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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, role=None, vpc_config_override=VPC_CONFIG_DEFAULT, entry_point=None, source_dir=None, dependencies=None):
 """Create a SageMaker ``RLEstimatorModel`` 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. 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. entry_point (str):
 Path (absolute or relative) to the Python source file which should be executed as the entry point for MXNet hosting. This should be compatible with Python 3.5 (default: self.entry_point) source_dir (str):
 Path (absolute or relative) to a directory with any other training source code dependencies aside from tne entry point file (default: self.source_dir). Structure within this directory are preserved when hosting on Amazon SageMaker. dependencies (list[str]):
 A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: self.dependencies). The library folders will be copied to SageMaker in the same folder where the entry_point is copied. If the ```source_dir``` points to S3, code will be uploaded and the S3 location will be used instead. Returns: sagemaker.model.FrameworkModel: Depending on input parameters returns one of the following: * sagemaker.model.FrameworkModel - in case image_name was specified on the estimator; * sagemaker.mxnet.MXNetModel - if image_name wasn't specified and MXNet was used as RL backend; * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and TensorFlow was used as RL backend. """

        base_args = dict(model_data=self.model_data,
                         role=role or self.role,
                         image=self.image_name,
                         name=self._current_job_name,
                         container_log_level=self.container_log_level,
                         sagemaker_session=self.sagemaker_session,
                         vpc_config=self.get_vpc_config(vpc_config_override))

        if not entry_point and (source_dir or dependencies):
            raise AttributeError('Please provide an `entry_point`.')

        entry_point = entry_point or self.entry_point
        source_dir = source_dir or self._model_source_dir()
        dependencies = dependencies or self.dependencies

        extended_args = dict(entry_point=entry_point,
                             source_dir=source_dir,
                             code_location=self.code_location,
                             dependencies=dependencies,
                             enable_cloudwatch_metrics=self.enable_cloudwatch_metrics)
        extended_args.update(base_args)

        if self.image_name:
            return FrameworkModel(**extended_args)

        if self.toolkit == RLToolkit.RAY.value:
            raise NotImplementedError(
                'Automatic deployment of Ray models is not currently available.'
                ' Train policy parameters are available in model checkpoints'
                ' in the TrainingJob output.'
            )

        if self.framework == RLFramework.TENSORFLOW.value:
            from sagemaker.tensorflow.serving import Model as tfsModel
            return tfsModel(framework_version=self.framework_version, **base_args)
        elif self.framework == RLFramework.MXNET.value:
            return MXNetModel(framework_version=self.framework_version, py_version=PYTHON_VERSION,
                              **extended_args)