 *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
        >>> from datasets import load_dataset

        >>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
        >>> model = AutoModelForQuestionAnswering.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")

        >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
        >>> example = dataset[0]
        >>> words = example["tokens"]
        >>> boxes = example["bboxes"]

        >>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")

        >>> outputs = model(**encoding)

        >>> answer_start_index = outputs.start_logits.argmax()
        >>> answer_end_index = outputs.end_logits.argmax()

        >>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
        >>> predicted_answer = tokenizer.decode(predict_answer_tokens)
        ```Nrū