code: def test_pipeline():
      X, y = make_classification(random_state=0)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=0
      )
      pipe = Pipeline(
          steps=[("scaler", StandardScaler()), ("svc", SVC())]
      )
      pipe.fit(X_train, y_train)
      score = pipe.score(X_test, y_test)
      assert score > 0.8
  
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 1 for calling a global function with example usage: test_pipeline() without return types