code: def test_sdeint_dependencies(sde_cls, method, adaptive, device):
      """This test uses diagonal noise. This checks if the solvers still work when some of the functions don't depend on
      the states/params and when some states/params don't require gradients.
      """
  
      if method == "milstein_grad_free":
          method = "milstein"
          options = dict(grad_free=True)
      else:
          options = dict()
  
      sde = sde_cls(d=d).to(device)
      bm = None
      logqp = False
      should_fail = False
      _test_sdeint(
          sde, bm, method, adaptive, logqp, device, should_fail, options
      )
  
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 6 for calling a global function with example usage: test_sdeint_dependencies(sde_cls, method, adaptive, device) without return types