e and true, respectively.

        Returns:

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
        >>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")

        >>> text = "Beyoncé lives in Los Angeles"
        # List all possible entity spans in the text

        >>> word_start_positions = [0, 8, 14, 17, 21]  # character-based start positions of word tokens
        >>> word_end_positions = [7, 13, 16, 20, 28]  # character-based end positions of word tokens
        >>> entity_spans = []
        >>> for i, start_pos in enumerate(word_start_positions):
        ...     for end_pos in word_end_positions[i:]:
        ...         entity_spans.append((start_pos, end_pos))

        >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
        >>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
        ...     if predicted_class_idx != 0:
        ...         print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
        Beyoncé PER
        Los Angeles LOC
        ```NTr,