code: def get_qformer(model):
      print("loading qformer")
  
      qformer = qformer_model_from_original_config()
      qformer_diffusers_checkpoint = (
          qformer_original_checkpoint_to_diffusers_checkpoint(model)
      )
  
      load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer)
  
      print("done loading qformer")
      return qformer
  
imports: import argparse
  import os
  import tempfile
  import torch
  from lavis.models import load_model_and_preprocess
  from transformers import CLIPTokenizer
  from transformers.models.blip_2.configuration_blip_2 import Blip2Config
  from diffusers import AutoencoderKL, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines import BlipDiffusionPipeline
  from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
  from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
  from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
label_desc: Use image node_id 10 for calling a global function with example usage: get_qformer(model) and returns: qformer