| import torch | |
| class Model(torch.nn.Module): | |
| def __init__(self): | |
| super(Model, self).__init__() | |
| self.fc1 = torch.nn.Linear(10, 5) | |
| self.threshold = 0. | |
| def forward(self, x): | |
| ## generates a random float the same size as x | |
| return torch.randn(x.shape[0]).to(x.device) | |