code: def _rtreeviz_univar(
      shadow_tree,
      fontsize,
      ticks_fontsize,
      fontname,
      show,
      split_linewidth,
      mean_linewidth,
      markersize,
      colors,
      feature,
      figsize,
      ax,
  ):
      featidx = shadow_tree.feature_names.index(feature)
      X_train = shadow_tree.X_train
      y_train = shadow_tree.y_train
      if X_train is None or y_train is None:
          raise ValueError(f"X_train and y_train must not be none")
  
      if not is_numeric(X_train[:, featidx]):
          raise ValueError(
              f"rtree_feature_space only supports numeric feature spaces"
          )
  
      if ax is None:
          if figsize:
              fig, ax = plt.subplots(figsize=figsize)
          else:
              fig, ax = plt.subplots()
  
      colors = adjust_colors(colors)
  
      y_range = (min(y_train), max(y_train))  # same y axis for all
      overall_feature_range = (
          np.min(X_train[:, featidx]),
          np.max(X_train[:, featidx]),
      )
  
      splits = []
      for node in shadow_tree.internal:
          if node.feature() == featidx:
              splits.append(node.split())
      splits = sorted(splits)
      bins = (
          [overall_feature_range[0]]
          + splits
          + [overall_feature_range[1]]
      )
  
      means = []
      for i in range(len(bins) - 1):
          left = bins[i]
          right = bins[i + 1]
          inrange = y_train[
              (X_train[:, featidx] >= left)
              & (X_train[:, featidx] <= right)
          ]
          means.append(np.mean(inrange))
  
      ax.scatter(
          X_train[:, featidx],
          y_train,
          marker="o",
          alpha=colors["scatter_marker_alpha"],
          c=colors["scatter_marker"],
          s=markersize,
          edgecolor=colors["scatter_edge"],
          lw=0.3,
      )
  
      if "splits" in show:
          for split in splits:
              ax.plot(
                  [split, split],
                  [*y_range],
                  "--",
                  color=colors["split_line"],
                  linewidth=split_linewidth,
              )
  
          prevX = overall_feature_range[0]
          for i, m in enumerate(means):
              split = overall_feature_range[1]
              if i < len(splits):
                  split = splits[i]
              ax.plot(
                  [prevX, split],
                  [m, m],
                  "-",
                  color=colors["mean_line"],
                  linewidth=mean_linewidth,
              )
              prevX = split
  
      _format_axes(
          ax,
          shadow_tree.feature_names[featidx],
          shadow_tree.target_name,
          colors,
          fontsize,
          fontname,
          ticks_fontsize=ticks_fontsize,
          grid=False,
      )
  
      if "title" in show:
          title = f"Regression Tree Depth {shadow_tree.get_max_depth()}, Samples per Leaf {shadow_tree.get_min_samples_leaf()},\nTraining $R^2$={shadow_tree.get_score()}"
          ax.set_title(title, fontsize=fontsize, color=colors["title"])
  
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 13 for calling a global function with example usage: _rtreeviz_univar(shadow_tree, fontsize, ticks_fontsize, fontname, show, split_linewidth, mean_linewidth, markersize, colors, feature, figsize, ax) without return types