code: def _get_denoised_and_pred_x0(
      self,
      model_output: Tensor,
      ts: Tensor,
      image: Tensor,
      quantize_denoised: bool,
      temperature: float,
      noise_dropout: float,
  ) -> Tuple[Tensor, Tensor]:
      # c_skip, c_out
      sigma_data = 0.5
      t_div = self._t / 0.1
      c_skip = sigma_data**2 / (t_div**2 + sigma_data**2)
      c_out = t_div / (t_div**2 + sigma_data**2) ** 0.5
      # denoise
      _, pred_x0 = self._get_pred_x0(
          model_output, ts, image, quantize_denoised
      )
      denoised = c_out * pred_x0 + c_skip * image
      return denoised, pred_x0
  
file_path: carefree-learn/cflearn/models/cv/diffusion/samplers/lcm.py
imports: from torch import Tensor
  from typing import Any
  from typing import Tuple
  from typing import Optional
  from .ddim import DDIMSampler
  from .ddim import IGetModelOutput
  from .ddim import IGetDenoised
  from .schema import ISampler
  from ..utils import cond_type
  from ..utils import get_timesteps
label_desc: Use image node_id 2 for calling the LCMSampler obj's underlying member method code with example usage: obj._get_denoised_and_pred_x0(model_output, ts, image, quantize_denoised, temperature, noise_dropout) and returns: denoised,  pred_x0