code: def test_gradcam_ones():
      activations = np.ones((1, 1, 2))
      weights = np.ones((2,))
      lmap = gradcam(weights, activations)
      # all within eps distance to one
      assert np.isclose(lmap, np.ones((1, 1)), rtol=epsilon())
  
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 13 for calling a global function with example usage: test_gradcam_ones() without return types