code: def test_given_continuous_data_with_default_attribution_func_when_estimate_arrow_strength_then_returns_expected_results():
      causal_strengths = arrow_strength(_create_causal_model(), "X2")
  
      # By default, the strength is measure with respect to the variance.
      assert causal_strengths[("X0", "X2")] == approx(9, abs=0.5)
      assert causal_strengths[("X1", "X2")] == approx(1, abs=0.2)
  
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 3 for calling a global function with example usage: test_given_continuous_data_with_default_attribution_func_when_estimate_arrow_strength_then_returns_expected_results() without return types