code: def __init__(
      self,
      in_channels: int,
      num_classes: int,
      conv_block: Optional[Callable[..., nn.Module]] = None,
  ) -> None:
      super().__init__()
      if conv_block is None:
          conv_block = BasicConv2d
      self.conv0 = conv_block(in_channels, 128, kernel_size=1)
      self.conv1 = conv_block(128, 768, kernel_size=5)
      self.conv1.stddev = 0.01  # type: ignore[assignment]
      self.fc = nn.Linear(768, num_classes)
      self.fc.stddev = 0.001
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, 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 InceptionAux object from inherited base classes: nn with example: obj = InceptionAux(in_channels, num_classes, conv_block)