code: def forward(self, x: Tensor) -> Dict[str, Tensor]:
      # L2 regularization + Rescaling of 1st block's feature map
      x = self.features(x)
      rescaled = self.scale_weight.view(1, -1, 1, 1) * F.normalize(x)
      output = [rescaled]
  
      # Calculating Feature maps for the rest blocks
      for block in self.extra:
          x = block(x)
          output.append(x)
  
      return OrderedDict([(str(i), v) for i, v in enumerate(output)])
  
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 2 for calling the SSDFeatureExtractorVGG obj's underlying member method code with example usage: obj.forward(x) and returns: OrderedDict