class_docstr: VGGish :cite:`45611` inference pipeline ported from
  `torchvggish <https://github.com/harritaylor/torchvggish>`__
  and `tensorflow-models <https://github.com/tensorflow/models/tree/master/research/audioset>`__.
  
  Example:
      >>> import torchaudio
      >>> from torchaudio.prototype.pipelines import VGGISH
      >>>
      >>> input_sr = VGGISH.sample_rate
      >>> input_proc = VGGISH.get_input_processor()
      >>> model = VGGISH.get_model()
      >>>
      >>> waveform, sr = torchaudio.load(
      >>>     "Chopin_Ballade_-1_In_G_Minor,_Op._23.mp3",
      >>> )
      >>> waveform = waveform.squeeze(0)
      >>> waveform = torchaudio.functional.resample(waveform, sr, input_sr)
      >>> mono_output = model(input_proc(waveform))
docstr: Constructs pre-trained VGGish model. Downloads and caches weights as necessary.
  
  Returns:
      VGGish: VGGish model with pre-trained weights loaded.
code: def get_model(self) -> VGGish:
      """Constructs pre-trained VGGish model. Downloads and caches weights as necessary.
  
      Returns:
          VGGish: VGGish model with pre-trained weights loaded.
      """
      model = self.VGGish()
      state_dict = self._state_dict_func()
      model.load_state_dict(state_dict)
      model.eval()
      return model
  
file_path: audio/src/torchaudio/prototype/pipelines/_vggish/_vggish_pipeline.py
imports: from dataclasses import dataclass
  from typing import Callable, Dict
  import torch
  import torchaudio
  from ._vggish_impl import _SAMPLE_RATE, VGGish, VGGishInputProcessor
label_desc: Use image node_id 2 for calling the VGGishBundle obj's underlying member method code with example usage: obj.get_model() and returns: model