code: def __init__(self, n_units, n_out):
      super(MLP, self).__init__()
      with self.init_scope():
          # the size of the inputs to each layer will be inferred
          self.l1 = L.Linear(None, n_units)  # n_in -> n_units
          self.l2 = L.Linear(None, n_units)  # n_units -> n_units
          self.l3 = L.Linear(None, n_out)
  
file_path: chainer/examples/mnist/train_mnist.py
imports: import argparse
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
  import matplotlib
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: chainer with example: obj = MLP(n_units, n_out)