A real implementation of dropout with proper normalization.

true_dropout.py

class TrueDropout(torch.nn.Module):
    
    def __init__(self, p: float=0.5):
        super(TrueDropout, self).__init__()
        self.p = p
        if self.p < 0 or self.p > 1:
            raise ValueError("p must be a probability")
            
    def forward(self, x):
        if self.training:
            x = x.mul(torch.empty(x.size()[1]).uniform_(0, 1) >= self.p) * (1 / (1 - self.p))
        return x