code: def __init__(
      self,
      batch_size,
      nfft,
      window_length,
      window_step,
      center,
      layout="ft",
      num_threads=1,
      device_id=0,
      spectrogram_func=spectrogram_func_librosa,
  ):
      super(AudioSpectrogramPythonPipeline, self).__init__(
          batch_size,
          num_threads,
          device_id,
          seed=12345,
          exec_async=False,
          exec_pipelined=False,
      )
  
      self.input = ops.readers.File(device="cpu", files=audio_files)
      self.decode = ops.decoders.Audio(
          device="cpu", dtype=types.FLOAT, downmix=True
      )
  
      function = partial(
          spectrogram_func,
          nfft,
          window_length,
          window_step,
          None,
          center,
      )
      self.spectrogram = ops.PythonFunction(
          function=function, output_layouts=["ft"]
      )
      self.layout = layout
  
imports: from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.types
  import nvidia.dali
  import numpy
  from functools import partial
  from test_utils import get_files
  from test_utils import compare_pipelines
  from test_utils import RandomDataIterator
  from test_utils import ConstantDataIterator
  import librosa
  import math
label_desc: Use image node_id 1 to create a new AudioSpectrogramPythonPipeline object from inherited base classes: Pipeline with example: obj = AudioSpectrogramPythonPipeline(batch_size, nfft, window_length, window_step, center, layout, num_threads, device_id, spectrogram_func)