def METHOD_NAME(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