code: def plot_chains_leaves(
      model: SklearnModel,
      ax=None,
      title="Tree Structure/Prediction Variation",
      x_label=False,
      X=None,
      y=None,
  ):
      if ax is None:
          _, ax = plt.subplots(1, 1)
      complexity = {i: [] for i in range(model.n_trees)}
      n_chains = model.n_chains
      for sample in model.model_samples:
          for i, tree in enumerate(sample.trees):
              complexity[i].append(len(tree.leaf_nodes))
  
      chain_len = int(len(model.model_samples) / n_chains)
      color = iter(cm.rainbow(np.linspace(0, 1, n_chains)))
  
      for c in range(n_chains):
          clr = next(color)
          chain_preds = model.predict_chain(X, c)
          chain_std = np.round(model.chain_mse_std(X, y, c), 2)
          mse_chain = np.round(mean_squared_error(chain_preds, y), 2)
  
          trees_chain = np.stack(
              [
                  complexity[t][c * chain_len : (c + 1) * chain_len]
                  for t in range(model.n_trees)
              ],
              axis=1,
          )
          y_plt = np.mean(trees_chain, axis=1)
          ax.plot(
              np.arange(chain_len),
              y_plt,
              color=clr,
              label=f"Chain {c} (mse: {mse_chain} std: {chain_std})",
          )
  
      ax.set_ylabel("# Leaves")
      if x_label:
          ax.set_xlabel("Iteration")
      ax.legend()
      ax.set_title(title)
      return ax
  
imports: import argparse
  import itertools
  import os
  from functools import partial
  import numpy
  import pandas
  import matplotlib.pyplot
  from matplotlib import cm
  from tqdm import tqdm
  from sklearn import model_selection, datasets
  from sklearn.metrics import mean_squared_error
  from imodels import get_clean_dataset
  from imodels.experimental.bartpy.model import Model
  from imodels.experimental.bartpy.tree import Tree
  from ..sklearnmodel import BART, SklearnModel
label_desc: Use image node_id 5 for calling a global function with example usage: plot_chains_leaves(model, ax, title, x_label, X, y) and returns: ax