code: def test_shape(random_xy_dataset_regr):
      X, y = random_xy_dataset_regr
      m = X.shape[0]
      pipeline = Pipeline(
          [
              (
                  "ml_features",
                  FeatureUnion(
                      [
                          (
                              "model_1",
                              EstimatorTransformer(LinearRegression()),
                          ),
                          ("model_2", EstimatorTransformer(Ridge())),
                      ]
                  ),
              )
          ]
      )
  
      assert pipeline.fit(X, y).transform(X).shape == (m, 2)
  
imports: from unittest.mock import Mock, patch
  import numpy
  import pytest
  from sklearn import clone
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import HistGradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, Ridge
  from sklearn.multioutput import MultiOutputRegressor
  from sklearn.pipeline import FeatureUnion, Pipeline
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.utils import check_X_y
  from sklego.common import flatten
  from sklego.meta import EstimatorTransformer
  from tests.conftest import general_checks, transformer_checks
label_desc: Use image node_id 5 for calling a global function with example usage: test_shape(random_xy_dataset_regr) without return types