code: def compute_hessian_vector_product(y, params, vec):
      grads = torch.autograd.grad([y], params, create_graph=True)
      flat_grads = trpo._flatten_and_concat_variables(grads)
      return trpo._hessian_vector_product(flat_grads, params, vec)
  
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 1 for calling a global function with example usage: compute_hessian_vector_product(y, params, vec) and returns: trpo