code: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  __all__ = ['Inception3', 'inception_v3']
  
  def inception_v3(**kwargs):
      r"""Inception v3 model architecture from
      `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      # """
  
      return Inception3(**kwargs)
  
  
  class Inception3(nn.Module):
  
      def __init__(self, Embed_dim=512, num_classes=100, dropout=0.5, aux_logits=False, transform_input=False):
          super(Inception3, self).__init__()
          self.aux_logits = aux_logits
          self.transform_input = transform_input
          self.dropout = dropout
          self.Embed_dim = Embed_dim
          self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
          self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
          self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
          self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
          self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
          self.Mixed_5b = InceptionA(192, pool_features=32)
          self.Mixed_5c = InceptionA(256, pool_features=64)
          self.Mixed_5d = InceptionA(288, pool_features=64)
          self.Mixed_6a = InceptionB(288)
          self.Mixed_6b = InceptionC(768, channels_7x7=128)
          self.Mixed_6c = InceptionC(768, channels_7x7=160)
          self.Mixed_6d = InceptionC(768, channels_7x7=160)
          self.Mixed_6e = InceptionC(768, channels_7x7=192)
          if aux_logits:
              self.AuxLogits = InceptionAux(768, num_classes)
          self.Mixed_7a = InceptionD(768)
          self.Mixed_7b = InceptionE(1280)
          self.Mixed_7c = InceptionE(2048)
  
          if self.Embed_dim > 0:
              self.Embed = Embedding(2048, self.Embed_dim)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                  import scipy.stats as stats
                  stddev = m.stddev if hasattr(m, 'stddev') else 0.001
                  X = stats.truncnorm(-2, 2, scale=stddev)
                  values = torch.Tensor(X.rvs(m.weight.data.numel()))
                  m.weight.data.copy_(values)
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def forward(self, x):
          if self.transform_input:
              x = x.clone()
              x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
              x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
              x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
          # 299 x 299 x 3
          x = self.Conv2d_1a_3x3(x)
          # 149 x 149 x 32
          x = self.Conv2d_2a_3x3(x)
          # 147 x 147 x 32
          x = self.Conv2d_2b_3x3(x)
          # 147 x 147 x 64
          x = F.max_pool2d(x, kernel_size=3, stride=2)
          # 73 x 73 x 64
          x = self.Conv2d_3b_1x1(x)
          # 73 x 73 x 80
          x = self.Conv2d_4a_3x3(x)
          # 71 x 71 x 192
          x = F.max_pool2d(x, kernel_size=3, stride=2)
          # 35 x 35 x 192
          x = self.Mixed_5b(x)
          # 35 x 35 x 256
          x = self.Mixed_5c(x)
          # 35 x 35 x 288
          x = self.Mixed_5d(x)
          # 35 x 35 x 288
          x = self.Mixed_6a(x)
          # 17 x 17 x 768
          x = self.Mixed_6b(x)
          # 17 x 17 x 768
          x = self.Mixed_6c(x)
          # 17 x 17 x 768
          x = self.Mixed_6d(x)
          # 17 x 17 x 768
          x = self.Mixed_6e(x)
          # 17 x 17 x 768
          if self.training and self.aux_logits:
              aux = self.AuxLogits(x)
          # 17 x 17 x 768
          x = self.Mixed_7a(x)
          # 8 x 8 x 1280
          x = self.Mixed_7b(x)
          # 8 x 8 x 2048
          x = self.Mixed_7c(x)
          # 8 x 8 x 2048
          x = F.adaptive_avg_pool2d(x, output_size=1)
          # 1 x 1 x 2048
          x = F.dropout(x, training=self.training)
          # 1 x 1 x 2048
          x = x.view(x.size(0), -1)
          # 2048
          if self.Embed_dim > 0:
              x = self.Embed(x)
          return x
  
  
  class Embedding(nn.Module):
      def __init__(self, in_dim, out_dim, dropout=None, normalized=True):
          super(Embedding, self).__init__()
          self.bn = nn.BatchNorm2d(in_dim, eps=0.001)
          self.linear = nn.Linear(in_features=in_dim, out_features=out_dim)
          self.dropout = dropout
          self.normalized = normalized
  
      def forward(self, x):
          x = self.bn(x)
          x = F.relu(x, inplace=True)
          if self.dropout is not None:
              x = nn.Dropout(p=self.dropout)(x, inplace=True)
          x = self.linear(x)
          if self.normalized:
              norm = x.norm(dim=1, p=2, keepdim=True)
              x = x.div(norm.expand_as(x))
          return x
  
  
  class InceptionA(nn.Module):
  
      def __init__(self, in_channels, pool_features):
          super(InceptionA, self).__init__()
          self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)
  
          self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
          self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)
  
          self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
          self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
          self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)
  
          self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1)
  
      def forward(self, x):
          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 torch.cat(outputs, 1)
  
  
  class InceptionB(nn.Module):
  
      def __init__(self, in_channels):
          super(InceptionB, self).__init__()
          self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)
  
          self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
          self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
          self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2)
  
      def forward(self, x):
          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 torch.cat(outputs, 1)
  
  
  class InceptionC(nn.Module):
  
      def __init__(self, in_channels, channels_7x7):
          super(InceptionC, self).__init__()
          self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)
  
          c7 = channels_7x7
          self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)
          self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0))
  
          self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)
          self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3))
  
          self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
  
      def forward(self, x):
          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 torch.cat(outputs, 1)
  
  
  class InceptionD(nn.Module):
  
      def __init__(self, in_channels):
          super(InceptionD, self).__init__()
          self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
          self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2)
  
          self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
          self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3))
          self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0))
          self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2)
  
      def forward(self, x):
          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 torch.cat(outputs, 1)
  
  
  class InceptionE(nn.Module):
  
      def __init__(self, in_channels):
          super(InceptionE, self).__init__()
          self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)
  
          self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)
          self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
          self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
  
          self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)
          self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)
          self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))
          self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))
  
          self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
  
      def forward(self, x):
          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 torch.cat(outputs, 1)
  
  
  class InceptionAux(nn.Module):
  
      def __init__(self, in_channels, num_classes):
          super(InceptionAux, self).__init__()
          self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
          self.conv1 = BasicConv2d(128, 768, kernel_size=5)
          self.conv1.stddev = 0.01
          self.fc = nn.Linear(768, num_classes)
          self.fc.stddev = 0.001
  
      def forward(self, x):
          # 17 x 17 x 768
          x = F.avg_pool2d(x, kernel_size=5, stride=3)
          # 5 x 5 x 768
          x = self.conv0(x)
          # 5 x 5 x 128
          x = self.conv1(x)
          # 1 x 1 x 768
          x = x.view(x.size(0), -1)
          # 768
          x = self.fc(x)
          # 1000
          return x
  
  
  class BasicConv2d(nn.Module):
  
      def __init__(self, in_channels, out_channels, **kwargs):
          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):
          x = self.conv(x)
          x = self.bn(x)
          return F.relu(x, inplace=True)
  
  
apis: torch.nn.BatchNorm2d torch.nn.Linear torch.nn.functional.avg_pool2d torch.nn.functional.dropout torch.nn.functional.max_pool2d torch.nn.functional.relu scipy.stats.truncnorm torch.nn.functional.adaptive_avg_pool2d torch.nn.Conv2d torch.cat torch.nn.Dropout