code: def sample_euler(
      model,
      x,
      sigmas,
      extra_args=None,
      callback=None,
      disable=None,
      s_churn=0.0,
      s_tmin=0.0,
      s_tmax=float("inf"),
      s_noise=1.0,
  ):
      """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
      extra_args = {} if extra_args is None else extra_args
      s_in = x.new_ones([x.shape[0]])
      for i in trange(len(sigmas) - 1, disable=disable):
          gamma = (
              min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
              if s_tmin <= sigmas[i] <= s_tmax
              else 0.0
          )
          eps = torch.randn_like(x) * s_noise
          sigma_hat = sigmas[i] * (gamma + 1)
          if gamma > 0:
              x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
          denoised = model(x, sigma_hat * s_in, **extra_args)
          d = to_d(x, sigma_hat, denoised)
          if callback is not None:
              callback(
                  {
                      "x": x,
                      "i": i,
                      "sigma": sigmas[i],
                      "sigma_hat": sigma_hat,
                      "denoised": denoised,
                  }
              )
          dt = sigmas[i + 1] - sigma_hat
          # Euler method
          x = x + d * dt
      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 10 for calling a global function with example usage: sample_euler(model, x, sigmas, extra_args, callback, disable, s_churn, s_tmin, s_tmax, s_noise) and returns: x