code: def classification_dataset(request):
      X, y = make_classification(
          n_samples=10, n_features=5, random_state=0
      )
      return train_test_split(X, y, random_state=0)
  
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 4 for calling a global function with example usage: classification_dataset(request) and returns: train_test_split