code: def test_second_order(self):
      # Second order, so its Hessian will be non-zero
      params, y = self._generate_params_and_second_order_output()
  
      def test_hessian_vector_product_nonzero(vec):
          hvp = compute_hessian_vector_product(y, params, vec)
          hessian = compute_hessian(y, params)
          self.assertGreater(np.count_nonzero(hvp.numpy()), 0)
          self.assertGreater(np.count_nonzero(hessian.numpy()), 0)
          torch_assert_allclose(
              hvp, torch.matmul(hessian, vec), atol=1e-3
          )
  
      # Test with two different random vectors, reusing y
      test_hessian_vector_product_nonzero(torch.rand(4))
      test_hessian_vector_product_nonzero(torch.rand(4))
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import trpo
  from pfrl.envs.abc import ABC
  from pfrl.experiments import train_agent_batch_with_evaluation, train_agent_with_evaluation
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes, run_evaluation_episodes
  from pfrl.nn import RecurrentSequential
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 4 for calling the TestHessianVectorProduct obj's underlying member method code with example usage: obj.test_second_order() without return types