code: def test_should_produce_gradients_and_filters(
      convolutional_model, random_data
  ):
      images, _ = random_data
      layer_name = "activation_1"
      use_guided_grads = True
      output, grads = GradCAM.get_gradients_and_filters(
          convolutional_model, images, layer_name, 0, use_guided_grads
      )
  
      assert output.shape == [len(images)] + list(
          convolutional_model.get_layer(layer_name).output.shape[1:]
      )
      assert grads.shape == output.shape
  
file_path: tf-explain/tests/core/test_grad_cam.py
imports: import numpy
  import pytest
  import tensorflow
  from tf_explain.core.grad_cam import GradCAM
label_desc: Use image node_id 3 for calling a global function with example usage: test_should_produce_gradients_and_filters(convolutional_model, random_data) without return types