docstr: Perform a forward pass on the supplied list of words.
  
  The RNN is unrolled for a number of time slices equal to the
  length of the supplied word sequence.
  
  Args:
      words_labels (list of Variable): The list of input words to the
          unrolled neural network.
  
  Returns the corresponding lest of output variables of the same
  length as the input sequence.
docstr_tok: ["Perform","a","forward","pass","on","the","supplied","list","of","words",".","The","RNN","is","unrolled","for","a","number","of","time","slices","equal","to","the","length","of","the","supplied","word","sequence",".","Args",":","words_labels","(","list","of","Variable",")",":","The","list","of","input","words","to","the","unrolled","neural","network",".","Returns","the","corresponding","lest","of","output","variables","of","the","same","length","as","the","input","sequence","."]
code: def __call__(self, words):
      """Perform a forward pass on the supplied list of words.
  
      The RNN is unrolled for a number of time slices equal to the
      length of the supplied word sequence.
  
      Args:
          words_labels (list of Variable): The list of input words to the
              unrolled neural network.
  
      Returns the corresponding lest of output variables of the same
      length as the input sequence.
      """
      outputs = []
      for ind in range(len(words)):
          word = words[ind]
          y = self.rnn(word)
          outputs.append(y)
  
      return outputs
  
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 2 for calling the RNNForLMUnrolled obj's underlying member method code with example usage: obj.__call__(words) and returns: outputs