code: def load_model(self, vision_tower_path=None):
      self.image_processor = CLIPImageProcessor_GIT.from_pretrained(
          self.vision_tower_name
      )
      if vision_tower_path is not None:
          (
              self.vision_tower,
              loading_info,
          ) = CLIPVisionModel.from_pretrained(
              vision_tower_path, output_loading_info=True
          )
          print("loading_info:", loading_info)
      else:
          self.vision_tower = CLIPVisionModel.from_pretrained(
              self.vision_tower_name
          )
      self.vision_tower.requires_grad_(False)
  
      self.is_loaded = True
  
imports: import torch
  import torch.nn
  from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
  import math
  import numpy
  from typing import Dict
  from transformers.image_utils import PILImageResampling, ChannelDimension
  from transformers.image_processing_utils import get_size_dict
  from transformers.image_transforms import get_resize_output_image_size, resize
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 2 for calling the CLIPVisionTower obj's underlying member method code with example usage: obj.load_model(vision_tower_path) without return types