code: def test_calc_gradients_of_top_class_if_prediction_is_wrong(
      mocker, model
  ):
      backprop = Backprop(model)
  
      top_class = torch.tensor(5)
      target_class = 7
      input_ = torch.zeros([1, 3, 224, 224])
  
      target = make_expected_gradient_target(top_class)
  
      mock_output = make_mock_output(mocker, model, top_class)
  
      with pytest.warns(UserWarning):
          backprop.calculate_gradients(input_, target_class)
  
      args, kwargs = mock_output.backward.call_args
  
      assert torch.all(kwargs["gradient"].eq(target))
  
imports: import inspect
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.models
  from flashtorch.saliency import Backprop
label_desc: Use image node_id 12 for calling a global function with example usage: test_calc_gradients_of_top_class_if_prediction_is_wrong(mocker, model) without return types