def create_model(self, role=None, image=None, predictor_cls=None, serializer=None, deserializer=None,
                     content_type=None, accept=None, vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT, **kwargs):
        """
        Create a model to deploy.

        Args:
            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
                transform jobs. If not specified, the role from the Estimator will be used.
            image (str): An container image to use for deploying the model. Defaults to the image used for training.
            predictor_cls (RealTimePredictor): The predictor class to use when deploying the model.
            serializer (callable): Should accept a single argument, the input data, and return a sequence
                of bytes. May provide a content_type attribute that defines the endpoint request content type
            deserializer (callable): Should accept two arguments, the result data and the response content type,
                and return a sequence of bytes. May provide a content_type attribute that defines th endpoint
                response Accept content type.
            content_type (str): The invocation ContentType, overriding any content_type from the serializer
            accept (str): The invocation Accept, overriding any accept from the deserializer.
            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model.
                Default: use subnets and security groups from this Estimator.
                * 'Subnets' (list[str]): List of subnet ids.
                * 'SecurityGroupIds' (list[str]): List of security group ids.

            The serializer, deserializer, content_type, and accept arguments are only used to define a default
            RealTimePredictor. They are ignored if an explicit predictor class is passed in. Other arguments
            are passed through to the Model class.

        Returns: a Model ready for deployment.
        """
        if predictor_cls is None:
            def predict_wrapper(endpoint, session):
                return RealTimePredictor(endpoint, session, serializer, deserializer, content_type, accept)
            predictor_cls = predict_wrapper

        role = role or self.role

        return Model(self.model_data, image or self.train_image(), role,
                     vpc_config=self.get_vpc_config(vpc_config_override),
                     sagemaker_session=self.sagemaker_session, predictor_cls=predictor_cls, **kwargs)
Create a model to deploy.

        Args:
            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
                transform jobs. If not specified, the role from the Estimator will be used.
            image (str): An container image to use for deploying the model. Defaults to the image used for training.
            predictor_cls (RealTimePredictor): The predictor class to use when deploying the model.
            serializer (callable): Should accept a single argument, the input data, and return a sequence
                of bytes. May provide a content_type attribute that defines the endpoint request content type
            deserializer (callable): Should accept two arguments, the result data and the response content type,
                and return a sequence of bytes. May provide a content_type attribute that defines th endpoint
                response Accept content type.
            content_type (str): The invocation ContentType, overriding any content_type from the serializer
            accept (str): The invocation Accept, overriding any accept from the deserializer.
            vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model.
                Default: use subnets and security groups from this Estimator.
                * 'Subnets' (list[str]): List of subnet ids.
                * 'SecurityGroupIds' (list[str]): List of security group ids.

            The serializer, deserializer, content_type, and accept arguments are only used to define a default
            RealTimePredictor. They are ignored if an explicit predictor class is passed in. Other arguments
            are passed through to the Model class.

        Returns: a Model ready for deployment.