code: def create_clip_encoding(self, text=None, img=None, encoding=None):
      self.text = text
      self.img = img
      if encoding is not None:
          encoding = encoding.to(self.device)
      elif self.create_story:
          encoding = self.update_story_encoding(epoch=0, iteration=1)
      elif text is not None and img is not None:
          encoding = (
              self.create_text_encoding(text)
              + self.create_img_encoding(img)
          ) / 2
      elif text is not None:
          encoding = self.create_text_encoding(text)
      elif img is not None:
          encoding = self.create_img_encoding(img)
      return encoding
  
imports: import os
  import subprocess
  import sys
  import random
  from datetime import datetime
  from pathlib import Path
  import torch
  import torch.nn.functional
  from siren_pytorch import SirenNet, SirenWrapper
  from torch import nn
  from torch.cuda.amp import GradScaler, autocast
  from torch_optimizer import DiffGrad, AdamP
  import numpy
  from PIL import Image
  from imageio import imread, mimsave
  import torchvision.transforms
  from tqdm import trange, tqdm
  from .clip import load, tokenize
label_desc: Use image node_id 2 for calling the Imagine obj's underlying member method code with example usage: obj.create_clip_encoding(text, img, encoding) and returns: encoding