code: def test_actual_results_quantile_transformer_normal():
      """Test that the actual results are the expected ones."""
      X_ppf = norm.ppf(np.linspace(0, 1, 1000)[1:-1])
      weights = np.round(norm.pdf(X_ppf) * 1000).astype("int64")
      X = []
      for value, weight in zip(X_ppf, weights):
          X += [value] * weight
      X = np.asarray(X).reshape(1, -1)
      transformer = QuantileTransformer(
          n_quantiles=11, output_distribution="normal"
      )
      arr_actual = transformer.fit_transform(X)
      arr_desired = X
      atol = 0.01 * X.shape[1]
      np.testing.assert_allclose(
          arr_actual, arr_desired, atol=atol, rtol=0.0
      )
  
imports: import numpy
  from scipy.stats import boxcox, norm, yeojohnson
  from pyts.preprocessing import StandardScaler
  from pyts.preprocessing import PowerTransformer, QuantileTransformer
label_desc: Use image node_id 4 for calling a global function with example usage: test_actual_results_quantile_transformer_normal() without return types