class_docstr: Recurrent LSTM language model.
  
  Generate captions given features extracted from images.
docstr: Batch of image features to captions.
docstr_tok: ["Batch","of","image","features","to","captions","."]
code: def predict(self, img_feats, bos, eos, max_caption_length):
      """Batch of image features to captions."""
      self.reset(img_feats)
  
      captions = self.xp.full(
          (img_feats.shape[0], 1), bos, dtype=np.int32
      )
      for _ in range(max_caption_length):
          x = Variable(captions[:, -1])  # Previous word token as input
          y = self.step(x)
          pred = y.array.argmax(axis=1).astype(np.int32)
          captions = self.xp.hstack((captions, pred[:, None]))
          if (pred == eos).all():
              break
      return captions
  
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 3 for calling the LSTMLanguageModel obj's underlying member method code with example usage: obj.predict(img_feats, bos, eos, max_caption_length) and returns: captions