code: def get_train_data(n_features=20, n_samples=3000, nnz=5):
      np.random.seed(12)
      weights0 = weights_sparse_gauss(n_features, nnz=nnz)
      interc0 = 0.1
      features, y = SimuLogReg(
          weights0, interc0, n_samples=n_samples, verbose=False
      ).simulate()
      return features, y
  
imports: import itertools
  import unittest
  import numpy
  from sklearn.metrics import roc_auc_score
  from tick.base.inference import InferenceTest
  from tick.linear_model import SimuLogReg, LogisticRegression
  from tick.simulation import weights_sparse_gauss
  from tick.preprocessing.features_binarizer import FeaturesBinarizer
  from tick.prox import ProxZero, ProxL1, ProxL2Sq, ProxElasticNet, ProxTV, ProxBinarsity
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.get_train_data(n_features, n_samples, nnz) and returns: features,  y