ontext_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*):
                Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the
                retriever.

                If the model is not initialized with a `retriever` or `input_ids` is not given, `context_input_ids` and
                `context_attention_mask` have to be provided to the forward pass. They are returned by
                [`~RagRetriever.__call__`].
            doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`):
                Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and
                `question_encoder_last_hidden_state`.

                If the model is not initialized with a `retriever` or `input_ids` is not given, `doc_scores` has to be
                provided to the forward pass. `doc_scores` are returned by [`~RagRetriever.__call__`].
            do_deduplication (`bool`, *optional*):
                Whether or not to deduplicate the generations from different context documents for a given input. Has
                to be set to `False` if used while training with distributed backend.
            num_return_sequences(`int`, *optional*, defaults to 1):
                The number of independently computed returned sequences for each element in the batch. Note that this
                is not the value we pass to the `generator`'s `[`~generation.GenerationMixin.generate`]` function,
                where we set `num_return_sequences` to `num_beams`.
            num_beams (`int`, *optional*, defaults to 1):
                Number of beams for beam search. 1 means no beam search.
            n_docs (`int`, *optional*, defaults to `config.n_docs`)
                Number of documents to retrieve and/or number of documents for which to generate an answer.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional kwargs will be passed to [`~generation.GenerationMixin.generate`].

        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`.
        Nz= At least one of input_ids or context_input_ids must be givenŠ