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 _bootstrap_multi_fit(self, model_list, coeffs_list):
      solvers, proxes = ([] for i in range(2))  # 2 on the left
      for i in range(len(model_list)):
          proxes.append(
              self._construct_prox_obj(
                  coeffs=coeffs_list[i], project=True
              )
          )
          solvers.append(
              self._construct_solver_obj_with_class(*self._solver_info)
          )
          solvers[i].step = self.step
          solvers[i].set_model(model_list[i]).set_prox(proxes[-1])
          solvers[i]._solver.set_epoch_size(
              model_list[i]._model.get_epoch_size()
          )
  
      return solvers[0].multi_solve(
          coeffs_list, solvers, self.max_iter, self.threads, True
      )
  
imports: from tick.survival.convolutional_sccs import *
label_desc: Use image node_id 4 for calling the StreamConvSCCS obj's underlying member method code with example usage: obj._bootstrap_multi_fit(model_list, coeffs_list) and returns: solvers