train
device = get_device()
print(device)
def train(train_set):
    epochs = 1000

    learning_rate = 0.01
    optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)
    loss_f = nn.MSELoss()
    epoch = 0
    while epoch < epochs:
        for input,label in train_set:
            optimizer.zero_grad()
            input = input.to(device)
            label = label.to(device)
            print(input.shape)
            print(label.shape)
            output = model(input)
            print(output.shape)
            loss = loss_f(output,label)

            loss.backward()
            optimizer.step()

        print("epoch:{},loss:{}".format(epoch,loss))
        epoch+=1

output
cuda
torch.Size([270, 93])
torch.Size([270])
torch.Size([270])

How can i fix this