code: def extract_features(
      self,
      source: torch.Tensor,
      padding_mask: Optional[torch.Tensor] = None,
      mask: bool = False,
      ret_conv: bool = False,
      output_layer: Optional[int] = None,
      ret_layer_results: bool = False,
  ):
      if self.feature_grad_mult > 0:
          features = self.feature_extractor(source)
          if self.feature_grad_mult != 1.0:
              features = GradMultiply.apply(
                  features, self.feature_grad_mult
              )
      else:
          with torch.no_grad():
              features = self.feature_extractor(source)
  
      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)
  
      features = self.dropout_input(features)
  
      if mask:
          x, mask_indices = self.apply_mask(features, padding_mask)
      else:
          x = features
  
      # feature: (B, T, D), float
      # target: (B, T), long
      # x: (B, T, D), float
      # padding_mask: (B, T), bool
      # mask_indices: (B, T), bool
      x, layer_results = self.encoder(
          x,
          padding_mask=padding_mask,
          layer=None if output_layer is None else output_layer - 1,
      )
  
      res = {
          "x": x,
          "padding_mask": padding_mask,
          "features": features,
          "layer_results": layer_results,
      }
  
      feature = res["features"] if ret_conv else res["x"]
      if ret_layer_results:
          feature = (feature, res["layer_results"])
      return feature, res["padding_mask"]
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from TTS.vc.modules.freevc.wavlm.modules import Fp32GroupNorm, Fp32LayerNorm, GLU_Linear, GradMultiply, MultiheadAttention, SamePad, TransposeLast, get_activation_fn, init_bert_params
label_desc: Use image node_id 4 for calling the WavLM obj's underlying member method code with example usage: obj.extract_features(source, padding_mask, mask, ret_conv, output_layer, ret_layer_results) and returns: feature,  res