class_docstr: A multilayer perceptron.
  
  Args:
      n_vocab (int): The size of vocabulary.
      n_units (int): The number of units in a hidden or output layer.
      dropout (float): The dropout ratio.
code: def __init__(self, n_layers, n_units, dropout=0.1):
      super(MLP, self).__init__()
      for i in range(n_layers):
          self.add_link(L.Linear(None, n_units))
      self.dropout = dropout
      self.out_units = n_units
  
imports: import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import reporter
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: chainer with example: obj = MLP(n_layers, n_units, dropout)