class_docstr: A BOW encoder with word embedding and MLP.
  
  This model encodes a sentence as just a set of words by averaging.
  Additionally, its output is fed into a multilayer perceptron.
  
  Args:
      n_layers (int): The number of layers of MLP.
      n_vocab (int): The size of vocabulary.
      n_units (int): The number of units of MLP and word embedding.
      dropout (float): The dropout ratio.
code: def __init__(self, n_layers, n_vocab, n_units, dropout=0.1):
      super(BOWMLPEncoder, self).__init__()
      with self.init_scope():
          self.bow_encoder = BOWEncoder(n_vocab, n_units, dropout)
          self.mlp_encoder = MLP(n_layers, n_units, 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 BOWMLPEncoder object from inherited base classes: chainer with example: obj = BOWMLPEncoder(n_layers, n_vocab, n_units, dropout)