code: def forward(self, x, t):
      h = F.relu(self.conv1(x))
      h = F.local_response_normalization(
          F.max_pooling_2d(h, 3, stride=2), n=5
      )
      h = F.relu(self.conv2_reduce(h))
      h = F.relu(self.conv2(h))
      h = F.max_pooling_2d(
          F.local_response_normalization(h, n=5), 3, stride=2
      )
  
      h = self.inc3a(h)
      h = self.inc3b(h)
      h = F.max_pooling_2d(h, 3, stride=2)
      h = self.inc4a(h)
  
      l = F.average_pooling_2d(h, 5, stride=3)
      l = F.relu(self.loss1_conv(l))
      l = F.relu(self.loss1_fc1(l))
      l = self.loss1_fc2(l)
      loss1 = F.softmax_cross_entropy(l, t)
  
      h = self.inc4b(h)
      h = self.inc4c(h)
      h = self.inc4d(h)
  
      l = F.average_pooling_2d(h, 5, stride=3)
      l = F.relu(self.loss2_conv(l))
      l = F.relu(self.loss2_fc1(l))
      l = self.loss2_fc2(l)
      loss2 = F.softmax_cross_entropy(l, t)
  
      h = self.inc4e(h)
      h = F.max_pooling_2d(h, 3, stride=2)
      h = self.inc5a(h)
      h = self.inc5b(h)
  
      h = F.average_pooling_2d(h, 7, stride=1)
      h = self.loss3_fc(F.dropout(h, 0.4))
      loss3 = F.softmax_cross_entropy(h, t)
  
      loss = 0.3 * (loss1 + loss2) + loss3
      accuracy = F.accuracy(h, t)
  
      chainer.report(
          {
              "loss": loss,
              "loss1": loss1,
              "loss2": loss2,
              "loss3": loss3,
              "accuracy": accuracy,
          },
          self,
      )
      return loss
  
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 2 for calling the GoogLeNet obj's underlying member method code with example usage: obj.forward(x, t) and returns: loss