code: def __init__(self, weightfile, use_cuda=True):
      if use_cuda and not torch.cuda.is_available():
          raise Exception(
              "Selected use_cuda=True, but cuda is not available to Pytorch"
          )
      self.use_cuda = use_cuda
      self.model = Yolov4(
          yolov4conv137weight=None, n_classes=80, inference=True
      )
      pretrained_dict = torch.load(
          weightfile,
          map_location=torch.device("cuda" if use_cuda else "cpu"),
      )
      self.model.load_state_dict(pretrained_dict)
  
      if self.use_cuda:
          self.model.cuda()
  
file_path: norfair/demos/yolov4/src/demo.py
imports: import argparse
  import cv2
  import numpy
  import torch
  from models import Yolov4
  from tool.torch_utils import do_detect
  from tool.utils import load_class_names, plot_boxes_cv2
  import norfair
  from norfair import Detection, Tracker, Video
label_desc: Use image node_id 1 to create a new YOLO object  with example: obj = YOLO(weightfile, use_cuda)