code: def test_lars_collinear(datatype, nrows, column_info, precompute):
      ncols, n_info = column_info
      if (
          nrows == 500000
          and ncols == 1000
          and pytest.max_gpu_memory < 32
      ):
          if pytest.adapt_stress_test:
              nrows = nrows * pytest.max_gpu_memory // 32
          else:
              pytest.skip(
                  "Insufficient GPU memory for this test."
                  "Re-run with 'CUML_ADAPT_STRESS_TESTS=True'"
              )
  
      X_train, X_test, y_train, y_test = make_regression_dataset(
          datatype, nrows, ncols, n_info
      )
      n_duplicate = min(ncols, 100)
      X_train = np.concatenate(
          (X_train, X_train[:, :n_duplicate]), axis=1
      )
      X_test = np.concatenate((X_test, X_test[:, :n_duplicate]), axis=1)
  
      params = {
          "precompute": precompute,
          "n_nonzero_coefs": ncols + n_duplicate,
      }
      culars = cuLars(**params)
      culars.fit(X_train, y_train)
  
      assert culars.score(X_train, y_train) > 0.85
      assert culars.score(X_test, y_test) > 0.85
  
file_path: cuml/python/cuml/tests/test_lars.py
imports: import sklearn
  from sklearn.linear_model import Lars
  from sklearn.datasets import fetch_california_housing
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.experimental.linear_model import Lars
  import sys
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
  from test_linear_model import make_regression_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: test_lars_collinear(datatype, nrows, column_info, precompute) without return types