__init__.py 文件源码

def sample(self):
        """
        :returns: a random subsample of `range(size)`
        :rtype: torch.autograd.Variable of torch.LongTensor
        """
        subsample_size = self.subsample_size
        if subsample_size is None or subsample_size > self.size:
            subsample_size = self.size
        if subsample_size == self.size:
            result = Variable(torch.LongTensor(list(range(self.size))))
        else:
            result = Variable(torch.randperm(self.size)[:self.subsample_size])
        return result.cuda() if self.use_cuda else result