code: def __init__(
      self,
      device,
      batch_size,
      iterator,
      axis=0,
      dct_type=2,
      lifter=1.0,
      n_mfcc=20,
      norm=None,
      num_threads=1,
      device_id=0,
  ):
      super(MFCCPipeline, self).__init__(
          batch_size, num_threads, device_id
      )
      self.device = device
      self.iterator = iterator
      self.inputs = ops.ExternalSource()
      self.mfcc = ops.MFCC(
          device=self.device,
          axis=axis,
          dct_type=dct_type,
          lifter=lifter,
          n_mfcc=n_mfcc,
          normalize=norm,
      )
  
imports: from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import numpy
  from functools import partial
  from test_utils import compare_pipelines
  from test_utils import RandomDataIterator
  import librosa
  from nose_utils import assert_raises
label_desc: Use image node_id 1 to create a new MFCCPipeline object from inherited base classes: Pipeline with example: obj = MFCCPipeline(device, batch_size, iterator, axis, dct_type, lifter, n_mfcc, norm, num_threads, device_id)