code: def conformer_wav2vec2_pretrain_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: int,
      encoder_dropout: float,
      encoder_convolution_first: bool,
      encoder_use_group_norm: bool,
      mask_prob: float,
      mask_selection: str,
      mask_other: float,
      mask_length: int,
      no_mask_overlap: bool,
      mask_min_space: int,
      mask_channel_prob: float,
      mask_channel_selection: str,
      mask_channel_other: float,
      mask_channel_length: int,
      no_mask_channel_overlap: bool,
      mask_channel_min_space: int,
      num_negatives: int,
      cross_sample_negatives: int,
  ) -> ConformerWav2Vec2PretrainModel:
      """Build a custom Conformer Wav2Vec2 Model for pre-training
  
      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.
          mask_prob (float):
              Probability for each token to be chosen as start of the span to be masked.
          mask_selection (str)
              How to choose the mask length. Options: [``static``, ``uniform``, ``normal``, ``poisson``].
          mask_other (float):
              Secondary mask argument (used for more complex distributions).
          mask_length (int):
              The lengths of the mask.
          no_mask_overlap (bool):
              Whether to allow masks to overlap.
          mask_min_space (int):
              Minimum space between spans (if no overlap is enabled).
          mask_channel_prob: (float):
              The probability of replacing a feature with 0.
          mask_channel_selection (str):
              How to choose the mask length for channel masking.
              Options: [``static``, ``uniform``, ``normal``, ``poisson``].
          mask_channel_other (float):
              Secondary mask argument for channel masking (used for more complex distributions).
          mask_channel_length (int):
              Minimum space between spans (if no overlap is enabled) for channel masking.
          no_mask_channel_overlap (bool):
              Whether to allow channel masks to overlap.
          mask_channel_min_space (int):
              Minimum space between spans for channel masking (if no overlap is enabled).
          num_negatives (int):
              Number of negatives to sample.
          cross_sample_negatives (int):
              Number of cross sampled negatives.
  
      Returns:
          ConformerWav2Vec2PretrainModel:
              The resulting model.
      """
      wav2vec2 = 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,
      )
  
      mask_generator = components.MaskGenerator(
          extractor_output_dim,
          mask_prob,
          mask_selection,
          mask_other,
          mask_length,
          no_mask_overlap,
          mask_min_space,
          mask_channel_prob,
          mask_channel_selection,
          mask_channel_other,
          mask_channel_length,
          no_mask_channel_overlap,
          mask_channel_min_space,
      )
  
      negative_sampler = _get_conformer_negativer_sampler(
          extractor_output_dim,
          encoder_embed_dim,
          num_negatives,
          cross_sample_negatives,
      )
  
      return ConformerWav2Vec2PretrainModel(
          wav2vec2=wav2vec2,
          mask_generator=mask_generator,
          negative_sampler=negative_sampler,
      )
  
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 8 for calling a global function with example usage: conformer_wav2vec2_pretrain_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, mask_prob, mask_selection, mask_other, mask_length, no_mask_overlap, mask_min_space, mask_channel_prob, mask_channel_selection, mask_channel_other, mask_channel_length, no_mask_channel_overlap, mask_channel_min_space, num_negatives, cross_sample_negatives) and returns: ConformerWav2Vec2PretrainModel