code: def rn50_pipeline(data_path):
      uniform = fn.random.uniform(range=(0.0, 1.0), shape=2)
      resize_uniform = fn.random.uniform(range=(256.0, 480.0))
      mirror = fn.random.coin_flip(probability=0.5)
      jpegs, _ = fn.readers.file(file_root=data_path)
      images = fn.decoders.image(jpegs, output_type=types.RGB)
      resized_images = fn.fast_resize_crop_mirror(
          images,
          crop=(224, 224),
          crop_pos_x=uniform[0],
          crop_pos_y=uniform[1],
          mirror=mirror,
          resize_shorter=resize_uniform,
      )
      output = fn.crop_mirror_normalize(
          resized_images.gpu(),
          device="gpu",
          dtype=types.FLOAT16,
          mean=[128.0, 128.0, 128.0],
          std=[1.0, 1.0, 1.0],
      )
      return output
  
file_path: DALI/dali/test/python/test_pipeline_debug_resnet50.py
imports: import argparse
  import numpy
  import nvidia.dali.fn
  import nvidia.dali.types
  import os
  from itertools import product
  from nvidia.dali.pipeline.experimental import pipeline_def
  from time import time
  from test_utils import get_dali_extra_path
label_desc: Use image node_id 1 for calling a global function with example usage: rn50_pipeline(data_path) and returns: output