code: def export(self, decision_tree, ax=None):
      import matplotlib.pyplot as plt
      from matplotlib.text import Annotation
  
      if ax is None:
          ax = plt.gca()
      ax.clear()
      ax.set_axis_off()
      my_tree = self._make_tree(
          0, decision_tree.tree_, decision_tree.criterion
      )
      draw_tree = buchheim(my_tree)
  
      # important to make sure we're still
      # inside the axis after drawing the box
      # this makes sense because the width of a box
      # is about the same as the distance between boxes
      max_x, max_y = draw_tree.max_extents() + 1
      ax_width = ax.get_window_extent().width
      ax_height = ax.get_window_extent().height
  
      scale_x = ax_width / max_x
      scale_y = ax_height / max_y
      self.recurse(draw_tree, decision_tree.tree_, ax, max_x, max_y)
  
      anns = [
          ann
          for ann in ax.get_children()
          if isinstance(ann, Annotation)
      ]
  
      # update sizes of all bboxes
      renderer = ax.figure.canvas.get_renderer()
  
      for ann in anns:
          ann.update_bbox_position_size(renderer)
  
      if self.fontsize is None:
          # get figure to data transform
          # adjust fontsize to avoid overlap
          # get max box width and height
          extents = [
              ann.get_bbox_patch().get_window_extent() for ann in anns
          ]
          max_width = max([extent.width for extent in extents])
          max_height = max([extent.height for extent in extents])
          # width should be around scale_x in axis coordinates
          size = anns[0].get_fontsize() * min(
              scale_x / max_width, scale_y / max_height
          )
          for ann in anns:
              ann.set_fontsize(size)
  
      return anns
  
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 3 for calling the _MPLTreeExporter obj's underlying member method code with example usage: obj.export(decision_tree, ax) and returns: anns