class_docstr: Handle end-to-end training and deployment of custom RLEstimator code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","custom","RLEstimator","code","."]
docstr: Placeholder docstring.
docstr_tok: ["Placeholder","docstring","."]
code: def _validate_toolkit_format(cls, toolkit):
      """Placeholder docstring."""
      if toolkit and toolkit not in list(RLToolkit):
          raise ValueError(
              "Invalid type: {}, valid RL toolkits types are: {}".format(
                  toolkit, list(RLToolkit)
              )
          )
  
imports: from __future__ import absolute_import
  import enum
  import logging
  import re
  from typing import Union, Optional, List, Dict
  from sagemaker import image_uris, fw_utils
  from sagemaker.estimator import Framework, EstimatorBase
  from sagemaker.model import FrameworkModel, SAGEMAKER_OUTPUT_LOCATION
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 8 for calling the RLEstimator obj's underlying member method code with example usage: obj._validate_toolkit_format(cls, toolkit) without return types