code: def test_get_score_model_returns_suitable_model(
      convolutional_model_with_separate_activation_layer,
  ):
      explainer = VanillaGradients()
      # The last two layers of the original model are a Dense layer with no activation (i.e. linear)
      # followed by a Softmax layer.
      score_layer = (
          convolutional_model_with_separate_activation_layer.layers[-2]
      )
      softmax_layer = (
          convolutional_model_with_separate_activation_layer.layers[-1]
      )
      score_model = explainer.get_score_model(
          convolutional_model_with_separate_activation_layer
      )
      # The score model should exclude the final activation layer.
      assert score_model.layers[-1] == score_layer
      assert softmax_layer not in score_model.layers
  
imports: import pytest
  from tf_explain.core.vanilla_gradients import VanillaGradients, UNSUPPORTED_ARCHITECTURE_WARNING
label_desc: Use image node_id 3 for calling a global function with example usage: test_get_score_model_returns_suitable_model(convolutional_model_with_separate_activation_layer) without return types