code: def EfficientnetInferencePipeline():
      images = fn.external_source(device="cpu", name=DALI_INPUT_NAME)
      images = fn.decoders.image(
          images,
          device="mixed" if args.device == "gpu" else "cpu",
          output_type=types.RGB,
          hw_decoder_load=args.hw_load,
      )
      images = fn.resize(images, resize_x=224, resize_y=224)
      images = fn.crop_mirror_normalize(
          images,
          dtype=types.FLOAT,
          output_layout="CHW",
          crop=(224, 224),
          mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
          std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
      )
      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 3 for calling a global function with example usage: EfficientnetInferencePipeline() and returns: images