class_docstr: Contains common processing steps used by each vectorizer.
  
  These are the preprocessing steps that in applied in order:
  
  Parameters
  ----------
  on
      The name of the feature that contains the text to vectorize. If `None`, then each
      `learn_one` and `transform_one` should treat `x` as a `str` and not as a `dict`.
  strip_accents
      Whether or not to strip accent characters.
  lowercase
      Whether or not to convert all characters to lowercase.
  preprocessor
      An optional preprocessing function which overrides the `strip_accents` and `lowercase`
      steps, while preserving the tokenizing and n-grams generation steps.
  tokenizer_pattern
      The tokenization pattern which is used when no `tokenizer` function is passed. A single
      capture group may optionally be specified.
  tokenizer
      A function used to convert preprocessed text into a `dict` of tokens. A default tokenizer
      is used if `None` is passed. Set to `False` to disable tokenization.
  stop_words
      An optional set of tokens to remove.
  ngram_range
      The lower and upper boundary of the range n-grams to be extracted. All values of n such
      that `min_n <= n <= max_n` will be used. For example an `ngram_range` of `(1, 1)` means
      only unigrams, `(1, 2)` means unigrams and bigrams, and `(2, 2)` means only bigrams.
  
  Attributes
  ----------
  processing_steps : list
      A list of preprocessing steps that are applied to each text.
code: def _more_tags(self):
      if self.on is None:
          return {base.tags.TEXT_INPUT}
      return {}
  
imports: from __future__ import annotations
  import collections
  import functools
  import itertools
  import math
  import operator
  import re
  import typing
  import unicodedata
  import pandas
  from scipy import sparse
  from river import base
label_desc: Use image node_id 3 for calling the VectorizerMixin obj's underlying member method code with example usage: obj._more_tags() and returns: dict, unknown