code: def test_df(penguins_df):
      meta = GroupedTransformer(
          StandardScaler(), groups=["island", "sex"]
      )
  
      transformed = meta.fit_transform(penguins_df)
  
      # 2 columns for grouping not in the result
      assert transformed.shape == (
          penguins_df.shape[0],
          penguins_df.shape[1] - 2,
      )
  
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 18 for calling a global function with example usage: test_df(penguins_df) without return types