class_docstr: Base class for Bagging meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","Bagging","meta-estimator",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def _get_estimators_indices(self):
      # Get drawn indices along both sample and feature axes
      for seed in self._seeds:
          # Operations accessing random_state must be performed identically
          # to those in `_parallel_build_estimators()`
          feature_indices, sample_indices = _generate_bagging_indices(
              seed,
              self.bootstrap_features,
              self.bootstrap,
              self.n_features_in_,
              self._n_samples,
              self._max_features,
              self._max_samples,
          )
  
          yield feature_indices, sample_indices
  
imports: import itertools
  import numbers
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from numbers import Integral
  from warnings import warn
  import numpy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import check_random_state, column_or_1d, indices_to_mask
  from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble, _partition_estimators
label_desc: Use image node_id 7 for calling the BaseBagging obj's underlying member method code with example usage: obj._get_estimators_indices() without return types