code: def __init__(self, backbone: nn.Module, highres: bool):
      super().__init__()
  
      _, _, maxpool3_pos, maxpool4_pos, _ = (
          i
          for i, layer in enumerate(backbone)
          if isinstance(layer, nn.MaxPool2d)
      )
  
      # Patch ceil_mode for maxpool3 to get the same WxH output sizes as the paper
      backbone[maxpool3_pos].ceil_mode = True
  
      # parameters used for L2 regularization + rescaling
      self.scale_weight = nn.Parameter(torch.ones(512) * 20)
  
      # Multiple Feature maps - page 4, Fig 2 of SSD paper
      self.features = nn.Sequential(
          *backbone[:maxpool4_pos]
      )  # until conv4_3
  
      # SSD300 case - page 4, Fig 2 of SSD paper
      extra = nn.ModuleList(
          [
              nn.Sequential(
                  nn.Conv2d(1024, 256, kernel_size=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(
                      256, 512, kernel_size=3, padding=1, stride=2
                  ),  # conv8_2
                  nn.ReLU(inplace=True),
              ),
              nn.Sequential(
                  nn.Conv2d(512, 128, kernel_size=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(
                      128, 256, kernel_size=3, padding=1, stride=2
                  ),  # conv9_2
                  nn.ReLU(inplace=True),
              ),
              nn.Sequential(
                  nn.Conv2d(256, 128, kernel_size=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(128, 256, kernel_size=3),  # conv10_2
                  nn.ReLU(inplace=True),
              ),
              nn.Sequential(
                  nn.Conv2d(256, 128, kernel_size=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(128, 256, kernel_size=3),  # conv11_2
                  nn.ReLU(inplace=True),
              ),
          ]
      )
      if highres:
          # Additional layers for the SSD512 case. See page 11, footernote 5.
          extra.append(
              nn.Sequential(
                  nn.Conv2d(256, 128, kernel_size=1),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(128, 256, kernel_size=4),  # conv12_2
                  nn.ReLU(inplace=True),
              )
          )
      _xavier_init(extra)
  
      fc = nn.Sequential(
          nn.MaxPool2d(
              kernel_size=3, stride=1, padding=1, ceil_mode=False
          ),  # add modified maxpool5
          nn.Conv2d(
              in_channels=512,
              out_channels=1024,
              kernel_size=3,
              padding=6,
              dilation=6,
          ),  # FC6 with atrous
          nn.ReLU(inplace=True),
          nn.Conv2d(
              in_channels=1024, out_channels=1024, kernel_size=1
          ),  # FC7
          nn.ReLU(inplace=True),
      )
      _xavier_init(fc)
      extra.insert(
          0,
          nn.Sequential(
              *backbone[
                  maxpool4_pos:-1
              ],  # until conv5_3, skip maxpool5
              fc,
          ),
      )
      self.extra = extra
  
imports: import warnings
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ...ops import boxes
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..vgg import VGG, vgg16, VGG16_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 to create a new SSDFeatureExtractorVGG object from inherited base classes: nn with example: obj = SSDFeatureExtractorVGG(backbone, highres)