class_docstr: Conformer Wav2Vec2 pre-train model for training from scratch.
  
  Note:
      To build the model, please use one of the factory functions,
      :py:func:`conformer_wav2vec2_base` or :py:func:`conformer_wav2vec2_large`
  
  Args:
      wav2vec2 (nn.Module):
          Conformer based Wav2Vec2 model, including feature extractor and conformer encoder components.
      mask_generator (nn.Module):
          Mask generator that generates the mask for masked prediction during training.
      negative_sampler (nn.Module):
          Negative sampler to apply after masking.
code: def __init__(
      self,
      wav2vec2: Wav2Vec2Model,
      mask_generator: Module,
      negative_sampler: Module,
  ):
      super().__init__()
      self.wav2vec2 = wav2vec2
      self.mask_generator = mask_generator
      self.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 1 to create a new ConformerWav2Vec2PretrainModel object from inherited base classes: Module with example: obj = ConformerWav2Vec2PretrainModel(wav2vec2, mask_generator, negative_sampler)