code: def test_adjoint(self):
      f, y0, t_points, sol = construct_problem(
          device="cpu", ode="constant"
      )
  
      def event_fn(t, y):
          return torch.sum(y - sol[-1])
  
      t, y = torchdiffeq.odeint_adjoint(
          f, y0, t_points[0:2], event_fn=event_fn, method="dopri5"
      )
      y = y[-1]
      self.assertLess(rel_error(sol[-1], y), 1e-4)
      self.assertLess(rel_error(t_points[-1], t), 1e-4)
  
      # Make sure adjoint mode backward code can still be run.
      t.backward(retain_graph=True)
      y.sum().backward()
  
file_path: torchdiffeq/tests/event_tests.py
imports: import unittest
  import torch
  import torchdiffeq
  from problems import construct_problem, DTYPES, DEVICES, METHODS, FIXED_METHODS
label_desc: Use image node_id 2 for calling the TestEventHandling obj's underlying member method code with example usage: obj.test_adjoint() and returns: torch