code: def __init__(
      self,
      num_classes: int = 1000,
      aux_logits: bool = True,
      transform_input: bool = False,
      init_weights: Optional[bool] = None,
      blocks: Optional[List[Callable[..., nn.Module]]] = None,
      dropout: float = 0.2,
      dropout_aux: float = 0.7,
  ) -> None:
      super().__init__()
      _log_api_usage_once(self)
      if blocks is None:
          blocks = [BasicConv2d, Inception, InceptionAux]
      if init_weights is None:
          warnings.warn(
              "The default weight initialization of GoogleNet will be changed in future releases of "
              "torchvision. If you wish to keep the old behavior (which leads to long initialization times"
              " due to scipy/scipy#11299), please set init_weights=True.",
              FutureWarning,
          )
          init_weights = True
      if len(blocks) != 3:
          raise ValueError(
              f"blocks length should be 3 instead of {len(blocks)}"
          )
      conv_block = blocks[0]
      inception_block = blocks[1]
      inception_aux_block = blocks[2]
  
      self.aux_logits = aux_logits
      self.transform_input = transform_input
  
      self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
      self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
      self.conv2 = conv_block(64, 64, kernel_size=1)
      self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)
      self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
  
      self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)
      self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)
      self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
  
      self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)
      self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)
      self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)
      self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)
      self.inception4e = inception_block(
          528, 256, 160, 320, 32, 128, 128
      )
      self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
      self.inception5a = inception_block(
          832, 256, 160, 320, 32, 128, 128
      )
      self.inception5b = inception_block(
          832, 384, 192, 384, 48, 128, 128
      )
  
      if aux_logits:
          self.aux1 = inception_aux_block(
              512, num_classes, dropout=dropout_aux
          )
          self.aux2 = inception_aux_block(
              528, num_classes, dropout=dropout_aux
          )
      else:
          self.aux1 = None  # type: ignore[assignment]
          self.aux2 = None  # type: ignore[assignment]
  
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
      self.dropout = nn.Dropout(p=dropout)
      self.fc = nn.Linear(1024, num_classes)
  
      if init_weights:
          for m in self.modules():
              if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                  torch.nn.init.trunc_normal_(
                      m.weight, mean=0.0, std=0.01, a=-2, b=2
                  )
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  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 GoogLeNet object from inherited base classes: nn with example: obj = GoogLeNet(num_classes, aux_logits, transform_input, init_weights, blocks, dropout, dropout_aux)