def forward(self, x, encoder_padding_mask, layer_head_mask, output_attentions=False):
        """
        Args:
            x (`torch.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
            encoder_padding_mask (`torch.ByteTensor`): binary ByteTensor of shape
                *(batch, src_len)* where padding elements are indicated by `1`.
            for t_tgt, t_src is excluded (or masked out), =0 means it is
            included in attention
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                *(config.encoder_attention_heads,)*.

        Returns:
            encoded output of shape *(seq_len, batch, embed_dim)*
        """
        residual = x
        x, attn_weights = self.self_attn(
            query=x,
            key=x,
            key_padding_mask=encoder_padding_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.self_attn_layer_norm(x)

        residual = x
        x = self.activation_fn(self.fc1(x))
        x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        x = self.final_layer_norm(x)
        return x, attn_weights

        Args:
            x (`torch.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
            encoder_padding_mask (`torch.ByteTensor`): binary ByteTensor of shape
                *(batch, src_len)* where padding elements are indicated by `1`.
            for t_tgt, t_src is excluded (or masked out), =0 means it is
            included in attention
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                *(config.encoder_attention_heads,)*.

        Returns:
            encoded output of shape *(seq_len, batch, embed_dim)*
        
dpkd/transformers/src/transformers/models/fsmt/modeling_fsmt.py