    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.
        is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels whether a question has an answer or no answer (SQuAD 2.0)
        cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the classification token to use as input for computing plausibility of the
            answer.
        p_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...). 1.0 means token should be
            masked. 0.0 mean token is not masked.

        Returns:

        Example:

        ```python
        >>> from transformers import XLMTokenizer, XLMForQuestionAnswering
        >>> import torch

        >>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
        >>> model = XLMForQuestionAnswering.from_pretrained("xlm-mlm-en-2048")

        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
        ...     0
        ... )  # Batch size 1
        >>> start_positions = torch.tensor([1])
        >>> end_positions = torch.tensor([3])

        >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
        >>> loss = outputs.loss
        ```Nr