code: def test_newton_cg():
      # Test that newton_cg gives same result as scipy's fmin_ncg
  
      rng = np.random.RandomState(0)
      A = rng.normal(size=(10, 10))
      x0 = np.ones(10)
  
      def func(x):
          Ax = A.dot(x)
          return 0.5 * (Ax).dot(Ax)
  
      def grad(x):
          return A.T.dot(A.dot(x))
  
      def hess(x, p):
          return p.dot(A.T.dot(A.dot(x.all())))
  
      def grad_hess(x):
          return grad(x), lambda x: A.T.dot(A.dot(x))
  
      assert_array_almost_equal(
          _newton_cg(grad_hess, func, grad, x0, tol=1e-10)[0],
          fmin_ncg(f=func, x0=x0, fprime=grad, fhess_p=hess),
      )
  
imports: import numpy
  from scipy.optimize import fmin_ncg
  from sklearn.utils._testing import assert_array_almost_equal
  from sklearn.utils.optimize import _newton_cg
label_desc: Use image node_id 1 for calling a global function with example usage: test_newton_cg() and returns: unknown, A, p, grad,  function