code: def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
          
          init_params = super(TensorFlow, cls)._prepare_init_params_from_job_description(job_details,
                                                                                         model_channel_name)
  
          # Move some of the tensorflow specific init params from hyperparameters into the main init params.
          for argument in ('checkpoint_path', 'training_steps', 'evaluation_steps', 'model_dir'):
              value = init_params['hyperparameters'].pop(argument, None)
              if value is not None:
                  init_params[argument] = value
  
          image_name = init_params.pop('image')
          framework, py_version, tag, script_mode = fw.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
  
          if script_mode:
              init_params['script_mode'] = True
  
          init_params['py_version'] = py_version
  
          # We switched image tagging scheme from regular image version (e.g. '1.0') to more expressive
          # containing framework version, device type and python version (e.g. '1.5-gpu-py2').
          # For backward compatibility map deprecated image tag '1.0' to a '1.4' framework version
          # otherwise extract framework version from the tag itself.
          init_params['framework_version'] = '1.4' if tag == '1.0' else fw.framework_version_from_tag(
              tag)
  
          training_job_name = init_params['base_job_name']
          if framework != cls.__framework_name__:
              raise ValueError("Training job: {} didn't use image for requested framework".format(
                  training_job_name))
  
          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.
  
          Returns:
               dictionary: The transformed init_params