code: def get_image(
      img_name="street_small.jpg",
      img_url=None,
      img_width=1280,
      img_height=736,
  ):
      def get_single_image_input(img_url, save_dir="/tmp"):
          img_name = img_url.split("/")[-1]
          img_path = os.path.join(save_dir, img_name)
          download(img_url, img_path)
          orig_img = Image.open(img_path)
          orig_img = orig_img.resize(
              (img_width, img_height), Image.LANCZOS
          )
          img = np.array(orig_img)[:, :, (0, 1, 2)].astype("uint8")
          return img, orig_img, img_path
  
      def get_transforms():
          tforms = T.Compose(
              [
                  T.ToTensor(),
                  T.Normalize(
                      mean=[0.485, 0.456, 0.406],
                      std=[0.229, 0.224, 0.225],
                  ),
              ]
          )
          return tforms
  
      if img_url is None:
          img_url = f"https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/detection/{img_name}"
      img, orig_img, img_path = get_single_image_input(img_url)
  
      tforms = get_transforms()
      input_data = tforms(img).unsqueeze(0)
      return input_data, orig_img
  
file_path: gluon-cv/scripts/pose/directpose/demo_directpose.py
imports: import os
  import argparse
  from gluoncv.torch import model_zoo
  from gluoncv.torch.engine.config import get_cfg_defaults
  from gluoncv.torch.utils.model_utils import download
  from gluoncv.torch.utils.visualizer import ColorMode, Visualizer
  from gluoncv.torch.data.registry.catalog import MetadataCatalog
  from PIL import Image
  from matplotlib import pyplot
  import numpy
  import torch
  import torchvision.transforms
label_desc: Use image node_id 2 for calling a global function with example usage: get_image(img_name, img_url, img_width, img_height) and returns: input_data,  orig_img, img,  orig_img,  img_path, tforms