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 _is_combination_supported(
      cls, toolkit, toolkit_version, framework
  ):
      """Placeholder docstring."""
      supported_versions = TOOLKIT_FRAMEWORK_VERSION_MAP.get(
          toolkit, None
      )
      if supported_versions:
          supported_frameworks = supported_versions.get(
              toolkit_version, None
          )
          if supported_frameworks and supported_frameworks.get(
              framework, None
          ):
              return True
      return False
  
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 10 for calling the RLEstimator obj's underlying member method code with example usage: obj._is_combination_supported(cls, toolkit, toolkit_version, framework) and returns: False, True