code: def test_determinism_simple(
      device, levy_area_approximation, return_U, return_A
  ):
      if device == gpu and not torch.cuda.is_available():
          pytest.skip(msg="CUDA not available.")
  
      ta, tb, bm = _setup(
          device, levy_area_approximation, (SMALL_BATCH_SIZE, D)
      )
      vals = [
          bm(ta, tb, return_U=return_U, return_A=return_A)
          for _ in range(REPS)
      ]
      for val in vals[1:]:
          if torch.is_tensor(val):
              val = (val,)
          if torch.is_tensor(vals[0]):
              val0 = (vals[0],)
          else:
              val0 = vals[0]
          for v, v0 in zip(val, val0):
              assert (v == v0).all()
  
file_path: torchsde/tests/test_brownian_interval.py
imports: import sys
  import math
  import numpy.random
  import torch
  from scipy.stats import kstest
  import pytest
  import torchsde
label_desc: Use image node_id 5 for calling a global function with example usage: test_determinism_simple(device, levy_area_approximation, return_U, return_A) without return types