code: def create_img_encoding(self, img):
      if isinstance(img, str):
          img = Image.open(img)
      normed_img = self.clip_transform(img).unsqueeze(0).to(self.device)
      with torch.no_grad():
          img_encoding = self.perceptor.encode_image(
              normed_img
          ).detach()
      return img_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 4 for calling the Imagine obj's underlying member method code with example usage: obj.create_img_encoding(img) and returns: img_encoding