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 __init__(self, n_layers, n_vocab, n_units, dropout=0.1):
      super(RNNEncoder, self).__init__()
      with self.init_scope():
          self.embed = L.EmbedID(n_vocab, n_units, initialW=embed_init)
          self.encoder = L.NStepLSTM(
              n_layers, n_units, n_units, dropout
          )
  
      self.n_layers = n_layers
      self.out_units = n_units
      self.dropout = dropout
  
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 RNNEncoder object from inherited base classes: chainer with example: obj = RNNEncoder(n_layers, n_vocab, n_units, dropout)