code: def test_entropy_determinism(
      random_order, device, levy_area_approximation, return_U, return_A
  ):
      if device == gpu and not torch.cuda.is_available():
          pytest.skip(msg="CUDA not available.")
  
      t0, t1 = 0.0, 1.0
      entropy = 56789
      points1 = torch.rand(1000)
      points2 = torch.rand(1000)
      outs = []
  
      tol = 1e-6 if random_order else 0.0
  
      bm = torchsde.BrownianInterval(
          t0=t0,
          t1=t1,
          size=(),
          device=device,
          levy_area_approximation=levy_area_approximation,
          entropy=entropy,
          tol=tol,
          halfway_tree=random_order,
      )
      for point1, point2 in zip(points1, points2):
          point1, point2 = sorted([point1, point2])
          outs.append(
              bm(point1, point2, return_U=return_U, return_A=return_A)
          )
  
      bm = torchsde.BrownianInterval(
          t0=t0,
          t1=t1,
          size=(),
          device=device,
          levy_area_approximation=levy_area_approximation,
          entropy=entropy,
          tol=tol,
          halfway_tree=random_order,
      )
      if random_order:
          perm = torch.randperm(1000)
          points1 = points1[perm]
          points2 = points2[perm]
          outs = [outs[i.item()] for i in perm]
      for point1, point2, out in zip(points1, points2, outs):
          point1, point2 = sorted([point1, point2])
          out_ = bm(
              point1, point2, return_U=return_U, return_A=return_A
          )
  
          # Assert equal
          if torch.is_tensor(out):
              out = (out,)
          if torch.is_tensor(out_):
              out_ = (out_,)
          for outi, outi_ in zip(out, out_):
              if torch.is_tensor(outi):
                  assert (outi == outi_).all()
              else:
                  assert outi == outi_
  
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 10 for calling a global function with example usage: test_entropy_determinism(random_order, device, levy_area_approximation, return_U, return_A) without return types