code: def test_get_hvp():
      # This represents some train data point input.
      X = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
      # This represents the weights of a model.
      w = torch.nn.Parameter(
          torch.tensor([1, 2]).float(), requires_grad=True
      )
      # This is the vector in the HVP.
      v = torch.tensor([10, 20]).float()
      # And this is the forward pass / loss calculation of the model.
      loss = 1 / 2 * (w @ X @ w.T)
  
      expected_answer = 1 / 2 * (X + X.T) @ v
  
      hessian_vector_product = get_hvp(loss, [w], [v])[0]
      assert torch.equal(hessian_vector_product, expected_answer)
  
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 1 for calling a global function with example usage: test_get_hvp() without return types