code: def test_given_multiple_atomic_intervention_with_specific_input_when_draw_interventional_samples_then_return_correct_sample_values():
      (
          causal_model,
          _,
      ) = _create_and_fit_simple_probabilistic_causal_model()
  
      observed_data = pd.DataFrame(
          {"X0": [0], "X1": [1], "X2": [2], "X3": [3]}
      )
  
      sample = interventional_samples(
          causal_model,
          dict(X0=lambda x: 10, X2=lambda x: x + 5),
          observed_data,
      ).to_numpy()
      sample = sample.squeeze()
      assert sample[0] == 10
      assert sample[1] == approx(20, abs=0.3)
      assert sample[2] == approx(10, abs=0.3)
      assert sample[3] == approx(5, abs=0.3)
  
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 7 for calling a global function with example usage: test_given_multiple_atomic_intervention_with_specific_input_when_draw_interventional_samples_then_return_correct_sample_values() without return types