ation call. `**kwargs`
                passed to generate matching the attributes of `generation_config` will override them. If
                `generation_config` is not provided, the default will be used, which has the following loading
                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
                default values, whose documentation should be checked to parameterize generation.
            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
                If provided, this function constraints the beam search to allowed tokens only at each step. If not
                provided no constraint is applied. This function takes 2 arguments `inputs_ids` and the batch ID
                `batch_id`. It has to return a list with the allowed tokens for the next generation step conditioned on
                the previously generated tokens `inputs_ids` and the batch ID `batch_id`. This argument is useful for
                constrained generation conditioned on the prefix, as described in [Autoregressive Entity
                Retrieval](https://arxiv.org/abs/2010.00904).
            logits_processor (`LogitsProcessorList`, *optional*):
                Custom logits processors that complement the default logits processors built from arguments and a
                model's config. If a logit processor is passed that is already created with the arguments or a model's
                config an error is thrown.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                Custom stopping criteria that complement the default stopping criteria built from arguments and a
                model's config. If a stopping criteria is passed that is already created with the arguments or a
                model's config an error is thrown.
            kwargs (`Dict[str, Any]`, *optional*):
                Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                forwarded to the `forward` function of the model.

        Return:
            `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`: The generated
            sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches
            finished early due to the `eos_token_id`.
        Nr·