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:
            data = pb_utils.get_input_tensor_by_name(request,
                                                     self.input_names[0])
            data = data.as_numpy()
            outputs, im_info = self.preprocess_.run(data)

            # PaddleSeg preprocess has two outputs
            dlpack_tensor = outputs[0].to_dlpack()
            output_tensor_0 = pb_utils.Tensor.from_dlpack(self.output_names[0],
                                                          dlpack_tensor)
            output_tensor_1 = pb_utils.Tensor(
                self.output_names[1], np.array(
                    [im_info], dtype=np.object_))
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[output_tensor_0, output_tensor_1])
            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/preprocess/1/model.py