align/models.py [364:373]:
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        lengths = [len(x) for x in tokens]
        max_length = max(lengths)
        for i in range(len(tokens)):
            word_ids.append(self.tokenizer.convert_tokens_to_ids(['<pad>'] * (max_length - len(tokens[i])) + tokens[i]))
        encoder_input = {
            'src_tokens': torch.tensor(word_ids).to(self.device),
            'src_lengths': torch.tensor(lengths).to(self.device)
        }
        encoder_outs = self.model.forward_encoder(encoder_input)
        np_encoder_outs = encoder_outs[0].encoder_out.float().detach()
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src/models.py [40:49]:
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        lengths = [len(x) for x in tokens]
        max_length = max(lengths)
        for i in range(len(tokens)):
            word_ids.append(self.tokenizer.convert_tokens_to_ids(['<pad>'] * (max_length - len(tokens[i])) + tokens[i]))
        encoder_input = {
            'src_tokens': torch.tensor(word_ids).to(self.device),
            'src_lengths': torch.tensor(lengths).to(self.device)
        }
        encoder_outs = self.model.forward_encoder(encoder_input)
        np_encoder_outs = encoder_outs[0].encoder_out.float().detach()
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