code: def __init__(self, cfg=None):
      self.input_patch_size: int = -1  # path size of patch embedding
      self.embed_dim: int = 512  # patch embedding dimension
      self.conv_bias: bool = False  # include bias in conv encoder
  
      self.encoder_layers: int = (
          12  # num encoder layers in the transformer
      )
      self.encoder_embed_dim: int = 768  # encoder embedding dimension
      self.encoder_ffn_embed_dim: int = (
          3072  # encoder embedding dimension for FFN
      )
      self.encoder_attention_heads: int = (
          12  # num encoder attention heads
      )
      self.activation_fn: str = "gelu"  # activation function to use
  
      self.layer_norm_first: bool = (
          False  # apply layernorm first in the transformer
      )
      self.deep_norm: bool = (
          False  # apply deep_norm first in the transformer
      )
  
      # dropouts
      self.dropout: float = (
          0.1  # dropout probability for the transformer
      )
      self.attention_dropout: float = (
          0.1  # dropout probability for attention weights
      )
      self.activation_dropout: float = (
          0.0  # dropout probability after activation in FFN
      )
      self.encoder_layerdrop: float = (
          0.0  # probability of dropping a tarnsformer layer
      )
      self.dropout_input: float = (
          0.0  # dropout to apply to the input (after feat extr)
      )
  
      # positional embeddings
      self.conv_pos: int = 128  # number of filters for convolutional positional embeddings
      self.conv_pos_groups: int = (
          16  # number of groups for convolutional positional embedding
      )
  
      # relative position embedding
      self.relative_position_embedding: bool = (
          False  # apply relative position embedding
      )
      self.num_buckets: int = (
          320  # number of buckets for relative position embedding
      )
      self.max_distance: int = (
          1280  # maximum distance for relative position embedding
      )
      self.gru_rel_pos: bool = (
          False  # apply gated relative position embedding
      )
  
      # quantizer
      self.quant_n: int = 1024  # codebook number in quantizer
      self.quant_dim: int = 256  # codebook dimension in quantizer
  
      if cfg is not None:
          self.update(cfg)
  
file_path: lavis/lavis/models/beats/Tokenizers.py
imports: import torch
  import torch.nn
  from torch.nn import LayerNorm
  import torchaudio.compliance.kaldi
  from lavis.models.beats.backbone import TransformerEncoder
  from lavis.models.beats.quantizer import NormEMAVectorQuantizer
  import logging
  from typing import Optional
label_desc: Use image node_id 1 to create a new TokenizersConfig object  with example: obj = TokenizersConfig(cfg)