code: def sample_dpmpp_2m(
      model, x, sigmas, extra_args=None, callback=None, disable=None
  ):
      """DPM-Solver++(2M)."""
      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()
      old_denoised = None
  
      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,
                  }
              )
          t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
          h = t_next - t
          if old_denoised is None or sigmas[i + 1] == 0:
              x = (sigma_fn(t_next) / sigma_fn(t)) * x - (
                  -h
              ).expm1() * denoised
          else:
              h_last = t - t_fn(sigmas[i - 1])
              r = h_last / h
              denoised_d = (1 + 1 / (2 * r)) * denoised - (
                  1 / (2 * r)
              ) * old_denoised
              x = (sigma_fn(t_next) / sigma_fn(t)) * x - (
                  -h
              ).expm1() * denoised_d
          old_denoised = denoised
      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 22 for calling a global function with example usage: sample_dpmpp_2m(model, x, sigmas, extra_args, callback, disable) and returns: x