code: def recall_wilson(y_true, y_pred):
      """Return a Wilson confidence interval for the recall metric.
  
      Parameters
      ----------
      y_true : array-like of shape (n_samples,)
          Ground truth labels
      y_pred : array-like of shape (n_samples,)
          Predicted labels
  
      Returns
      -------
          np.ndarray
          Array of length 2 of form: [lower_bound, upper_bound]
      """
      check_consistent_length(y_true, y_pred)
      tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
      bounds = general_wilson(
          tp / (tp + fn), tp + fn, digits_of_precision, z_score
      )
      return bounds
  
file_path: fairlearn/examples/plot_plotting_metrics_with_error.py
imports: import numpy
  from sklearn.compose import ColumnTransformer
  from sklearn.compose import make_column_selector
  from fairlearn.datasets import fetch_adult
  from sklearn.impute import SimpleImputer
  from sklearn.metrics import accuracy_score, confusion_matrix, recall_score
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import check_consistent_length
  from fairlearn.experimental.enable_metric_frame_plotting import plot_metric_frame
  from fairlearn.metrics import MetricFrame
label_desc: Use image node_id 2 for calling a global function with example usage: recall_wilson(y_true, y_pred) and returns: bounds