code: def test_missing_groups_transform_noglobal(
      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, use_global_model=False
      )
      transformer.fit(X_with_groups, y)
  
      # Array with 2 rows, first column a new group. Remaining top are out of range so should be the range
      X_test = np.concatenate(
          [
              np.array([[3], [3]]),
              np.stack([X.min(axis=0) - 1, X.max(axis=0) + 1], axis=0),
          ],
          axis=1,
      )
  
      with pytest.raises(ValueError):
          transformer.transform(X_test)
  
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 15 for calling a global function with example usage: test_missing_groups_transform_noglobal(dataset_with_single_grouping, scaling_range) without return types