(self, size, type_as=None)
| 2259 | return torch.randn(size=size, generator=self.rng_) |
| 2260 | |
| 2261 | def randperm(self, size, type_as=None): |
| 2262 | if not isinstance(size, int): |
| 2263 | raise ValueError("size must be an integer") |
| 2264 | if type_as is not None: |
| 2265 | generator = ( |
| 2266 | self.rng_cuda_ if self.device_type(type_as) == "GPU" else self.rng_ |
| 2267 | ) |
| 2268 | return torch.randperm( |
| 2269 | n=size, |
| 2270 | dtype=type_as.dtype, |
| 2271 | generator=generator, |
| 2272 | device=type_as.device, |
| 2273 | ) |
| 2274 | else: |
| 2275 | return torch.randperm(n=size, generator=self.rng_) |
| 2276 | |
| 2277 | def coo_matrix(self, data, rows, cols, shape=None, type_as=None): |
| 2278 | if type_as is None: |
nothing calls this directly
no test coverage detected