(self, a, axis=None, keepdims=False)
| 2205 | return torch.unique(a, return_inverse=return_inverse) |
| 2206 | |
| 2207 | def logsumexp(self, a, axis=None, keepdims=False): |
| 2208 | if axis is not None: |
| 2209 | return torch.logsumexp(a, dim=axis, keepdim=keepdims) |
| 2210 | else: |
| 2211 | return torch.logsumexp(a, dim=tuple(range(len(a.shape))), keepdim=keepdims) |
| 2212 | |
| 2213 | def stack(self, arrays, axis=0): |
| 2214 | return torch.stack(arrays, dim=axis) |