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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 compile(self, target_instance_family, input_shape, output_path, role, tags=None, job_name=None, compile_max_run=5 * 60, framework=None, framework_version=None):
 """Compile this ``Model`` with SageMaker Neo. Args: target_instance_family (str):
 Identifies the device that you want to run your model after compilation, for example: ml_c5. Allowed strings are: ml_c5, ml_m5, ml_c4, ml_m4, jetsontx1, jetsontx2, ml_p2, ml_p3, deeplens, rasp3b input_shape (dict):
 Specifies the name and shape of the expected inputs for your trained model in json dictionary form, for example: {'data':[1,3,1024,1024]}, or {'var1': [1,1,28,28], 'var2':[1,1,28,28]} output_path (str):
 Specifies where to store the compiled model role (str):
 Execution role tags (list[dict]):
 List of tags for labeling a compilation job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html. job_name (str):
 The name of the compilation job compile_max_run (int):
 Timeout in seconds for compilation (default: 3 * 60). After this amount of time Amazon SageMaker Neo terminates the compilation job regardless of its current status. framework (str):
 The framework that is used to train the original model. Allowed values: 'mxnet', 'tensorflow', 'pytorch', 'onnx', 'xgboost' framework_version (str) Returns: sagemaker.model.Model: A SageMaker ``Model`` object. See :func:`~sagemaker.model.Model` for full details. """

        framework = self._framework() or framework
        if framework is None:
            raise ValueError("You must specify framework, allowed values {}".format(NEO_ALLOWED_FRAMEWORKS))
        if framework not in NEO_ALLOWED_FRAMEWORKS:
            raise ValueError("You must provide valid framework, allowed values {}".format(NEO_ALLOWED_FRAMEWORKS))
        if job_name is None:
            raise ValueError("You must provide a compilation job name")

        framework = framework.upper()
        framework_version = self._get_framework_version() or framework_version

        config = self._compilation_job_config(target_instance_family, input_shape, output_path, role,
                                              compile_max_run, job_name, framework, tags)
        self.sagemaker_session.compile_model(**config)
        job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
        self.model_data = job_status['ModelArtifacts']['S3ModelArtifacts']
        if target_instance_family.startswith('ml_'):
            self.image = self._neo_image(self.sagemaker_session.boto_region_name, target_instance_family, framework,
                                         framework_version)
            self._is_compiled_model = True
        else:
            LOGGER.warning("The intance type {} is not supported to deploy via SageMaker,"
                           "please deploy the model on the device by yourself.".format(target_instance_family))
        return self