code: def test_all_groups_scaled(
      dataset_with_single_grouping, scaling_range
  ):
      (
          X,
          y,
          groups,
          X_with_groups,
          grouper,
      ) = dataset_with_single_grouping
  
      trf = MinMaxScaler(scaling_range)
      transformer = GroupedTransformer(trf, groups=grouper)
      transformed = transformer.fit(X_with_groups, y).transform(
          X_with_groups
      )
  
      df_with_groups = pd.concat(
          [
              pd.Series(groups.flatten(), name="G"),
              pd.DataFrame(transformed),
          ],
          axis=1,
      )
  
      assert np.allclose(
          df_with_groups.groupby("G").min(), scaling_range[0]
      )
      assert np.allclose(
          df_with_groups.groupby("G").max(), scaling_range[1]
      )
  
imports: import itertools
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from sklearn.utils import check_X_y
  from sklego.common import flatten
  from sklego.datasets import load_penguins
  from sklego.meta import GroupedTransformer
  from tests.conftest import general_checks, k_vals, n_vals, np_types, select_tests, transformer_checks
label_desc: Use image node_id 8 for calling a global function with example usage: test_all_groups_scaled(dataset_with_single_grouping, scaling_range) without return types