code: def plot_group_metrics_with_error_bars(
      metricframe, metric, error_name
  ):
      """Plot the disaggregated metric for each group with an associated
      error bar. Both metric and the error bar are provided as columns in the
      provided MetricFrame.
  
      Parameters
      ----------
      metricframe : MetricFrame
          The MetricFrame containing the metrics and their associated
          uncertainty quantification.
      metric : str
          The metric to plot
      error_name : str
          The associated standard error for each metric in metric
  
      Returns
      -------
      Matplotlib Plot of point estimates with error bars
      """
      grouped_metrics = metricframe.by_group
      point_estimates = grouped_metrics[metric]
      error_bars = grouped_metrics[error_name]
      lower_bounds = point_estimates - error_bars
      upper_bounds = point_estimates + error_bars
  
      x_axis_names = [
          str(name)
          for name in error_bars.index.to_flat_index().tolist()
      ]
      plt.vlines(
          x_axis_names,
          lower_bounds,
          upper_bounds,
          linestyles="dashed",
          alpha=0.45,
      )
      plt.scatter(x_axis_names, point_estimates, s=25)
      plt.xticks(rotation=0)
      y_start, y_end = np.round(
          min(lower_bounds), decimals=2
      ), np.round(max(upper_bounds), decimals=2)
      plt.yticks(np.arange(y_start, y_end, 0.05))
      plt.ylabel(metric)
  
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import lightgbm
  from sklearn.metrics import balanced_accuracy_score, roc_auc_score
  from sklearn.metrics import confusion_matrix
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from fairlearn.metrics import MetricFrame
  from fairlearn.metrics import count, selection_rate, equalized_odds_difference, false_positive_rate, false_negative_rate
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.reductions import ExponentiatedGradient
  from fairlearn.reductions import EqualizedOdds
  from sklearn.model_selection import train_test_split
  import warnings
label_desc: Use image node_id 6 for calling a global function with example usage: plot_group_metrics_with_error_bars(metricframe, metric, error_name) without return types