code: def transform_image(image_bytes):
      my_transforms = transforms.Compose(
          [
              transforms.Resize(255),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
              ),
          ]
      )
      image = Image.open(io.BytesIO(image_bytes))
      if image.mode != "RGB":
          raise ValueError("input file does not RGB image...")
      return my_transforms(image).unsqueeze(0).to(device)
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/pytorch_flask_service/main.py
imports: import os
  import io
  import json
  import torch
  import torchvision.transforms
  from PIL import Image
  from flask import Flask, jsonify, request, render_template
  from flask_cors import CORS
  from model import MobileNetV2
label_desc: Use image node_id 1 for calling a global function with example usage: transform_image(image_bytes) and returns: my_transforms