code: def init_Qformer(
      cls, num_query_token, vision_width, cross_attention_freq=2
  ):
      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
      encoder_config.query_length = num_query_token
      Qformer = BertLMHeadModel.from_pretrained(
          "bert-base-uncased", config=encoder_config
      )
      query_tokens = nn.Parameter(
          torch.zeros(1, num_query_token, encoder_config.hidden_size)
      )
      query_tokens.data.normal_(
          mean=0.0, std=encoder_config.initializer_range
      )
      return Qformer, query_tokens
  
imports: import contextlib
  import logging
  import os
  import time
  import datetime
  import torch
  import torch.nn
  import torch.distributed
  import torch.nn.functional
  import lavis.common.dist_utils
  from lavis.common.dist_utils import download_cached_file
  from lavis.common.utils import is_url
  from lavis.common.logger import MetricLogger
  from lavis.models.base_model import BaseModel
  from lavis.models.blip2_models.Qformer import BertConfig, BertLMHeadModel
  from lavis.models.eva_vit import create_eva_vit_g
  from lavis.models.clip_vit import create_clip_vit_L
  from transformers import BertTokenizer
label_desc: Use image node_id 3 for calling the Blip2Base obj's underlying member method code with example usage: obj.init_Qformer(cls, num_query_token, vision_width, cross_attention_freq) and returns: Qformer,  query_tokens