code: def cov_cluster(results, group, use_correction=True):
      """cluster robust covariance matrix
  
      Calculates sandwich covariance matrix for a single cluster, i.e. grouped
      variables.
  
      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 : ndarray, (k_vars, k_vars)
          cluster robust covariance matrix for parameter estimates
  
      Notes
      -----
      same result as Stata in UCLA example and same as Peterson
  
      """
      # TODO: currently used version of groupsums requires 2d resid
      xu, hessian_inv = _get_sandwich_arrays(results, cov_type="clu")
  
      if not hasattr(group, "dtype") or group.dtype != np.dtype("int"):
          clusters, group = np.unique(group, return_inverse=True)
      else:
          clusters = np.unique(group)
  
      scale = S_crosssection(xu, group)
  
      nobs, k_params = xu.shape
      n_groups = len(
          clusters
      )  # replace with stored group attributes if available
  
      cov_c = _HCCM2(hessian_inv, scale)
  
      if use_correction:
          cov_c *= (
              n_groups
              / (n_groups - 1.0)
              * ((nobs - 1.0) / float(nobs - k_params))
          )
  
      return cov_c
  
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 16 for calling a global function with example usage: cov_cluster(results, group, use_correction) and returns: cov_c