code: def inspect_samples(
      y0: Tensor,
      ts: Vector,
      dt: Scalar,
      sde: BaseSDE,
      bm: BaseBrownian,
      img_dir: str,
      methods: Tuple[str, ...],
      options: Optional[Tuple] = None,
      labels: Optional[Tuple[str, ...]] = None,
      vis_dim=0,
      dt_true: Optional[float] = 2**-14,
  ):
      if options is None:
          options = (None,) * len(methods)
      if labels is None:
          labels = methods
  
      solns = [
          sdeint(
              sde, y0, ts, bm, method=method, dt=dt, options=options_
          )
          for method, options_ in zip(methods, options)
      ]
  
      method_for_true = (
          "euler" if sde.sde_type == SDE_TYPES.ito else "midpoint"
      )
      true = sdeint(sde, y0, ts, bm, method=method_for_true, dt=dt_true)
  
      labels += ("true",)
      solns += [true]
  
      # (T, batch_size, d) -> (T, batch_size) -> (batch_size, T).
      solns = [soln[..., vis_dim].t() for soln in solns]
  
      for i, samples in enumerate(zip(*solns)):
          utils.swiss_knife_plotter(
              img_path=os.path.join(img_dir, f"{i}"),
              plots=[
                  {"x": ts, "y": sample, "label": label, "marker": "x"}
                  for sample, label in zip(samples, labels)
              ],
          )
  
file_path: torchsde/diagnostics/inspection.py
imports: import os
  import sys
  import torch
  import tqdm
  from torchsde import BaseBrownian, BaseSDE, sdeint
  from torchsde.settings import SDE_TYPES
  from torchsde.types import Tensor, Vector, Scalar, Tuple, Optional, Callable
  from .None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: inspect_samples(y0, ts, dt, sde, bm, img_dir, methods, options, labels, vis_dim, dt_true) without return types