code: def decode_latents(self, latents: torch.Tensor):
      latents = 1 / self.vae.config.scaling_factor * latents
      image = self.vae.decode(latents).sample
      image = (image / 2 + 0.5).clamp(0, 1)
      return image
  
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 8 for calling the EDICTPipeline obj's underlying member method code with example usage: obj.decode_latents(latents) and returns: image