class_docstr: The actual dynamic feature quantization mechanism.
  
  The input numerical feature is partitioned using equal-sized intervals that are defined
  by the `radius` parameter. For each interval, estimators of target's mean and variance are
  kept, as well as an estimator of the feature's mean value. At any time, one can iterate
  over the ordered stored values in the hash. The ordering is defined by the quantized values
  of the feature.
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code: def all(self):
      branch_ids = []
      split_stats = []
      for slot_id in sorted(self.hash.keys()):
          branch_ids.append(slot_id)
          split_stats.append(self.hash[slot_id].y_stats)
  
      return branch_ids, split_stats
  
imports: from __future__ import annotations
  import functools
  import math
  import typing
  from river import stats
  from ..utils import BranchFactory
  from .base import Splitter
label_desc: Use image node_id 6 for calling the FeatureQuantizer obj's underlying member method code with example usage: obj.all() and returns: branch_ids,  split_stats