code: def test_composite_lrp():
      model = kmodels.Sequential(
          [
              klayers.Input(shape=(28, 28, 3)),
              klayers.Conv2D(8, 3, activation="relu"),
              klayers.Conv2D(4, 5, activation="relu"),
              klayers.Flatten(),
              klayers.Dense(16, activation="relu"),
              klayers.Dense(2, activation="softmax"),
          ]
      )
      analyzer = LRP(
          model,
          rule="Z",
          input_layer_rule="Flat",
          until_layer_idx=2,
          until_layer_rule="Epsilon",
      )
      correct_rules = [
          "Flat",
          "Epsilon",
          "Epsilon",
          "Z",
          "Z",
      ]  # Correct rules corresponding to analyzer input args
  
      for i, layer in enumerate(model.layers):
          for condition, rule in analyzer._rules:
              if condition(layer):
                  rule_class = rule
                  break
          assert rule_class == correct_rules[i]
  
imports: import pytest
  import tensorflow.keras.layers
  import tensorflow.keras.models
  from innvestigate.analyzer.relevance_based.relevance_analyzer import LRP
label_desc: Use image node_id 1 for calling a global function with example usage: test_composite_lrp() without return types