code: def _test(self, device):
      # A must be symmetric and positive-definite
      random_mat = torch.normal(0, 1, size=(self.n, self.n))
      random_mat.to(device)
      A = torch.matmul(random_mat, random_mat.T)
      x_ans = torch.normal(0, 1, size=(self.n,))
      x_ans.to(device)
      b = torch.matmul(A, x_ans)
  
      def A_product_func(vec):
          assert vec.shape == b.shape
          return torch.matmul(A, vec)
  
      x = pfrl.utils.conjugate_gradient(A_product_func, b)
      torch_assert_allclose(x, x_ans, rtol=1e-1)
  
file_path: pfrl/tests/utils_tests/test_conjugate_gradient.py
imports: import pytest
  import torch
  import pfrl
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 2 for calling the TestConjugateGradient obj's underlying member method code with example usage: obj._test(device) and returns: torch