code: def __init__(
      self,
      device,
      batch_size,
      iterator,
      nfft,
      window_length,
      window_step,
      window=None,
      center=None,
      num_threads=1,
      device_id=0,
  ):
      super(SpectrogramPipeline, self).__init__(
          batch_size, num_threads, device_id
      )
      self.device = device
      self.iterator = iterator
      self.inputs = ops.ExternalSource()
      window_fn = (
          window(window_length).tolist() if window is not None else None
      )
      self.fft = ops.Spectrogram(
          device=self.device,
          nfft=nfft,
          window_length=window_length,
          window_step=window_step,
          window_fn=window_fn,
          center_windows=center,
          power=2,
      )
      # randomly insert extra axis (channels?)
      self.r = np.random.randint(-1, 2)
  
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 SpectrogramPipeline object from inherited base classes: Pipeline with example: obj = SpectrogramPipeline(device, batch_size, iterator, nfft, window_length, window_step, window, center, num_threads, device_id)