def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        position_bias: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, hidden_size)`
            attention_mask (`torch.FloatTensor`):
                attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
                large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(config.encoder_attention_heads,)`.
            position_bias (`torch.FloatTensor`):
                relative position embeddings of size `(seq_len, seq_len, hidden_size // encoder_attention_heads)`
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states, attn_weights, _ = self.attention(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
        )

        hidden_states = self.dropout(hidden_states)
        hidden_states = residual + hidden_states

        hidden_states = self.layer_norm(hidden_states)
        hidden_states = hidden_states + self.feed_forward(hidden_states)
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs

        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, hidden_size)`
            attention_mask (`torch.FloatTensor`):
                attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
                large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(config.encoder_attention_heads,)`.
            position_bias (`torch.FloatTensor`):
                relative position embeddings of size `(seq_len, seq_len, hidden_size // encoder_attention_heads)`
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        
dpkd/transformers/src/transformers/models/speecht5/modeling_speecht5.py