pytorch程序需要返回“石头、布、剪刀”游戏的结果。输入以单热Tensor的形式给出：[ [1，0，0]，[0，1，0] ]（[1，0，0]-石头，[0，1，0] -剪刀）输出必须为：[1，0]（第一个玩家赢，第二个玩家输）。这段代码有什么问题？

import torch
from torch import nn
import torch.utils.data as data
torch.manual_seed(42)

input = [[1, 0, 0], [0, 1, 0]], [[1, 0, 0], [0, 0, 1]], [[0, 1, 0], [0, 1, 0]], [[0, 0, 1], [1, 0, 0]]
input = torch.tensor(input, dtype=torch.float32)
result = [[1, 0], [0, 1], [1, 1], [1, 0]]
result = torch.tensor(result, dtype=torch.float32)

class LinearRegression(nn.Module):
    def __init__(self, num_inputs, num_outputs):
        super().__init__()
        self.linear = nn.Linear(num_inputs, num_outputs)
        self.act_fn = nn.Sigmoid()
    def forward(self, x):
        x = self.linear(x)
        x = self.act_fn(x)
        return x

model = LinearRegression(num_inputs=3, num_outputs=2)
print(model)

# Training loop

for name, param in model.named_parameters():
    print(f"Parameter {name}, shape {param.shape}")

for x in input:
  print(model(x))

print(model(input))
print('####################')
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=0.3)
lossfunc = nn.MSELoss()

# Training loop

for _ in range(1000):
    res = model(input)
    res = res.squeeze(dim=1)

    loss = lossfunc(res, result)
    # print(loss)

    ## Backpropagation

    optimizer.zero_grad()
    loss.backward()

    optimizer.step()

print(model(input))