ed**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*)
            Tuple consists of (`generator_enc_last_hidden_state`, *optional*: `generator_enc_hidden_states`,
            *optional*: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs *
            sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the
            generator's encoder.

            Used by the ([`RagModel`]) model during decoding.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Provide for generation tasks. `None` by default, construct as per instructions for the generator model
            you're using with your RAG instance.
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size,  target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        past_key_values (`tuple(tuple(torch.FloatTensor))`):
            Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and
            `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used
            in the ([`RagTokenForGeneration`]) model during decoding.
        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 has is not initialized with a `retriever` `doc_scores`
            has to be provided to the forward pass. `doc_scores` can be computed via
            `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
        context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, *optional*, returned when *output_retrieved=True*):
            Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the
            retriever. If the model was not initialized with a `retriever` ``context_input_ids` has to be provided to
            the forward pass. `context_input_ids` are returned by [`~RagRetriever.__call__`].
        context_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 has is not initialized with a `retriever` `context_attention_mask` has to be
            provided to the forward pass. `context_attention_mask` are returned by [`~RagRetriever.__call__`].
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        output_retrieved(`bool`, *optional*):
            Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask`. See returned tensors for more detail.
        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.
c