code: def latent2image(self, latents):
      latents = 1 / 0.18215 * latents.detach()
      image = self.vae.decode(latents)["sample"].detach()
      image = self.processor.postprocess(image, output_type="pil")[0]
      return image
  
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 4 for calling the NullTextPipeline obj's underlying member method code with example usage: obj.latent2image(latents) and returns: image