t are attention masks?](../glossary#attention-mask)
            head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

            inputs_embeds (`tf.Tensor` 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. This argument can be used only in eager mode, in graph mode the value
                in the config will be used instead.
            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. This argument can be used only in eager mode, in graph mode the value in the config
                will be used instead.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
                in eager mode, in graph mode the value will always be set to True.
            training (`bool`, *optional*, defaults to `False`):
                Whether or not to use the model in training mode (some modules like dropout modules have different
                behaviors between training and evaluation).
        NzDYou cannot specify both input_ids and inputs_embeds at the same timer$