class_docstr: Wrapper class to run inference on Wav2Vec 2.0 model.
  Helps extract features for a given audio file.
code: def get_feats(self, file_path, channel_id=None):
      x = self.read_audio(file_path, channel_id)
      with torch.no_grad():
          source = torch.from_numpy(x).view(1, -1).float()
          if self.use_cuda:
              source = source.cuda()
          res = self.model(
              source=source,
              mask=False,
              features_only=True,
              layer=self.layer,
          )
          return res["layer_results"][self.layer][0].squeeze(1)
  
imports: import torch
  import fairseq
  import soundfile
label_desc: Use image node_id 3 for calling the Wav2VecFeatureReader obj's underlying member method code with example usage: obj.get_feats(file_path, channel_id) and returns: res