omputed for the tokens with labels in `[0, ..., config.vocab_size]`.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            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.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

        Returns:

        Example:

        ```python
        >>> from transformers import (
        ...     SpeechEncoderDecoderModel,
        ...     Speech2Text2ForCausalLM,
        ...     Wav2Vec2Model,
        ...     Speech2Text2Config,
        ...     Wav2Vec2Config,
        ...     Wav2Vec2FeatureExtractor,
        ...     Speech2Text2Tokenizer,
        ... )
        >>> from datasets import load_dataset

        >>> feature_extractor = Wav2Vec2FeatureExtractor()
        >>> tokenizer = Speech2Text2Tokenizer.from_pretrained("facebook/s2t-wav2vec2-large-en-de")

        >>> encoder = Wav2Vec2Model(Wav2Vec2Config())
        >>> decoder = Speech2Text2ForCausalLM(Speech2Text2Config())
        >>> # init random speech2text model

        >>> model = SpeechEncoderDecoderModel(encoder=encoder, decoder=decoder)
        >>> model.config.pad_token_id = tokenizer.pad_token_id
        >>> model.config.decoder_start_token_id = tokenizer.bos_token_id
        >>> # pre-process inputs and labels

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> inputs = feature_extractor(
        ...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
        ... )
        >>> input_values = inputs.input_values
        >>> decoder_input_ids = tokenizer(ds[0]["text"], return_tensors="pt").input_ids
        >>> # compute loss

        >>> loss = model(inputs=input_values, labels=decoder_input_ids).loss
        >>> # backprop loss

        >>> loss.backward()  # doctest: +IGNORE_RESULT
        ```N)