帮我用pytorch写一段CNN和GRU结合的网络模型代码

当然可以帮您写一段使用PyTorch实现CNN和GRU结合的网络模型代码。以下是一个简单的示例：

import torch
import torch.nn as nn

class CNN_GRU_Model(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_filters, filter_sizes, dropout):
        super(CNN_GRU_Model, self).__init__()
        
        # Embedding layer
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        
        # Convolutional layers
        self.convs = nn.ModuleList([
            nn.Conv2d(in_channels=1,
                      out_channels=num_filters,
                      kernel_size=(fs, embedding_dim))
            for fs in filter_sizes
        ])
        
        # GRU layer
        self.gru = nn.GRU(embedding_dim*num_filters, hidden_dim)
        
        # Dropout layer
        self.dropout = nn.Dropout(dropout)
        
        # Fully-connected layer
        self.fc = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, text):
        # text shape: (seq_len, batch_size)
        
        embedded = self.embedding(text)
        # embedded shape: (seq_len, batch_size, embedding_dim)
        
        embedded = embedded.unsqueeze(1)
        # embedded shape: (batch_size, 1, seq_len, embedding_dim)
        
        conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]
        # conved[i] shape: (batch_size, num_filters, seq_len - filter_sizes[i] + 1)
        
        pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]
        # pooled[i] shape: (batch_size, num_filters)
        
        cat = self.dropout(torch.cat(pooled, dim=1))
        # cat shape: (batch_size, num_filters * len(filter_sizes))
        
        _, hidden = self.gru(cat.unsqueeze(0))
        # hidden shape: (1, batch_size, hidden_dim)
        
        hidden = self.dropout(hidden.squeeze(0))
        # hidden shape: (batch_size, hidden_dim)
        
        output = self.fc(hidden)
        # output shape: (batch_size, output_dim)
        
        return output

请注意，这只是一个简单的模型示例，您可能需要根据您的具体任务进行适当的修改。此模型将输入文本序列作为参数，并输出对应的预测结果。您可以根据需要调整模型的超参数和层配置。