code: def kernel(d1, d2, r=None, weights=None):
      """general product kernel
  
      hardcoded split for the example:
          cat1 is continuous (time), other categories are discrete
  
      weights is e.g. Bartlett for cat1
      r is (0,1) indicator vector for boolean weights 1{d1_i == d2_i}
  
      returns boolean if no continuous weights are used
      """
  
      diff = d1 - d2
      if (weights is None) or (r[0] == 0):
          # time is irrelevant or treated as categorical
          return np.all((r * diff) == 0)  # return bool
      else:
          # time uses continuous kernel, all other categorical
          return weights[diff] * np.all((r[1:] * diff[1:]) == 0)
  
file_path: statsmodels/statsmodels/sandbox/panel/sandwich_covariance_generic.py
imports: import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: kernel(d1, d2, r, weights) and returns: np, unknown