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 = []
    # print("num:", len(requests), flush=True)
    for request in requests:
        data = pb_utils.get_input_tensor_by_name(request,
                                                 self.input_names[0])
        data = data.as_numpy()
        # print("post data:", data)
        tokens_label = data.argmax(axis=-1).tolist()
        value = []
        for _, token_label in enumerate(tokens_label):
            start = -1
            label_name = ""
            items = []
            for i, label in enumerate(token_label):
                if self.label_names[label] == "O" and start >= 0:
                    items.append({
                        "pos": [start, i - 2],
                        "label": label_name,
                    })
                    start = -1
                elif "B-" in self.label_names[label]:
                    start = i - 1
                    label_name = self.label_names[label][2:]
            if start >= 0:
                items.append({
                    "pos": [start, len(token_label) - 1],
                    "label": label_name,
                })
            value.append(items)
        out_result = np.array(value, dtype='object')
        out_tensor = pb_utils.Tensor(self.output_names[0], out_result)
        inference_response = pb_utils.InferenceResponse(output_tensors=[
            out_tensor,
        ])
        responses.append(inference_response)
    return responses