(conds)
| 161 | @torch.no_grad() |
| 162 | @torch.inference_mode() |
| 163 | def clone_cond(conds): |
| 164 | results = [] |
| 165 | |
| 166 | for c, p in conds: |
| 167 | p = p["pooled_output"] |
| 168 | |
| 169 | if isinstance(c, torch.Tensor): |
| 170 | c = c.clone() |
| 171 | |
| 172 | if isinstance(p, torch.Tensor): |
| 173 | p = p.clone() |
| 174 | |
| 175 | results.append([c, {"pooled_output": p}]) |
| 176 | |
| 177 | return results |
| 178 | |
| 179 | |
| 180 | @torch.no_grad() |