docs: def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
                 use_compiled_model=False, update_endpoint=False, **kwargs):
          """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
  
          More information:
          http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html
  
          Args:
              initial_instance_count (int): Minimum number of EC2 instances to deploy to an endpoint for prediction.
              instance_type (str): Type of EC2 instance to deploy to an endpoint for prediction,
                  for example, 'ml.c4.xlarge'.
              accelerator_type (str): Type of Elastic Inference accelerator to attach to an endpoint for model loading
                  and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator
                  will be attached to the endpoint.
                  For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
              endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified, the name of
                  the training job is used.
              use_compiled_model (bool): Flag to select whether to use compiled (optimized) model. Default: False.
              update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
                  If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
                  corresponding to the previous EndpointConfig. Default: False
              tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
                      >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
                      For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
                      /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
  
              **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
                  ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
                  For more, see the implementation docs.
  
          Returns:
              sagemaker.predictor.RealTimePredictor: A predictor that provides a ``predict()`` method,
                  which can be used to send requests to the Amazon SageMaker endpoint and obtain inferences.
          """
          self._ensure_latest_training_job()
          endpoint_name = endpoint_name or self.latest_training_job.name
          self.deploy_instance_type = instance_type
          if use_compiled_model:
              family = '_'.join(instance_type.split('.')[:-1])
              if family not in self._compiled_models:
                  raise ValueError("No compiled model for {}. "
                                   "Please compile one with compile_model before deploying.".format(family))
              model = self._compiled_models[family]
          else:
              model = self.create_model(**kwargs)
          return model.deploy(
              instance_type=instance_type,
              initial_instance_count=initial_instance_count,
              accelerator_type=accelerator_type,
              endpoint_name=endpoint_name,
              update_endpoint=update_endpoint,
              tags=self.tags)