text: import warnings
  from collections import namedtuple
  from typing import Callable, Any, Optional, Tuple, List
  
  import torch
  import torch.nn.functional as F
  from torch import nn, Tensor
  
  from .._internally_replaced_utils import load_state_dict_from_url
  
  
  __all__ = ["Inception3", "inception_v3", "InceptionOutputs", "_InceptionOutputs"]
  
  
  model_urls = {
      # Inception v3 ported from TensorFlow
      "inception_v3_google": "https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth",
  }
  
  InceptionOutputs = namedtuple("InceptionOutputs", ["logits", "aux_logits"])
  InceptionOutputs.__annotations__ = {"logits": Tensor, "aux_logits": Optional[Tensor]}
  
  # Script annotations failed with _GoogleNetOutputs = namedtuple ...
  # _InceptionOutputs set here for backwards compat
  _InceptionOutputs = InceptionOutputs
  
  
  def inception_v3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> "Inception3":
      r"""Inception v3 model architecture from
      `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
      The required minimum input size of the model is 75x75.
  
      .. note::
          **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
          N x 3 x 299 x 299, so ensure your images are sized accordingly.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          aux_logits (bool): If True, add an auxiliary branch that can improve training.
              Default: *True*
          transform_input (bool): If True, preprocesses the input according to the method with which it
              was trained on ImageNet. Default: *False*
      """
      if pretrained:
          if "transform_input" not in kwargs:
              kwargs["transform_input"] = True
          if "aux_logits" in kwargs:
              original_aux_logits = kwargs["aux_logits"]
              kwargs["aux_logits"] = True
          else:
              original_aux_logits = True
          kwargs["init_weights"] = False  # we are loading weights from a pretrained model
          model = Inception3(**kwargs)
          state_dict = load_state_dict_from_url(model_urls["inception_v3_google"], progress=progress)
          model.load_state_dict(state_dict)
          if not original_aux_logits:
              model.aux_logits = False
              model.AuxLogits = None
          return model
  
      return Inception3(**kwargs)
  
  
  class Inception3(nn.Module):
      def __init__(
          self,
          num_classes: int = 1000,
          aux_logits: bool = True,
          transform_input: bool = False,
          inception_blocks: Optional[List[Callable[..., nn.Module]]] = None,
          init_weights: Optional[bool] = None,
          dropout: float = 0.5,
      ) -> None:
          super(Inception3, self).__init__()
          if inception_blocks is None:
              inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, InceptionAux]
          if init_weights is None:
              warnings.warn(
                  "The default weight initialization of inception_v3 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
          assert len(inception_blocks) == 7
          conv_block = inception_blocks[0]
          inception_a = inception_blocks[1]
          inception_b = inception_blocks[2]
          inception_c = inception_blocks[3]
          inception_d = inception_blocks[4]
          inception_e = inception_blocks[5]
          inception_aux = inception_blocks[6]
  
          self.aux_logits = aux_logits
          self.transform_input = transform_input
          self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)
          self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
          self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
          self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
          self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
          self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
          self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
          self.Mixed_5b = inception_a(192, pool_features=32)
          self.Mixed_5c = inception_a(256, pool_features=64)
          self.Mixed_5d = inception_a(288, pool_features=64)
          self.Mixed_6a = inception_b(288)
          self.Mixed_6b = inception_c(768, channels_7x7=128)
          self.Mixed_6c = inception_c(768, channels_7x7=160)
          self.Mixed_6d = inception_c(768, channels_7x7=160)
          self.Mixed_6e = inception_c(768, channels_7x7=192)
          self.AuxLogits: Optional[nn.Module] = None
          if aux_logits:
              self.AuxLogits = inception_aux(768, num_classes)
          self.Mixed_7a = inception_d(768)
          self.Mixed_7b = inception_e(1280)
          self.Mixed_7c = inception_e(2048)
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.dropout = nn.Dropout(p=dropout)
          self.fc = nn.Linear(2048, num_classes)
          if init_weights:
              for m in self.modules():
                  if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                      stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1  # type: ignore
                      torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=stddev, a=-2, b=2)
                  elif isinstance(m, nn.BatchNorm2d):
                      nn.init.constant_(m.weight, 1)
                      nn.init.constant_(m.bias, 0)
  
      def _transform_input(self, x: Tensor) -> Tensor:
          if self.transform_input:
              x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
              x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
              x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
              x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
          return x
  
      def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor]]:
          # N x 3 x 299 x 299
          x = self.Conv2d_1a_3x3(x)
          # N x 32 x 149 x 149
          x = self.Conv2d_2a_3x3(x)
          # N x 32 x 147 x 147
          x = self.Conv2d_2b_3x3(x)
          # N x 64 x 147 x 147
          x = self.maxpool1(x)
          # N x 64 x 73 x 73
          x = self.Conv2d_3b_1x1(x)
          # N x 80 x 73 x 73
          x = self.Conv2d_4a_3x3(x)
          # N x 192 x 71 x 71
          x = self.maxpool2(x)
          # N x 192 x 35 x 35
          x = self.Mixed_5b(x)
          # N x 256 x 35 x 35
          x = self.Mixed_5c(x)
          # N x 288 x 35 x 35
          x = self.Mixed_5d(x)
          # N x 288 x 35 x 35
          x = self.Mixed_6a(x)
          # N x 768 x 17 x 17
          x = self.Mixed_6b(x)
          # N x 768 x 17 x 17
          x = self.Mixed_6c(x)
          # N x 768 x 17 x 17
          x = self.Mixed_6d(x)
          # N x 768 x 17 x 17
          x = self.Mixed_6e(x)
          # N x 768 x 17 x 17
          aux: Optional[Tensor] = None
          if self.AuxLogits is not None:
              if self.training:
                  aux = self.AuxLogits(x)
          # N x 768 x 17 x 17
          x = self.Mixed_7a(x)
          # N x 1280 x 8 x 8
          x = self.Mixed_7b(x)
          # N x 2048 x 8 x 8
          x = self.Mixed_7c(x)
          # N x 2048 x 8 x 8
          # Adaptive average pooling
          x = self.avgpool(x)
          # N x 2048 x 1 x 1
          x = self.dropout(x)
          # N x 2048 x 1 x 1
          x = torch.flatten(x, 1)
          # N x 2048
          x = self.fc(x)
          # N x 1000 (num_classes)
          return x, aux
  
      @torch.jit.unused
      def eager_outputs(self, x: Tensor, aux: Optional[Tensor]) -> InceptionOutputs:
          if self.training and self.aux_logits:
              return InceptionOutputs(x, aux)
          else:
              return x  # type: ignore[return-value]
  
      def forward(self, x: Tensor) -> InceptionOutputs:
          x = self._transform_input(x)
          x, aux = self._forward(x)
          aux_defined = self.training and self.aux_logits
          if torch.jit.is_scripting():
              if not aux_defined:
                  warnings.warn("Scripted Inception3 always returns Inception3 Tuple")
              return InceptionOutputs(x, aux)
          else:
              return self.eager_outputs(x, aux)
  
  
  class InceptionA(nn.Module):
      def __init__(
          self, in_channels: int, pool_features: int, conv_block: Optional[Callable[..., nn.Module]] = None
      ) -> None:
          super(InceptionA, self).__init__()
          if conv_block is None:
              conv_block = BasicConv2d
          self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
  
          self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
          self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
  
          self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
          self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
          self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
  
          self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
  
      def _forward(self, x: Tensor) -> List[Tensor]:
          branch1x1 = self.branch1x1(x)
  
          branch5x5 = self.branch5x5_1(x)
          branch5x5 = self.branch5x5_2(branch5x5)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
  
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
          return outputs
  
      def forward(self, x: Tensor) -> Tensor:
          outputs = self._forward(x)
          return torch.cat(outputs, 1)
  
  
  class InceptionB(nn.Module):
      def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None:
          super(InceptionB, self).__init__()
          if conv_block is None:
              conv_block = BasicConv2d
          self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
  
          self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
          self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
          self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
  
      def _forward(self, x: Tensor) -> List[Tensor]:
          branch3x3 = self.branch3x3(x)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
  
          branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
  
          outputs = [branch3x3, branch3x3dbl, branch_pool]
          return outputs
  
      def forward(self, x: Tensor) -> Tensor:
          outputs = self._forward(x)
          return torch.cat(outputs, 1)
  
  
  class InceptionC(nn.Module):
      def __init__(
          self, in_channels: int, channels_7x7: int, conv_block: Optional[Callable[..., nn.Module]] = None
      ) -> None:
          super(InceptionC, self).__init__()
          if conv_block is None:
              conv_block = BasicConv2d
          self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
  
          c7 = channels_7x7
          self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
          self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
  
          self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
          self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
  
          self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
  
      def _forward(self, x: Tensor) -> List[Tensor]:
          branch1x1 = self.branch1x1(x)
  
          branch7x7 = self.branch7x7_1(x)
          branch7x7 = self.branch7x7_2(branch7x7)
          branch7x7 = self.branch7x7_3(branch7x7)
  
          branch7x7dbl = self.branch7x7dbl_1(x)
          branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
          branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
  
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
          return outputs
  
      def forward(self, x: Tensor) -> Tensor:
          outputs = self._forward(x)
          return torch.cat(outputs, 1)
  
  
  class InceptionD(nn.Module):
      def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None:
          super(InceptionD, self).__init__()
          if conv_block is None:
              conv_block = BasicConv2d
          self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
          self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
  
          self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
          self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
  
      def _forward(self, x: Tensor) -> List[Tensor]:
          branch3x3 = self.branch3x3_1(x)
          branch3x3 = self.branch3x3_2(branch3x3)
  
          branch7x7x3 = self.branch7x7x3_1(x)
          branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
          branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
          branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
  
          branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
          outputs = [branch3x3, branch7x7x3, branch_pool]
          return outputs
  
      def forward(self, x: Tensor) -> Tensor:
          outputs = self._forward(x)
          return torch.cat(outputs, 1)
  
  
  class InceptionE(nn.Module):
      def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None:
          super(InceptionE, self).__init__()
          if conv_block is None:
              conv_block = BasicConv2d
          self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
  
          self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
          self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
          self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
  
          self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
          self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
          self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
          self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
  
          self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
  
      def _forward(self, x: Tensor) -> List[Tensor]:
          branch1x1 = self.branch1x1(x)
  
          branch3x3 = self.branch3x3_1(x)
          branch3x3 = [
              self.branch3x3_2a(branch3x3),
              self.branch3x3_2b(branch3x3),
          ]
          branch3x3 = torch.cat(branch3x3, 1)
  
          branch3x3dbl = self.branch3x3dbl_1(x)
          branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
          branch3x3dbl = [
              self.branch3x3dbl_3a(branch3x3dbl),
              self.branch3x3dbl_3b(branch3x3dbl),
          ]
          branch3x3dbl = torch.cat(branch3x3dbl, 1)
  
          branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
          branch_pool = self.branch_pool(branch_pool)
  
          outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
          return outputs
  
      def forward(self, x: Tensor) -> Tensor:
          outputs = self._forward(x)
          return torch.cat(outputs, 1)
  
  
  class InceptionAux(nn.Module):
      def __init__(
          self, in_channels: int, num_classes: int, conv_block: Optional[Callable[..., nn.Module]] = None
      ) -> None:
          super(InceptionAux, self).__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  # type: ignore[assignment]
  
      def forward(self, x: Tensor) -> Tensor:
          # N x 768 x 17 x 17
          x = F.avg_pool2d(x, kernel_size=5, stride=3)
          # N x 768 x 5 x 5
          x = self.conv0(x)
          # N x 128 x 5 x 5
          x = self.conv1(x)
          # N x 768 x 1 x 1
          # Adaptive average pooling
          x = F.adaptive_avg_pool2d(x, (1, 1))
          # N x 768 x 1 x 1
          x = torch.flatten(x, 1)
          # N x 768
          x = self.fc(x)
          # N x 1000
          return x
  
  
  class BasicConv2d(nn.Module):
      def __init__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None:
          super(BasicConv2d, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
          self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x: Tensor) -> Tensor:
          x = self.conv(x)
          x = self.bn(x)
          return F.relu(x, inplace=True)
  