return_tensors="pt")

        >>> outputs = model(**encoding)
        >>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
        >>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
        >>> predicted_start_idx, predicted_end_idx
        (154, 287)

        >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
        >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
        >>> predicted_answer  # results are not very good without further fine-tuning
        'council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public ...
        ```

        ```python
        >>> target_start_index = torch.tensor([7])
        >>> target_end_index = torch.tensor([14])
        >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
        >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
        >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
        >>> predicted_answer_span_start, predicted_answer_span_end
        (154, 287)
        ```
        NrC