请用pytorch来实现一段用LSTM来预测的代码

好的，我可以为你提供一个使用PyTorch实现的LSTM预测代码段，请参考下面的代码：

import torch
import torch.nn as nn

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc1 = nn.Linear(hidden_size, output_size)
        
    def forward(self, input):
        hidden = (torch.zeros(1, 1, self.hidden_size), 
                  torch.zeros(1, 1, self.hidden_size))
        lstm_out, hidden = self.lstm(input.view(len(input), 1, -1), hidden)
        output = self.fc1(lstm_out.view(len(input), -1))
        return output[-1]

# 定义数据集
data = torch.FloatTensor([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
target = torch.FloatTensor([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])

# 实例化模型
input_size = 1
hidden_size = 2
output_size = 1
model = LSTMModel(input_size, hidden_size, output_size)

# 设置超参数
learning_rate = 0.1
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(1000):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

# 使用模型进行预测
input = torch.FloatTensor([1.0])
output = model(input)
print(output.item())

该代码段实现了一个简单的使用LSTM模型来预测数列下一个数的问题。如果你有任何问题，请随时问我！