code: def test_odeint(self):
      for reverse in (False, True):
          for dtype in DTYPES:
              for device in DEVICES:
                  for method in METHODS:
                      if (
                          method in SCIPY_METHODS
                          and dtype == torch.complex64
                      ):
                          # scipy solvers don't support complex types.
                          continue
  
                      kwargs = dict()
                      # Have to increase tolerance for dopri8.
                      if method == "dopri8" and dtype == torch.float64:
                          kwargs = dict(rtol=1e-12, atol=1e-14)
                      if method == "dopri8" and dtype == torch.float32:
                          kwargs = dict(rtol=1e-7, atol=1e-7)
  
                      problems = (
                          PROBLEMS
                          if method in ADAPTIVE_METHODS
                          else ("constant",)
                      )
                      for ode in problems:
                          if method in ["adaptive_heun", "bosh3"]:
                              eps = 4e-3
                          elif ode == "linear":
                              eps = 2e-3
                          else:
                              eps = 3e-4
  
                          with self.subTest(
                              reverse=reverse,
                              dtype=dtype,
                              device=device,
                              ode=ode,
                              method=method,
                          ):
                              f, y0, t_points, sol = construct_problem(
                                  dtype=dtype,
                                  device=device,
                                  ode=ode,
                                  reverse=reverse,
                              )
                              y = torchdiffeq.odeint(
                                  f,
                                  y0,
                                  t_points,
                                  method=method,
                                  **kwargs,
                              )
                              self.assertLess(rel_error(sol, y), eps)
  
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 1 for calling the TestSolverError obj's underlying member method code with example usage: obj.test_odeint() without return types