml"
  
      return None
  
imports: from __future__ import absolute_import
  import warnings
  import inspect
  from functools import wraps
  from typing import Dict, Optional, Callable
  from sagemaker.session import Session, SessionSettings
  from sagemaker.local import LocalSession
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
label_desc: Use image node_id 2 for calling a global function with example usage: retrieve_caller_name(job_instance) and returns: None, str, str, str, str, str