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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def create_model(self, model_server_workers=None, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
 """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. """

        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)