class_docstr: An architecuture which jointly learns translation and target words
  prediction, as described in http://aclweb.org/anthology/D17-1013.
class_docstr_tok: ["An","architecuture","which","jointly","learns","translation","and","target","words","prediction",",","as","described","in","http",":","\/\/aclweb.org\/anthology\/D17-1013","."]
code: def get_predictor_normalized_probs(self, pred_output, log_probs):
      return self.predictor.get_normalized_probs(pred_output, log_probs)
  
file_path: translate/pytorch_translate/word_prediction/word_prediction_model.py
imports: from fairseq.models import FairseqEncoderDecoderModel, register_model, register_model_architecture
  from pytorch_translate import rnn
  from pytorch_translate.rnn import LSTMSequenceEncoder, RNNDecoder, RNNEncoder, RNNModel
  from pytorch_translate.utils import torch_find
  from pytorch_translate.word_prediction import word_predictor
label_desc: Use image node_id 3 for calling the WordPredictionModel obj's underlying member method code with example usage: obj.get_predictor_normalized_probs(pred_output, log_probs) and returns: self