写一个LSTM分类模型

好的，下面是一个简单的LSTM分类模型的代码示例：

import torch
import torch.nn as nn

class LSTMClassifier(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(LSTMClassifier, self).__init__()
        self.hidden_size = hidden_size
        
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers=2, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
        
    def forward(self, x):
        embedded = self.embedding(x)
        output, _ = self.lstm(embedded)
        output = output[:, -1, :]
        logits = self.fc(output)
        return logits

这个模型接受一个输入序列 x，通过嵌入层将其转换为嵌入向量，然后输入到LSTM层中。最后将LSTM层的输出通过全连接层进行分类预测。