code: def sample_dpm_fast(
      model,
      x,
      sigma_min,
      sigma_max,
      n,
      extra_args=None,
      callback=None,
      disable=None,
      eta=0.0,
      s_noise=1.0,
      noise_sampler=None,
  ):
      """DPM-Solver-Fast (fixed 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(total=n, 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,
                  }
              )
          return dpm_solver.dpm_solver_fast(
              x,
              dpm_solver.t(torch.tensor(sigma_max)),
              dpm_solver.t(torch.tensor(sigma_min)),
              n,
              eta,
              s_noise,
              noise_sampler,
          )
  
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 18 for calling a global function with example usage: sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args, callback, disable, eta, s_noise, noise_sampler) and returns: dpm_solver