code: def get_synthetic_auuc(
      synthetic_preds,
      drop_learners=[],
      outcome_col="y",
      treatment_col="w",
      treatment_effect_col="tau",
      plot=True,
  ):
      """Get auuc values for cumulative gains of model estimates in quantiles.
  
      For details, reference get_cumgain() and plot_gain()
      Args:
          synthetic_preds (dict): dictionary of predictions generated by get_synthetic_preds()
          or get_synthetic_preds_holdout()
          outcome_col (str, optional): the column name for the actual outcome
          treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
          treatment_effect_col (str, optional): the column name for the true treatment effect
          plot (boolean,optional): plot the cumulative gain chart or not
  
      Returns:
          (pandas.DataFrame): auuc values by learner for cumulative gains of model estimates
      """
      synthetic_preds_df = synthetic_preds.copy()
      generated_data = synthetic_preds_df.pop(KEY_GENERATED_DATA)
      synthetic_preds_df = pd.DataFrame(synthetic_preds_df)
      synthetic_preds_df = synthetic_preds_df.drop(
          drop_learners, axis=1
      )
  
      synthetic_preds_df["y"] = generated_data[outcome_col]
      synthetic_preds_df["w"] = generated_data[treatment_col]
      if treatment_effect_col in generated_data.keys():
          synthetic_preds_df["tau"] = generated_data[
              treatment_effect_col
          ]
  
      assert (
          (outcome_col in synthetic_preds_df.columns)
          and (treatment_col in synthetic_preds_df.columns)
          or treatment_effect_col in synthetic_preds_df.columns
      )
  
      cumlift = get_cumgain(
          synthetic_preds_df,
          outcome_col="y",
          treatment_col="w",
          treatment_effect_col="tau",
      )
      auuc_df = pd.DataFrame(cumlift.columns)
      auuc_df.columns = ["Learner"]
      auuc_df["cum_gain_auuc"] = [
          auc(cumlift.index.values / 100, cumlift[learner].values)
          for learner in cumlift.columns
      ]
      auuc_df = auuc_df.sort_values("cum_gain_auuc", ascending=False)
  
      if plot:
          plot_gain(
              synthetic_preds_df,
              outcome_col=outcome_col,
              treatment_col=treatment_col,
              treatment_effect_col=treatment_effect_col,
          )
  
      return auuc_df
  
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 11 for calling a global function with example usage: get_synthetic_auuc(synthetic_preds, drop_learners, outcome_col, treatment_col, treatment_effect_col, plot) and returns: auuc_df