densenet + 注意力机制 的网络结构绘图代码，请列个例子

这是一个使用DenseNet和注意力机制的网络结构图代码的示例：

from torch import nn
from graphviz import Digraph

# 定义注意力机制模块
class Attention(nn.Module):
    def __init__(self, in_channels):
        super(Attention, self).__init__()
        self.conv = nn.Conv2d(in_channels, 1, kernel_size=1)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        att = self.sigmoid(self.conv(x))
        return att * x

# 定义DenseNet模块
class DenseNet(nn.Module):
    def __init__(self, in_channels, growth_rate, num_blocks):
        super(DenseNet, self).__init__()
        self.in_channels = in_channels
        self.growth_rate = growth_rate
        self.num_blocks = num_blocks
        
        self.layers = self._make_layers()
        self.attention = Attention(self.in_channels + self.growth_rate * self.num_blocks)
        
    def _make_layers(self):
        layers = []
        
        for i in range(self.num_blocks):
            layers.append(nn.Conv2d(self.in_channels + i * self.growth_rate, self.growth_rate, kernel_size=3))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = x
        
        for i in range(self.num_blocks):
            dense_out = self.layers[i](out)
            out = torch.cat([out, dense_out], dim=1)
        
        out = self.attention(out)
        return out

# 创建DenseNet模型实例
model = DenseNet(in_channels=3, growth_rate=32, num_blocks=4)

# 绘制网络结构图
dot = Digraph(comment='DenseNet with Attention')

# 输入节点
dot.node('input', 'Input')

# DenseNet模块节点
for i in range(model.num_blocks):
    dot.node(f'dense_block_{i+1}', f'Dense Block {i+1}')

# 注意力机制节点
dot.node('attention', 'Attention')

# 输出节点
dot.node('output', 'Output')

# 连接各个节点
dot.edge('input', 'dense_block_1')
for i in range(model.num_blocks-1):
    dot.edge(f'dense_block_{i+1}', f'dense_block_{i+2}')
dot.edge(f'dense_block_{model.num_blocks}', 'attention')
dot.edge('attention', 'output')

# 设置节点样式
dot.node_attr.update(style='filled', color='lightblue')

# 保存并展示图形
dot.render('densenet_attention.gv', view=True)

这个示例中，我们定义了一个具有4个DenseNet模块和注意力机制的网络结构。每个DenseNet模块由一个卷积层组成，注意力机制模块通过一个卷积层和Sigmoid激活函数实现。使用`graphviz`库创建有向图表示网络结构，并将其保存为`densenet_attention.gv`文件，并在终端展示图形。你可以根据需要调整模型的参数和网络结构。