我需要多输入变量多步预测的pytorch LSTM程序

以下是一个示例程序，用于多输入变量的多步预测：
import torch
import torch.nn as nn
import numpy as np

# 定义LSTM模型
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size)
self.fc = nn.Linear(hidden_size, output_size)

def forward(self, input):
h0 = torch.zeros(1, input.size(1), self.hidden_size).to(input.device)
c0 = torch.zeros(1, input.size(1), self.hidden_size).to(input.device)
output, _ = self.lstm(input, (h0, c0))
output = self.fc(output[-1])
return output

# 定义训练函数
def train(model, train_input, train_target, optimizer, criterion, num_epochs):
for epoch in range(num_epochs):
optimizer.zero_grad()
output = model(train_input)
loss = criterion(output, train_target)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch, num_epochs, loss.item()))

# 生成随机数据
data = np.random.rand(1000, 5)
target = np.random.rand(1000, 2)

# 划分训练集和测试集
train_data = data[:800, :]
train_target = target[:800, :]
test_data = data[800:, :]
test_target = target[800:, :]

# 将数据转换为PyTorch张量
train_input = torch.from_numpy(train_data[:, None, :]).float()
train_target = torch.from_numpy(train_target).float()
test_input = torch.from_numpy(test_data[:, None, :]).float()
test_target = torch.from_numpy(test_target).float()

# 定义模型和优化器
input_size = train_data.shape[1]
output_size = train_target.shape[1]
hidden_size = 10
model = LSTM(input_size, hidden_size, output_size)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()

# 训练模型
num_epochs = 1000
train(model, train_input, train_target, optimizer, criterion, num_epochs)

# 测试模型
with torch.no_grad():
test_output = model(test_input)
test_loss = criterion(test_output, test_target)
print('Test Loss: {:.4f}'.format(test_loss.item()))
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这个程序中，我们首先定义了一个LSTM类，该类包含一个LSTM层和一个全连接层。然后我们定义了一个train函数，用于训练模型。接下来，我们生成一些随机数据并将其划分为训练集和测试集，并将其转换为PyTorch张量。然后，我们定义了一个LSTM模型和一个优化器，并使用train函数训练模型。最后，我们使用训练好的模型对测试集进行预测，并计算测试误差。