(vocab_size, encoder_output_size, pooling)
| 22 | @pytest.mark.parametrize("encoder_output_size", [4, 21]) |
| 23 | @pytest.mark.parametrize("pooling", ["mean", "max", "CLS"]) |
| 24 | def test_score(vocab_size, encoder_output_size, pooling): |
| 25 | decoder = LinearDecoder(vocab_size, encoder_output_size, pooling) |
| 26 | x = torch.randn(10, encoder_output_size) |
| 27 | score, _ = decoder.score(ys=None, state=None, x=x) |
| 28 | assert score.shape == (vocab_size,), score.shape |
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