code: def test_given_misspecified_graph_when_estimating_direct_arrow_strength_with_observed_data_then_returns_correct_result():
      Z = np.random.normal(0, 1, 1000)
      X0 = Z + np.random.normal(0, 1, 1000)
      X1 = Z + 2 * X0 + np.random.normal(0, 1, 1000)
      X2 = X0 + X1
  
      data = pd.DataFrame({"Z": Z, "X0": X0, "X1": X1, "X2": X2})
  
      # Missing connection between X0 and X1.
      # For X0 and X1, we set the ground truth noise to further emphasize the misspecification. The inferred noise of X1
      # would otherwise have a dependency with Z due to the missing connection with X0.
      causal_model_without = ProbabilisticCausalModel(
          nx.DiGraph(
              [("Z", "X0"), ("Z", "X1"), ("X0", "X2"), ("X1", "X2")]
          )
      )
      causal_model_without.set_causal_mechanism(
          "X0",
          AdditiveNoiseModel(
              create_linear_regressor(),
              ScipyDistribution(stats.norm, loc=0, scale=1),
          ),
      )
      causal_model_without.set_causal_mechanism(
          "X1",
          AdditiveNoiseModel(
              create_linear_regressor(),
              ScipyDistribution(stats.norm, loc=0, scale=1),
          ),
      )
      assign_causal_mechanisms(causal_model_without, data)
      fit(causal_model_without, data)
  
      # Modelling connection between X0 and X1 explicitly.
      causal_model_with = ProbabilisticCausalModel(
          nx.DiGraph(
              [
                  ("Z", "X0"),
                  ("Z", "X1"),
                  ("X0", "X1"),
                  ("X0", "X2"),
                  ("X1", "X2"),
              ]
          )
      )
      causal_model_with.set_causal_mechanism(
          "X0",
          AdditiveNoiseModel(
              create_linear_regressor(),
              ScipyDistribution(stats.norm, loc=0, scale=1),
          ),
      )
      causal_model_with.set_causal_mechanism(
          "X1",
          AdditiveNoiseModel(
              create_linear_regressor(),
              ScipyDistribution(stats.norm, loc=0, scale=1),
          ),
      )
      assign_causal_mechanisms(
          causal_model_with, data, override_models=False
      )
      fit(causal_model_with, data)
  
      strength_missing_edge = arrow_strength(
          causal_model_without, "X2", parent_samples=data
      )
      strength_with_edge = arrow_strength(causal_model_with, "X2")
  
      assert strength_missing_edge[("X0", "X2")] == approx(
          strength_with_edge[("X0", "X2")], abs=0.2
      )
      assert strength_missing_edge[("X1", "X2")] == approx(
          strength_with_edge[("X1", "X2")], abs=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 7 for calling a global function with example usage: test_given_misspecified_graph_when_estimating_direct_arrow_strength_with_observed_data_then_returns_correct_result() without return types