code: def test_given_conditional_intervention_without_specific_input_when_draw_interventional_samples_then_return_correct_sample_values():
      (
          causal_model,
          training_data,
      ) = _create_and_fit_simple_probabilistic_causal_model()
  
      samples = interventional_samples(
          causal_model,
          dict(X2=lambda x: x + 10),
          num_samples_to_draw=10,
      )
      assert samples["X2"].to_numpy() == approx(
          np.mean(training_data["X2"].to_numpy()) + 10, abs=1
      )
  
imports: import networkx
  import numpy
  import pandas
  import pytest
  from flaky import flaky
  from pytest import approx
  from dowhy.gcm import AdditiveNoiseModel, ClassifierFCM, DiscreteAdditiveNoiseModel, EmpiricalDistribution, InvertibleStructuralCausalModel, ProbabilisticCausalModel, auto, average_causal_effect, counterfactual_samples, fit, interventional_samples
  from dowhy.gcm.ml import create_hist_gradient_boost_regressor, create_linear_regressor, create_logistic_regression_classifier
label_desc: Use image node_id 5 for calling a global function with example usage: test_given_conditional_intervention_without_specific_input_when_draw_interventional_samples_then_return_correct_sample_values() without return types