code: def denoise(self, model, x_t, sigmas, **model_kwargs):
      import torch.distributed as dist
  
      if not self.distillation:
          c_skip, c_out, c_in = [
              append_dims(x, x_t.ndim)
              for x in self.get_scalings(sigmas)
          ]
      else:
          c_skip, c_out, c_in = [
              append_dims(x, x_t.ndim)
              for x in self.get_scalings_for_boundary_condition(sigmas)
          ]
      rescaled_t = 1000 * 0.25 * th.log(sigmas + 1e-44)
      model_output = model(c_in * x_t, rescaled_t, **model_kwargs)
      denoised = c_out * model_output + c_skip * x_t
      return model_output, denoised
  
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 9 for calling the KarrasDenoiser obj's underlying member method code with example usage: obj.denoise(model, x_t, sigmas) and returns: model_output,  denoised