optimum/intel/openvino/modeling.py [231:257]:
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        )
    )
    def forward(
        self,
        input_ids: Union[torch.Tensor, np.ndarray],
        attention_mask: Union[torch.Tensor, np.ndarray],
        token_type_ids: Optional[Union[torch.Tensor, np.ndarray]] = None,
        **kwargs,
    ):
        self.compile()

        np_inputs = isinstance(input_ids, np.ndarray)
        if not np_inputs:
            input_ids = input_ids.cpu().numpy()
            attention_mask = attention_mask.cpu().numpy()
            token_type_ids = token_type_ids.cpu().numpy() if token_type_ids is not None else token_type_ids

        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }

        # Add the token_type_ids when needed
        if "token_type_ids" in self.input_names:
            inputs["token_type_ids"] = token_type_ids if token_type_ids is not None else np.zeros_like(input_ids)

        outputs = self._inference(inputs)
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optimum/intel/openvino/modeling.py [450:476]:
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        )
    )
    def forward(
        self,
        input_ids: Union[torch.Tensor, np.ndarray],
        attention_mask: Union[torch.Tensor, np.ndarray],
        token_type_ids: Optional[Union[torch.Tensor, np.ndarray]] = None,
        **kwargs,
    ):
        self.compile()

        np_inputs = isinstance(input_ids, np.ndarray)
        if not np_inputs:
            input_ids = input_ids.cpu().numpy()
            attention_mask = attention_mask.cpu().numpy()
            token_type_ids = token_type_ids.cpu().numpy() if token_type_ids is not None else token_type_ids

        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }

        # Add the token_type_ids when needed
        if "token_type_ids" in self.input_names:
            inputs["token_type_ids"] = token_type_ids if token_type_ids is not None else np.zeros_like(input_ids)

        outputs = self._inference(inputs)
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