code: def sample_dpm_adaptive(
      model,
      x,
      sigma_min,
      sigma_max,
      extra_args=None,
      callback=None,
      disable=None,
      order=3,
      rtol=0.05,
      atol=0.0078,
      h_init=0.05,
      pcoeff=0.0,
      icoeff=1.0,
      dcoeff=0.0,
      accept_safety=0.81,
      eta=0.0,
      s_noise=1.0,
      noise_sampler=None,
      return_info=False,
  ):
      """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
      if sigma_min <= 0 or sigma_max <= 0:
          raise ValueError("sigma_min and sigma_max must not be 0")
      with tqdm(disable=disable) as pbar:
          dpm_solver = DPMSolver(
              model, extra_args, eps_callback=pbar.update
          )
          if callback is not None:
              dpm_solver.info_callback = lambda info: callback(
                  {
                      "sigma": dpm_solver.sigma(info["t"]),
                      "sigma_hat": dpm_solver.sigma(info["t_up"]),
                      **info,
                  }
              )
          x, info = dpm_solver.dpm_solver_adaptive(
              x,
              dpm_solver.t(torch.tensor(sigma_max)),
              dpm_solver.t(torch.tensor(sigma_min)),
              order,
              rtol,
              atol,
              h_init,
              pcoeff,
              icoeff,
              dcoeff,
              accept_safety,
              eta,
              s_noise,
              noise_sampler,
          )
      if return_info:
          return x, info
      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 19 for calling a global function with example usage: sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args, callback, disable, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler, return_info) and returns: x, x,  info