code: def simu_linreg_data(
      dtype, n_samples=5000, n_features=50, interc=-1.0, p_nnz=0.3
  ):
      np.random.seed(123)
      idx = np.arange(1, n_features + 1)
      weights = (-1) ** (idx - 1) * np.exp(-idx / 10.0)
      corr = 0.5
      cov = toeplitz(corr ** np.arange(0, n_features))
      X = np.random.multivariate_normal(
          np.zeros(n_features), cov, size=n_samples
      )
      X *= np.random.binomial(1, p_nnz, size=X.shape)
      idx = np.nonzero(X.sum(axis=1))
      X = X[idx]
      n_samples = X.shape[0]
      noise = np.random.randn(n_samples)
      y = X.dot(weights) + noise
      if interc:
          y += interc
      X = X.astype(dtype)
      y = y.astype(dtype)
      return X, y
  
imports: import unittest
  from warnings import catch_warnings, simplefilter
  from itertools import product
  import numpy
  from scipy.linalg.special_matrices import toeplitz
  from scipy.sparse import csr_matrix
  from tick.linear_model import ModelLinReg, ModelLogReg, SimuLogReg
  from tick.prox import ProxL1, ProxL1w, ProxTV, ProxEquality, ProxElasticNet
  from tick.solver import SVRG
  from tick.solver.tests import TestSolver
  from tick.solver.build.solver import SVRG_VarianceReductionMethod_Last
  from tick.solver.build.solver import SVRG_VarianceReductionMethod_Average
  from tick.solver.build.solver import SVRG_VarianceReductionMethod_Random
  from tick.solver.build.solver import SVRG_StepType_Fixed
  from tick.solver.build.solver import SVRG_StepType_BarzilaiBorwein
  from tick.simulation import weights_sparse_gauss
label_desc: Use image node_id 1 for calling the SVRGTest obj's underlying member method code with example usage: obj.simu_linreg_data(dtype, n_samples, n_features, interc, p_nnz) and returns: X,  y