code: def test_given_gcm_with_misspecified_mechanism_when_evaluate_arrow_strength_with_observational_data_then_gives_expected_results():
      causal_model = ProbabilisticCausalModel(
          nx.DiGraph([("X1", "X2"), ("X0", "X2")])
      )
      # Here, we misspecify the mechanism on purpose by setting scale to 1 instead of 2.
      causal_model.set_causal_mechanism(
          "X0", ScipyDistribution(stats.norm, loc=0, scale=1)
      )
      causal_model.set_causal_mechanism(
          "X1", ScipyDistribution(stats.norm, loc=0, scale=1)
      )
      causal_model.set_causal_mechanism(
          "X2",
          AdditiveNoiseModel(
              prediction_model=create_linear_regressor()
          ),
      )
  
      X0 = np.random.normal(
          0, 2, 2000
      )  # The standard deviation in the data is actually 2.
      X1 = np.random.normal(0, 1, 2000)
  
      test_data = pd.DataFrame(
          {
              "X0": X0,
              "X1": X1,
              "X2": X0 + X1 + np.random.normal(0, 0.2, X0.shape[0]),
          }
      )
      fit(causal_model, test_data)
  
      # If we provide the observational data here, we can mitigate the misspecification of the causal mechanism.
      causal_strengths = arrow_strength(
          causal_model,
          "X2",
          parent_samples=test_data,
          difference_estimation_func=lambda x, y: np.var(y) - np.var(x),
      )
      assert causal_strengths[("X0", "X2")] == approx(4, abs=0.5)
      assert causal_strengths[("X1", "X2")] == approx(1, abs=0.1)
  
imports: import random
  import networkx
  import numpy
  import pandas
  import pytest
  from flaky import flaky
  from pytest import approx
  from scipy import stats
  from dowhy.gcm import AdditiveNoiseModel, ClassifierFCM, ProbabilisticCausalModel, ScipyDistribution, arrow_strength, fit
  from dowhy.gcm.auto import assign_causal_mechanisms
  from dowhy.gcm.divergence import estimate_kl_divergence_continuous_knn
  from dowhy.gcm.influence import arrow_strength_of_model
  from dowhy.gcm.ml import create_linear_regressor, create_linear_regressor_with_given_parameters, create_logistic_regression_classifier
label_desc: Use image node_id 8 for calling a global function with example usage: test_given_gcm_with_misspecified_mechanism_when_evaluate_arrow_strength_with_observational_data_then_gives_expected_results() without return types