code: def __init__(
      self,
      n_models: int,
      max_features: bool | str | int,
      lambda_value: int,
      drift_detector: base.DriftDetector,
      warning_detector: base.DriftDetector,
      metric: metrics.base.MultiClassMetric
      | metrics.base.RegressionMetric,
      disable_weighted_vote,
      seed,
  ):
      super().__init__([])  # type: ignore
      self.n_models = n_models
      self.max_features = max_features
      self.lambda_value = lambda_value
      self.metric = metric
      self.disable_weighted_vote = disable_weighted_vote
      self.drift_detector = drift_detector
      self.warning_detector = warning_detector
      self.seed = seed
  
      self._rng = random.Random(self.seed)
  
      self._warning_detectors: list[base.DriftDetector]
      self._warning_detection_disabled = True
      if not isinstance(self.warning_detector, NoDrift):
          self._warning_detectors = [
              self.warning_detector.clone()
              for _ in range(self.n_models)
          ]
          self._warning_detection_disabled = False
  
      self._drift_detectors: list[base.DriftDetector]
      self._drift_detection_disabled = True
      if not isinstance(self.drift_detector, NoDrift):
          self._drift_detectors = [
              self.drift_detector.clone() for _ in range(self.n_models)
          ]
          self._drift_detection_disabled = False
  
      # The background models
      self._background: list[
          BaseTreeClassifier | BaseTreeRegressor | None
      ] = (
          None if self._warning_detection_disabled else [None] * self.n_models  # type: ignore
      )
  
      # Performance metrics used for weighted voting/aggregation
      self._metrics = [
          self.metric.clone() for _ in range(self.n_models)
      ]
  
      # Drift and warning logging
      self._warning_tracker: dict = (
          collections.defaultdict(int) if not self._warning_detection_disabled else None  # type: ignore
      )
      self._drift_tracker: dict = (
          collections.defaultdict(int) if not self._drift_detection_disabled else None  # type: ignore
      )
  
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 1 to create a new BaseForest object from inherited base classes: base with example: obj = BaseForest(n_models, max_features, lambda_value, drift_detector, warning_detector, metric, disable_weighted_vote, seed)