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 __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,
      batch_size=1,
  ):
      _, _, _, kvs = inspect.getargvalues(inspect.currentframe())
      object.__setattr__(self, "batch_size", batch_size)
      del kvs["batch_size"]
      ConvSCCS.__init__(**kvs)
  
imports: from tick.survival.convolutional_sccs import *
label_desc: Use image node_id 1 to create a new BatchConvSCCS object from inherited base classes: ConvSCCS with example: obj = BatchConvSCCS(n_lags, penalized_features, C_tv, C_group_l1, step, tol, max_iter, verbose, print_every, record_every, random_state, batch_size)