帮我用pytorch写一个三模态的时序模型代码

我可以回答这个问题。以下是一个使用 PyTorch 编写的三模态时序模型的示例代码：

import torch
import torch.nn as nn

class ThreeModalLSTM(nn.Module):
    def __init__(self, input_size1, input_size2, input_size3, hidden_size, num_layers):
        super(ThreeModalLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers

        self.lstm1 = nn.LSTM(input_size1, hidden_size, num_layers, batch_first=True)
        self.lstm2 = nn.LSTM(input_size2, hidden_size, num_layers, batch_first=True)
        self.lstm3 = nn.LSTM(input_size3, hidden_size, num_layers, batch_first=True)

        self.fc = nn.Linear(hidden_size * 3, 1)

    def forward(self, x1, x2, x3):
        h = torch.zeros(self.num_layers, x1.size(), self.hidden_size).to(x1.device)
        c = torch.zeros(self.num_layers, x1.size(), self.hidden_size).to(x1.device)

        out1, _ = self.lstm1(x1, (h, c))
        out2, _ = self.lstm2(x2, (h, c))
        out3, _ = self.lstm3(x3, (h, c))

        out = torch.cat((out1[:, -1, :], out2[:, -1, :], out3[:, -1, :]), dim=1)
        out = self.fc(out)

        return out

这个模型接受三个输入张量 `x1`、`x2` 和 `x3`，分别表示三个模态的时序数据。模型使用三个 LSTM 层分别处理这三个输入，然后将三个 LSTM 层的最后一个时间步的输出拼接在一起，通过一个全连接层输出一个标量。