code: def get_train_data(n_samples=2000, n_features=20, fit_intercept=True):
      np.random.seed(12)
      weights0 = weights_sparse_gauss(n_features)
      if fit_intercept:
          intercept0 = -1.0
      else:
          intercept0 = None
      X, y = SimuLinReg(
          weights0, intercept0, n_samples=n_samples, verbose=False
      ).simulate()
      return X, y, weights0, intercept0
  
imports: import itertools
  import unittest
  from itertools import product
  import numpy
  from scipy.linalg import norm
  from tick.base.inference import InferenceTest
  from tick.linear_model import SimuLinReg, LinearRegression
  from tick.simulation import weights_sparse_gauss
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_samples, n_features, fit_intercept) and returns: X,  y,  weights0,  intercept0