NMT/src/model/attention.py [794:821]:
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        lm = LM(params, encoder, decoder)
        logger.info("")
    else:
        lm = None

    # cuda - models on CPU will be synchronized and don't need to be reloaded
    if cuda:
        encoder.cuda()
        decoder.cuda()
        if len(params.vocab) > 0:
            decoder.vocab_mask_neg = [x.cuda() for x in decoder.vocab_mask_neg]
        if discriminator is not None:
            discriminator.cuda()
        if lm is not None:
            lm.cuda()

        # initialize the model with pretrained embeddings
        assert not (getattr(params, 'cpu_thread', False)) ^ (data is None)
        if data is not None:
            initialize_embeddings(encoder, decoder, params, data)

        # reload encoder / decoder / discriminator
        if params.reload_model != '':
            assert os.path.isfile(params.reload_model)
            logger.info("Reloading model from %s ..." % params.reload_model)
            reloaded = torch.load(params.reload_model)
            if params.reload_enc:
                logger.info("Reloading encoder...")
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NMT/src/model/seq2seq.py [433:460]:
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        lm = LM(params, encoder, decoder)
        logger.info("")
    else:
        lm = None

    # cuda - models on CPU will be synchronized and don't need to be reloaded
    if cuda:
        encoder.cuda()
        decoder.cuda()
        if len(params.vocab) > 0:
            decoder.vocab_mask_neg = [x.cuda() for x in decoder.vocab_mask_neg]
        if discriminator is not None:
            discriminator.cuda()
        if lm is not None:
            lm.cuda()

        # initialize the model with pretrained embeddings
        assert not (getattr(params, 'cpu_thread', False)) ^ (data is None)
        if data is not None:
            initialize_embeddings(encoder, decoder, params, data)

        # reload encoder / decoder / discriminator
        if params.reload_model != '':
            assert os.path.isfile(params.reload_model)
            logger.info("Reloading model from %s ..." % params.reload_model)
            reloaded = torch.load(params.reload_model)
            if params.reload_enc:
                logger.info("Reloading encoder...")
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