给出一段Python代码，并调用pytorch，实现以下功能，将一个Excel表格中的5列数据处理成多变量lstm预测模型的输入类型

以下是一段Python代码，使用pytorch实现将Excel表格中的5列数据处理成多变量lstm预测模型的输入类型：

import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader

# 读取Excel表格数据
data = pd.read_excel('data.xlsx')

# 将数据转换为PyTorch张量
data_tensor = torch.tensor(data.values, dtype=torch.float32)

# 定义数据集类
class MyDataset(Dataset):
    def __init__(self, data_tensor):
        self.data_tensor = data_tensor

    def __len__(self):
        return len(self.data_tensor) - 1

    def __getitem__(self, idx):
        x = self.data_tensor[idx:idx+5]
        y = self.data_tensor[idx+5][-1]
        return x, y

# 定义数据加载器
batch_size = 32
data_loader = DataLoader(MyDataset(data_tensor), batch_size=batch_size, shuffle=True)

# 定义多变量LSTM模型
class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.lstm = torch.nn.LSTM(input_size=5, hidden_size=64, num_layers=2, batch_first=True)
        self.fc = torch.nn.Linear(64, 1)

    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :])
        return out

# 定义训练函数
def train(model, data_loader, optimizer, criterion, num_epochs):
    for epoch in range(num_epochs):
        for x, y in data_loader:
            optimizer.zero_grad()
            out = model(x)
            loss = criterion(out, y.unsqueeze(1))
            loss.backward()
            optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 训练模型
model = MyModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.MSELoss()
num_epochs = 100
train(model, data_loader, optimizer, criterion, num_epochs)

这段代码将Excel表格中的5列数据转换为PyTorch张量，并定义了一个数据集类和数据加载器，用于将数据输入到多变量LSTM模型中进行训练。训练过程中使用均方误差损失函数和Adam优化器进行优化。