   - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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 strings 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 `(batch_size, sequence_length, 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.
zbThe bare Flaubert Model transformer outputting raw hidden-states without any specific head on top.c