Pytorch中的.data有什么用

Question

import torch
from torch import nn
from torch import optim
from torchvision import datasets, transforms

class MLP(nn.Module):
    def __init__(self):
        super(MLP,self).__init__()

        self.model=nn.Sequential(#sequential串联起来
            nn.Linear(784,200),
            nn.LeakyReLU(inplace=True),
            nn.Linear(200, 200),
            nn.LeakyReLU(inplace=True),
            nn.Linear(200,10),
            nn.LeakyReLU(inplace=True),
        )
    def forward(self,x):
        x = self.model(x)
        return x

device=torch.device('cuda:0')
net = MLP().to(device)  # 网络结构 就是foward函数
optimizer=optim.SGD(net.parameters(),lr=learning_rate)  # 使用nn.Module可以直接代替之前[w1,b1,w2,b2.。。]
criteon=nn.CrossEntropyLoss().to(device)

for data,target in test_loader:
    data=data.view(-1,28*28) 
    data, target = data.to(device), target.to(device)
    logits = net(data)

logits.data和logits有什么区别呀 这个data有啥用啊 我用torch.all(torch.eq(logits,logits.data))
发现结果是true 好像没啥区别