code: def main(args, model_args):
      imgnet_classes = np.array(
          json.load(open("./LOC_synset_mapping.json", "r"))
      )
      try:
          model = available_models()[args.arch](**model_args.__dict__)
      except RuntimeError as e:
          print_in_box(
              "Error when creating model, did you forget to run checkpoint2model script?"
          )
          raise e
  
      if args.arch in [
          "efficientnet-quant-b0",
          "efficientnet-quant-b4",
      ]:
          check_quant_weight_correctness(
              model_args.pretrained_from_file, model
          )
  
      if not args.cpu:
          model = model.cuda()
      model.eval()
  
      input = load_jpeg_from_file(
          args.image, args.image_size, cuda=not args.cpu
      )
  
      with torch.no_grad(), autocast(enabled=args.precision == "AMP"):
          output = torch.nn.functional.softmax(model(input), dim=1)
  
      output = output.float().cpu().view(-1).numpy()
      top5 = np.argsort(output)[-5:][::-1]
  
      print(args.image)
      for c, v in zip(imgnet_classes[top5], output[top5]):
          print(f"{c}: {100*v:.1f}%")
  
file_path: DALI/docs/examples/use_cases/pytorch/efficientnet/classify.py
imports: from PIL import Image
  import argparse
  import numpy
  import json
  import torch
  from torch.cuda.amp import autocast
  import torch.backends.cudnn
  from image_classification import models
  import torchvision.transforms
  from image_classification.models import efficientnet_b0
label_desc: Use image node_id 5 for calling a global function with example usage: main(args, model_args) without return types