code: def test_elasticnet_solvers_eq(
      datatype, alpha, l1_ratio, nrows, column_info
  ):
      ncols, n_info = column_info
      X_train, X_test, y_train, y_test = make_regression_dataset(
          datatype, nrows, ncols, n_info
      )
  
      kwargs = {"alpha": alpha, "l1_ratio": l1_ratio}
      cd = cuElasticNet(solver="cd", **kwargs)
      cd.fit(X_train, y_train)
      cd_res = cd.predict(X_test)
  
      qn = cuElasticNet(solver="qn", **kwargs)
      qn.fit(X_train, y_train)
      # the results of the two models should be close (even if both are bad)
      assert qn.score(X_test, cd_res) > 0.95
      # coefficients of the two models should be close
      assert np.corrcoef(cd.coef_, qn.coef_)[0, 1] > 0.98
  
imports: from contextlib import nullcontext
  from distutils.version import LooseVersion
  from functools import lru_cache
  import pytest
  import sklearn
  from cuml.internals.array import elements_in_representable_range
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from, gpu_only_import
  from cuml.testing.strategies import regression_datasets, split_datasets, standard_classification_datasets, standard_regression_datasets
  from cuml.testing.utils import array_difference, array_equal, quality_param, small_classification_dataset, small_regression_dataset, stress_param, unit_param
  from hypothesis import assume, example, given, note
  from hypothesis import strategies
  from hypothesis import target
  from hypothesis.extra.numpy import floating_dtypes
  from sklearn.datasets import load_breast_cancer, load_digits, make_classification, make_regression
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from cuml import ElasticNet
  from cuml import LinearRegression
  from cuml import LogisticRegression
  from cuml import Ridge
label_desc: Use image node_id 29 for calling a global function with example usage: test_elasticnet_solvers_eq(datatype, alpha, l1_ratio, nrows, column_info) without return types