code: def mfcc_func(axis, dct_type, lifter, n_mfcc, norm, input_data):
      # Librosa works with frequency-major mel-spectrograms
      if axis == 1:
          input_data = np.transpose(input_data)
  
      in_shape = input_data.shape
      assert len(in_shape) == 2
  
      norm_str = "ortho" if norm else None
  
      out = librosa.feature.mfcc(
          S=input_data,
          n_mfcc=n_mfcc,
          dct_type=dct_type,
          norm=norm_str,
          lifter=lifter,
      )
  
      # Scipy DCT (used by Librosa) without normalization is scaled by a factor of 2 when comparing
      # with Wikipedia's formula
      if not norm:
          out = out / 2
  
      # Transpose back the output if necessary
      if axis == 1:
          out = np.transpose(out)
  
      return out
  
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 for calling a global function with example usage: mfcc_func(axis, dct_type, lifter, n_mfcc, norm, input_data) and returns: out