class_docstr: Common super class for decoder networks with output projection layers.
  
  This class couples common functionality for `FairseqDecoder`s with large
  output projection layers such as ONNX compatibility and vocabulary reduction.
class_docstr_tok: ["Common","super","class","for","decoder","networks","with","output","projection","layers",".","This","class","couples","common","functionality","for","`","FairseqDecoder","`","s","with","large","output","projection","layers","such","as","ONNX","compatibility","and","vocabulary","reduction","."]
docstr: Forward pass through the decoder without output projection.
docstr_tok: ["Forward","pass","through","the","decoder","without","output","projection","."]
code: def forward_unprojected(
      self,
      input_tokens,
      encoder_out,
      incremental_state=None,
      possible_translation_tokens=None,
  ):
      """Forward pass through the decoder without output projection."""
      raise NotImplementedError()
  
imports: import abc
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqIncrementalDecoder, transformer
  from pytorch_translate import rnn_cell, utils, vocab_reduction
  from pytorch_translate.research.lexical_choice import lexical_translation
label_desc: Use image node_id 4 for calling the DecoderWithOutputProjection obj's underlying member method code with example usage: obj.forward_unprojected(input_tokens, encoder_out, incremental_state, possible_translation_tokens) without return types