def evaluate(encoder, decoder, sentence, input_lang, output_lang):
    with [torch.no_grad](https://docs.pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad "torch.no_grad")():
        input_tensor = tensorFromSentence(input_lang, sentence)

        encoder_outputs, encoder_hidden = encoder(input_tensor)
        decoder_outputs, decoder_hidden, decoder_attn = decoder(encoder_outputs, encoder_hidden)

        _, topi = decoder_outputs.topk(1)
        decoded_ids = topi.squeeze()

        decoded_words = []
        for idx in decoded_ids:
            if idx.item() == EOS_token:
                decoded_words.append('<EOS>')
                break
            decoded_words.append(output_lang.index2word[idx.item()])
    return decoded_words, decoder_attn