src/transformers/models/bert/modeling_tf_bert.py/TFBertAttention/call
class TFBertAttention:    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[tf.Tensor],
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        # add attentions (possibly with past_key_value) if we output them
        outputs = (attention_output,) + self_outputs[1:]

        return outputs