def _batch_prepare_for_model(
        self,
        batch_ids_pairs: List[Tuple[List[int], None]],
        batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]],
        batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]],
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        max_entity_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[str] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_length: bool = False,
        verbose: bool = True,
    ) -> BatchEncoding:
        """
        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
        manages a moving window (with user defined stride) for overflowing tokens


        Args:
            batch_ids_pairs: list of tokenized input ids or input ids pairs
            batch_entity_ids_pairs: list of entity ids or entity ids pairs
            batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
            max_entity_length: The maximum length of the entity sequence.
        """

        batch_outputs = {}
        for input_ids, entity_ids, entity_token_span_pairs in zip(
            batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
        ):
            first_ids, second_ids = input_ids
            first_entity_ids, second_entity_ids = entity_ids
            first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
            outputs = self.prepare_for_model(
                first_ids,
                second_ids,
                entity_ids=first_entity_ids,
                pair_entity_ids=second_entity_ids,
                entity_token_spans=first_entity_token_spans,
                pair_entity_token_spans=second_entity_token_spans,
                add_special_tokens=add_special_tokens,
                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
                truncation=truncation_strategy.value,
                max_length=max_length,
                max_entity_length=max_entity_length,
                stride=stride,
                pad_to_multiple_of=None,  # we pad in batch afterward
                return_attention_mask=False,  # we pad in batch afterward
                return_token_type_ids=return_token_type_ids,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_length=return_length,
                return_tensors=None,  # We convert the whole batch to tensors at the end
                prepend_batch_axis=False,
                verbose=verbose,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                batch_outputs[key].append(value)

        batch_outputs = self.pad(
            batch_outputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)

        return batch_outputs

        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
        manages a moving window (with user defined stride) for overflowing tokens


        Args:
            batch_ids_pairs: list of tokenized input ids or input ids pairs
            batch_entity_ids_pairs: list of entity ids or entity ids pairs
            batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
            max_entity_length: The maximum length of the entity sequence.
        
dpkd/transformers/src/transformers/models/mluke/tokenization_mluke.py