给我写一个使用pytorch的LSTM模型代码

好的，这里是使用 PyTorch 实现 LSTM 模型的简单代码示例：

import torch
import torch.nn as nn

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out

# Instantiate the model with hyperparameters
input_size = 1
hidden_size = 32
num_layers = 1
num_classes = 1
model = LSTM(input_size, hidden_size, num_layers, num_classes)

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

在上述代码中，我们首先定义了一个包含 LSTM 层的 PyTorch 模型。然后，我们将模型移动到 GPU 上（如果可用），并定义损失函数和优化器。最后，我们在循环中训练模型，每次迭代时都会执行