code: def corrected_std(differences, n_train, n_test):
      """Corrects standard deviation using Nadeau and Bengio's approach.
  
      Parameters
      ----------
      differences : ndarray of shape (n_samples,)
          Vector containing the differences in the score metrics of two models.
      n_train : int
          Number of samples in the training set.
      n_test : int
          Number of samples in the testing set.
  
      Returns
      -------
      corrected_std : float
          Variance-corrected standard deviation of the set of differences.
      """
      # kr = k times r, r times repeated k-fold crossvalidation,
      # kr equals the number of times the model was evaluated
      kr = len(differences)
      corrected_var = np.var(differences, ddof=1) * (
          1 / kr + n_test / n_train
      )
      corrected_std = np.sqrt(corrected_var)
      return corrected_std
  
file_path: scikit-learn/examples/model_selection/plot_grid_search_stats.py
imports: import matplotlib.pyplot
  import seaborn
  from sklearn.datasets import make_moons
  from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
  from sklearn.svm import SVC
  import pandas
  import numpy
  from scipy.stats import t
  from itertools import combinations
  from math import factorial
label_desc: Use image node_id 1 for calling a global function with example usage: corrected_std(differences, n_train, n_test) and returns: corrected_std