class_docstr: This object provides the primary interface to the functionality of this library. You can think of it as
  an adaptor that adapts the various decision-tree based libraries for use with dtreeviz. In implementation,
  however, this object encapsulates the key functionality and API but delegates tree model adaptation
  from sklearn etc... to dtreeviz.models.ShadowDecTree subclasses.
class_docstr_tok: ["This","object","provides","the","primary","interface","to","the","functionality","of","this","library",".","You","can","think","of","it","as","an","adaptor","that","adapts","the","various","decision-tree","based","libraries","for","use","with","dtreeviz",".","In","implementation",",","however",",","this","object","encapsulates","the","key","functionality","and","API","but","delegates","tree","model","adaptation","from","sklearn","etc","...","to","dtreeviz.models.ShadowDecTree","subclasses","."]
code: def __init__(self, shahdow_tree: ShadowDecTree):
      self.shadow_tree = shahdow_tree
  
imports: import os
  import tempfile
  from typing import Mapping, List, Callable
  import matplotlib
  import matplotlib.patches
  import matplotlib.pyplot
  import numpy
  import pandas
  from colour import Color, rgb2hex
  from sklearn import tree
  from dtreeviz.colors import adjust_colors
  from dtreeviz.interpretation import explain_prediction_plain_english, explain_prediction_sklearn_default
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree
  from dtreeviz.models.shadow_decision_tree import ShadowDecTreeNode
  from dtreeviz.utils import myround, DTreeVizRender, add_classifier_legend, _format_axes, _draw_wedge, _set_wedge_ticks, tessellate, is_numeric
label_desc: Use image node_id 1 to create a new DTreeVizAPI object  with example: obj = DTreeVizAPI(shahdow_tree)