code: def test_warn_when_prediction_is_wrong(mocker, model):
      backprop = Backprop(model)
  
      top_class = torch.tensor(1)
      target_class = 5
  
      input_ = torch.zeros([1, 3, 224, 224])
  
      make_mock_output(mocker, model, top_class)
  
      with pytest.warns(UserWarning):
          backprop.calculate_gradients(input_, target_class)
  
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 16 for calling a global function with example usage: test_warn_when_prediction_is_wrong(mocker, model) without return types