class_docstr: Base class for the feature quantizers used in Stochastic Gradient Trees[^1].
  
  References
  ----------
  [^1]: Gouk, H., Pfahringer, B., & Frank, E. (2019, October). Stochastic Gradient Trees.
  In Asian Conference on Machine Learning (pp. 1094-1109).
class_docstr_tok: ["Base","class","for","the","feature","quantizers","used","in","Stochastic","Gradient","Trees","[","^1","]",".","References","--","--","--","--","--","[","^1","]",":","Gouk",",","H.",",","Pfahringer",",","B.",",","&","Frank",",","E.","(","2019",",","October",")",".","Stochastic","Gradient","Trees",".","In","Asian","Conference","on","Machine","Learning","(","pp",".","1094-1109",")","."]
code: def update(self, x_val, gh: GradHess, w: float):
      pass
  
file_path: river/river/tree/splitter/base.py
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
  from ..split_criterion.base import SplitCriterion
  from ..utils import BranchFactory, GradHess, GradHessStats
label_desc: Use image node_id 2 for calling the Quantizer obj's underlying member method code with example usage: obj.update(x_val, gh, w) without return types