code: def forward(self, x):
      if self.device0 != self.device1:
          # assume x is on device0
          x1 = F.copy(x, self.device1)
  
          z0 = self.first0(x)
          z1 = self.first1(x1)
  
          # synchronize
          h0 = z0 + F.copy(z1, self.device0)
          h1 = z1 + F.copy(z0, self.device1)
  
          y0 = self.second0(F.relu(h0))
          y1 = self.second1(F.relu(h1))
  
          y = y0 + F.copy(y1, self.device0)
          return y  # output is on device0
      else:
          z0 = self.first0(x)
          z1 = self.first1(x)
          h = z0 + z1
  
          y0 = self.second0(F.relu(h))
          y1 = self.second1(F.relu(h))
          y = y0 + y1
  
          return y
  
file_path: chainer/examples/mnist/train_mnist_model_parallel.py
imports: import argparse
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
  import train_mnist
label_desc: Use image node_id 2 for calling the ParallelMLP obj's underlying member method code with example usage: obj.forward(x) and returns: y, y