code: def problem(self, device):
      class Odefunc(torch.nn.Module):
          def __init__(self):
              super(Odefunc, self).__init__()
              self.A = torch.nn.Parameter(
                  torch.tensor([[-0.1, 2.0], [-2.0, -0.1]])
              )
              self.unused_module = torch.nn.Linear(2, 5)
  
          def forward(self, t, y):
              return torch.mm(y**3, self.A)
  
      y0 = torch.tensor([[2.0, 0.0]], device=device, requires_grad=True)
      t_points = torch.linspace(
          0.0, 25.0, 10, device=device, requires_grad=True
      )
      func = Odefunc().to(device)
      return func, y0, t_points
  
file_path: torchdiffeq/tests/gradient_tests.py
imports: import unittest
  import torch
  import torchdiffeq
  from problems import construct_problem, PROBLEMS, DEVICES, METHODS
label_desc: Use image node_id 1 for calling the TestCompareAdjointGradient obj's underlying member method code with example usage: obj.problem(device) and returns: func,  y0,  t_points, torch