code: def main():
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      torch.set_default_dtype(torch.float64)
      utils.manual_seed()
  
      small_batch_size, large_batch_size, d = 16, 16384, 5
      t0, t1, steps, dt = 0.0, 2.0, 10, 1e-1
      ts = torch.linspace(t0, t1, steps=steps, device=device)
      dts = tuple(
          2**-i for i in range(1, 7)
      )  # For checking strong order.
      sde = NeuralScalar(d=d).to(device)
      methods = ("euler", "milstein", "milstein", "srk")
      options = (None, None, dict(grad_free=True), None)
      labels = ("euler", "milstein", "gradient-free milstein", "srk")
      img_dir = os.path.join(
          os.path.dirname(__file__), "plots", "ito_scalar"
      )
  
      y0 = torch.full(
          (small_batch_size, d), fill_value=0.1, device=device
      )
      bm = BrownianInterval(
          t0=t0,
          t1=t1,
          size=(small_batch_size, 1),
          dtype=y0.dtype,
          device=device,
          levy_area_approximation=LEVY_AREA_APPROXIMATIONS.space_time,
      )
      inspection.inspect_samples(
          y0, ts, dt, sde, bm, img_dir, methods, options, labels
      )
  
      y0 = torch.full(
          (large_batch_size, d), fill_value=0.1, device=device
      )
      bm = BrownianInterval(
          t0=t0,
          t1=t1,
          size=(large_batch_size, 1),
          dtype=y0.dtype,
          device=device,
          levy_area_approximation=LEVY_AREA_APPROXIMATIONS.space_time,
      )
      inspection.inspect_orders(
          y0, t0, t1, dts, sde, bm, img_dir, methods, options, labels
      )
  
imports: import os
  import torch
  from tests.problems import NeuralScalar
  from torchsde import BrownianInterval
  from torchsde.settings import LEVY_AREA_APPROXIMATIONS
  from .None import inspection
  from .None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types