code: def __init__(self, n_vocab, n_units):
      super(RNNForLMSlice, 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 print_function
  import argparse
  import numpy
  import random
  import sys
  import chainer
  from chainer import configuration
  from chainer.dataset import convert
  import chainer.functions
  from chainer.functions.loss import softmax_cross_entropy
  import chainer.links
  from chainer import serializers
  from chainer import static_graph
  import chainerx
label_desc: Use image node_id 1 to create a new RNNForLMSlice object from inherited base classes: chainer with example: obj = RNNForLMSlice(n_vocab, n_units)