code: def extract_features(
      self,
      features: Tensor,
      lengths: Optional[Tensor] = None,
      num_layers: Optional[int] = None,
  ) -> List[Tensor]:
      x, masks = self._preprocess(features, lengths)
      return self.transformer.get_intermediate_outputs(
          x, attention_mask=masks, num_layers=num_layers
      )
  
imports: import logging
  from typing import List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, Parameter
  from .wavlm_attention import WavLMSelfAttention
label_desc: Use image node_id 4 for calling the Encoder obj's underlying member method code with example usage: obj.extract_features(features, lengths, num_layers) and returns: self