code: def add_parser_arguments(parser):
      model_names = available_models().keys()
      parser.add_argument("--image-size", default="224", type=int)
      parser.add_argument(
          "--arch",
          "-a",
          metavar="ARCH",
          default="efficientnet-b0",
          choices=model_names,
          help="model architecture: "
          + " | ".join(model_names)
          + " (default: efficientnet-b0)",
      )
      parser.add_argument(
          "--precision",
          metavar="PREC",
          default="AMP",
          choices=["AMP", "FP32"],
      )
      parser.add_argument(
          "--cpu", action="store_true", help="perform inference on CPU"
      )
      parser.add_argument(
          "--image", metavar="<path>", help="path to classified image"
      )
  
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 2 for calling a global function with example usage: add_parser_arguments(parser) without return types