def predict(self, inputs: List[Dict]) -> List[Dict]:
        """onnx predict method

        Args:
            inputs (List[Dict]): _description_

        Returns:
            List[Dict]: _description_
        """
        self.inputs = self._prepare_inputs(inputs)
        # couldn't find any documentation on passing tokenizer arguments through the pipeline
        # leaving these here for reference
        # https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/__init__.py#L750
        # https://stackoverflow.com/questions/67849833/how-to-truncate-input-in-the-huggingface-pipeline
        self.predictions = self.onnx_classifier(self.inputs, **self.tokenizer_kwargs)
        self.outputs = self._parepare_outputs(self.predictions)

        return self.outputs
onnx predict method

        Args:
            inputs (List[Dict]): _description_

        Returns:
            List[Dict]: _description_
        
src/marqo/s2_inference/reranking/model_utils.py