src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py/TFXLMRobertaModel/serving_output
class TFXLMRobertaModel:    def serving_output(
        self, output: TFBaseModelOutputWithPoolingAndCrossAttentions
    ) -> TFBaseModelOutputWithPoolingAndCrossAttentions:
        output_cache = self.config.use_cache and self.config.is_decoder
        pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
        if not (self.config.output_attentions and self.config.add_cross_attention):
            cross_attns = None

        return TFBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=output.last_hidden_state,
            pooler_output=output.pooler_output,
            past_key_values=pkv,
            hidden_states=hs,
            attentions=attns,
            cross_attentions=cross_attns,
        )