(vocab_size, encoder_output_size, pooling, dropout)
| 24 | @pytest.mark.parametrize("pooling", ["mean", "max", "CLS"]) |
| 25 | @pytest.mark.parametrize("dropout", [0.1, 0.0]) |
| 26 | def test_score(vocab_size, encoder_output_size, pooling, dropout): |
| 27 | decoder = LinearDecoder(vocab_size, encoder_output_size, pooling, dropout) |
| 28 | x = torch.randn(10, encoder_output_size) |
| 29 | score, _ = decoder.score(ys=None, state=None, x=x) |
| 30 | assert score.shape == (vocab_size,), score.shape |
| 31 | |
| 32 | |
| 33 | @pytest.mark.execution_timeout(30) |
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