class_docstr: Provides a consistent `Transform` interface to tokenizers operating on `DataFrame`s and folders
class_docstr_tok: ["Provides","a","consistent","`","Transform","`","interface","to","tokenizers","operating","on","`","DataFrame","`","s","and","folders"]
code: def from_df(cls, text_cols, tok=None, rules=None, sep=" ", **kwargs):
      if tok is None:
          tok = WordTokenizer()
      res = cls(tok, rules=rules, mode="df")
      res.kwargs, res.train_setup = merge({"tok": tok}, kwargs), False
      res.text_cols, res.sep = text_cols, sep
      default_val = (
          inspect.signature(tokenize_df)
          .parameters["tok_text_col"]
          .default
      )
      res.tok_text_col = kwargs.get("tok_text_col", default_val)
      return res
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  import html
label_desc: Use image node_id 2 for calling the Tokenizer obj's underlying member method code with example usage: obj.from_df(cls, text_cols, tok, rules, sep) and returns: res