基于pytorch写一个多对多预测代码，数据集名称为data.csv，前四列为特征，其余列为标签，前800行为训练集其余为测试集，将预测值与真实值输出到excel工作表中

在这里给出一个示例代码，它实现了你所描述的功能：

import pandas as pd
import torch
import torch.nn as nn

# 读入数据
df = pd.read_csv("data.csv")
train_df = df.iloc[:800, :]
test_df = df.iloc[800:, :]

# 特征和标签
X_train = train_df.iloc[:, :4]
y_train = train_df.iloc[:, 4:]
X_test = test_df.iloc[:, :4]
y_test = test_df.iloc[:, 4:]

# 转换为tensor
X_train = torch.tensor(X_train.values).float()
y_train = torch.tensor(y_train.values).float()
X_test = torch.tensor(X_test.values).float()
y_test = torch.tensor(y_test.values).float()

# 定义模型
class MultiOutputModel(nn.Module):
    def __init__(self):
        super(MultiOutputModel, self).__init__()
        self.linear = nn.Linear(4, y_train.shape[1])

    def forward(self, x):
        out = self.linear(x)
        return out

model = MultiOutputModel()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练
for epoch in range(1000):
    # 前向传播
    y_pred = model(X_train)
    # 计算损失
    loss = criterion(y_pred, y_train)
    # 反向传播
    loss.backward()
    # 更新参数
    optimizer.step()
    # 清空梯度
    optimizer.zero_grad()

# 测试
with torch.no_grad():
    y_test_pred = model(X_test)

# 将预测值和真实值写入excel
result_df = pd.DataFrame({"y_test": y_test.numpy().flatten(), "y_test_pred": y_test_pred.numpy().flatten()})
result_df.to_excel("result.xlsx", index=False)

请注意，这只是一个示例代码，它可能需要根据你