class_docstr: A mapping from indices to vocab elements.
class_docstr_tok: ["A","mapping","from","indices","to","vocab","elements","."]
code: def lookup_all(self, nested_values):
      res, unk_counter, total = self.lookup_all_internal(nested_values)
      self.unk_token_counter[0] += unk_counter
      self.unk_token_counter[1] += total
      self.unk_example_counter[1] += 1
      if total > 3 and (unk_counter / total) > 0.75:
          self.unk_example_counter[0] += 1
          if (
              self.unk_example_counter[0] % 100 == 0
              and self.messages_printed < 200
          ):
              self.messages_printed += 1
              c1, c2 = self.unk_token_counter
              print("")
              print(
                  f"{c1} out of {c2} ({int(100 * c1 / c2)}%) tokens not in vocab"
              )
              c1, c2 = self.unk_example_counter
              print(
                  f"{c1} out of {c2} ({int(100 * c1 / c2)}%) examples have >= 75% "
                  f"tokens not in vocab"
              )
              print("Example: (first 20 tokens)")
              print(nested_values[:20], flush=True)
      return res
  
imports: import itertools
  from collections import Counter
  from typing import Dict, List, Optional, Tuple
  import torch
  from pytext.common.constants import SpecialTokens, Token
  from pytext.utils import cuda, precision
label_desc: Use image node_id 3 for calling the Vocabulary obj's underlying member method code with example usage: obj.lookup_all(nested_values) and returns: res