code: def test_hessian_vector_product():
      """Test Hessian-vector product for a function with one variable."""
      a = torch.tensor([5.0])
      x = torch.tensor([10.0], requires_grad=True)
  
      def f():
          return a * (x**2)
  
      expected_hessian = 2 * a
      vector = torch.tensor([10.0])
      expected_hvp = (expected_hessian * vector).detach()
      f_Ax = _build_hessian_vector_product(f, [x])
      computed_hvp = f_Ax(vector).detach()
      assert np.allclose(computed_hvp, expected_hvp)
  
imports: import pickle
  import numpy
  import pytest
  import torch
  from garage.torch.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
  from garage.torch.optimizers.conjugate_gradient_optimizer import _build_hessian_vector_product
  from garage.torch.optimizers.conjugate_gradient_optimizer import _conjugate_gradient
label_desc: Use image node_id 2 for calling a global function with example usage: test_hessian_vector_product() and returns: unknown