class_docstr: New GoogLeNet of BatchNormalization version.
code: def forward(self, x, t):
      h = F.max_pooling_2d(
          F.relu(self.norm1(self.conv1(x))), 3, stride=2, pad=1
      )
      h = F.max_pooling_2d(
          F.relu(self.norm2(self.conv2(h))), 3, stride=2, pad=1
      )
  
      h = self.inc3a(h)
      h = self.inc3b(h)
      h = self.inc3c(h)
      h = self.inc4a(h)
  
      a = F.average_pooling_2d(h, 5, stride=3)
      a = F.relu(self.norma(self.conva(a)))
      a = F.relu(self.norma2(self.lina(a)))
      a = self.outa(a)
      loss1 = F.softmax_cross_entropy(a, t)
  
      h = self.inc4b(h)
      h = self.inc4c(h)
      h = self.inc4d(h)
  
      b = F.average_pooling_2d(h, 5, stride=3)
      b = F.relu(self.normb(self.convb(b)))
      b = F.relu(self.normb2(self.linb(b)))
      b = self.outb(b)
      loss2 = F.softmax_cross_entropy(b, t)
  
      h = self.inc4e(h)
      h = self.inc5a(h)
      h = F.average_pooling_2d(self.inc5b(h), 7)
      h = self.out(h)
      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 GoogLeNetBN obj's underlying member method code with example usage: obj.forward(x, t) and returns: loss