class_docstr: This is a dummy audio transform.
  
  It ignores actual audio data, and returns an empty tensor. It is useful when
  actual audio data is raw waveform and has a varying number of waveform samples
  which makes minibatch assembling impossible
class_docstr_tok: ["This","is","a","dummy","audio","transform",".","It","ignores","actual","audio","data",",","and","returns","an","empty","tensor",".","It","is","useful","when","actual","audio","data","is","raw","waveform","and","has","a","varying","number","of","waveform","samples","which","makes","minibatch","assembling","impossible"]
docstr: Callable function which applies the tranform to the input audio data.
  
  Args:
      audio: input audio data that will undergo the dummy transform
docstr_tok: ["Callable","function","which","applies","the","tranform","to","the","input","audio","data",".","Args",":","audio",":","input","audio","data","that","will","undergo","the","dummy","transform"]
code: def __call__(self, _audio):
      """Callable function which applies the tranform to the input audio data.
  
      Args:
          audio: input audio data that will undergo the dummy transform
  
      """
      return torch.zeros(0, 1, dtype=torch.float)
  
imports: import random
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torchvision.transforms
  import torchvision.transforms._transforms_video
  from .None import build_transforms, ClassyTransform, register_transform
  from .util import ApplyTransformToKey, ImagenetConstants, TupleToMapTransform
label_desc: Use image node_id 2 for calling the DummyAudioTransform obj's underlying member method code with example usage: obj.__call__(_audio) and returns: torch