class_docstr: Factory class to generate executors for given step based on their types
class_docstr_tok: ["Factory","class","to","generate","executors","for","given","step","based","on","their","types"]
code: def __init__(self, pipeline_executor: LocalPipelineExecutor):
      self.pipeline_executor = pipeline_executor
  
imports: from __future__ import absolute_import
  from abc import ABC, abstractmethod
  import json
  from datetime import datetime
  from typing import Dict, List, Union
  from botocore.exceptions import ClientError
  from sagemaker.workflow.conditions import ConditionTypeEnum
  from sagemaker.workflow.function_step import DelayedReturn
  from sagemaker.workflow.steps import StepTypeEnum, Step
  from sagemaker.workflow.step_collections import StepCollection
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.parameters import Parameter
  from sagemaker.workflow.functions import Join, JsonGet, PropertyFile
  from sagemaker.workflow.properties import Properties
  from sagemaker.workflow.execution_variables import ExecutionVariable, ExecutionVariables
  from sagemaker.workflow.pipeline import PipelineGraph
  from sagemaker.local.exceptions import StepExecutionException
  from sagemaker.local.utils import get_using_dot_notation
  from sagemaker.utils import unique_name_from_base
  from sagemaker.s3 import parse_s3_url, s3_path_join
label_desc: Use image node_id 1 to create a new _StepExecutorFactory object  with example: obj = _StepExecutorFactory(pipeline_executor)