code: def wavlm_model(
      extractor_mode: str,
      extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]],
      extractor_conv_bias: bool,
      encoder_embed_dim: int,
      encoder_projection_dropout: float,
      encoder_pos_conv_kernel: int,
      encoder_pos_conv_groups: int,
      encoder_num_layers: int,
      encoder_num_heads: int,
      encoder_num_buckets: int,
      encoder_max_distance: int,
      encoder_attention_dropout: float,
      encoder_ff_interm_features: int,
      encoder_ff_interm_dropout: float,
      encoder_dropout: float,
      encoder_layer_norm_first: bool,
      encoder_layer_drop: float,
      aux_num_out: Optional[int],
  ) -> Wav2Vec2Model:
      """Builds custom WaveLM model :cite:`chen2022wavlm`. The architecture is compatible
      with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output object is
      :class:`~torchaudio.models.Wav2Vec2Model`. Most of the arguments have the same meaning
      as in :py:func:`~torchaudio.models.wav2vec2_model` so please refer there for documentation.
  
      Args:
          extractor_mode (str): Operation mode of feature extractor.
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          extractor_conv_layer_config (list of integer tuples or None):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          extractor_conv_bias (bool):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_embed_dim (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_projection_dropout (float):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_pos_conv_kernel (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_pos_conv_groups (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_num_layers (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_num_heads (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_num_buckets (int):
              Number of buckets for relative position embedding.
          encoder_max_distance (int):
              Maximum distance for relative position embedding.
  
          encoder_attention_dropout (float):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_ff_interm_features (int):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_ff_interm_dropout (float):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_dropout (float):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_layer_norm_first (bool):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          encoder_layer_drop (float):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
          aux_num_out (int or None):
              See :py:func:`~torchaudio.models.wav2vec2_model`.
  
      Returns:
          Wav2Vec2Model:
              The resulting model.
      """
      if extractor_conv_layer_config is None:
          extractor_conv_layer_config = (
              [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
          )
  
      feature_extractor = components._get_feature_extractor(
          extractor_mode,
          extractor_conv_layer_config,
          extractor_conv_bias,
      )
      encoder = components._get_wavlm_encoder(
          in_features=extractor_conv_layer_config[-1][0],
          embed_dim=encoder_embed_dim,
          dropout_input=encoder_projection_dropout,
          pos_conv_kernel=encoder_pos_conv_kernel,
          pos_conv_groups=encoder_pos_conv_groups,
          num_layers=encoder_num_layers,
          num_heads=encoder_num_heads,
          num_buckets=encoder_num_buckets,
          max_distance=encoder_max_distance,
          attention_dropout=encoder_attention_dropout,
          ff_interm_features=encoder_ff_interm_features,
          ff_interm_dropout=encoder_ff_interm_dropout,
          dropout=encoder_dropout,
          layer_norm_first=encoder_layer_norm_first,
          layer_drop=encoder_layer_drop,
      )
      aux = None
      if aux_num_out is not None:
          aux = torch.nn.Linear(
              in_features=encoder_embed_dim, out_features=aux_num_out
          )
      return Wav2Vec2Model(feature_extractor, encoder, aux)
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Module
  from .None import components
label_desc: Use image node_id 13 for calling a global function with example usage: wavlm_model(extractor_mode, extractor_conv_layer_config, extractor_conv_bias, encoder_embed_dim, encoder_projection_dropout, encoder_pos_conv_kernel, encoder_pos_conv_groups, encoder_num_layers, encoder_num_heads, encoder_num_buckets, encoder_max_distance, encoder_attention_dropout, encoder_ff_interm_features, encoder_ff_interm_dropout, encoder_dropout, encoder_layer_norm_first, encoder_layer_drop, aux_num_out) and returns: Wav2Vec2Model