我构建了一个自定义的神经网络模型，如下所示：

class MyNNet(torch.nn.Module):    def __init__(self, inp_dim, n_classes):    super(MyNNet, self).__init__()    self.flat = torch.nn.Flatten()    self.l1 = torch.nn.Linear(inp_dim * inp_dim, 32)    self.l2 = torch.nn.Linear(32, 16)    self.l3 = torch.nn.Linear(16, n_classes)    def forward(self, X):    out = self.flat(X)    out = F.relu(self.l1(out))    out = F.relu(self.l2(out))    return self.l3(out)

以及一个简单的训练脚本，用于更新模型参数：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model = MyNNet(28, 10)model.to(device)optimizer = torch.optim.Adam(model.parameters())loss = torch.nn.CrossEntropyLoss()epochs = 20for e in range(epochs):  train_l = 0.  for i, (s, c) in enumerate(train_loader):        s.to(device)    c.to(device)    y_hat = model(s)    l = loss(y_hat, c)    train_l += l    l.backward()    optimizer.step()    optimizer.zero_grad()  print(f'Epoch: {e}, AvgLoss: {train_l / len(train_loader)}')

在脚本中，我将模型存储到cuda，并对数据集（MNIST）的每一批次也这样做。然而，出现了以下错误： Expected all tensors to be on the same device, but found at least two devices但当我注释掉 model.to(device) 后，脚本就能正常工作。这是否意味着PyTorch会自动将自定义模型存储到cuda？
谢谢。

回答：

与Module（其中.to(...)以原地方式工作）不同，在将Tensor移动到设备时，需要重新赋值：

s = s.to(device)c = c.to(device)