code: def main(image_path):
      print("image_path:{}".format(image_path))
  
      # Define what device we are using
      logging.info(
          "CUDA Available: {}".format(torch.cuda.is_available())
      )
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
  
      orig = cv2.imread(image_path)[..., ::-1]
      orig = cv2.resize(orig, (224, 224))
      img = orig.copy().astype(np.float32)
  
      mean = [0.485, 0.456, 0.406]
      std = [0.229, 0.224, 0.225]
      img /= 255.0
      img = old_div((img - mean), std)
      img = img.transpose(2, 0, 1)
  
      img = np.expand_dims(img, axis=0)
  
      img = Variable(torch.from_numpy(img).to(device).float())
  
      # Initialize the network
      # model = models.resnet18(pretrained=True).to(device).eval()
      model = models.alexnet(pretrained=True).to(device).eval()
  
      label = np.argmax(model(img).data.cpu().numpy())
  
      print("label={}".format(label))
  
file_path: AdvBox/tutorials/imagenet_tools_pytorch.py
imports: from __future__ import division
  from __future__ import print_function
  from past.utils import old_div
  import logging
  import sys
  import torch
  import torchvision
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  import torch.utils.data.dataloader
  import torch.nn
  from torchvision import models
  import numpy
  import cv2
label_desc: Use image node_id 1 for calling a global function with example usage: main(image_path) without return types