code: def __init__(
      self,
      src_dict,
      dst_dict,
      vocab_reduction_params,
      predictor=None,
      fp16: bool = False,
  ):
      super().__init__()
      self.src_dict = src_dict
      self.dst_dict = dst_dict
      self.vocab_reduction_params = vocab_reduction_params
      self.predictor = predictor
      self.fp16 = fp16
      self.translation_candidates = None
  
      if (
          self.vocab_reduction_params is not None
          and self.vocab_reduction_params[
              "max_translation_candidates_per_word"
          ]
          > 0
      ):
          translation_candidates = get_translation_candidates(
              self.src_dict,
              self.dst_dict,
              self.vocab_reduction_params["lexical_dictionaries"],
              self.vocab_reduction_params["num_top_words"],
              self.vocab_reduction_params[
                  "max_translation_candidates_per_word"
              ],
          )
          self.translation_candidates = nn.Parameter(
              torch.Tensor(translation_candidates).long(),
              requires_grad=False,
          )
  
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 1 to create a new VocabReduction object from inherited base classes: nn with example: obj = VocabReduction(src_dict, dst_dict, vocab_reduction_params, predictor, fp16)