code: def test_LearnerSCCS_fit_KFold_CV(self):
      lrn_scores = []
  
      def fit(lrn):
          self.setUp()
          lrn.fit(self.features, self.labels, self.censoring)
          score = lrn.score()
          tv_range = (-5, -1)
          groupl1_range = (-5, -1)
          lrn.fit_kfold_cv(
              self.features,
              self.labels,
              self.censoring,
              C_tv_range=tv_range,
              C_group_l1_range=groupl1_range,
              n_cv_iter=4,
          )
          lrn_scores.append(lrn.score())
          self.assertTrue(lrn_scores[-1] <= score)
  
      fit(
          ConvSCCS(
              n_lags=self.n_lags,
              penalized_features=np.arange(self.n_features),
              random_state=self.seed,
              C_tv=1e-1,
              C_group_l1=1e-1,
          )
      )
      fit(
          BatchConvSCCS(
              n_lags=self.n_lags,
              penalized_features=np.arange(self.n_features),
              random_state=self.seed,
              C_tv=1e-1,
              C_group_l1=1e-1,
          )
      )
      fit(
          StreamConvSCCS(
              n_lags=self.n_lags,
              penalized_features=np.arange(self.n_features),
              random_state=self.seed,
              C_tv=1e-1,
              C_group_l1=1e-1,
          )
      )
  
file_path: tick/tick/survival/tests/convolutional_sccs_test.py
imports: import unittest
  import numpy
  from tick.survival import SimuSCCS, ConvSCCS
  from scipy.sparse import csr_matrix
  from tick.survival.sccs import *
label_desc: Use image node_id 7 for calling the Test obj's underlying member method code with example usage: obj.test_LearnerSCCS_fit_KFold_CV() without return types