code: def get_dense_and_sparse_linreg_model(
      X_dense, y, dtype, fit_intercept=True
  ):
      X_sparse = csr_matrix(X_dense).astype(dtype)
      model_dense = ModelLinReg(fit_intercept).fit(X_dense, y)
      model_spars = ModelLinReg(fit_intercept).fit(X_sparse, y)
      return model_dense, model_spars
  
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 2 for calling the SVRGTest obj's underlying member method code with example usage: obj.get_dense_and_sparse_linreg_model(X_dense, y, dtype, fit_intercept) and returns: model_dense,  model_spars