code: def invert(
      self,
      image_path: str,
      prompt: str,
      num_inner_steps=10,
      early_stop_epsilon=1e-6,
      num_inference_steps=50,
  ):
      self.num_inference_steps = num_inference_steps
      context = self.get_context(prompt)
      latent = self.image2latent(image_path)
      ddim_latents = self.ddim_inversion_loop(latent, context)
      if os.path.exists(image_path + ".pt"):
          uncond_embeddings = torch.load(image_path + ".pt")
      else:
          uncond_embeddings = self.null_optimization(
              ddim_latents, context, num_inner_steps, early_stop_epsilon
          )
          uncond_embeddings = torch.stack(uncond_embeddings, 0)
          torch.save(uncond_embeddings, image_path + ".pt")
      return ddim_latents[-1], uncond_embeddings
  
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 10 for calling the NullTextPipeline obj's underlying member method code with example usage: obj.invert(image_path, prompt, num_inner_steps, early_stop_epsilon, num_inference_steps) and returns: ddim_latents,  uncond_embeddings