code: def _make_vit_b16_backbone(
      model,
      features=[96, 192, 384, 768],
      size=[384, 384],
      hooks=[2, 5, 8, 11],
      vit_features=768,
      use_readout="ignore",
      start_index=1,
  ):
      pretrained = nn.Module()
  
      pretrained.model = model
      pretrained.model.blocks[hooks[0]].register_forward_hook(
          get_activation("1")
      )
      pretrained.model.blocks[hooks[1]].register_forward_hook(
          get_activation("2")
      )
      pretrained.model.blocks[hooks[2]].register_forward_hook(
          get_activation("3")
      )
      pretrained.model.blocks[hooks[3]].register_forward_hook(
          get_activation("4")
      )
  
      pretrained.activations = activations
  
      readout_oper = get_readout_oper(
          vit_features, features, use_readout, start_index
      )
  
      # 32, 48, 136, 384
      pretrained.act_postprocess1 = nn.Sequential(
          readout_oper[0],
          Transpose(1, 2),
          nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
          nn.Conv2d(
              in_channels=vit_features,
              out_channels=features[0],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.ConvTranspose2d(
              in_channels=features[0],
              out_channels=features[0],
              kernel_size=4,
              stride=4,
              padding=0,
              bias=True,
              dilation=1,
              groups=1,
          ),
      )
  
      pretrained.act_postprocess2 = nn.Sequential(
          readout_oper[1],
          Transpose(1, 2),
          nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
          nn.Conv2d(
              in_channels=vit_features,
              out_channels=features[1],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.ConvTranspose2d(
              in_channels=features[1],
              out_channels=features[1],
              kernel_size=2,
              stride=2,
              padding=0,
              bias=True,
              dilation=1,
              groups=1,
          ),
      )
  
      pretrained.act_postprocess3 = nn.Sequential(
          readout_oper[2],
          Transpose(1, 2),
          nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
          nn.Conv2d(
              in_channels=vit_features,
              out_channels=features[2],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
      )
  
      pretrained.act_postprocess4 = nn.Sequential(
          readout_oper[3],
          Transpose(1, 2),
          nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
          nn.Conv2d(
              in_channels=vit_features,
              out_channels=features[3],
              kernel_size=1,
              stride=1,
              padding=0,
          ),
          nn.Conv2d(
              in_channels=features[3],
              out_channels=features[3],
              kernel_size=3,
              stride=2,
              padding=1,
          ),
      )
  
      pretrained.model.start_index = start_index
      pretrained.model.patch_size = [16, 16]
  
      # We inject this function into the VisionTransformer instances so that
      # we can use it with interpolated position embeddings without modifying the library source.
      pretrained.model.forward_flex = types.MethodType(
          forward_flex, pretrained.model
      )
      pretrained.model._resize_pos_embed = types.MethodType(
          _resize_pos_embed, pretrained.model
      )
  
      return pretrained
  
imports: import torch
  import torch.nn
  import types
  import math
  import torch.nn.functional
label_desc: Use image node_id 6 for calling a global function with example usage: _make_vit_b16_backbone(model, features, size, hooks, vit_features, use_readout, start_index) and returns: pretrained