code: def __init__(self, vision_tower, args, delay_load=False):
      super().__init__()
  
      self.is_loaded = False
  
      self.vision_tower_name = vision_tower
      self.select_layer = args.mm_vision_select_layer
      self.select_feature = getattr(
          args, "mm_vision_select_feature", "patch"
      )
  
      if not delay_load:
          self.load_model()
      else:
          self.cfg_only = CLIPVisionConfig.from_pretrained(
              self.vision_tower_name
          )
  
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 1 to create a new CLIPVisionTower object from inherited base classes: nn with example: obj = CLIPVisionTower(vision_tower, args, delay_load)