code: def test_given_list_of_invariant_nodes_to_remove_return_expected_results():
      original_observations, outlier_observations = _generate_data()
  
      causal_model = ProbabilisticCausalModel(
          nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])
      )
      _assign_causal_mechanisms(causal_model)
  
      results = distribution_change(
          causal_model,
          original_observations,
          outlier_observations,
          "X3",
          shapley_config=ShapleyConfig(n_jobs=1),
          invariant_nodes=["X0", "X1"],
      )
  
      assert results["X3"] > results["X2"]
      assert results["X0"] == approx(0)
  
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from pytest import approx
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, distribution_change, distribution_change_of_graphs, fit
  from dowhy.gcm.auto import AssignmentQuality
  from dowhy.gcm.distribution_change import mechanism_change_test
  from dowhy.gcm.ml import create_linear_regressor
  from dowhy.gcm.shapley import ShapleyConfig
label_desc: Use image node_id 3 for calling a global function with example usage: test_given_list_of_invariant_nodes_to_remove_return_expected_results() without return types