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()
        data = [i[0].decode("utf-8") for i in data]
        data = self.tokenizer(data, max_length=128, padding=True, truncation=True)
        input_ids = np.array(data["input_ids"], dtype=self.output_dtype[0])
        token_type_ids = np.array(data["token_type_ids"], dtype=self.output_dtype[1])
        out_tensor1 = pb_utils.Tensor(self.output_names[0], input_ids)
        out_tensor2 = pb_utils.Tensor(self.output_names[1], token_type_ids)
        inference_response = pb_utils.InferenceResponse(output_tensors=[out_tensor1, out_tensor2])
        responses.append(inference_response)
    return responses