class_docstr: Recurrent LSTM language model.
  
  Generate captions given features extracted from images.
code: def __init__(
      self, vocab_size, hidden_size, dropout_ratio, ignore_label
  ):
      super(LSTMLanguageModel, self).__init__()
      with self.init_scope():
          self.embed_word = L.EmbedID(
              vocab_size,
              hidden_size,
              initialW=initializers.Normal(1.0),
              ignore_label=ignore_label,
          )
          self.embed_img = L.Linear(
              hidden_size, initialW=initializers.Normal(0.01)
          )
          self.lstm = L.LSTM(hidden_size, hidden_size)
          self.out_word = L.Linear(
              hidden_size,
              vocab_size,
              initialW=initializers.Normal(0.01),
          )
  
      self.dropout_ratio = dropout_ratio
  
imports: import numpy
  import chainer
  from chainer import functions
  from chainer import initializers
  from chainer import links
  from chainer import reporter
  from chainer import Variable
label_desc: Use image node_id 1 to create a new LSTMLanguageModel object from inherited base classes: chainer with example: obj = LSTMLanguageModel(vocab_size, hidden_size, dropout_ratio, ignore_label)