code: def __init__(
      self,
      vision_tower,
      image_processor,
      args,
      use_checkpoint=False,
      drop_path_rate=0.0,
      delay_load=False,
      dtype=torch.float32,
  ):
      super().__init__()
  
      self.is_loaded = False
      self.use_checkpoint = use_checkpoint
      self.vision_tower_name = vision_tower
      self.image_processor_name = image_processor
      self.drop_path_rate = drop_path_rate
      self.patch_size = 14
      self.out_channel = 1408
      if not delay_load:
          self.load_model()
  
      self.vision_config = CLIPVisionConfig.from_pretrained(
          image_processor
      )
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from transformers import CLIPImageProcessor, CLIPVisionConfig
label_desc: Use image node_id 1 to create a new EVAVisionTowerLavis object from inherited base classes: nn with example: obj = EVAVisionTowerLavis(vision_tower, image_processor, args, use_checkpoint, drop_path_rate, delay_load, dtype)