class_docstr: Base class for Bagging meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
docstr: The subset of drawn samples for each base estimator.
  
  Returns a dynamically generated list of boolean masks identifying
  the samples used for for fitting each member of the ensemble, i.e.,
  the in-bag samples.
  
  Note: the list is re-created at each call to the property in order
  to reduce the object memory footprint by not storing the sampling
  data. Thus fetching the property may be slower than expected.
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code: def estimators_samples_(self):
      """The subset of drawn samples for each base estimator.
  
      Returns a dynamically generated list of boolean masks identifying
      the samples used for for fitting each member of the ensemble, i.e.,
      the in-bag samples.
  
      Note: the list is re-created at each call to the property in order
      to reduce the object memory footprint by not storing the sampling
      data. Thus fetching the property may be slower than expected.
      """
      sample_masks = []
      for _, sample_indices in self._get_estimators_indices():
          mask = indices_to_mask(sample_indices, self._n_samples)
          sample_masks.append(mask)
  
      return sample_masks
  
imports: from __future__ import division
  import itertools
  import numbers
  import numpy
  from warnings import warn
  from abc import ABCMeta, abstractmethod
  from ..base import ClassifierMixin, RegressorMixin
  from ..externals.joblib import Parallel, delayed
  from ..externals.six import with_metaclass
  from ..externals.six.moves import zip
  from ..metrics import r2_score, accuracy_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import check_random_state, check_X_y, check_array, column_or_1d
  from ..utils.random import sample_without_replacement
  from ..utils.validation import has_fit_parameter, check_is_fitted
  from ..utils import indices_to_mask
  from ..utils.fixes import bincount
  from ..utils.metaestimators import if_delegate_has_method
  from ..utils.multiclass import check_classification_targets
  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.estimators_samples_() and returns: sample_masks