code: def create_model(self, model_server_workers=None, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
          
          role = role or self.role
          return ChainerModel(self.model_data, role, self.entry_point, source_dir=self._model_source_dir(),
                              enable_cloudwatch_metrics=self.enable_cloudwatch_metrics, name=self._current_job_name,
                              container_log_level=self.container_log_level, code_location=self.code_location,
                              py_version=self.py_version, framework_version=self.framework_version,
                              model_server_workers=model_server_workers, image=self.image_name,
                              sagemaker_session=self.sagemaker_session,
                              vpc_config=self.get_vpc_config(vpc_config_override), dependencies=self.dependencies)
docs: Create a SageMaker ``ChainerModel`` object that can be deployed to an ``Endpoint``.
  
          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.
              model_server_workers (int): Optional. The number of worker processes used by the inference server.
                  If None, server will use one worker per vCPU.
              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.
  
          Returns:
              sagemaker.chainer.model.ChainerModel: A SageMaker ``ChainerModel`` object.
                  See :func:`~sagemaker.chainer.model.ChainerModel` for full details.