code: def _ctreeviz_bivar(
      shadow_tree,
      fontsize,
      ticks_fontsize,
      fontname,
      show,
      colors,
      features,
      figsize,
      ax,
  ):
      """
      Show tesselated 2D feature space for bivariate classification tree. X_train can
      have lots of features but features lists indexes of 2 features to train tree with.
      """
      if ax is None:
          if figsize:
              fig, ax = plt.subplots(figsize=figsize)
          else:
              fig, ax = plt.subplots()
  
      featidx = [shadow_tree.feature_names.index(f) for f in features]
      X_train = shadow_tree.X_train
      y_train = shadow_tree.y_train
      colors = adjust_colors(colors)
      tessellation = tessellate(shadow_tree.root, X_train, featidx)
      n_classes = shadow_tree.nclasses()
      class_values = shadow_tree.classes()
      color_values = colors["classes"][n_classes]
      color_map = {
          v: color_values[i] for i, v in enumerate(class_values)
      }
  
      if not is_numeric(X_train[:, featidx[0]]) or not is_numeric(
          X_train[:, featidx[1]]
      ):
          raise ValueError(
              f"ctree_feature_space only supports numeric feature spaces"
          )
  
      dot_w = 25
      X_hist = [X_train[y_train == cl] for cl in class_values]
      for i, h in enumerate(X_hist):
          ax.scatter(
              h[:, featidx[0]],
              h[:, featidx[1]],
              marker="o",
              s=dot_w,
              c=color_map[i],
              edgecolors=colors["scatter_edge"],
              lw=0.3,
          )
  
      _format_axes(
          ax,
          shadow_tree.feature_names[featidx[0]],
          shadow_tree.feature_names[featidx[1]],
          colors,
          fontsize,
          fontname,
          ticks_fontsize=ticks_fontsize,
          grid=False,
      )
  
      if "splits" in show:
          plt.draw()
          for node, bbox in tessellation:
              x = bbox[0]
              y = bbox[1]
              w = bbox[2] - bbox[0]
              h = bbox[3] - bbox[1]
              rect = patches.Rectangle(
                  (x, y),
                  w,
                  h,
                  angle=0,
                  linewidth=0.3,
                  alpha=colors["tessellation_alpha"],
                  edgecolor=colors["rect_edge"],
                  facecolor=color_map[node.prediction()],
              )
              ax.add_patch(rect)
  
      if "legend" in show:
          add_classifier_legend(
              ax,
              shadow_tree.class_names,
              class_values,
              color_map,
              shadow_tree.target_name,
              colors,
              fontname=fontname,
          )
  
      if "title" in show:
          accur = shadow_tree.get_score()
          title = f"Classifier Tree Depth {shadow_tree.get_max_depth()}, Training Accuracy={accur * 100:.2f}%"
          ax.set_title(
              title,
              fontsize=fontsize,
              color=colors["title"],
          )
  
      return None
  
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 12 for calling a global function with example usage: _ctreeviz_bivar(shadow_tree, fontsize, ticks_fontsize, fontname, show, colors, features, figsize, ax) and returns: None