code: def RN50Pipeline():
      device = "mixed" if args.device == "gpu" else "cpu"
      jpegs, _ = fn.readers.file(file_root=args.images_dir)
      images = fn.decoders.image_random_crop(
          jpegs,
          device=device,
          output_type=types.RGB,
          hw_decoder_load=args.hw_load,
          preallocate_width_hint=args.width_hint,
          preallocate_height_hint=args.height_hint,
      )
      images = fn.resize(images, resize_x=224, resize_y=224)
      layout = types.NCHW
      out_type = types.FLOAT16
      coin_flip = fn.random.coin_flip(probability=0.5)
      images = fn.crop_mirror_normalize(
          images,
          dtype=out_type,
          output_layout=layout,
          crop=(224, 224),
          mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
          std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
          mirror=coin_flip,
      )
      return images
  
file_path: DALI/tools/hw_decoder_bench.py
imports: import argparse
  import nvidia.dali.fn
  import nvidia.dali.types
  import time
  from nvidia.dali.pipeline import pipeline_def
  import random
  import numpy
  import os
  from nvidia.dali.auto_aug import auto_augment
label_desc: Use image node_id 2 for calling a global function with example usage: RN50Pipeline() and returns: images