docs: def transformer(self, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None,
                      output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None,
                      max_payload=None, tags=None, volume_kms_key=None):
          """Return a ``Transformer`` that uses this Model.
  
          Args:
              instance_count (int): Number of EC2 instances to use.
              instance_type (str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.
              strategy (str): The strategy used to decide how to batch records in a single request (default: None).
                  Valid values: 'MULTI_RECORD' and 'SINGLE_RECORD'.
              assemble_with (str): How the output is assembled (default: None). Valid values: 'Line' or 'None'.
              output_path (str): S3 location for saving the transform result. If not specified, results are stored to
                  a default bucket.
              output_kms_key (str): Optional. KMS key ID for encrypting the transform output (default: None).
              accept (str): The content type accepted by the endpoint deployed during the transform job.
              env (dict): Environment variables to be set for use during the transform job (default: None).
              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.
              tags (list[dict]): List of tags for labeling a transform job. If none specified, then the tags used for
                  the training job are used for the transform job.
              volume_kms_key (str): Optional. KMS key ID for encrypting the volume attached to the ML
                  compute instance (default: None).
          """
          self._create_sagemaker_model(instance_type)
          if self.enable_network_isolation():
              env = None
  
          return Transformer(self.name, instance_count, instance_type, strategy=strategy, assemble_with=assemble_with,
                             output_path=output_path, output_kms_key=output_kms_key, accept=accept,
                             max_concurrent_transforms=max_concurrent_transforms, max_payload=max_payload,
                             env=env, tags=tags, base_transform_job_name=self.name,
                             volume_kms_key=volume_kms_key, sagemaker_session=self.sagemaker_session)