code: def decode(self, latent):
      latent = (1 / 0.18215) * latent
      with torch.no_grad():
          img = self.vae.decode(latent).sample
      img = (img / 2 + 0.5).clamp(0, 1)
      img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
      img = (img * 255).round().astype("uint8")
      return Image.fromarray(img[0])
  
file_path: diffusers/examples/community/magic_mix.py
imports: from typing import Union
  import torch
  from PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
label_desc: Use image node_id 3 for calling the MagicMixPipeline obj's underlying member method code with example usage: obj.decode(latent) and returns: Image