code: def transform(self, job_name, model_name, strategy, max_concurrent_transforms, max_payload, env,
                    input_config, output_config, resource_config, tags):
          
          transform_request = {
              'TransformJobName': job_name,
              'ModelName': model_name,
              'TransformInput': input_config,
              'TransformOutput': output_config,
              'TransformResources': resource_config,
          }
  
          if strategy is not None:
              transform_request['BatchStrategy'] = strategy
  
          if max_concurrent_transforms is not None:
              transform_request['MaxConcurrentTransforms'] = max_concurrent_transforms
  
          if max_payload is not None:
              transform_request['MaxPayloadInMB'] = max_payload
  
          if env is not None:
              transform_request['Environment'] = env
  
          if tags is not None:
              transform_request['Tags'] = tags
  
          LOGGER.info('Creating transform job with name: {}'.format(job_name))
          LOGGER.debug('Transform request: {}'.format(json.dumps(transform_request, indent=4)))
          self.sagemaker_client.create_transform_job(**transform_request)
docs: Create an Amazon SageMaker transform job.
  
          Args:
              job_name (str): Name of the transform job being created.
              model_name (str): Name of the SageMaker model being used for the transform job.
              strategy (str): The strategy used to decide how to batch records in a single request.
                  Possible values are 'MULTI_RECORD' and 'SINGLE_RECORD'.
              max_concurrent_transforms (int): The maximum number of HTTP requests to be made to
                  each individual transform container at one time.
              max_payload (int): Maximum size of the payload in a single HTTP request to the container in MB.
              env (dict): Environment variables to be set for use during the transform job.
              input_config (dict): A dictionary describing the input data (and its location) for the job.
              output_config (dict): A dictionary describing the output location for the job.
              resource_config (dict): A dictionary describing the resources to complete the job.
              tags (list[dict]): List of tags for labeling a training job. For more, see
                  https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.