code: def __init__(self, num_classes, pretrain_backbone: bool = False):
      super(VGG16UNet, self).__init__()
      backbone = vgg16_bn(pretrained=pretrain_backbone)
  
      # if pretrain_backbone:
      #     # 载入vgg16_bn预训练权重
      #     # https://download.pytorch.org/models/vgg16_bn-6c64b313.pth
      #     backbone.load_state_dict(torch.load("vgg16_bn.pth", map_location='cpu'))
  
      backbone = backbone.features
  
      stage_indices = [5, 12, 22, 32, 42]
      self.stage_out_channels = [64, 128, 256, 512, 512]
      return_layers = dict(
          [(str(j), f"stage{i}") for i, j in enumerate(stage_indices)]
      )
      self.backbone = IntermediateLayerGetter(
          backbone, return_layers=return_layers
      )
  
      c = self.stage_out_channels[4] + self.stage_out_channels[3]
      self.up1 = Up(c, self.stage_out_channels[3])
      c = self.stage_out_channels[3] + self.stage_out_channels[2]
      self.up2 = Up(c, self.stage_out_channels[2])
      c = self.stage_out_channels[2] + self.stage_out_channels[1]
      self.up3 = Up(c, self.stage_out_channels[1])
      c = self.stage_out_channels[1] + self.stage_out_channels[0]
      self.up4 = Up(c, self.stage_out_channels[0])
      self.conv = OutConv(
          self.stage_out_channels[0], num_classes=num_classes
      )
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/unet/src/vgg_unet.py
imports: from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from torch import Tensor
  from torchvision.models import vgg16_bn
  from .unet import Up, OutConv
label_desc: Use image node_id 1 to create a new VGG16UNet object from inherited base classes: nn with example: obj = VGG16UNet(num_classes, pretrain_backbone)