code: def _corr_kendalltau(X):
      n_variables = X.shape[1]
      mat = np.empty((n_variables, n_variables), dtype=float)
      for i in range(n_variables):
          for j in range(i):
              v = kendalltau(X[:, i], X[:, j]).correlation
              mat[i, j] = v
              mat[j, i] = v
      return mat
  
imports: import numbers
  from joblib import Parallel, delayed
  import numpy
  from scipy.stats import kendalltau, rankdata, spearmanr
  from sklearn.base import BaseEstimator, clone
  from sklearn.model_selection import check_cv
  from sklearn.utils._param_validation import Interval, StrOptions
  from .base import _fit_and_score
  from .stacking import Stacking
label_desc: Use image node_id 1 for calling a global function with example usage: _corr_kendalltau(X) and returns: mat