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 = []
            for name in self.input_names:
                infer_output = pb_utils.get_input_tensor_by_name(request, name)
                if infer_output:
                    infer_output = infer_output.as_numpy()
                    infer_outputs.append(infer_output)

            results = self.postprocess_.run(infer_outputs)
            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/serving/models/postprocess/1/model.py