code: def forward(self, x):
      h0 = self.embed(x)
      h1 = self.l1(F.dropout(h0))
      h2 = self.l2(F.dropout(h1))
      y = self.l3(F.dropout(h2))
      return y
  
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 3 for calling the RNNForLM obj's underlying member method code with example usage: obj.forward(x) and returns: y