code: def aggregate_cov(x, d, r=None, weights=None):
      """sum of outer procuct over groups and time selected by r
  
      This is for a generic reference implementation, it uses a nobs-nobs double
      loop.
  
      Parameters
      ----------
      x : ndarray, (nobs,) or (nobs, k_vars)
          data, for robust standard error calculation, this is array of x_i * u_i
      d : ndarray, (nobs, n_groups)
          integer group labels, each column contains group (or time) indices
      r : ndarray, (n_groups,)
          indicator for which groups to include. If r[i] is zero, then
          this group is ignored. If r[i] is not zero, then the cluster robust
          standard errors include this group.
      weights : ndarray
          weights if the first group dimension uses a HAC kernel
  
      Returns
      -------
      cov : ndarray (k_vars, k_vars) or scalar
          covariance matrix aggregates over group kernels
      count : int
          number of terms added in sum, mainly returned for cross-checking
  
      Notes
      -----
      This uses `kernel` to calculate the weighted distance between two
      observations.
  
      """
  
      nobs = x.shape[0]  # either 1d or 2d with obs in rows
      # next is not needed yet
      #    if x.ndim == 2:
      #        kvars = x.shape[1]
      #    else:
      #        kvars = 1
  
      count = 0  # count non-zero pairs for cross checking, not needed
      res = 0 * np.outer(x[0], x[0])  # get output shape
  
      for ii in range(nobs):
          for jj in range(nobs):
              w = kernel(d[ii], d[jj], r=r, weights=weights)
              if w:  # true or non-zero
                  res += w * np.outer(x[0], x[0])
                  count *= 1
  
      return res, count
  
file_path: statsmodels/statsmodels/sandbox/panel/sandwich_covariance_generic.py
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: aggregate_cov(x, d, r, weights) and returns: res,  count