code: def test_steps(self):
      for forward, adjoint in (
          (False, True),
          (True, False),
          (True, True),
      ):
          for method in FIXED_METHODS + ADAPTIVE_METHODS:
              if method == "dopri8":  # using torch.float32
                  continue
              with self.subTest(
                  forward=forward, adjoint=adjoint, method=method
              ):
                  f = _NeuralF(width=10, oscillate=False)
  
                  if forward:
                      forward_counter = 0
                      forward_accept_counter = 0
                      forward_reject_counter = 0
  
                      def callback_step(t0, y0, dt):
                          nonlocal forward_counter
                          forward_counter += 1
  
                      def callback_accept_step(t0, y0, dt):
                          nonlocal forward_accept_counter
                          forward_accept_counter += 1
  
                      def callback_reject_step(t0, y0, dt):
                          nonlocal forward_reject_counter
                          forward_reject_counter += 1
  
                      f.callback_step = callback_step
                      if method in ADAPTIVE_METHODS:
                          f.callback_accept_step = callback_accept_step
                          f.callback_reject_step = callback_reject_step
  
                  if adjoint:
                      adjoint_counter = 0
                      adjoint_accept_counter = 0
                      adjoint_reject_counter = 0
  
                      def callback_step_adjoint(t0, y0, dt):
                          nonlocal adjoint_counter
                          adjoint_counter += 1
  
                      def callback_accept_step_adjoint(t0, y0, dt):
                          nonlocal adjoint_accept_counter
                          adjoint_accept_counter += 1
  
                      def callback_reject_step_adjoint(t0, y0, dt):
                          nonlocal adjoint_reject_counter
                          adjoint_reject_counter += 1
  
                      f.callback_step_adjoint = callback_step_adjoint
                      if method in ADAPTIVE_METHODS:
                          f.callback_accept_step_adjoint = (
                              callback_accept_step_adjoint
                          )
                          f.callback_reject_step_adjoint = (
                              callback_reject_step_adjoint
                          )
  
                  x0 = torch.tensor([1.0, 2.0])
                  t = torch.tensor([0.0, 1.0])
  
                  if method in FIXED_METHODS:
                      kwargs = dict(options=dict(step_size=0.1))
                  elif method == "implicit_adams":
                      kwargs = dict(rtol=1e-3, atol=1e-4)
                  else:
                      kwargs = {}
                  xs = torchdiffeq.odeint_adjoint(
                      f, x0, t, method=method, **kwargs
                  )
  
                  if forward:
                      if method in FIXED_METHODS:
                          self.assertEqual(forward_counter, 10)
                      if method in ADAPTIVE_METHODS:
                          self.assertGreater(forward_counter, 0)
                          self.assertEqual(
                              forward_accept_counter
                              + forward_reject_counter,
                              forward_counter,
                          )
                  if adjoint:
                      xs.sum().backward()
                      if method in FIXED_METHODS:
                          self.assertEqual(adjoint_counter, 10)
                      if method in ADAPTIVE_METHODS:
                          self.assertGreater(adjoint_counter, 0)
                          self.assertEqual(
                              adjoint_accept_counter
                              + adjoint_reject_counter,
                              adjoint_counter,
                          )
  
imports: from functools import partial
  import unittest
  import warnings
  import torch
  import torchdiffeq
  from problems import construct_problem, PROBLEMS, DTYPES, DEVICES, METHODS, ADAPTIVE_METHODS, FIXED_METHODS, SCIPY_METHODS
label_desc: Use image node_id 2 for calling the TestCallbacks obj's underlying member method code with example usage: obj.test_steps() without return types