class_docstr: Base class for forests of trees.
code: def __init__(
      self,
      base_estimator,
      n_estimators=100,
      estimator_params=tuple(),
      bootstrap=False,
      oob_score=False,
      n_jobs=None,
      random_state=None,
      verbose=0,
      warm_start=False,
      class_weight=None,
      max_samples=None,
  ):
      super().__init__(
          base_estimator,
          n_estimators=n_estimators,
          estimator_params=estimator_params,
      )
      self.bootstrap = bootstrap
      self.oob_score = oob_score
      self.n_jobs = n_jobs
      self.random_state = random_state
      self.verbose = verbose
      self.warm_start = warm_start
      self.class_weight = class_weight
      self.max_samples = max_samples
  
imports: from abc import abstractmethod
  from warnings import catch_warnings, simplefilter
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from numpy import float64
  from sklearn.base import clone
  from sklearn.ensemble._forest import MAX_INT, BaseForest, _generate_sample_indices, _get_n_samples_bootstrap
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils import check_array, check_random_state, compute_sample_weight
  from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
  from sktime.utils.random_state import set_random_state
  from sktime.utils.warnings import warn
label_desc: Use image node_id 1 to create a new BaseTimeSeriesForest object from inherited base classes: BaseForest with example: obj = BaseTimeSeriesForest(base_estimator, n_estimators, estimator_params, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, class_weight, max_samples)