code: def cache_epsilons(dstore, oq, assetcol, riskmodel, E):
      
      if oq.ignore_covs or not riskmodel.covs:
          return
      A = len(assetcol)
      hdf5path = dstore.hdf5cache()
      logging.info('Storing the epsilon matrix in %s', hdf5path)
      if oq.calculation_mode == 'scenario_risk':
          eps = make_eps(assetcol.array, E, oq.master_seed, oq.asset_correlation)
      else:  # event based
          if oq.asset_correlation:
              numpy.random.seed(oq.master_seed)
              eps = numpy.array([numpy.random.normal(size=E)] * A)
          else:
              seeds = oq.master_seed + numpy.arange(E)
              eps = numpy.zeros((A, E), F32)
              for i, seed in enumerate(seeds):
                  numpy.random.seed(seed)
                  eps[:, i] = numpy.random.normal(size=A)
      with hdf5.File(hdf5path) as cache:
          cache['epsilon_matrix'] = eps
      return hdf5path
docs: Do nothing if there are no coefficients of variation of ignore_covs is
      set. Otherwise, generate an epsilon matrix of shape (A, E) and save it
      in the cache file, by returning the path to it.