class_docstr: A class for handling creating and interacting with Amazon SageMaker transform jobs.
docstr: Attach an existing transform job to a new Transformer instance
  
  Args:
      transform_job_name (str): Name for the transform job to be attached.
      sagemaker_session (sagemaker.session.Session): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, one will be created using
          the default AWS configuration chain.
  
  Returns:
      sagemaker.transformer.Transformer: The Transformer instance with the
      specified transform job attached.
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code: def attach(cls, transform_job_name, sagemaker_session=None):
      """Attach an existing transform job to a new Transformer instance
  
      Args:
          transform_job_name (str): Name for the transform job to be attached.
          sagemaker_session (sagemaker.session.Session): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, one will be created using
              the default AWS configuration chain.
  
      Returns:
          sagemaker.transformer.Transformer: The Transformer instance with the
          specified transform job attached.
      """
      sagemaker_session = sagemaker_session or Session()
  
      job_details = (
          sagemaker_session.sagemaker_client.describe_transform_job(
              TransformJobName=transform_job_name
          )
      )
      init_params = cls._prepare_init_params_from_job_description(
          job_details
      )
      transformer = cls(
          sagemaker_session=sagemaker_session, **init_params
      )
      transformer.latest_transform_job = _TransformJob(
          sagemaker_session=sagemaker_session,
          job_name=init_params["base_transform_job_name"],
      )
  
      return transformer
  
imports: from __future__ import absolute_import
  from typing import Union, Optional, Dict
  import logging
  import copy
  import time
  from botocore import exceptions
  from sagemaker import s3
  from sagemaker.config import TRANSFORM_JOB_ENVIRONMENT_PATH, TRANSFORM_OUTPUT_KMS_KEY_ID_PATH, TRANSFORM_JOB_KMS_KEY_ID_PATH, TRANSFORM_RESOURCES_VOLUME_KMS_KEY_ID_PATH, PIPELINE_ROLE_ARN_PATH
  from sagemaker.job import _Job
  from sagemaker.session import Session, get_execution_role
  from sagemaker.inputs import BatchDataCaptureConfig
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.functions import Join
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline, PipelineSession
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from sagemaker.utils import base_name_from_image, name_from_base, check_and_get_run_experiment_config, resolve_value_from_config, resolve_class_attribute_from_config, format_tags, Tags
label_desc: Use image node_id 10 for calling the Transformer obj's underlying member method code with example usage: obj.attach(cls, transform_job_name, sagemaker_session) and returns: transformer