code: def sample_euler_ancestral(
      model,
      x,
      sigmas,
      extra_args=None,
      callback=None,
      disable=None,
      eta=1.0,
      s_noise=1.0,
      noise_sampler=None,
  ):
      """Ancestral sampling with Euler method steps."""
      extra_args = {} if extra_args is None else extra_args
      noise_sampler = (
          default_noise_sampler(x)
          if noise_sampler is None
          else noise_sampler
      )
      s_in = x.new_ones([x.shape[0]])
      for i in trange(len(sigmas) - 1, disable=disable):
          denoised = model(x, sigmas[i] * s_in, **extra_args)
          sigma_down, sigma_up = get_ancestral_step(
              sigmas[i], sigmas[i + 1], eta=eta
          )
          if callback is not None:
              callback(
                  {
                      "x": x,
                      "i": i,
                      "sigma": sigmas[i],
                      "sigma_hat": sigmas[i],
                      "denoised": denoised,
                  }
              )
          d = to_d(x, sigmas[i], denoised)
          # Euler method
          dt = sigma_down - sigmas[i]
          x = x + d * dt
          if sigmas[i + 1] > 0:
              x = (
                  x
                  + noise_sampler(sigmas[i], sigmas[i + 1])
                  * s_noise
                  * sigma_up
              )
      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 11 for calling a global function with example usage: sample_euler_ancestral(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) and returns: x