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.

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