写一个复杂的LSTM的nn.module类，并给出各参数

好的，下面是一个复杂的LSTM的nn.module类的实现，其中包含了多个LSTM层、Dropout层和全连接层：

import torch.nn as nn

class ComplexLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, dropout=0.5):
        super(ComplexLSTM, self).__init__()
        
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        
        self.lstm_layers = nn.ModuleList()
        self.dropout_layers = nn.ModuleList()
        
        for i in range(num_layers):
            if i == 0:
                lstm_layer = nn.LSTM(input_size, hidden_size, bidirectional=True)
            else:
                lstm_layer = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True)
                
            dropout_layer = nn.Dropout(p=dropout)
            
            self.lstm_layers.append(lstm_layer)
            self.dropout_layers.append(dropout_layer)
            
        self.fc_layer = nn.Linear(hidden_size * 2, 1)
        
    def forward(self, input_seq):
        h_t = torch.zeros(2 * self.num_layers, input_seq.size(0), self.hidden_size).to(device) # initial hidden state
        c_t = torch.zeros(2 * self.num_layers, input_seq.size(0), self.hidden_size).to(device) # initial cell state
        
        for i in range(self.num_layers):
            lstm_out, (h_t, c_t) = self.lstm_layers[i](input_seq, (h_t, c_t))
            input_seq = lstm_out
            input_seq = self.dropout_layers[i](input_seq)
        
        output = self.fc_layer(input_seq)
        
        return output

其中各参数含义如下：

- `input_size`：输入特征的维度。
- `hidden_size`：LSTM的隐藏层维度。
- `num_layers`：LSTM的层数。
- `dropout`：Dropout层的概率。默认为0.5。