code: def __init__(self, n_vocab, n_units):
      super(RNNForLM, self).__init__()
      with self.init_scope():
          self.embed = L.EmbedID(n_vocab, n_units)
          self.l1 = L.LSTM(n_units, n_units)
          self.l2 = L.LSTM(n_units, n_units)
          self.l3 = L.Linear(n_units, n_vocab)
  
      for param in self.params():
          param.array[...] = np.random.uniform(-0.1, 0.1, param.shape)
  
imports: from __future__ import division
  import argparse
  import sys
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
label_desc: Use image node_id 1 to create a new RNNForLM object from inherited base classes: chainer with example: obj = RNNForLM(n_vocab, n_units)