class_docstr: Adapted version of the Quantizer Observer (QO)[^1] that is applied to Stochastic Gradient
  Trees (SGT).
  
  This feature quantizer starts by partitioning the inputs using the passed `radius` value.
  As more splits are created in the SGTs, new feature quantizers will use `std * std_prop` as
  the quantization radius. In the expression, `std` represents the standard deviation of the
  input data, which is calculated incrementally.
  
  Parameters
  ----------
  radius
      The initial quantization radius.
  std_prop
      The proportion of the standard deviation that is going to be used to define the radius
      value for new quantizer instances following the initial one.
  
  References
  ----------
  [^1]: Mastelini, S.M. and de Leon Ferreira, A.C.P., 2021. Using dynamical quantization
  to perform split attempts in online tree regressors. Pattern Recognition Letters.
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code: def __len__(self):
      return len(self.hash)
  
file_path: river/river/tree/splitter/sgt_quantizer.py
imports: from __future__ import annotations
  import math
  from river import stats
  from ..utils import GradHess, GradHessStats
  from .base import Quantizer
label_desc: Use image node_id 3 for calling the DynamicQuantizer obj's underlying member method code with example usage: obj.__len__() and returns: len