class_docstr: Base class for forest-based estimators for survival analysis.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
code: def __init__(
      self,
      estimator,
      n_estimators=100,
      *,
      estimator_params=tuple(),
      bootstrap=False,
      oob_score=False,
      n_jobs=None,
      random_state=None,
      verbose=0,
      warm_start=False,
      max_samples=None,
  ):
      super().__init__(
          estimator,
          n_estimators=n_estimators,
          estimator_params=estimator_params,
          bootstrap=bootstrap,
          oob_score=oob_score,
          n_jobs=n_jobs,
          random_state=random_state,
          verbose=verbose,
          warm_start=warm_start,
          class_weight=None,
          max_samples=max_samples,
      )
  
imports: from abc import ABCMeta, abstractmethod
  from functools import partial
  import threading
  import warnings
  from joblib import Parallel, delayed
  import numpy
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.ensemble._forest import BaseForest, _accumulate_prediction, _generate_unsampled_indices, _get_n_samples_bootstrap, _parallel_build_trees
  from sklearn.tree._tree import DTYPE
  from sklearn.utils.validation import check_is_fitted, check_random_state
  from ..base import SurvivalAnalysisMixin
  from ..metrics import concordance_index_censored
  from ..tree import SurvivalTree
  from ..tree._criterion import get_unique_times
  from ..tree.tree import _array_to_step_function
  from ..util import check_array_survival
label_desc: Use image node_id 1 to create a new _BaseSurvivalForest object from inherited base classes: BaseForest, SurvivalAnalysisMixin with example: obj = _BaseSurvivalForest(estimator, n_estimators)