code: def winsorized_rank_correlation(X, rowvar=False, weighted=False):
      """
      Computes rank correlations using a winsorized ranks.
      Relevant to high dimensional settings where the n_samples < n_features
      resulting too large variance in the ranks.
  
      Parameters
      ----------
      X: array-like, shape = [n_samples, n_features]
          Data matrix using which we compute the empirical
          correlation
  
      Returns
      -------
      rank_correlation
  
      References
      ----------
  
      Liu, Han, John Lafferty, and Larry Wasserman.
      "The nonparanormal: Semiparametric estimation of high dimensional
      undirected graphs."
      Journal of Machine Learning Research 10.Oct (2009): 2295-2328.
      """
      pass
  
imports: from __future__ import absolute_import
  import numpy
  from scipy.stats import rankdata, kendalltau, weightedtau
label_desc: Use image node_id 4 for calling a global function with example usage: winsorized_rank_correlation(X, rowvar, weighted) without return types