code: def learn_one(self, x: dict, y: base.typing.Target, **kwargs):
      if len(self) == 0:
          self._init_ensemble(sorted(x.keys()))
  
      for i, model in enumerate(self):
          y_pred = model.predict_one(x)
  
          # Update performance evaluator
          self._metrics[i].update(
              y_true=y,
              y_pred=model.predict_proba_one(x)
              if isinstance(
                  self.metric, metrics.base.ClassificationMetric
              )
              and not self.metric.requires_labels
              else y_pred,
          )
  
          k = poisson(rate=self.lambda_value, rng=self._rng)
          if k > 0:
              if (
                  not self._warning_detection_disabled
                  and self._background[i] is not None
              ):
                  self._background[i].learn_one(x=x, y=y, w=k)  # type: ignore
  
              model.learn_one(x=x, y=y, w=k)
  
              drift_input = None
              if not self._warning_detection_disabled:
                  drift_input = self._drift_detector_input(i, y, y_pred)
                  self._warning_detectors[i].update(drift_input)
  
                  if self._warning_detectors[i].drift_detected:
                      self._background[i] = self._new_base_model()  # type: ignore
                      # Reset the warning detector for the current object
                      self._warning_detectors[
                          i
                      ] = self.warning_detector.clone()
  
                      # Update warning tracker
                      self._warning_tracker[i] += 1
  
              if not self._drift_detection_disabled:
                  drift_input = (
                      drift_input
                      if drift_input is not None
                      else self._drift_detector_input(i, y, y_pred)
                  )
                  self._drift_detectors[i].update(drift_input)
  
                  if self._drift_detectors[i].drift_detected:
                      if (
                          not self._warning_detection_disabled
                          and self._background[i] is not None
                      ):
                          self.data[i] = self._background[i]
                          self._background[i] = None
                          self._warning_detectors[
                              i
                          ] = self.warning_detector.clone()
                          self._drift_detectors[
                              i
                          ] = self.drift_detector.clone()
                          self._metrics[i] = self.metric.clone()
                      else:
                          self.data[i] = self._new_base_model()
                          self._drift_detectors[
                              i
                          ] = self.drift_detector.clone()
                          self._metrics[i] = self.metric.clone()
  
                      # Update warning tracker
                      self._drift_tracker[i] += 1
  
imports: from __future__ import annotations
  import abc
  import collections
  import copy
  import math
  import random
  import typing
  import numpy
  from river import base, metrics, stats
  from river.drift import ADWIN, NoDrift
  from river.tree.hoeffding_tree_classifier import HoeffdingTreeClassifier
  from river.tree.hoeffding_tree_regressor import HoeffdingTreeRegressor
  from river.tree.nodes.arf_htc_nodes import RandomLeafMajorityClass, RandomLeafNaiveBayes, RandomLeafNaiveBayesAdaptive
  from river.tree.nodes.arf_htr_nodes import RandomLeafAdaptive, RandomLeafMean, RandomLeafModel
  from river.tree.splitter import Splitter
  from river.utils.random import poisson
label_desc: Use image node_id 9 for calling the BaseForest obj's underlying member method code with example usage: obj.learn_one(x, y) without return types