class_docstr: Retrieves job specific data from the environment.
docstr: Init for _RunEnvironment.
  
  Args:
      environment_type (_EnvironmentType): The environment type.
      source_arn (str): The ARN of the current job.
docstr_tok: ["Init","for","_RunEnvironment",".","Args",":","environment_type","(","_EnvironmentType",")",":","The","environment","type",".","source_arn","(","str",")",":","The","ARN","of","the","current","job","."]
code: def __init__(
      self, environment_type: _EnvironmentType, source_arn: str
  ):
      """Init for _RunEnvironment.
  
      Args:
          environment_type (_EnvironmentType): The environment type.
          source_arn (str): The ARN of the current job.
      """
      self.environment_type = environment_type
      self.source_arn = source_arn
  
file_path: sagemaker-python-sdk/src/sagemaker/experiments/_environment.py
imports: from __future__ import absolute_import
  import enum
  import json
  import logging
  import os
  from sagemaker import Session
  from sagemaker.experiments import trial_component
  from sagemaker.utils import retry_with_backoff
label_desc: Use image node_id 1 to create a new _RunEnvironment object from inherited base classes: object with example: obj = _RunEnvironment(environment_type, source_arn)