class_docstr: BatchConvSCCS provides parallel batch solving for ConvSCCS k_fold
      and bootstrap
  
  Parameters
  ----------
  batch_size : integer
   for kfold must be multiple of n_fold, and divisor of 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(coeffs_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[-1].step = self.step
          solvers[-1].set_model(model_list[i]).set_prox(proxes[-1])
          solvers[-1]._solver.set_epoch_size(
              model_list[i]._model.get_epoch_size()
          )
  
      return solvers[0].multi_solve(
          coeffs_list, solvers, self.max_iter, self.batch_size, True
      )
  
imports: from tick.survival.convolutional_sccs import *
label_desc: Use image node_id 4 for calling the BatchConvSCCS obj's underlying member method code with example usage: obj._bootstrap_multi_fit(model_list, coeffs_list) and returns: solvers