code: def test_cg():
      """Solve Ax = b using Conjugate gradient method."""
      a = np.linspace(-np.pi, np.pi, 25).reshape((5, 5))
      a = a.T.dot(a)  # make sure a is positive semi-definite
  
      def hvp(v):
          return torch.tensor(a.dot(v))
  
      b = torch.tensor(np.linspace(-np.pi, np.pi, 5))
      x = _conjugate_gradient(hvp, b, 5)
      assert np.allclose(a.dot(x), b)
  
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 1 for calling a global function with example usage: test_cg() and returns: torch