code: def cov_cluster_2groups(
      results, group, group2=None, use_correction=True
  ):
      """cluster robust covariance matrix for two groups/clusters
  
      Parameters
      ----------
      results : result instance
         result of a regression, uses results.model.exog and results.resid
         TODO: this should use wexog instead
      use_correction : bool
         If true (default), then the small sample correction factor is used.
  
      Returns
      -------
      cov_both : ndarray, (k_vars, k_vars)
          cluster robust covariance matrix for parameter estimates, for both
          clusters
      cov_0 : ndarray, (k_vars, k_vars)
          cluster robust covariance matrix for parameter estimates for first
          cluster
      cov_1 : ndarray, (k_vars, k_vars)
          cluster robust covariance matrix for parameter estimates for second
          cluster
  
      Notes
      -----
  
      verified against Peterson's table, (4 decimal print precision)
      """
  
      if group2 is None:
          if group.ndim != 2 or group.shape[1] != 2:
              raise ValueError(
                  "if group2 is not given, then groups needs to be "
                  + "an array with two columns"
              )
          group0 = group[:, 0]
          group1 = group[:, 1]
      else:
          group0 = group
          group1 = group2
          group = (group0, group1)
  
      cov0 = cov_cluster(results, group0, use_correction=use_correction)
      # [0] because we get still also returns bse
      cov1 = cov_cluster(results, group1, use_correction=use_correction)
  
      # cov of cluster formed by intersection of two groups
      cov01 = cov_cluster(
          results,
          combine_indices(group)[0],
          use_correction=use_correction,
      )
  
      # robust cov matrix for union of groups
      cov_both = cov0 + cov1 - cov01
  
      # return all three (for now?)
      return cov_both, cov0, cov1
  
imports: import numpy
  from statsmodels.tools.grouputils import combine_indices, group_sums
  from statsmodels.stats.moment_helpers import se_cov
label_desc: Use image node_id 17 for calling a global function with example usage: cov_cluster_2groups(results, group, group2, use_correction) and returns: cov_both,  cov0,  cov1