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 __init__(
      self,
      n_lags: np.array,
      penalized_features: np.array = None,
      C_tv=None,
      C_group_l1=None,
      step: float = None,
      tol: float = 1e-5,
      max_iter: int = 100,
      verbose: bool = False,
      print_every: int = 10,
      record_every: int = 10,
      random_state: int = None,
      threads=1,
  ):
      _, _, _, kvs = inspect.getargvalues(inspect.currentframe())
      object.__setattr__(self, "threads", threads)
      del kvs["threads"]
      ConvSCCS.__init__(**kvs)
  
imports: from tick.survival.convolutional_sccs import *
label_desc: Use image node_id 1 to create a new StreamConvSCCS object from inherited base classes: ConvSCCS with example: obj = StreamConvSCCS(n_lags, penalized_features, C_tv, C_group_l1, step, tol, max_iter, verbose, print_every, record_every, random_state, threads)