code: def init_bert(
      self,
      vision_width,
      cross_attention_freq=2,
      truncation_side="right",
  ):
      # initialize BERT tokenizer
      tokenizer = BertTokenizer.from_pretrained(
          "bert-base-uncased", truncation_side=truncation_side
      )
      tokenizer.add_special_tokens({"bos_token": "[DEC]"})
      # initialize BERT
      encoder_config = BertConfig.from_pretrained("bert-base-uncased")
      encoder_config.encoder_width = vision_width
      # insert cross-attention layer every other block
      encoder_config.add_cross_attention = True
      encoder_config.cross_attention_freq = cross_attention_freq
      query_tokens = None
  
      if "qformer" in self.config.bert_type:
          mm_model = BertLMHeadModelQF.from_pretrained(
              "bert-base-uncased", config=encoder_config
          )
          query_tokens = nn.Parameter(
              torch.zeros(
                  1, self.config.num_query, encoder_config.hidden_size
              )
          )
          query_tokens.data.normal_(
              mean=0.0, std=encoder_config.initializer_range
          )
      elif "raw" in self.config.bert_type:
          encoder_config.is_decoder = True
          mm_model = BertLMHeadModelRaw.from_pretrained(
              "bert-base-uncased", config=encoder_config
          )
      else:
          raise NotImplementedError("BERT type not implemented...")
  
      mm_model.resize_token_embeddings(len(tokenizer))
      mm_model.cls = None
  
      if "layer" in self.config.bert_type:
          layer_num = int(self.config.bert_type.split(":")[-1])
          mm_model.bert.encoder.layer = mm_model.bert.encoder.layer[
              :layer_num
          ]
          print(f"Only use {layer_num} layers in BERT...")
  
      return tokenizer, mm_model, query_tokens
  
imports: from abc import ABC, abstractmethod
  import os
  import json
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import BertTokenizer
  from transformers.models.bert.modeling_bert import BertLMHeadModel
  from .qformer import BertConfig
  from .qformer import BertLMHeadModel
  from .multimodal_encoder.builder import build_vision_tower
  from .multimodal_projector.builder import build_vision_projector
  from llamavid.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
label_desc: Use image node_id 5 for calling the LLaMAVIDMetaModel obj's underlying member method code with example usage: obj.init_bert(vision_width, cross_attention_freq, truncation_side) and returns: tokenizer,  mm_model,  query_tokens