code: def test_zero_column(sparse_X):
      X, y = build_dataset(
          n_samples=60, n_features=50, sparse_X=sparse_X
      )
      n_zero_columns = 20
      if sparse_X:
          X.data[: X.indptr[n_zero_columns]].fill(0.0)
      else:
          X[:, :n_zero_columns].fill(0.0)
      alpha_max = norm(X.T.dot(y), ord=np.inf) / X.shape[0]
      tol = 1e-6
      _, coefs, gaps = celer_path(
          X,
          y,
          "lasso",
          alphas=[alpha_max / 10.0],
          tol=tol,
          p0=50,
          prune=0,
      )
      w = coefs.T[0]
      assert_array_less(gaps, tol * norm(y) ** 2 / len(y))
      np.testing.assert_equal(w.shape[0], X.shape[1])
  
imports: import warnings
  from itertools import product
  import numpy
  from numpy.linalg import norm
  from numpy.testing import assert_allclose, assert_array_less, assert_array_equal
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LassoCV, Lasso, lasso_path
  from celer import celer_path
  from celer.dropin_sklearn import Lasso, LassoCV
  from celer.utils.testing import build_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: test_zero_column(sparse_X) without return types