docstr: If possible_unk_token is not None, then all UNK id's will be replaced
  by possible_unk_token instead of the default UNK string which is <UNK>.
  This is a simple way to resolve UNK's when there's a correspondence
  between source and target translations.
docstr_tok: ["If","possible_unk_token","is","not","None",",","then","all","UNK","id","'s","will","be","replaced","by","possible_unk_token","instead","of","the","default","UNK","string","which","is","<","UNK",">",".","This","is","a","simple","way","to","resolve","UNK","'s","when","there","'s","a","correspondence","between","source","and","target","translations","."]
code: def lookup_words_1d(
      self,
      values: torch.Tensor,
      filter_token_list: List[int] = (),
      possible_unk_token: Optional[str] = None,
  ) -> List[str]:
      """If possible_unk_token is not None, then all UNK id's will be replaced
      by possible_unk_token instead of the default UNK string which is <UNK>.
      This is a simple way to resolve UNK's when there's a correspondence
      between source and target translations.
      """
      result: List[str] = []
      for idx in range(values.size(0)):
          value = int(values[idx])
          if not (value in filter_token_list):
              result.append(self.lookup_word(value, possible_unk_token))
      return result
  
file_path: pytext/pytext/torchscript/vocab.py
imports: from typing import Dict, List, Optional
  import torch
  import torch.nn
  from pytext.common.constants import SpecialTokens
label_desc: Use image node_id 7 for calling the ScriptVocabulary obj's underlying member method code with example usage: obj.lookup_words_1d(values, filter_token_list, possible_unk_token) and returns: result