class_docstr: Base class for decision trees.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
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docstr: Return the index of the leaf that each sample is predicted as.
  
  .. versionadded:: 0.17
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csr_matrix``.
  
  check_input : bool, default=True
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you do.
  
  Returns
  -------
  X_leaves : array-like of shape (n_samples,)
      For each datapoint x in X, return the index of the leaf x
      ends up in. Leaves are numbered within
      ``[0; self.tree_.node_count)``, possibly with gaps in the
      numbering.
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code: def apply(self, X, check_input=True):
      """Return the index of the leaf that each sample is predicted as.
  
      .. versionadded:: 0.17
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csr_matrix``.
  
      check_input : bool, default=True
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you do.
  
      Returns
      -------
      X_leaves : array-like of shape (n_samples,)
          For each datapoint x in X, return the index of the leaf x
          ends up in. Leaves are numbered within
          ``[0; self.tree_.node_count)``, possibly with gaps in the
          numbering.
      """
      check_is_fitted(self)
      X = self._validate_X_predict(X, check_input)
      return self.tree_.apply(X)
  
imports: from abc import ABCMeta
  from abc import abstractmethod
  from sklearn.base import MultiOutputMixin
  from sklearn.base import BaseEstimator
  from sklearn.base import is_classifier, clone
  from sklearn.utils import Bunch
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from scipy.sparse import issparse
  from ._tree import Tree
  from ._tree import _build_pruned_tree_ccp
  from ._tree import ccp_pruning_path
  from .None import _tree, _splitter
label_desc: Use image node_id 7 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj.apply(X, check_input) and returns: self