code: def inceptionv3(num_classes=1000, pretrained="imagenet"):
      r"""Inception v3 model architecture from
      `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
      """
      model = models.inception_v3(pretrained=False)
      if pretrained is not None:
          settings = pretrained_settings["inceptionv3"][pretrained]
          model = load_pretrained(model, num_classes, settings)
  
      # Modify attributs
      model.last_linear = model.fc
      del model.fc
  
      def features(self, input):
          # 299 x 299 x 3
          x = self.Conv2d_1a_3x3(input)  # 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:
              self._out_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
          return x
  
      def logits(self, features):
          x = F.avg_pool2d(features, kernel_size=8)  # 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
          x = self.last_linear(x)  # 1000 (num_classes)
          if self.training and self.aux_logits:
              aux = self._out_aux
              self._out_aux = None
              return x, aux
          return x
  
      def forward(self, input):
          x = self.features(input)
          x = self.logits(x)
          return x
  
      # Modify methods
      model.features = types.MethodType(features, model)
      model.logits = types.MethodType(logits, model)
      model.forward = types.MethodType(forward, model)
      return model
  
imports: from __future__ import print_function, division, absolute_import
  import torchvision.models
  import torch.utils.model_zoo
  import torch.nn.functional
  import types
  import re
label_desc: Use image node_id 10 for calling a global function with example usage: inceptionv3(num_classes, pretrained) and returns: model, x, x, x, x,  aux