code: def sample_dpmpp_sde(
      model,
      x,
      sigmas,
      extra_args=None,
      callback=None,
      disable=None,
      eta=1.0,
      s_noise=1.0,
      noise_sampler=None,
      r=1 / 2,
  ):
      """DPM-Solver++ (stochastic)."""
      sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
      noise_sampler = (
          BrownianTreeNoiseSampler(x, sigma_min, sigma_max)
          if noise_sampler is None
          else noise_sampler
      )
      extra_args = {} if extra_args is None else extra_args
      s_in = x.new_ones([x.shape[0]])
      sigma_fn = lambda t: t.neg().exp()
      t_fn = lambda sigma: sigma.log().neg()
  
      for i in trange(len(sigmas) - 1, disable=disable):
          denoised = model(x, sigmas[i] * s_in, **extra_args)
          if callback is not None:
              callback(
                  {
                      "x": x,
                      "i": i,
                      "sigma": sigmas[i],
                      "sigma_hat": sigmas[i],
                      "denoised": denoised,
                  }
              )
          if sigmas[i + 1] == 0:
              # Euler method
              d = to_d(x, sigmas[i], denoised)
              dt = sigmas[i + 1] - sigmas[i]
              x = x + d * dt
          else:
              # DPM-Solver++
              t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
              h = t_next - t
              s = t + h * r
              fac = 1 / (2 * r)
  
              # Step 1
              sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
              s_ = t_fn(sd)
              x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (
                  t - s_
              ).expm1() * denoised
              x_2 = (
                  x_2
                  + noise_sampler(sigma_fn(t), sigma_fn(s))
                  * s_noise
                  * su
              )
              denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
  
              # Step 2
              sd, su = get_ancestral_step(
                  sigma_fn(t), sigma_fn(t_next), eta
              )
              t_next_ = t_fn(sd)
              denoised_d = (1 - fac) * denoised + fac * denoised_2
              x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (
                  t - t_next_
              ).expm1() * denoised_d
              x = (
                  x
                  + noise_sampler(sigma_fn(t), sigma_fn(t_next))
                  * s_noise
                  * su
              )
      return x
  
imports: import math
  import torch
  import torchsde
  from scipy import integrate
  from torch import nn
  from torchdiffeq import odeint
  from tqdm.auto import tqdm, trange
label_desc: Use image node_id 21 for calling a global function with example usage: sample_dpmpp_sde(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler, r) and returns: x