class_docstr: A LSTM-RNN Encoder with Word Embedding.
  
  This model encodes a sentence sequentially using LSTM.
  
  Args:
      n_layers (int): The number of LSTM layers.
      n_vocab (int): The size of vocabulary.
      n_units (int): The number of units of a LSTM layer and word embedding.
      dropout (float): The dropout ratio.
code: def forward(self, xs):
      exs = sequence_embed(self.embed, xs, self.dropout)
      last_h, last_c, ys = self.encoder(None, None, exs)
      assert last_h.shape == (self.n_layers, len(xs), self.out_units)
      concat_outputs = last_h[-1]
      return concat_outputs
  
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 RNNEncoder obj's underlying member method code with example usage: obj.forward(xs) and returns: concat_outputs