code: def __init__(self, n_units, n_out, device0, device1):
      super(ParallelMLP, self).__init__()
      self.device0 = device0
      self.device1 = device1
  
      with self.init_scope():
          # the input size, 784, is inferred
          self.first0 = train_mnist.MLP(n_units // 2, n_units)
          self.first1 = train_mnist.MLP(n_units // 2, n_units)
          self.first0.to_device(device0)
          self.first1.to_device(device1)
  
          # the input size, n_units, is inferred
          self.second0 = train_mnist.MLP(n_units // 2, n_out)
          self.second1 = train_mnist.MLP(n_units // 2, n_out)
          self.second0.to_device(device0)
          self.second1.to_device(device1)
  
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 1 to create a new ParallelMLP object from inherited base classes: chainer with example: obj = ParallelMLP(n_units, n_out, device0, device1)