Forward pass placeholder for subclasses. Args: x (torch.Tensor): Input spike count tensor. T (int | None): Optional number of timesteps. If None, determine from x. Returns: torch.Tensor: Spike sequence of shape [T, *x.shape].
(self,)
| 16 | self.is_two_complement = False |
| 17 | |
| 18 | def forward(self,): |
| 19 | """ |
| 20 | Forward pass placeholder for subclasses. |
| 21 | |
| 22 | Args: |
| 23 | x (torch.Tensor): Input spike count tensor. |
| 24 | T (int | None): Optional number of timesteps. If None, determine from x. |
| 25 | |
| 26 | Returns: |
| 27 | torch.Tensor: Spike sequence of shape [T, *x.shape]. |
| 28 | """ |
| 29 | raise NotImplementedError |
| 30 | |
| 31 | def neuronal_charge(self,): |
| 32 | raise NotImplementedError |
nothing calls this directly
no outgoing calls
no test coverage detected