code: def test_rnnt_data_pipeline_throughput(
      pad_amount=0,
      preemph_coeff=0.97,
      window_size=0.02,
      window_stride=0.01,
      window="hann",
      nfeatures=64,
      n_fft=512,
      frame_splicing_stack=1,
      frame_splicing_subsample=1,
      speed_perturb=True,
      silence_trim=True,
      lowfreq=0.0,
      highfreq=None,
      normalize_type="per_feature",
      batch_size=32,
  ):
      sample_rate = npy_files_sr
      device = "gpu"
      pipe = rnnt_train_pipe(
          audio_files,
          sample_rate,
          pad_amount,
          preemph_coeff,
          window_size,
          window_stride,
          window,
          nfeatures,
          n_fft,
          frame_splicing_stack,
          frame_splicing_subsample,
          lowfreq,
          highfreq,
          normalize_type,
          speed_perturb,
          silence_trim,
          device,
          seed=42,
          batch_size=batch_size,
      )
      pipe.build()
  
      import time
      from test_utils import AverageMeter
  
      end = time.time()
      data_time = AverageMeter()
      iters = 1000
      for j in range(iters):
          pipe.run()
          data_time.update(time.time() - end)
          if j % 100 == 0:
              print(
                  f"run {j+1}/ {iters}, avg time: {data_time.avg} [s], "
                  f"worst time: {data_time.max_val} [s], "
                  f"speed: {batch_size / data_time.avg} [recordings/s]"
              )
          end = time.time()
  
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 17 for calling a global function with example usage: test_rnnt_data_pipeline_throughput(pad_amount, preemph_coeff, window_size, window_stride, window, nfeatures, n_fft, frame_splicing_stack, frame_splicing_subsample, speed_perturb, silence_trim, lowfreq, highfreq, normalize_type, batch_size) without return types