class_docstr: A C4.5 tree classifier.
  
  Parameters
  ----------
  max_rules : int, optional (default=None)
      Maximum number of split nodes allowed in the tree
class_docstr_tok: ["A","C4.5","tree","classifier",".","Parameters","--","--","--","--","--","max_rules",":","int",",","optional","(","default=None",")","Maximum","number","of","split","nodes","allowed","in","the","tree"]
docstr: Returns
  ---
  the leaf by which this sample would be classified
docstr_tok: ["Returns","--","-","the","leaf","by","which","this","sample","would","be","classified"]
code: def impute_nodes(self, X, y):
      """
      Returns
      ---
      the leaf by which this sample would be classified
      """
      source_node = self.root
      for i in range(len(y)):
          sample, label = X[i, ...], y[i]
          _add_label(source_node, label)
          nodes = [source_node]
          while len(nodes) > 0:
              node = nodes.pop()
              if not node.hasChildNodes():
                  continue
              else:
                  att_name = node.firstChild.nodeName
                  if att_name != "#text":
                      att = sample[self.feature_names.index(att_name)]
                      next_node = _get_next_node(node.childNodes, att)
                  else:
                      next_node = node.firstChild
                  _add_label(next_node, label)
                  nodes.append(next_node)
  
      self._calc_probs(source_node)
  
imports: import copy
  from copy import deepcopy
  from typing import List
  from xml.dom import minidom
  from xml.etree import ElementTree
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.model_selection import cross_val_score
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  from imodels.util.arguments import check_fit_arguments
  from ..c45_tree.c45_utils import decision, is_numeric_feature, gain, gain_ratio, get_best_split, set_as_leaf_node
  from ...util.data_util import get_clean_dataset
label_desc: Use image node_id 3 for calling the C45TreeClassifier obj's underlying member method code with example usage: obj.impute_nodes(X, y) without return types