code: def test_pipeline_with_regression(
      regression_dataset, model_key, instantiation
  ):
      X_train, X_test, y_train, y_test = regression_dataset
      model_const = models[model_key]
      if model_key == "RandomForestRegressor":
          model = model_const(n_bins=2)
      else:
          model = model_const()
  
      if instantiation == "Pipeline":
          pipe = Pipeline(
              steps=[("scaler", StandardScaler()), ("model", model)]
          )
      elif instantiation == "make_pipeline":
          pipe = make_pipeline(StandardScaler(), model)
      pipe.fit(X_train, y_train)
      prediction = pipe.predict(X_test)
      assert isinstance(prediction, cupy.ndarray)
      _ = pipe.score(X_test, y_test)
  
file_path: cuml/python/cuml/tests/test_meta_estimators.py
imports: from cuml.svm import SVC
  from cuml.preprocessing import StandardScaler
  from sklearn.datasets import load_iris
  from cuml.model_selection import train_test_split
  from cuml.datasets import make_regression, make_classification
  from cuml.testing.utils import ClassEnumerator
  from cuml.model_selection import GridSearchCV
  from cuml.pipeline import Pipeline, make_pipeline
  import pytest
  import cuml
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 5 for calling a global function with example usage: test_pipeline_with_regression(regression_dataset, model_key, instantiation) without return types