code: def init_qformer(
      cls,
      num_query_token,
      vision_width,
      cross_attention_freq=2,
      pretrain=True,
  ):
      encoder_config = BertConfig()
      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(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 copy
  import os
  import sys
  import contextlib
  import torch.utils.checkpoint
  from torch.nn import LayerNorm
  from torchvision import transforms
  from torchvision.transforms.functional import InterpolationMode
  from PIL import Image
  from .modeling_perceive_sampler import BertConfig, BertLMHeadModel
  from .modeling_vit import *
  from .modeling_InternLM import *
  from .modeling_utils import *
  from transformers.utils import logging
label_desc: Use image node_id 5 for calling the InternLMXComposerForCausalLM obj's underlying member method code with example usage: obj.init_qformer(cls, num_query_token, vision_width, cross_attention_freq, pretrain) and returns: Qformer,  query_tokens