code: def _optimize_CLIP(self, original_img, pos_prompts, neg_prompts):
      vector = torch.randn_like(
          self.latent, requires_grad=True, device=self.device
      )
      optim = torch.optim.Adam([vector], lr=self.lr)
  
      for i in range(self.iterations):
          optim.zero_grad()
          transformed_img = self._add_vector(vector)
          processed_img = loop_post_process(transformed_img)
          clip_loss = self._get_CLIP_loss(
              pos_prompts, neg_prompts, processed_img
          )
          print("CLIP loss", clip_loss)
          if self.log:
              wandb.log({"CLIP Loss": clip_loss})
          clip_loss.backward(retain_graph=True)
          optim.step()
          if self.return_val == "image":
              yield custom_to_pil(transformed_img[0])
          else:
              yield vector
  
imports: import os
  from glob import glob
  import imageio
  import torch
  import torchvision
  import wandb
  from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
  from loaders import load_vqgan
  from PIL import Image
  from torch import nn
  from transformers import CLIPModel, CLIPTokenizerFast
  from utils import get_device, get_timestamp, show_pil
label_desc: Use image node_id 7 for calling the VQGAN_CLIP obj's underlying member method code with example usage: obj._optimize_CLIP(original_img, pos_prompts, neg_prompts) without return types