请写一个LSTM的pytorch代码

好的，以下是一个基本的LSTM模型的PyTorch代码：

import torch
import torch.nn as nn

class MyLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(MyLSTM, 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):
        # Initialize hidden state with zeros
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
        # Initialize cell state with zeros
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)

        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0,c0))

        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out

这个模型使用LSTM作为主要的循环神经网络层，使用了一个线性全连接层将LSTM的输出转换成预测值。