code: def prev_step(self, model_output, timestep, sample):
      prev_timestep = (
          timestep
          - self.scheduler.config.num_train_timesteps
          // self.scheduler.num_inference_steps
      )
      alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
      alpha_prod_t_prev = (
          self.scheduler.alphas_cumprod[prev_timestep]
          if prev_timestep >= 0
          else self.scheduler.final_alpha_cumprod
      )
      beta_prod_t = 1 - alpha_prod_t
      pred_original_sample = (
          sample - beta_prod_t**0.5 * model_output
      ) / alpha_prod_t**0.5
      pred_sample_direction = (
          1 - alpha_prod_t_prev
      ) ** 0.5 * model_output
      prev_sample = (
          alpha_prod_t_prev**0.5 * pred_original_sample
          + pred_sample_direction
      )
      return prev_sample
  
imports: import inspect
  import os
  import numpy
  import torch
  import torch.nn.functional
  from PIL import Image
  from torch.optim.adam import Adam
  from tqdm import tqdm
  from diffusers import StableDiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
label_desc: Use image node_id 5 for calling the NullTextPipeline obj's underlying member method code with example usage: obj.prev_step(model_output, timestep, sample) and returns: prev_sample