code: def denoise_step(
      self,
      base: torch.Tensor,
      model_input: torch.Tensor,
      model_output: torch.Tensor,
      timestep: torch.Tensor,
  ):
      prev_timestep = (
          timestep
          - self.scheduler.config.num_train_timesteps
          / self.scheduler.num_inference_steps
      )
  
      alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)
      alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(
          prev_timestep
      )
  
      a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
      b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
      next_model_input = a_t * base + b_t * model_output
  
      return model_input, next_model_input.to(base.dtype)
  
file_path: llmga/llmga/diffusers/examples/community/edict_pipeline.py
imports: from typing import Optional
  import torch
  from PIL import Image
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import deprecate
label_desc: Use image node_id 7 for calling the EDICTPipeline obj's underlying member method code with example usage: obj.denoise_step(base, model_input, model_output, timestep) and returns: model_input,  next_model_input