class_docstr: Reversible transform of tokenized texts to numericalized ids
class_docstr_tok: ["Reversible","transform","of","tokenized","texts","to","numericalized","ids"]
code: def __init__(
      self, vocab=None, min_freq=3, max_vocab=60000, special_toks=None
  ):
      store_attr("vocab,min_freq,max_vocab,special_toks")
      self.o2i = (
          None
          if vocab is None
          else defaultdict(int, {v: k for k, v in enumerate(vocab)})
      )
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
label_desc: Use image node_id 1 to create a new Numericalize object from inherited base classes: Transform with example: obj = Numericalize(vocab, min_freq, max_vocab, special_toks)