def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):

model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.nlayers, args.dr

opout, args.tied)

        # Optionally tie weights as in:
        # "Using the Output Embedding to Improve Language Models" (Press & Wolf 2017)
        # https://arxiv.org/abs/1608.05859
        # and
        # "Tying Word Vectors and Word Classifiers:
        # A Loss Framework for Language Modeling" (Inan et al. 2017)
        # https://arxiv.org/abs/1611.01462
        if tie_weights:
            if nhid != ninp:
                raise ValueError('When using the tied flag, nhid must be equal to emsize')
            self.decoder.weight = self.encoder.weight