(clamp_range: SoftClampRange)
| 91 | """ |
| 92 | |
| 93 | def normalize(clamp_range: SoftClampRange) -> torch.Tensor: |
| 94 | value0, value1 = clamp_range |
| 95 | return value0 + (value1 - value0) * torch.tanh((tensor - value0) / (value1 - value0)) |
| 96 | |
| 97 | tensor_clamped = tensor |
| 98 | if min is not None: |