code: def lagged_groups(x, lag, groupidx):
      """
      assumes sorted by time, groupidx is tuple of start and end values
      not optimized, just to get a working version, loop over groups
      """
      out0 = []
      out_lagged = []
      for l, u in groupidx:
          if l + lag < u:  # group is longer than lag
              out0.append(x[l + lag : u])
              out_lagged.append(x[l : u - lag])
  
      if out0 == []:
          raise ValueError("all groups are empty taking lags")
      # return out0, out_lagged
      return np.vstack(out0), np.vstack(out_lagged)
  
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 20 for calling a global function with example usage: lagged_groups(x, lag, groupidx) and returns: np,  np