code: def start(self, input_data, output_data, transform_resources, **kwargs):
          
          self.transform_resources = transform_resources
          self.input_data = input_data
          self.output_data = output_data
  
          image = self.primary_container['Image']
          instance_type = transform_resources['InstanceType']
          instance_count = 1
  
          environment = self._get_container_environment(**kwargs)
  
          # Start the container, pass the environment and wait for it to start up
          self.container = _SageMakerContainer(instance_type, instance_count, image, self.local_session)
          self.container.serve(self.primary_container['ModelDataUrl'], environment)
  
          serving_port = get_config_value('local.serving_port', self.local_session.config) or 8080
          _wait_for_serving_container(serving_port)
  
          # Get capabilities from Container if needed
          endpoint_url = 'http://localhost:%s/execution-parameters' % serving_port
          response, code = _perform_request(endpoint_url)
          if code == 200:
              execution_parameters = json.loads(response.read())
              # MaxConcurrentTransforms is ignored because we currently only support 1
              for setting in ('BatchStrategy', 'MaxPayloadInMB'):
                  if setting not in kwargs and setting in execution_parameters:
                      kwargs[setting] = execution_parameters[setting]
  
          # Apply Defaults if none was provided
          kwargs.update(self._get_required_defaults(**kwargs))
  
          self.start_time = datetime.datetime.now()
          self.batch_strategy = kwargs['BatchStrategy']
          if 'Environment' in kwargs:
              self.environment = kwargs['Environment']
  
          # run the batch inference requests
          self._perform_batch_inference(input_data, output_data, **kwargs)
          self.end_time = datetime.datetime.now()
          self.state = self._COMPLETED
docs: Start the Local Transform Job
  
          Args:
              input_data (dict): Describes the dataset to be transformed and the location where it is stored.
              output_data (dict): Identifies the location where to save the results from the transform job
              transform_resources (dict): compute instances for the transform job. Currently only supports local or
                  local_gpu
              **kwargs: additional arguments coming from the boto request object