code: def test_get_inverse_hvp_lissa():
      vs = [torch.tensor([1.0, 1.0])]
      # create a fake model
      vocab = Vocabulary()
      params = torch.tensor([1, 2]).float()
      model = DummyBilinearModelForTestingIF(vocab, params)
      used_params = list(model.parameters())
  
      # create a fake instance: just a matrix
      A = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
      fake_instance = Instance({"tensors": TensorField(A)})
  
      # wrap fake instance into dataloader
      lissa_data_loader = SimpleDataLoader(
          [fake_instance], batch_size=1, batches_per_epoch=1
      )
  
      inverse_hvp = get_inverse_hvp_lissa(
          vs=vs,
          model=model,
          used_params=used_params,
          lissa_data_loader=lissa_data_loader,
          damping=0.0,
          num_samples=1,
          scale=1.0,
      )
      # I tried to increase recursion depth to actually approx the inverse Hessian vector product,
      # but I suspect due to extremely small number of data point, the algorithm doesn't work well
      # on this toy example
      ans = torch.tensor([-1.5, -4.5])
      assert torch.equal(inverse_hvp, ans)
  
file_path: allennlp/tests/interpret/simple_influence_test.py
imports: import torch
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.fields import TensorField
  from allennlp.data import Instance
  from allennlp.models.model import Model
  from allennlp.data.data_loaders import SimpleDataLoader
  from allennlp.interpret import InfluenceInterpreter
  from allennlp.interpret.influence_interpreters.simple_influence import _flatten_tensors, get_hvp, get_inverse_hvp_lissa
label_desc: Use image node_id 3 for calling a global function with example usage: test_get_inverse_hvp_lissa() without return types