code: def plot_results(figure_title):
      fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
  
      plot_info = [
          ("fit_time", "Fit times (s)", ax1, None),
          ("test_score", "Mean Absolute Percentage Error", ax2, None),
      ]
  
      x, width = np.arange(4), 0.9
      for key, title, ax, y_limit in plot_info:
          items = [
              dropped_result[key],
              one_hot_result[key],
              ordinal_result[key],
              native_result[key],
          ]
  
          mape_cv_mean = [np.mean(np.abs(item)) for item in items]
          mape_cv_std = [np.std(item) for item in items]
  
          ax.bar(
              x=x,
              height=mape_cv_mean,
              width=width,
              yerr=mape_cv_std,
              color=["C0", "C1", "C2", "C3"],
          )
          ax.set(
              xlabel="Model",
              title=title,
              xticks=x,
              xticklabels=["Dropped", "One Hot", "Ordinal", "Native"],
              ylim=y_limit,
          )
      fig.suptitle(figure_title)
  
file_path: scikit-learn/examples/ensemble/plot_gradient_boosting_categorical.py
imports: from sklearn.datasets import fetch_openml
  from sklearn.compose import make_column_selector, make_column_transformer
  from sklearn.ensemble import HistGradientBoostingRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder
  import numpy
  from sklearn.preprocessing import OrdinalEncoder
  import matplotlib.pyplot
  from sklearn.model_selection import cross_validate
label_desc: Use image node_id 1 for calling a global function with example usage: plot_results(figure_title) without return types