    def extract_features(
        self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
    ):
        """
        Similar to *forward* but only return features.

        Returns:
            tuple:
                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                - a dictionary with any model-specific outputs
        """

        # embed positions
        positions = (
            self.embed_positions(
                prev_output_tokens, incremental_state=incremental_state
            )
            if self.embed_positions is not None
            else None
        )

        if incremental_state is not None:
            prev_output_tokens = prev_output_tokens[:, -1:]
            if positions is not None:
                positions = positions[:, -1:]

        # embed tokens and positions
        x = self.embed_scale * self.embed_tokens(prev_output_tokens)

        if self.project_in_dim is not None:
            x = self.project_in_dim(x)

        if positions is not None:
            x += positions
        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)
        attn = None

        inner_states = [x]

        # decoder layers
        self_attn_padding_mask = None
        if prev_output_tokens.eq(self.padding_idx).any():
            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
        for layer in self.layers:
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, attn, _ = layer(
                    x,
                    encoder_out["encoder_out"] if encoder_out is not None else None,
                    encoder_out["padding_mask"] if encoder_out is not None else None,
                    incremental_state,
                    self_attn_mask=self.buffered_future_mask(x)
                    if incremental_state is None
                    else None,
                    self_attn_padding_mask=self_attn_padding_mask,
                )
                inner_states.append(x)

        if self.layer_norm:
            x = self.layer_norm(x)

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, {"attn": attn, "inner_states": inner_states}
Similar to *forward* but only return features.

Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
fairseq/models/wav2vec/wav2vec2_asr.py