code: def __init__(self, cfg=None):
      self.extractor_mode: str = "default"  # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
      self.encoder_layers: int = (
          12  # num encoder layers in the transformer
      )
  
      self.encoder_embed_dim: int = 768  # encoder embedding dimension
      self.encoder_ffn_embed_dim: int = (
          3072  # encoder embedding dimension for FFN
      )
      self.encoder_attention_heads: int = (
          12  # num encoder attention heads
      )
      self.activation_fn: str = "gelu"  # activation function to use
  
      self.layer_norm_first: bool = (
          False  # apply layernorm first in the transformer
      )
      self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"  # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
      self.conv_bias: bool = False  # include bias in conv encoder
      self.feature_grad_mult: float = (
          1.0  # multiply feature extractor var grads by this
      )
  
      self.normalize: bool = False  # normalize input to have 0 mean and unit variance during training
  
      # dropouts
      self.dropout: float = (
          0.1  # dropout probability for the transformer
      )
      self.attention_dropout: float = (
          0.1  # dropout probability for attention weights
      )
      self.activation_dropout: float = (
          0.0  # dropout probability after activation in FFN
      )
      self.encoder_layerdrop: float = (
          0.0  # probability of dropping a tarnsformer layer
      )
      self.dropout_input: float = (
          0.0  # dropout to apply to the input (after feat extr)
      )
      self.dropout_features: float = (
          0.0  # dropout to apply to the features (after feat extr)
      )
  
      # masking
      self.mask_length: int = 10  # mask length
      self.mask_prob: float = (
          0.65  # probability of replacing a token with mask
      )
      self.mask_selection: str = "static"  # how to choose mask length
      self.mask_other: float = 0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
      self.no_mask_overlap: bool = (
          False  # whether to allow masks to overlap
      )
      self.mask_min_space: int = (
          1  # min space between spans (if no overlap is enabled)
      )
  
      # channel masking
      self.mask_channel_length: int = (
          10  # length of the mask for features (channels)
      )
      self.mask_channel_prob: float = (
          0.0  # probability of replacing a feature with 0
      )
      self.mask_channel_selection: str = (
          "static"  # how to choose mask length for channel masking
      )
      self.mask_channel_other: float = 0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
      self.no_mask_channel_overlap: bool = (
          False  # whether to allow channel masks to overlap
      )
      self.mask_channel_min_space: int = (
          1  # min space between spans (if no overlap is enabled)
      )
  
      # positional embeddings
      self.conv_pos: int = 128  # number of filters for convolutional positional embeddings
      self.conv_pos_groups: int = (
          16  # number of groups for convolutional positional embedding
      )
  
      # relative position embedding
      self.relative_position_embedding: bool = (
          False  # apply relative position embedding
      )
      self.num_buckets: int = (
          320  # number of buckets for relative position embedding
      )
      self.max_distance: int = (
          1280  # maximum distance for relative position embedding
      )
      self.gru_rel_pos: bool = (
          False  # apply gated relative position embedding
      )
  
      if cfg is not None:
          self.update(cfg)
  
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 WavLMConfig object  with example: obj = WavLMConfig(cfg)