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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, name, role, container_defs, vpc_config=None, enable_network_isolation=False, primary_container=None, tags=None):
 """Create an Amazon SageMaker ``Model``. Specify the S3 location of the model artifacts and Docker image containing the inference code. Amazon SageMaker uses this information to deploy the model in Amazon SageMaker. This method can also be used to create a Model for an Inference Pipeline if you pass the list of container definitions through the containers parameter. Args: name (str):
 Name of the Amazon SageMaker ``Model`` to create. role (str):
 An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role. container_defs (list[dict[str, str]] or [dict[str, str]]):
 A single container definition or a list of container definitions which will be invoked sequentially while performing the prediction. If the list contains only one container, then it'll be passed to SageMaker Hosting as the ``PrimaryContainer`` and otherwise, it'll be passed as ``Containers``.You can also specify the return value of ``sagemaker.get_container_def()`` or ``sagemaker.pipeline_container_def()``, which will used to create more advanced container configurations ,including model containers which need artifacts from S3. vpc_config (dict[str, list[str]]):
 The VpcConfig set on the model (default: None) * 'Subnets' (list[str]):
 List of subnet ids. * 'SecurityGroupIds' (list[str]):
 List of security group ids. enable_network_isolation (bool):
 Wether the model requires network isolation or not. primary_container (str or dict[str, str]):
 Docker image which defines the inference code. You can also specify the return value of ``sagemaker.container_def()``, which is used to create more advanced container configurations, including model containers which need artifacts from S3. This field is deprecated, please use container_defs instead. tags(List[dict[str, str]]):
 Optional. The list of tags to add to the model. Example: For more information about tags, see https://boto3.amazonaws.com/v1/documentation\ /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags Returns: str: Name of the Amazon SageMaker ``Model`` created. """

        if container_defs and primary_container:
            raise ValueError('Both container_defs and primary_container can not be passed as input')

        if primary_container:
            msg = 'primary_container is going to be deprecated in a future release. Please use container_defs instead.'
            warnings.warn(msg, DeprecationWarning)
            container_defs = primary_container

        role = self.expand_role(role)

        if isinstance(container_defs, list):
            container_definition = container_defs
        else:
            container_definition = _expand_container_def(container_defs)

        create_model_request = _create_model_request(name=name,
                                                     role=role,
                                                     container_def=container_definition,
                                                     tags=tags)

        if vpc_config:
            create_model_request['VpcConfig'] = vpc_config

        if enable_network_isolation:
            create_model_request['EnableNetworkIsolation'] = True

        LOGGER.info('Creating model with name: {}'.format(name))
        LOGGER.debug('CreateModel request: {}'.format(json.dumps(create_model_request, indent=4)))

        try:
            self.sagemaker_client.create_model(**create_model_request)
        except ClientError as e:
            error_code = e.response['Error']['Code']
            message = e.response['Error']['Message']

            if error_code == 'ValidationException' and 'Cannot create already existing model' in message:
                LOGGER.warning('Using already existing model: {}'.format(name))
            else:
                raise

        return name