code: def karras_sample(
      diffusion,
      model,
      shape,
      steps,
      clip_denoised=True,
      progress=False,
      callback=None,
      model_kwargs=None,
      device=None,
      sigma_min=0.002,
      sigma_max=80,  # higher for highres?
      rho=7.0,
      sampler="heun",
      s_churn=0.0,
      s_tmin=0.0,
      s_tmax=float("inf"),
      s_noise=1.0,
      generator=None,
      ts=None,
  ):
      if generator is None:
          generator = get_generator("dummy")
  
      if sampler == "progdist":
          sigmas = get_sigmas_karras(
              steps + 1, sigma_min, sigma_max, rho, device=device
          )
      else:
          sigmas = get_sigmas_karras(
              steps, sigma_min, sigma_max, rho, device=device
          )
  
      x_T = generator.randn(*shape, device=device) * sigma_max
  
      sample_fn = {
          "heun": sample_heun,
          "dpm": sample_dpm,
          "ancestral": sample_euler_ancestral,
          "onestep": sample_onestep,
          "progdist": sample_progdist,
          "euler": sample_euler,
          "multistep": stochastic_iterative_sampler,
      }[sampler]
  
      if sampler in ["heun", "dpm"]:
          sampler_args = dict(
              s_churn=s_churn,
              s_tmin=s_tmin,
              s_tmax=s_tmax,
              s_noise=s_noise,
          )
      elif sampler == "multistep":
          sampler_args = dict(
              ts=ts,
              t_min=sigma_min,
              t_max=sigma_max,
              rho=diffusion.rho,
              steps=steps,
          )
      else:
          sampler_args = {}
  
      def denoiser(x_t, sigma):
          _, denoised = diffusion.denoise(
              model, x_t, sigma, **model_kwargs
          )
          if clip_denoised:
              denoised = denoised.clamp(-1, 1)
          return denoised
  
      x_0 = sample_fn(
          denoiser,
          x_T,
          sigmas,
          generator,
          progress=progress,
          callback=callback,
          **sampler_args,
      )
      return x_0.clamp(-1, 1)
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from piq import LPIPS
  from torchvision.transforms import RandomCrop
  from .None import dist_util
  from .nn import mean_flat, append_dims, append_zero
  from .random_util import get_generator
label_desc: Use image node_id 2 for calling a global function with example usage: karras_sample(diffusion, model, shape, steps, clip_denoised, progress, callback, model_kwargs, device, sigma_min, sigma_max, rho, sampler, s_churn, s_tmin, s_tmax, s_noise, generator, ts) and returns: x_0, denoised