code: def __init__(
      self, model_path: str, transform=transformTorchVision, device=None
  ):
      self.transform = transform
  
      if device == None:
          self.device = torch.device(
              "cuda:0" if torch.cuda.is_available() else "cpu"
          )
      else:
          self.device = device
  
      self.model_path = (
          model_path if model_path.endswith("/") else model_path + "/"
      )
  
      self.model = torch.load(
          self.model_path + "best_model.pth", map_location=self.device
      )
  
      self.config = json.load(
          open(self.model_path + "config.json", "r")
      )
  
      print("Model loaded!")
  
file_path: HugsVision/hugsvision/inference/TorchVisionClassifierInference.py
imports: import json
  import torch
  from torchvision import transforms
  from PIL import Image
label_desc: Use image node_id 1 to create a new TorchVisionClassifierInference object  with example: obj = TorchVisionClassifierInference(model_path, transform, device)