code: def test_rnnt_data_pipeline():
      preemph_coeff = 0.97
      window_size = 0.02
      window_stride = 0.01
      window = "hann"
      nfeatures = 64
      n_fft = 512
      lowfreq = 0.0
      highfreq = None
      for device in ["cpu", "gpu"]:
          for frame_splicing_stack, frame_splicing_subsample in [
              (1, 1),
              (3, 2),
          ]:
              for normalize_type in ["per_feature", "all_features"]:
                  pad_amount = random.choice([0, 16])
                  yield (
                      _testimpl_rnnt_data_pipeline,
                      device,
                      pad_amount,
                      preemph_coeff,
                      window_size,
                      window_stride,
                      window,
                      nfeatures,
                      n_fft,
                      frame_splicing_stack,
                      frame_splicing_subsample,
                      lowfreq,
                      highfreq,
                      normalize_type,
                  )
  
imports: import nose_utils
  import nvidia.dali.fn
  from nvidia.dali import pipeline_def
  import nvidia.dali.types
  from test_utils import get_files, to_array
  import numpy
  import librosa
  import torch
  import math
  import random
  import os
  from nose.tools import nottest
label_desc: Use image node_id 16 for calling a global function with example usage: test_rnnt_data_pipeline() without return types