code: def test_calc_gradient_nondifferentiable(nondifferentiable_model):
      with pytest.raises(ValueError):
          grads = _calc_gradient(
              nondifferentiable_model.output,
              [nondifferentiable_model.input],
          )
  
imports: import pytest
  import keras.backend
  from keras.models import Sequential, Model
  from keras.layers import Dense, Activation, Conv2D, GlobalAveragePooling2D, Input, Lambda
  from keras.backend import epsilon
  import numpy
  from eli5.keras.explain_prediction import explain_prediction, _validate_doc, _get_activation_layer
  from eli5.keras.gradcam import _get_target_prediction, _calc_gradient, gradcam
label_desc: Use image node_id 11 for calling a global function with example usage: test_calc_gradient_nondifferentiable(nondifferentiable_model) without return types