code: def test_given_less_samples_than_num_conditionals_specified_when_evaluate_arrow_strength_with_observational_data_then_does_not_throw_error():
      causal_model = ProbabilisticCausalModel(
          nx.DiGraph([("X0", "X1")])
      )
      causal_model.set_causal_mechanism(
          "X0", ScipyDistribution(stats.norm, loc=0, scale=1)
      )
      causal_model.set_causal_mechanism(
          "X1",
          AdditiveNoiseModel(
              prediction_model=create_linear_regressor()
          ),
      )
  
      X0 = np.random.normal(0, 1, 10)
      X1 = np.random.normal(0, 1, 10)
  
      test_data = pd.DataFrame({"X0": X0, "X1": X1})
      fit(causal_model, test_data)
  
      arrow_strength(
          causal_model,
          "X1",
          parent_samples=test_data,
          num_samples_conditional=10000,
      )
  
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 9 for calling a global function with example usage: test_given_less_samples_than_num_conditionals_specified_when_evaluate_arrow_strength_with_observational_data_then_does_not_throw_error() without return types