code: def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
          
          init_params = super(RLEstimator, cls)\
              ._prepare_init_params_from_job_description(job_details, model_channel_name)
  
          image_name = init_params.pop('image')
          framework, _, tag, _ = fw_utils.framework_name_from_image(image_name)
  
          if not framework:
              # If we were unable to parse the framework name from the image it is not one of our
              # officially supported images, in this case just add the image to the init params.
              init_params['image_name'] = image_name
              return init_params
  
          toolkit, toolkit_version = cls._toolkit_and_version_from_tag(tag)
  
          if not cls._is_combination_supported(toolkit, toolkit_version, framework):
              training_job_name = init_params['base_job_name']
              raise ValueError(
                  "Training job: {} didn't use image for requested framework".format(
                      training_job_name)
              )
  
          init_params['toolkit'] = RLToolkit(toolkit)
          init_params['toolkit_version'] = toolkit_version
          init_params['framework'] = RLFramework(framework)
  
          return init_params
docs: Convert the job description to init params that can be handled by the class constructor
  
          Args:
              job_details: the returned job details from a describe_training_job API call.
              model_channel_name (str): Name of the channel where pre-trained model data will be
                  downloaded.
  
          Returns:
               dictionary: The transformed init_params