     used for the single-label classification. In this case, labels should contain the indices that should be in
            `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
            loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
            and 1 indicate false and true, respectively.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, LukeForEntityPairClassification

        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
        >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")

        >>> text = "Beyoncé lives in Los Angeles."
        >>> entity_spans = [
        ...     (0, 7),
        ...     (17, 28),
        ... ]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> predicted_class_idx = logits.argmax(-1).item()
        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
        Predicted class: per:cities_of_residence
        ```NTr,