code: def __init__(
      self,
      in_channels: int,
      ch1x1: int,
      ch3x3red: int,
      ch3x3: int,
      ch5x5red: int,
      ch5x5: int,
      pool_proj: int,
      conv_block: Optional[Callable[..., nn.Module]] = None,
  ) -> None:
      super().__init__()
      if conv_block is None:
          conv_block = BasicConv2d
      self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)
  
      self.branch2 = nn.Sequential(
          conv_block(in_channels, ch3x3red, kernel_size=1),
          conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1),
      )
  
      self.branch3 = nn.Sequential(
          conv_block(in_channels, ch5x5red, kernel_size=1),
          # Here, kernel_size=3 instead of kernel_size=5 is a known bug.
          # Please see https://github.com/pytorch/vision/issues/906 for details.
          conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1),
      )
  
      self.branch4 = nn.Sequential(
          nn.MaxPool2d(
              kernel_size=3, stride=1, padding=1, ceil_mode=True
          ),
          conv_block(in_channels, pool_proj, kernel_size=1),
      )
  
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 Inception object from inherited base classes: nn with example: obj = Inception(in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj, conv_block)