code: def test_given_additive_noise_data_when_evaluate_invertibility_assumptions_then_does_not_reject():
      X0 = np.random.normal(0, 1, 5000)
      Y = X0 + np.random.normal(0, 0.1, 5000)
      data = pd.DataFrame({"X0": X0, "Y": Y})
  
      causal_model = InvertibleStructuralCausalModel(
          nx.DiGraph([("X0", "Y")])
      )
  
      assign_causal_mechanisms(causal_model, data)
      causal_model.set_causal_mechanism(
          "Y", AdditiveNoiseModel(create_linear_regressor())
      )
      fit(causal_model, data)
  
      assert not _evaluate_invertibility_assumptions(
          causal_model, data, kernel_based, 0.05, None, 1
      )["Y"][1]
  
imports: import networkx
  import numpy
  import pandas
  from _pytest.python_api import approx
  from flaky import flaky
  from scipy import stats
  from dowhy.gcm import AdditiveNoiseModel, ClassifierFCM, InvertibleStructuralCausalModel, ScipyDistribution, fit, kernel_based
  from dowhy.gcm.auto import assign_causal_mechanisms
  from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_linear_regressor, create_linear_regressor_with_given_parameters, create_logistic_regression_classifier
  from dowhy.gcm.model_evaluation import EvaluateCausalModelConfig, _estimate_conditional_expectations, _evaluate_invertibility_assumptions, crps, evaluate_causal_model, nmse
label_desc: Use image node_id 11 for calling a global function with example usage: test_given_additive_noise_data_when_evaluate_invertibility_assumptions_then_does_not_reject() without return types