code: def test_celer_path_logreg(solver):
      X, y = build_dataset(n_samples=60, n_features=100, sparse_X=True)
      y = np.sign(y)
      alpha_max = norm(X.T.dot(y), ord=np.inf) / 2
      alphas = alpha_max * np.geomspace(1, 1e-2, 10)
  
      tol = 1e-11
      coefs, Cs, n_iters = _logistic_regression_path(
          X,
          y,
          Cs=1.0 / alphas,
          fit_intercept=False,
          penalty="l1",
          solver="liblinear",
          tol=tol,
          max_iter=1000,
          random_state=0,
      )
  
      _, coefs_c, gaps = celer_path(
          X,
          y,
          "logreg",
          alphas=alphas,
          tol=tol,
          verbose=0,
          use_PN=(solver == "celer-pn"),
      )
  
      assert_array_less(gaps, tol * len(y) * np.log(2))
      assert_allclose(coefs != 0, coefs_c.T != 0)
      assert_allclose(coefs, coefs_c.T, atol=1e-5, rtol=1e-3)
  
file_path: celer/celer/tests/test_logreg.py
imports: import pytest
  import numpy
  from numpy.linalg import norm
  from numpy.testing import assert_allclose, assert_array_less
  from sklearn.linear_model._logistic import _logistic_regression_path
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.linear_model import LogisticRegression
  from celer import celer_path
  from celer.dropin_sklearn import LogisticRegression
  from celer.utils.testing import build_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_celer_path_logreg(solver) without return types