code: def next_step(self, model_output, timestep, sample):
      timestep, next_timestep = (
          min(
              timestep
              - self.scheduler.config.num_train_timesteps
              // self.num_inference_steps,
              999,
          ),
          timestep,
      )
      alpha_prod_t = (
          self.scheduler.alphas_cumprod[timestep]
          if timestep >= 0
          else self.scheduler.final_alpha_cumprod
      )
      alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
      beta_prod_t = 1 - alpha_prod_t
      next_original_sample = (
          sample - beta_prod_t**0.5 * model_output
      ) / alpha_prod_t**0.5
      next_sample_direction = (
          1 - alpha_prod_t_next
      ) ** 0.5 * model_output
      next_sample = (
          alpha_prod_t_next**0.5 * next_original_sample
          + next_sample_direction
      )
      return next_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 6 for calling the NullTextPipeline obj's underlying member method code with example usage: obj.next_step(model_output, timestep, sample) and returns: next_sample