code: def test_sklearn_after_primitives():
      """Test that sklearn transformer after primitives is correctly applied."""
      t = SummaryTransformer() * StandardScaler()
      assert t.get_tag("scitype:transform-output") == "Primitives"
  
      X = get_examples("pd-multiindex")[0]
      X_out = t.fit_transform(X)
      X_summary = SummaryTransformer().fit_transform(X)
  
      assert (X_out.index == X_summary.index).all()
      assert deep_equals(X_out.columns, X_summary.columns)
      # var_0 is the same for all three instances
      # so summary statistics are all the same, thus StandardScaler transforms to 0
      assert X_out.iloc[0, 0] > -0.01
      assert X_out.iloc[0, 0] < 0.01
      # var_1 has some variation between three instances
      # fix this to one value to tie the output to current behaviour
      assert X_out.iloc[0, 10] > -1.37
      assert X_out.iloc[0, 10] < -1.36
  
imports: import pandas
  import pytest
  from sklearn.preprocessing import StandardScaler
  from sktime.datasets import load_airline
  from sktime.datatypes import get_examples
  from sktime.transformations.compose import FeatureUnion, InvertTransform, OptionalPassthrough, TransformerPipeline
  from sktime.transformations.panel.padder import PaddingTransformer
  from sktime.transformations.series.boxcox import LogTransformer
  from sktime.transformations.series.exponent import ExponentTransformer
  from sktime.transformations.series.impute import Imputer
  from sktime.transformations.series.subset import ColumnSelect
  from sktime.transformations.series.summarize import SummaryTransformer
  from sktime.transformations.series.theta import ThetaLinesTransformer
  from sktime.utils._testing.deep_equals import deep_equals
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 6 for calling a global function with example usage: test_sklearn_after_primitives() without return types