or details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        langs (`torch.LongTensor` of shape `({0})`, *optional*):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the *language name
            to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
            *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the [multilingual documentation](../multilingual).
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        lengths (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use *attention_mask* for the same result (see above), kept here for compatibility. Indices selected in
            `[0, ..., input_ids.size(-1)]`.
        cache (`Dict[str, torch.FloatTensor]`, *optional*):
            Dictionary string to `torch.FloatTensor` that contains precomputed hidden states (key and values in the
            attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
            decoding.

            The dictionary object will be modified in-place during the forward pass to add newly computed
            hidden-states.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        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.
z]The bare XLM Model transformer outputting raw hidden-states without any specific head on top.c