def execute(self, requests):
        """`execute` must be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference is requested
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse.
        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest
        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        """
        responses = []
        for request in requests:
            infer_outputs = pb_utils.get_input_tensor_by_name(
                request, self.input_names[0])
            im_info = pb_utils.get_input_tensor_by_name(request,
                                                        self.input_names[1])
            infer_outputs = infer_outputs.as_numpy()
            im_info = im_info.as_numpy()
            for i in range(im_info.shape[0]):
                im_info[i] = json.loads(im_info[i].decode('utf-8').replace(
                    "'", '"'))

            results = self.postprocess_.run([infer_outputs], im_info[0])
            r_str = fd.vision.utils.fd_result_to_json(results)

            r_np = np.array(r_str, dtype=np.object_)
            out_tensor = pb_utils.Tensor(self.output_names[0], r_np)
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[out_tensor, ])
            responses.append(inference_response)
        return responses
`execute` must be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference is requested
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse.
        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest
        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        
deploy/fastdeploy/semantic_segmentation/serving/fastdeploy_serving/models/postprocess/1/model.py