code: def __init__(
      self,
      cfg: WavLMConfig,
  ) -> None:
      super().__init__()
      logger.info(f"WavLM Config: {cfg.__dict__}")
  
      self.cfg = cfg
      feature_enc_layers = eval(cfg.conv_feature_layers)
      self.embed = feature_enc_layers[-1][0]
  
      self.feature_extractor = ConvFeatureExtractionModel(
          conv_layers=feature_enc_layers,
          dropout=0.0,
          mode=cfg.extractor_mode,
          conv_bias=cfg.conv_bias,
      )
  
      self.post_extract_proj = (
          nn.Linear(self.embed, cfg.encoder_embed_dim)
          if self.embed != cfg.encoder_embed_dim
          else None
      )
  
      self.mask_prob = cfg.mask_prob
      self.mask_selection = cfg.mask_selection
      self.mask_other = cfg.mask_other
      self.mask_length = cfg.mask_length
      self.no_mask_overlap = cfg.no_mask_overlap
      self.mask_min_space = cfg.mask_min_space
  
      self.mask_channel_prob = cfg.mask_channel_prob
      self.mask_channel_selection = cfg.mask_channel_selection
      self.mask_channel_other = cfg.mask_channel_other
      self.mask_channel_length = cfg.mask_channel_length
      self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
      self.mask_channel_min_space = cfg.mask_channel_min_space
  
      self.dropout_input = nn.Dropout(cfg.dropout_input)
      self.dropout_features = nn.Dropout(cfg.dropout_features)
  
      self.feature_grad_mult = cfg.feature_grad_mult
  
      self.mask_emb = nn.Parameter(
          torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
      )
  
      self.encoder = TransformerEncoder(cfg)
      self.layer_norm = LayerNorm(self.embed)
  
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 1 to create a new WavLM object from inherited base classes: nn with example: obj = WavLM(cfg)