code: def test_specialised_functions(sde_type, method):
      vector = torch.randn(m)
      fg = problems.FGSDE(sde_type, vector)
      f_and_g = problems.FAndGSDE(sde_type, vector)
      g_prod = problems.GProdSDE(sde_type, vector)
      f_and_g_prod = problems.FAndGProdSDE(sde_type, vector)
      f_and_g_with_g_prod1 = problems.FAndGGProdSDE1(sde_type, vector)
      f_and_g_with_g_prod2 = problems.FAndGGProdSDE2(sde_type, vector)
  
      y0 = torch.randn(batch_size, d)
  
      outs = []
      for sde in (
          fg,
          f_and_g,
          g_prod,
          f_and_g_prod,
          f_and_g_with_g_prod1,
          f_and_g_with_g_prod2,
      ):
          bm = torchsde.BrownianInterval(
              t0, t1, (batch_size, m), entropy=45678
          )
          outs.append(
              torchsde.sdeint(sde, y0, [t0, t1], dt=dt, bm=bm)[1]
          )
      for o in outs[1:]:
          # Equality of floating points, because we expect them to do everything exactly the same.
          assert o.shape == outs[0].shape
          assert (o == outs[0]).all()
  
file_path: torchsde/tests/test_sdeint.py
imports: import sys
  import pytest
  import torch
  import torchsde
  from torchsde.settings import NOISE_TYPES
  from .None import problems
label_desc: Use image node_id 4 for calling a global function with example usage: test_specialised_functions(sde_type, method) without return types