class_docstr: An example of a model with side-effects.
  
  This uses the same network as ``MLP`` except that it includes an
  example of side-effect code.
code: def __init__(self, n_units, n_out):
      super(MLPSideEffect, 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)  # n_units -> n_out
      self.side_effect_counter = 0
  
imports: from __future__ import print_function
  import argparse
  import warnings
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import static_code
  from chainer import static_graph
  from chainer import training
  from chainer.training import extensions
  import chainerx
  import matplotlib
label_desc: Use image node_id 1 to create a new MLPSideEffect object from inherited base classes: chainer with example: obj = MLPSideEffect(n_units, n_out)