class_docstr: Base class for stacking method.
docstr: Number of features seen during :term:`fit`.
docstr_tok: ["Number","of","features","seen","during",":","term",":","`","fit","`","."]
code: def n_features_in_(self):
      """Number of features seen during :term:`fit`."""
      try:
          check_is_fitted(self)
      except NotFittedError as nfe:
          raise AttributeError(
              f"{self.__class__.__name__} object has no attribute n_features_in_"
          ) from nfe
      return self.estimators_[0].n_features_in_
  
imports: from abc import ABCMeta, abstractmethod
  from copy import deepcopy
  from numbers import Integral
  import numpy
  import scipy.sparse
  from ..base import ClassifierMixin, RegressorMixin, TransformerMixin, _fit_context, clone, is_classifier, is_regressor
  from ..exceptions import NotFittedError
  from ..linear_model import LogisticRegression, RidgeCV
  from ..model_selection import check_cv, cross_val_predict
  from ..preprocessing import LabelEncoder
  from ..utils import Bunch
  from ..utils._estimator_html_repr import _VisualBlock
  from ..utils._param_validation import HasMethods, StrOptions
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets, type_of_target
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import _check_feature_names_in, _check_response_method, check_is_fitted, column_or_1d
  from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator
label_desc: Use image node_id 6 for calling the _BaseStacking obj's underlying member method code with example usage: obj.n_features_in_() and returns: self