class_docstr: StreamConvSCCS provides parallel solving for k_fold and bootstrap ConvSCCS
     using a threadpool. Everything needed is precalculated and handed of to C++
  
  Parameters
  ----------
  threads : integer, number of threads for the thread pool,
   for kfold must be < n_folds * n_cv_iter
   for bootstrap must be <= rep
code: def _multi_fit(self, model_list, coeffs_list, C_s, n_folds):
      solvers, proxes = ([] for i in range(2))  # 2 on the left
      for c in range(len(C_s)):
          self.C_tv, self.C_group_l1 = C_s[c]
          for i in range(n_folds):
              index = i + (c * n_folds)
              proxes.append(self._construct_prox_obj())
              solvers.append(
                  self._construct_solver_obj_with_class(
                      *self._solver_info
                  )
              )
              solvers[-1].step = self.step
              solvers[-1].set_model(model_list[index]).set_prox(
                  proxes[-1]
              )
              solvers[-1]._solver.set_epoch_size(
                  model_list[index]._model.get_epoch_size()
              )
      # no point calling "set_start_iterate" as it is always 0s
      return solvers[0].multi_solve(
          coeffs_list,
          solvers,
          self.max_iter,
          self.threads,
          set_start=False,
      )
  
imports: from tick.survival.convolutional_sccs import *
label_desc: Use image node_id 2 for calling the StreamConvSCCS obj's underlying member method code with example usage: obj._multi_fit(model_list, coeffs_list, C_s, n_folds) and returns: solvers