code: def __init__(
      self, num_classes: int = 1000, dropout: float = 0.5
  ) -> None:
      super().__init__()
      _log_api_usage_once(self)
      self.features = nn.Sequential(
          nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
          nn.Conv2d(64, 192, kernel_size=5, padding=2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
          nn.Conv2d(192, 384, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(384, 256, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(256, 256, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=3, stride=2),
      )
      self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
      self.classifier = nn.Sequential(
          nn.Dropout(p=dropout),
          nn.Linear(256 * 6 * 6, 4096),
          nn.ReLU(inplace=True),
          nn.Dropout(p=dropout),
          nn.Linear(4096, 4096),
          nn.ReLU(inplace=True),
          nn.Linear(4096, num_classes),
      )
  
file_path: vision/torchvision/models/alexnet.py
imports: from functools import partial
  from typing import Any, Optional
  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 AlexNet object from inherited base classes: nn with example: obj = AlexNet(num_classes, dropout)