code: def test_issue_1310():
      X, y = sk_datasets.make_regression(n_samples=5000, n_features=2)
      X = pd.DataFrame(X)
      X.columns = ["feat_1", "feat_2"]
      X["cat"] = np.random.randint(1, 100, X.shape[0])
      X["cat"] = X["cat"].astype("string")
      y = pd.Series(y)
  
      group1 = compose.Select("cat") | preprocessing.OneHotEncoder()
      group2 = compose.Select("feat_2") | preprocessing.StandardScaler()
      model = (
          group1 + group1 * group2 * group2
          | compat.convert_sklearn_to_river(
              sk_linear_model.SGDRegressor()
          )
      )
  
      model.predict_many(X)
      model.learn_many(X, y)
  
file_path: river/river/compose/test_product.py
imports: from __future__ import annotations
  import numpy
  import pandas
  from sklearn import datasets
  from sklearn import linear_model
  from river import compat, compose, datasets, preprocessing, stream
label_desc: Use image node_id 11 for calling a global function with example usage: test_issue_1310() without return types