src/transformers/models/hubert/modeling_tf_hubert.py/TFHubertEncoderLayerStableLayerNorm/call
class TFHubertEncoderLayerStableLayerNorm:    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: Optional[tf.Tensor] = None,
        output_attentions: Optional[bool] = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attn_residual = hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.attention(
            hidden_states, attention_mask=attention_mask, training=training
        )
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = attn_residual + hidden_states
        hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs