code: def conformer_wav2vec2_model(
      extractor_input_dim: int,
      extractor_output_dim: int,
      extractor_stride: int,
      encoder_embed_dim: int,
      encoder_projection_dropout: float,
      encoder_num_layers: int,
      encoder_num_heads: int,
      encoder_ff_interm_features: int,
      encoder_depthwise_conv_kernel_size: Union[int, List[int]],
      encoder_dropout: float,
      encoder_convolution_first: bool,
      encoder_use_group_norm: bool,
  ) -> Wav2Vec2Model:
      """Build a custom Conformer Wav2Vec2Model
  
      Args:
          extractor_input_dim (int): Input dimension of the features.
          extractor_output_dim (int): Output dimension after feature extraction.
          extractor_stride (int): Stride used in time reduction layer of feature extraction.
          encoder_embed_dim (int): The dimension of the embedding in the feature projection.
          encoder_projection_dropout (float):
              The dropout probability applied after the input feature is projected to ``embed_dim``
          encoder_num_layers (int): Number of Conformer layers in the encoder.
          encoder_num_heads (int): Number of heads in each Conformer layer.
          encoder_ff_interm_features (int):
              Hidden layer dimension of the feedforward network in each Conformer layer.
          encoder_depthwise_conv_kernel_size (int or List[int]):
              List of kernel sizes corresponding to each of the Conformer layers.
              If int is provided, all layers will have the same kernel size.
          encoder_dropout (float): Dropout probability in each Conformer layer.
          encoder_convolution_first (bool):
              Whether to apply the convolution module ahead of the attention module
              in each Conformer layer.
          encoder_use_group_norm (bool):
              Whether to use ``GroupNorm`` rather than ``BatchNorm1d`` in the convolution
              module in each Conformer layer.
  
      Returns:
          Wav2Vec2Model:
              The resulting wav2vec2 model with a conformer encoder.
      """
      feature_extractor = _get_conformer_feature_extractor(
          extractor_input_dim,
          extractor_output_dim,
          extractor_stride,
      )
  
      encoder = _get_conformer_encoder(
          in_features=extractor_output_dim,
          embed_dim=encoder_embed_dim,
          dropout_input=encoder_projection_dropout,
          num_layers=encoder_num_layers,
          num_heads=encoder_num_heads,
          ff_interm_features=encoder_ff_interm_features,
          depthwise_conv_kernel_size=encoder_depthwise_conv_kernel_size,
          dropout=encoder_dropout,
          convolution_first=encoder_convolution_first,
          use_group_norm=encoder_use_group_norm,
      )
  
      return Wav2Vec2Model(feature_extractor, encoder)
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, ModuleList
  from torchaudio.models import Wav2Vec2Model
  from torchaudio.models.conformer import ConformerLayer
  from torchaudio.models.rnnt import _TimeReduction
  from torchaudio.models.wav2vec2 import components
label_desc: Use image node_id 6 for calling a global function with example usage: conformer_wav2vec2_model(extractor_input_dim, extractor_output_dim, extractor_stride, encoder_embed_dim, encoder_projection_dropout, encoder_num_layers, encoder_num_heads, encoder_ff_interm_features, encoder_depthwise_conv_kernel_size, encoder_dropout, encoder_convolution_first, encoder_use_group_norm) and returns: Wav2Vec2Model