code: def test_lasso(
      datatype, X_type, alpha, algorithm, nrows, column_info
  ):
      ncols, n_info = column_info
      X, y = make_regression(
          n_samples=nrows,
          n_features=ncols,
          n_informative=n_info,
          random_state=0,
      )
      X = X.astype(datatype)
      y = y.astype(datatype)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=0
      )
      cu_lasso = cuLasso(
          alpha=np.array([alpha]),
          fit_intercept=True,
          max_iter=1000,
          selection=algorithm,
          tol=1e-10,
      )
  
      cu_lasso.fit(X_train, y_train)
      assert cu_lasso.coef_ is not None
      cu_predict = cu_lasso.predict(X_test)
  
      cu_r2 = r2_score(y_test, cu_predict)
  
      if nrows < 500000:
          sk_lasso = Lasso(
              alpha=alpha,
              fit_intercept=True,
              max_iter=1000,
              selection=algorithm,
              tol=1e-10,
          )
          sk_lasso.fit(X_train, y_train)
          sk_predict = sk_lasso.predict(X_test)
          sk_r2 = r2_score(y_test, sk_predict)
          assert cu_r2 >= sk_r2 - 0.07
  
file_path: cuml/python/cuml/tests/test_coordinate_descent.py
imports: from sklearn.model_selection import train_test_split
  from sklearn.datasets import make_regression
  from sklearn.linear_model import Lasso, ElasticNet
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from cuml.metrics import r2_score
  from cuml.linear_model import ElasticNet
  from cuml import Lasso
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: test_lasso(datatype, X_type, alpha, algorithm, nrows, column_info) without return types