class_docstr: Reversible transform of tokenized texts to numericalized ids
class_docstr_tok: ["Reversible","transform","of","tokenized","texts","to","numericalized","ids"]
code: def setups(self, dsets):
      if dsets is None:
          return
      if self.vocab is None:
          count = (
              dsets.counter
              if getattr(dsets, "counter", None) is not None
              else Counter(p for o in dsets for p in o)
          )
          if self.special_toks is None and hasattr(
              dsets, "special_toks"
          ):
              self.special_toks = dsets.special_toks
          self.vocab = make_vocab(
              count,
              min_freq=self.min_freq,
              max_vocab=self.max_vocab,
              special_toks=self.special_toks,
          )
          self.o2i = defaultdict(
              int,
              {v: k for k, v in enumerate(self.vocab) if v != "xxfake"},
          )
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
label_desc: Use image node_id 2 for calling the Numericalize obj's underlying member method code with example usage: obj.setups(dsets) without return types