code: def rn50_pipeline_2(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, device="mixed", output_type=types.RGB
      )
      resized_images = fn.resize(
          images,
          device="gpu",
          interp_type=types.INTERP_LINEAR,
          resize_shorter=resize_uniform,
      )
      output = fn.crop_mirror_normalize(
          resized_images,
          device="gpu",
          dtype=types.FLOAT16,
          crop=(224, 224),
          mean=[128.0, 128.0, 128.0],
          std=[1.0, 1.0, 1.0],
          mirror=mirror,
          crop_pos_x=uniform[0],
          crop_pos_y=uniform[1],
      )
      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 2 for calling a global function with example usage: rn50_pipeline_2(data_path) and returns: output