code: def conformer_wav2vec2_base(
      extractor_input_dim: int = 64,
      extractor_output_dim: int = 256,
      encoder_projection_dropout: float = 0.0,
  ) -> Wav2Vec2Model:
      """
      Build Conformer Wav2Vec2 Model with "small" architecture from
      *Conformer-Based Slef-Supervised Learning for Non-Speech Audio Tasks* :cite:`9746490`
  
      Args:
          extractor_input_dim (int, optional): Input dimension of feature extractor. (Default: 64)
          extractor_output_dim (int, optional): Output dimension of feature extractor. (Default: 256)
          encoder_projection_dropout (float, optional):
              Dropout probability applied after feature projection. (Default: 0.0)
  
      Returns:
          Wav2Vec2Model:
              The resulting wav2vec2 model with a conformer encoder and ``base`` configuration.
      """
      return conformer_wav2vec2_model(
          extractor_input_dim=extractor_input_dim,
          extractor_output_dim=extractor_output_dim,
          extractor_stride=4,
          encoder_embed_dim=256,
          encoder_projection_dropout=encoder_projection_dropout,
          encoder_num_layers=12,
          encoder_num_heads=8,
          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,
      )
  
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 7 for calling a global function with example usage: conformer_wav2vec2_base(extractor_input_dim, extractor_output_dim, encoder_projection_dropout) and returns: conformer_wav2vec2_model