class_docstr: Base class for Bagging meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
code: def _validate_y(self, y):
      if len(y.shape) == 1 or y.shape[1] == 1:
          return column_or_1d(y, warn=True)
      return y
  
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
  from ..utils._tags import _safe_tags
  from ..utils.metadata_routing import _raise_for_unsupported_routing, _RoutingNotSupportedMixin
  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 6 for calling the BaseBagging obj's underlying member method code with example usage: obj._validate_y(y) and returns: y, column_or_1d