code: def extract_features(
      self,
      fbank: torch.Tensor,
      padding_mask: Optional[torch.Tensor] = None,
      fbank_mean: float = 15.41663,
      fbank_std: float = 6.55582,
  ):
      ## NOTE: preprocessing is done separately in lavis.processsors.audio_processors.BeatsAudioProcessor
      # fbank = self.preprocess(source, fbank_mean=fbank_mean, fbank_std=fbank_std)
  
      if padding_mask is not None:
          padding_mask = self.forward_padding_mask(fbank, padding_mask)
  
      fbank = fbank.unsqueeze(1)
      features = self.patch_embedding(fbank)
      features = features.reshape(
          features.shape[0], features.shape[1], -1
      )
      features = features.transpose(1, 2)
      features = self.layer_norm(features)
  
      if padding_mask is not None:
          padding_mask = self.forward_padding_mask(
              features, padding_mask
          )
  
      if self.post_extract_proj is not None:
          features = self.post_extract_proj(features)
  
      x = self.dropout_input(features)
  
      x, layer_results = self.encoder(
          x,
          padding_mask=padding_mask,
      )
  
      if self.predictor is not None:
          x = self.predictor_dropout(x)
          logits = self.predictor(x)
  
          if padding_mask is not None and padding_mask.any():
              logits[padding_mask] = 0
              logits = logits.sum(dim=1)
              logits = logits / (~padding_mask).sum(dim=1).unsqueeze(
                  -1
              ).expand_as(logits)
          else:
              logits = logits.mean(dim=1)
  
          lprobs = torch.sigmoid(logits)
  
          return lprobs, padding_mask
      else:
          return x, padding_mask
  
file_path: lavis/lavis/models/beats/BEATs.py
imports: import torch
  import torch.nn
  from torch.nn import LayerNorm
  import torchaudio.compliance.kaldi
  from lavis.models.beats.backbone import TransformerEncoder
  import logging
  from typing import Optional
label_desc: Use image node_id 4 for calling the BEATs obj's underlying member method code with example usage: obj.extract_features(fbank, padding_mask, fbank_mean, fbank_std) and returns: lprobs,  padding_mask, x,  padding_mask