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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, endpoint_type=None):
 """Create a SageMaker ``TensorFlowModel`` 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. endpoint_type: Optional. Selects the software stack used by the inference server. If not specified, the model will be configured to use the default SageMaker model server. If 'tensorflow-serving', the model will be configured to use the SageMaker Tensorflow Serving container. Returns: sagemaker.tensorflow.model.TensorFlowModel: A SageMaker ``TensorFlowModel`` object. See :func:`~sagemaker.tensorflow.model.TensorFlowModel` for full details. """


        role = role or self.role
        if endpoint_type == 'tensorflow-serving' or self._script_mode_enabled():
            return self._create_tfs_model(role=role, vpc_config_override=vpc_config_override)

        return self._create_default_model(model_server_workers=model_server_workers, role=role,
                                          vpc_config_override=vpc_config_override)