NMT/src/model/attention.py [180:206]:
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        self.eos_index = params.eos_index
        self.pad_index = params.pad_index
        self.bos_index = params.bos_index

        # words allowed for generation
        self.vocab_mask_neg = params.vocab_mask_neg if len(params.vocab) > 0 else None

        # embedding layers
        if self.share_encdec_emb:
            logger.info("Sharing encoder and decoder input embeddings")
            embeddings = encoder.embeddings
        else:
            if self.share_lang_emb:
                logger.info("Sharing decoder input embeddings")
                layer_0 = nn.Embedding(self.n_words[0], self.emb_dim, padding_idx=self.pad_index)
                nn.init.normal_(layer_0.weight, 0, 0.1)
                nn.init.constant_(layer_0.weight[self.pad_index], 0)
                embeddings = [layer_0 for _ in range(self.n_langs)]
            else:
                embeddings = []
                for n_words in self.n_words:
                    layer_i = nn.Embedding(n_words, self.emb_dim, padding_idx=self.pad_index)
                    nn.init.normal_(layer_i.weight, 0, 0.1)
                    nn.init.constant_(layer_i.weight[self.pad_index], 0)
                    embeddings.append(layer_i)
            embeddings = nn.ModuleList(embeddings)
        self.embeddings = embeddings
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NMT/src/model/seq2seq.py [185:213]:
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        self.eos_index = params.eos_index
        self.pad_index = params.pad_index
        self.bos_index = params.bos_index

        # words allowed for generation
        self.vocab_mask_neg = params.vocab_mask_neg if len(params.vocab) > 0 else None

        # embedding layers
        if self.share_encdec_emb:
            logger.info("Sharing encoder and decoder input embeddings")
            embeddings = encoder.embeddings
        else:
            if self.share_lang_emb:
                logger.info("Sharing decoder input embeddings")
                layer_0 = nn.Embedding(self.n_words[0], self.emb_dim, padding_idx=self.pad_index)
                nn.init.normal_(layer_0.weight, 0, 0.1)
                nn.init.constant_(layer_0.weight[self.pad_index], 0)

                embeddings = [layer_0 for _ in range(self.n_langs)]
            else:
                embeddings = []
                for n_words in self.n_words:
                    layer_i = nn.Embedding(n_words, self.emb_dim, padding_idx=self.pad_index)
                    nn.init.normal_(layer_i.weight, 0, 0.1)
                    nn.init.constant_(layer_i.weight[self.pad_index], 0)

                    embeddings.append(layer_i)
            embeddings = nn.ModuleList(embeddings)
        self.embeddings = embeddings
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