code: def conformer_wav2vec2_pretrain_large(
      extractor_input_dim: int = 64,
      extractor_output_dim: int = 256,
      encoder_projection_dropout: float = 0.0,
      mask_prob: float = 0.3,
      mask_length: int = 3,
      num_negatives: int = 100,
      cross_sample_negatives: int = 0,
  ) -> ConformerWav2Vec2PretrainModel:
      """Build Conformer Wav2Vec2 Model for pre-training with "large" architecture from
      *Conformer-Based Slef-Supervised Learning for Non-Speech Audio Tasks* :cite:`9746490`
  
      Args:
          extractor_input_dim (int, optional): Input dimension of the features. (Default: 64)
          extractor_output_dim (int, optional): Output dimension after feature extraction. (Default: 256)
          encoder_projection_dropout (float, optional):
              The dropout probability applied after the input feature is projected to
              ``embed_dim``. (Default: 0.0)
          mask_prob (float, optional):
              Probability for each token to be chosen as start of the span to be masked. (Default: 0.3)
          mask_length (int, optional):
              The lengths of the mask. (Default: 3)
          num_negatives (int, optional):
              Number of sampled negatives. (Default: 0)
          cross_sample_negatives (int, optional):
              Number of cross sampled negatives. (Default: 0)
  
      Returns:
          ConformerWav2Vec2PretrainModel:
              The resulting model.
      """
      return conformer_wav2vec2_pretrain_model(
          extractor_input_dim=extractor_input_dim,
          extractor_output_dim=extractor_output_dim,
          extractor_stride=4,
          encoder_embed_dim=768,
          encoder_projection_dropout=encoder_projection_dropout,
          encoder_num_layers=12,
          encoder_num_heads=12,
          encoder_ff_interm_features=1024,
          encoder_depthwise_conv_kernel_size=[31] + [15] * 11,
          encoder_dropout=0.1,
          encoder_convolution_first=True,
          encoder_use_group_norm=True,
          mask_prob=mask_prob,
          mask_selection="static",
          mask_other=0.0,
          mask_length=mask_length,
          no_mask_overlap=False,
          mask_min_space=0,
          mask_channel_prob=0,
          mask_channel_selection="static",
          mask_channel_other=0,
          mask_channel_length=10,
          no_mask_channel_overlap=False,
          mask_channel_min_space=1,
          num_negatives=num_negatives,
          cross_sample_negatives=cross_sample_negatives,
      )
  
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 10 for calling a global function with example usage: conformer_wav2vec2_pretrain_large(extractor_input_dim, extractor_output_dim, encoder_projection_dropout, mask_prob, mask_length, num_negatives, cross_sample_negatives) and returns: conformer_wav2vec2_pretrain_model