class_docstr: Base class for decision trees.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
class_docstr_tok: ["Base","class","for","decision","trees",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: Return the depth of the decision tree.
  
  The depth of a tree is the maximum distance between the root
  and any leaf.
  
  Returns
  -------
  self.tree_.max_depth : int
      The maximum depth of the tree.
docstr_tok: ["Return","the","depth","of","the","decision","tree",".","The","depth","of","a","tree","is","the","maximum","distance","between","the","root","and","any","leaf",".","Returns","--","--","--","-","self.tree_.max_depth",":","int","The","maximum","depth","of","the","tree","."]
code: def get_depth(self):
      """Return the depth of the decision tree.
  
      The depth of a tree is the maximum distance between the root
      and any leaf.
  
      Returns
      -------
      self.tree_.max_depth : int
          The maximum depth of the tree.
      """
      check_is_fitted(self)
      return self.tree_.max_depth
  
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 3 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj.get_depth() and returns: self