src/transformers/models/deberta/modeling_tf_deberta.py/TFDebertaAttention/call
class TFDebertaAttention:    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        query_states: tf.Tensor = None,
        relative_pos: tf.Tensor = None,
        rel_embeddings: tf.Tensor = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
            output_attentions=output_attentions,
            training=training,
        )
        if query_states is None:
            query_states = input_tensor
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=query_states, training=training
        )

        output = (attention_output,) + self_outputs[1:]

        return output