class_docstr: Builds and/or loads a dictionary.
  
  All code is TorchScriptable.
class_docstr_tok: ["Builds","and\/or","loads","a","dictionary",".","All","code","is","TorchScriptable","."]
docstr: Convert a vector of IDs to a string.
  
  Converts a vector (iterable of ints) into a string, with each token separated by
  the delimiter (default ``' '``).
docstr_tok: ["Convert","a","vector","of","IDs","to","a","string",".","Converts","a","vector","(","iterable","of","ints",")","into","a","string",",","with","each","token","separated","by","the","delimiter","(","default","``","'","'","``",")","."]
code: def vec2txt(self, vector: List[int], dict_tokenizer: str) -> str:
      """
      Convert a vector of IDs to a string.
  
      Converts a vector (iterable of ints) into a string, with each token separated by
      the delimiter (default ``' '``).
      """
      tokens = [self._index_lookup(idx) for idx in vector]
      if (
          dict_tokenizer == "gpt2"
          or dict_tokenizer == "slow_bytelevel_bpe"
      ):
          text = self.gpt2_bpe.decode(tokens)
      elif dict_tokenizer == "bpe":
          text = self.subword_bpe.decode(tokens)
      else:
          raise NotImplementedError
      return text
  
imports: from typing import Dict, List, Tuple
  import torch.jit
label_desc: Use image node_id 7 for calling the ScriptableDictionaryAgent obj's underlying member method code with example usage: obj.vec2txt(vector, dict_tokenizer) and returns: text