code: def simple_cnn(x, t, n_class):
      c1 = PF.convolution(x, 16, (5, 5), name="conv1")
      c1 = F.relu(F.max_pooling(c1, (2, 2)))
      c2 = PF.convolution(c1, 8, (5, 5), name="conv2")
      c2 = F.relu(F.max_pooling(c2, (2, 2)))
      c3 = F.relu(PF.affine(c2, 10, name="fc3"))
      c4 = PF.affine(c3, n_class, name="fc4")
      l = F.mean(F.softmax_cross_entropy(c4, t))
  
      return l
  
label_desc: Use image node_id 1 for calling a global function with example usage: simple_cnn(x, t, n_class) and returns: l