code: def create_model(self, model_server_workers=None, role=None,
                       vpc_config_override=VPC_CONFIG_DEFAULT, endpoint_type=None):
          
  
          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)
docs: 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.