code: def compute_corrected_ttest(differences, df, n_train, n_test):
      """Computes right-tailed paired t-test with corrected variance.
  
      Parameters
      ----------
      differences : array-like of shape (n_samples,)
          Vector containing the differences in the score metrics of two models.
      df : int
          Degrees of freedom.
      n_train : int
          Number of samples in the training set.
      n_test : int
          Number of samples in the testing set.
  
      Returns
      -------
      t_stat : float
          Variance-corrected t-statistic.
      p_val : float
          Variance-corrected p-value.
      """
      mean = np.mean(differences)
      std = corrected_std(differences, n_train, n_test)
      t_stat = mean / std
      p_val = t.sf(np.abs(t_stat), df)  # right-tailed t-test
      return t_stat, p_val
  
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 2 for calling a global function with example usage: compute_corrected_ttest(differences, df, n_train, n_test) and returns: t_stat,  p_val