class_docstr: Similar to @torchvision.transforms.Lambda, applies a user-defined lambda on the
  audio samples as a transform.
code: def __call__(self, data: Dict, *args, **kwargs) -> Dict:
      data["samples"]["audio"] = self.func(data["samples"]["audio"])
      return data
  
imports: import argparse
  import math
  import pathlib
  import random
  from typing import Callable, Dict, List, Optional, Union
  import torch
  import torchaudio
  from data.transforms import TRANSFORMATIONS_REGISTRY, BaseTransformation
  from data.transforms.audio_aux import mfccs
label_desc: Use image node_id 2 for calling the LambdaAudio obj's underlying member method code with example usage: obj.__call__(data) and returns: data