code: def forward(
      self, x, with_latent=False, fake_relu=False, no_relu=False
  ):
      assert (not fake_relu) and (
          not no_relu
      ), "fake_relu and no_relu not yet supported for this architecture"
      # 32 -> 30
      x = self.Conv2d_1a_3x3(x)
      x = self.Conv2d_2a_3x3(x)
      x = self.Conv2d_2b_3x3(x)
      x = self.Conv2d_3b_1x1(x)
      x = self.Conv2d_4a_3x3(x)
  
      # 30 -> 30
      x = self.Mixed_5b(x)
      x = self.Mixed_5c(x)
      x = self.Mixed_5d(x)
  
      # 30 -> 14
      # Efficient Grid Size Reduction to avoid representation
      # bottleneck
      x = self.Mixed_6a(x)
  
      # 14 -> 14
      # """In practice, we have found that employing this factorization does not
      # work well on early layers, but it gives very good results on medium
      # grid-sizes (On m x m feature maps, where m ranges between 12 and 20).
      # On that level, very good results can be achieved by using 1 x 7 convolutions
      # followed by 7 x 1 convolutions."""
      x = self.Mixed_6b(x)
      x = self.Mixed_6c(x)
      x = self.Mixed_6d(x)
      x = self.Mixed_6e(x)
  
      # 14 -> 6
      # Efficient Grid Size Reduction
      x = self.Mixed_7a(x)
  
      # 6 -> 6
      # We are using this solution only on the coarsest grid,
      # since that is the place where producing high dimensional
      # sparse representation is the most critical as the ratio of
      # local processing (by 1 x 1 convolutions) is increased compared
      # to the spatial aggregation."""
      x = self.Mixed_7b(x)
      x = self.Mixed_7c(x)
  
      # 6 -> 1
      x = self.avgpool(x)
      x = self.dropout(x)
      latent = x.view(x.size(0), -1)
      out = self.linear(latent)
      if with_latent:
          return out, latent
      return out
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the InceptionV3 obj's underlying member method code with example usage: obj.forward(x, with_latent, fake_relu, no_relu) and returns: out, out,  latent