class_docstr: A BoW encoder with word embedding.
  
  This model encodes a sentence as just a set of words by averaging.
  
  Args:
      n_vocab (int): The size of vocabulary.
      n_units (int): The number of units of word embedding.
      dropout (float): The dropout ratio.
code: def forward(self, xs):
      x_block = chainer.dataset.convert.concat_examples(xs, padding=-1)
      ex_block = block_embed(self.embed, x_block)
      x_len = self.xp.array([len(x) for x in xs], numpy.int32)[
          :, None, None
      ]
      h = F.sum(ex_block, axis=2) / x_len
      return h
  
imports: import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import reporter
label_desc: Use image node_id 2 for calling the BOWEncoder obj's underlying member method code with example usage: obj.forward(xs) and returns: h