code: def test_gradient(self):
      for device in DEVICES:
          f, y0, t_points, sol = construct_problem(device=device)
          tuple_f = lambda t, y: (f(t, y[0]), f(t, y[1]))
          for method in ADAPTIVE_METHODS:
              if method == "scipy_solver":
                  continue
  
              with self.subTest(device=device, method=method):
                  for i in range(2):
                      func = lambda y0, t_points: torchdiffeq.odeint(
                          tuple_f, (y0, y0), t_points, method=method
                      )[i]
                      self.assertTrue(
                          torch.autograd.gradcheck(func, (y0, t_points))
                      )
  
imports: import unittest
  import torch
  import torchdiffeq
  from problems import construct_problem, DTYPES, DEVICES, ADAPTIVE_METHODS
label_desc: Use image node_id 2 for calling the TestCollectionState obj's underlying member method code with example usage: obj.test_gradient() without return types