code: def ddim_inversion_loop(self, latent, context):
      self.scheduler.set_timesteps(self.num_inference_steps)
      _, cond_embeddings = context.chunk(2)
      all_latent = [latent]
      latent = latent.clone().detach()
      with torch.no_grad():
          for i in range(0, self.num_inference_steps):
              t = self.scheduler.timesteps[
                  len(self.scheduler.timesteps) - i - 1
              ]
              noise_pred = self.unet(
                  latent, t, encoder_hidden_states=cond_embeddings
              )["sample"]
              latent = self.next_step(noise_pred, t, latent)
              all_latent.append(latent)
      return all_latent
  
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 8 for calling the NullTextPipeline obj's underlying member method code with example usage: obj.ddim_inversion_loop(latent, context) and returns: all_latent