code: def get_train_data(fit_intercept=True):
      np.random.seed(12)
      n_samples = Test.n_samples
      n_features = Test.n_features
      noise_level = Test.noise_level
      nnz_outliers = Test.nnz_outliers
      outliers_intensity = Test.outliers_intensity
      if fit_intercept:
          interc0 = Test.interc0
      else:
          interc0 = 0.0
      weights0 = np.sqrt(
          2 * np.log(np.linspace(1, 10, n_features) * n_features)
      )
      sample_intercepts0 = weights_sparse_gauss(
          n_weights=n_samples, nnz=nnz_outliers
      )
      sample_intercepts0[sample_intercepts0 != 0] = (
          outliers_intensity
          * np.sqrt(
              2 * np.log(np.linspace(1, 10, nnz_outliers) * n_samples)
          )
          * np.sign(sample_intercepts0[sample_intercepts0 != 0])
      )
  
      X = features_normal_cov_toeplitz(n_samples, n_features, 0.5)
      y = (
          X.dot(weights0)
          + noise_level * np.random.randn(n_samples)
          + interc0
      )
      y += sample_intercepts0
      return X, y, weights0, interc0, sample_intercepts0
  
imports: import itertools
  import unittest
  import numpy
  from tick.base.inference import InferenceTest
  from tick.robust import RobustLinearRegression
  from tick.simulation import weights_sparse_gauss, features_normal_cov_toeplitz
  from tick.metrics import support_fdp, support_recall
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.get_train_data(fit_intercept) and returns: X,  y,  weights0,  interc0,  sample_intercepts0