code: def inference(weight, name, img):
      if img is None:
          img = np.random.randint(
              0, 255, size=(112, 112, 3), dtype=np.uint8
          )
      else:
          img = cv2.imread(img)
          img = cv2.resize(img, (112, 112))
  
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      img = np.transpose(img, (2, 0, 1))
      img = torch.from_numpy(img).unsqueeze(0).float()
      img.div_(255).sub_(0.5).div_(0.5)
      net = get_model(name, fp16=False)
      net.load_state_dict(torch.load(weight))
      net.eval()
      feat = net(img).numpy()
      print(feat)
  
imports: import argparse
  import cv2
  import numpy
  import torch
  from backbones import get_model
label_desc: Use image node_id 1 for calling a global function with example usage: inference(weight, name, img) without return types