code: def bar_plot_summary_holdout(
      train_summary,
      validation_summary,
      k,
      drop_learners=[],
      drop_cols=[],
  ):
      """Generates a bar plot comparing learner performance by training and validation
  
      Args:
          train_summary (pd.DataFrame): summary for training synthetic data generated by get_synthetic_summary_holdout()
          validation_summary (pd.DataFrame): summary for validation synthetic data generated by
              get_synthetic_summary_holdout()
          k (int): number of simulations (used only for plot title text)
          drop_learners (list, optional): list of learners (str) to omit when plotting
          drop_cols (list, optional): list of metrics (str) to omit when plotting
      """
      train_summary = train_summary.drop([KEY_ACTUAL])
      train_summary["Learner"] = train_summary.index
  
      validation_summary = validation_summary.drop([KEY_ACTUAL])
      validation_summary["Learner"] = validation_summary.index
  
      for metric in ["Abs % Error of ATE", "MSE", "KL Divergence"]:
          plot_data_sub = pd.DataFrame(
              train_summary.Learner
          ).reset_index(drop=True)
          plot_data_sub["train"] = train_summary[metric].values
          plot_data_sub["validation"] = validation_summary[
              metric
          ].values
          plot_data_sub = plot_data_sub.set_index("Learner")
          plot_data_sub = plot_data_sub.drop(drop_learners).drop(
              drop_cols, axis=1
          )
          plot_data_sub = plot_data_sub.sort_values(
              "train", ascending=True
          )
  
          plot_data_sub.plot(
              kind="bar", color=["red", "blue"], figsize=(12, 8)
          )
          plt.xticks(rotation=30)
          plt.title(
              "Learner Performance of {} (averaged over k={} simulations)".format(
                  metric, k
              )
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from matplotlib import pyplot
  import numpy
  import pandas
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import auc
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from xgboost import XGBRegressor
  from scipy.stats import entropy
  import warnings
  from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor
  from causalml.inference.tree.causal.causaltree import CausalTreeRegressor
  from causalml.propensity import ElasticNetPropensityModel
  from causalml.metrics import plot_gain, get_cumgain
label_desc: Use image node_id 10 for calling a global function with example usage: bar_plot_summary_holdout(train_summary, validation_summary, k, drop_learners, drop_cols) without return types