class_docstr: LSTM decoder.
docstr: Project features to the vocabulary size.
docstr_tok: ["Project","features","to","the","vocabulary","size","."]
code: def output_layer(self, x):
      """Project features to the vocabulary size."""
      if self.adaptive_softmax is None:
          if self.share_input_output_embed:
              x = F.linear(x, self.embed_tokens.weight)
          else:
              x = self.fc_out(x)
      return x
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax, FairseqDropout
  from torch import Tensor
label_desc: Use image node_id 4 for calling the LSTMDecoder obj's underlying member method code with example usage: obj.output_layer(x) and returns: x