    def output_layer(self, features, **kwargs):
        """Project features to the vocabulary size."""
        if self.adaptive_softmax is None:
            # project back to size of vocabulary
            if self.share_input_output_embed:
                if isinstance(self.embed_tokens, nn.ModuleList):
                    output = None
                    for i, emb in enumerate(self.embed_tokens):
                        sidx = i * emb.embedding_dim
                        eidx = (i + 1) * emb.embedding_dim
                        if output is None:
                            output = F.linear(features[:, :, sidx:eidx], emb.weight)
                        else:
                            output += F.linear(features[:, :, sidx:eidx], emb.weight)

                    return output
                else:
                    return F.linear(features, self.embed_tokens.weight)
            else:
                return F.linear(features, self.embed_tokens)
        else:
            return features
Project features to the vocabulary size.
fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py