code: def create_clip_img_transform(image_width):
      clip_mean = [0.48145466, 0.4578275, 0.40821073]
      clip_std = [0.26862954, 0.26130258, 0.27577711]
      transform = T.Compose(
          [
              # T.ToPILImage(),
              T.Resize(image_width),
              T.CenterCrop((image_width, image_width)),
              T.ToTensor(),
              T.Normalize(mean=clip_mean, std=clip_std),
          ]
      )
      return transform
  
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 5 for calling a global function with example usage: create_clip_img_transform(image_width) and returns: transform