code: def forward(
      self, src_tokens, encoder_output=None, decoder_input_tokens=None
  ):
      assert self.dst_dict.pad() == 0, (
          f"VocabReduction only works correctly when the padding ID is 0 "
          "(to ensure its position in possible_translation_tokens is also 0), "
          f"instead of {self.dst_dict.pad()}."
      )
      vocab_list = [src_tokens.new_tensor([self.dst_dict.pad()])]
  
      if decoder_input_tokens is not None:
          flat_decoder_input_tokens = decoder_input_tokens.view(-1)
          vocab_list.append(flat_decoder_input_tokens)
  
      if self.translation_candidates is not None:
          reduced_vocab = self.translation_candidates.index_select(
              dim=0, index=src_tokens.view(-1)
          ).view(-1)
          vocab_list.append(reduced_vocab)
      if (
          self.vocab_reduction_params is not None
          and self.vocab_reduction_params["num_top_words"] > 0
      ):
          top_words = torch.arange(
              self.vocab_reduction_params["num_top_words"],
              device=vocab_list[0].device,
          ).long()
          vocab_list.append(top_words)
  
      # Get bag of words predicted by word predictor
      if self.predictor is not None:
          assert encoder_output is not None
          pred_output = self.predictor(encoder_output)
          # [batch, k]
          topk_indices = self.predictor.get_topk_predicted_tokens(
              pred_output, src_tokens, log_probs=True
          )
          # flatten indices for entire batch [1, batch * k]
          topk_indices = topk_indices.view(-1)
          vocab_list.append(topk_indices.detach())
  
      all_translation_tokens = torch.cat(vocab_list, dim=0)
      possible_translation_tokens = torch.unique(
          all_translation_tokens,
          # Sorting helps ensure that the padding ID (0) remains in position 0.
          sorted=True,
          # The decoder_input_tokens used here are very close to the targets
          # tokens that we also need to map to the reduced vocab space later
          # on, except that decoder_input_tokens have <eos> prepended, while
          # the targets will have <eos> at the end of the sentence. This
          # prevents us from being able to directly use the inverse indices
          # that torch.unique can return.
          return_inverse=False,
      ).type_as(src_tokens)
  
      # Pad to a multiple of 8 to ensure training with fp16 will activate
      # NVIDIA Tensor Cores.
      len_mod_eight = possible_translation_tokens.shape[0] % 8
      if self.training and self.fp16 and len_mod_eight != 0:
          possible_translation_tokens = torch.cat(
              [
                  possible_translation_tokens,
                  possible_translation_tokens.new_tensor(
                      [self.dst_dict.pad()] * (8 - len_mod_eight)
                  ),
              ]
          )
  
      return possible_translation_tokens
  
file_path: translate/pytorch_translate/vocab_reduction.py
imports: import codecs
  import logging
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the VocabReduction obj's underlying member method code with example usage: obj.forward(src_tokens, encoder_output, decoder_input_tokens) and returns: possible_translation_tokens