code: def export(self, decision_tree):
      # Check length of feature_names before getting into the tree node
      # Raise error if length of feature_names does not match
      # n_features_in_ in the decision_tree
      if self.feature_names is not None:
          if len(self.feature_names) != decision_tree.n_features_in_:
              raise ValueError(
                  "Length of feature_names, %d does not match number of features, %d"
                  % (
                      len(self.feature_names),
                      decision_tree.n_features_in_,
                  )
              )
      # each part writes to out_file
      self.head()
      # Now recurse the tree and add node & edge attributes
      if isinstance(decision_tree, _tree.Tree):
          self.recurse(decision_tree, 0, criterion="impurity")
      else:
          self.recurse(
              decision_tree.tree_, 0, criterion=decision_tree.criterion
          )
  
      self.tail()
  
imports: from collections.abc import Iterable
  from io import StringIO
  from numbers import Integral
  import numpy
  from ..base import is_classifier
  from ..utils._param_validation import HasMethods, Interval, StrOptions, validate_params
  from ..utils.validation import check_array, check_is_fitted
  from .None import DecisionTreeClassifier, DecisionTreeRegressor, _criterion, _tree
  from ._reingold_tilford import Tree, buchheim
label_desc: Use image node_id 2 for calling the _DOTTreeExporter obj's underlying member method code with example usage: obj.export(decision_tree) without return types