code: def test_ridge_regression_model_default(dataset):
      assume(sklearn_compatible_dataset(*dataset))
      assume(cuml_compatible_dataset(*dataset))
      X_train, X_test, y_train, _ = dataset
  
      curidge = cuRidge()
  
      # fit and predict cuml ridge regression model
      curidge.fit(X_train, y_train)
      curidge_predict = curidge.predict(X_test)
  
      # sklearn ridge regression model initialization, fit and predict
      skridge = skRidge()
      skridge.fit(X_train, y_train)
      skridge_predict = skridge.predict(X_test)
  
      equal = array_equal(
          skridge_predict, curidge_predict, 1e-1, with_sign=True
      )
      note(equal)
      target(float(np.abs(equal.compute_difference())))
      assert equal
  
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 12 for calling a global function with example usage: test_ridge_regression_model_default(dataset) without return types