|  | 
| 7 | 7 | 
 | 
| 8 | 8 | 
 | 
| 9 | 9 | class TestNegConverter(DispatchTestCase): | 
| 10 |  | -    def test_neg(self): | 
|  | 10 | +    @parameterized.expand( | 
|  | 11 | +        [ | 
|  | 12 | +            ("2d_dim_dtype_float", (2, 2), torch.float), | 
|  | 13 | +            ("3d_dim_dtype_float", (2, 2, 2), torch.float), | 
|  | 14 | +         | 
|  | 15 | +        ] | 
|  | 16 | +    ) | 
|  | 17 | +    def test_neg_float(self, _, x, type): | 
| 11 | 18 |         class neg(nn.Module): | 
| 12 | 19 |             def forward(self, input): | 
| 13 | 20 |                 return torch.neg(input) | 
| 14 |  | - | 
| 15 |  | -        inputs = [torch.randn(1, 10)] | 
|  | 21 | +             | 
|  | 22 | +        inputs = [torch.randn(x, dtype=type)] | 
| 16 | 23 |         self.run_test( | 
| 17 | 24 |             neg(), | 
| 18 | 25 |             inputs, | 
| 19 | 26 |             expected_ops={torch.ops.aten.neg.default}, | 
| 20 | 27 |         ) | 
| 21 | 28 | 
 | 
|  | 29 | +    @parameterized.expand( | 
|  | 30 | +        [ | 
|  | 31 | +            ("2d_dim_dtype_int", (2, 2), torch.int32, 0, 5), | 
|  | 32 | +            ("3d_dim_dtype_int", (2, 2, 2), torch.int32, 0, 5), | 
|  | 33 | +        ] | 
|  | 34 | +    ) | 
|  | 35 | + | 
|  | 36 | +    def test_neg_int(self, _, x, type, min, max): | 
|  | 37 | +        class neg(nn.Module): | 
|  | 38 | +            def forward(self, input): | 
|  | 39 | +                return torch.neg(input) | 
|  | 40 | +             | 
|  | 41 | +        inputs = [torch.randint(min, max, (x), dtype=type)] | 
|  | 42 | + | 
|  | 43 | +        self.run_test( | 
|  | 44 | +            neg(), | 
|  | 45 | +            inputs, | 
|  | 46 | +            expected_ops={torch.ops.aten.neg.default}, | 
|  | 47 | +        ) | 
| 22 | 48 | 
 | 
| 23 | 49 | if __name__ == "__main__": | 
| 24 | 50 |     run_tests() | 
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