def forward(self, x):
        """
        Args:
            x (:class:`torch.FloatTensor` [batch size, sequence length, rnn hidden size]): Input to
                apply dropout too.
        """
        if not self.training or not self.p:
            return x
        x = x.clone()
        mask = x.new_empty(1, x.size(1), x.size(2), requires_grad=False).bernoulli_(1 - self.p)
        mask = mask.div_(1 - self.p)
        mask = mask.expand_as(x)
        return x * mask