ensors="pt")

        >>> with torch.no_grad():
        ...     logits = model(**inputs).logits

        >>> predicted_class_id = logits.argmax().item()
        >>> label = model.config.id2label[predicted_class_id]
        ```

        ```python
        >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
        >>> num_labels = len(model.config.id2label)
        >>> model = ReformerForSequenceClassification.from_pretrained(
        ...     "google/reformer-crime-and-punishment", num_labels=num_labels
        ... )

        >>> labels = torch.tensor(1)
        >>> loss = model(**inputs, labels=labels).loss
        ```
        N)rz