code: def __init__(
      self,
      features: nn.Module,
      num_classes: int = 1000,
      init_weights: bool = True,
      dropout: float = 0.5,
  ) -> None:
      super().__init__()
      _log_api_usage_once(self)
      self.features = features
      self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
      self.classifier = nn.Sequential(
          nn.Linear(512 * 7 * 7, 4096),
          nn.ReLU(True),
          nn.Dropout(p=dropout),
          nn.Linear(4096, 4096),
          nn.ReLU(True),
          nn.Dropout(p=dropout),
          nn.Linear(4096, num_classes),
      )
      if init_weights:
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(
                      m.weight, mode="fan_out", nonlinearity="relu"
                  )
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, 0, 0.01)
                  nn.init.constant_(m.bias, 0)
  
imports: from functools import partial
  from typing import Any, cast, Dict, List, Optional, Union
  import torch
  import torch.nn
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 1 to create a new VGG object from inherited base classes: nn with example: obj = VGG(features, num_classes, init_weights, dropout)