code: def test_when_calling_intrinsic_causal_influence_then_the_shape_of_inputs_in_the_attribution_function_should_be_equal():
      causal_model = StructuralCausalModel(nx.DiGraph([("X0", "X1")]))
      X0 = np.random.normal(0, 1, 10000)
      X1 = X0 + np.random.normal(0, 0.001, 10000)
      training_data = pd.DataFrame({"X0": X0, "X1": X1})
      auto.assign_causal_mechanisms(
          causal_model, training_data, auto.AssignmentQuality.GOOD
      )
  
      fit(causal_model, training_data)
  
      def my_attr_func(X, Y):
          assert np.all(X.shape == Y.shape)
          return 0
  
      intrinsic_causal_influence(
          causal_model, "X1", attribution_func=my_attr_func
      )
  
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from pytest import approx
  from scipy import stats
  from sklearn.linear_model import LogisticRegression
  from dowhy.gcm import AdditiveNoiseModel, InvertibleStructuralCausalModel, ScipyDistribution, StructuralCausalModel, auto, fit, intrinsic_causal_influence
  from dowhy.gcm._noise import noise_samples_of_ancestors
  from dowhy.gcm.influence import intrinsic_causal_influence_sample
  from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_linear_regressor, create_linear_regressor_with_given_parameters
  from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance
  from dowhy.gcm.util.general import apply_one_hot_encoding, fit_one_hot_encoders
  from dowhy.graph import node_connected_subgraph_view
  from tests.gcm.test_noise import _persist_parents
label_desc: Use image node_id 5 for calling a global function with example usage: test_when_calling_intrinsic_causal_influence_then_the_shape_of_inputs_in_the_attribution_function_should_be_equal() and returns: int