把上面两段代码合在一起

好的，如果你想要将这两段代码合并，你可以创建一个新的类，把原有的`enhance_net_nopool`和前面提到的`forward`方法整合在一个类里。例如：

import torch.nn as nn

class EnhancedNet(nn.Module):  # 继承nn.Module
    def __init__(self):
        super(EnhancedNet, self).__init__()
        
        self.relu = nn.ReLU(inplace=True)
        self.number_f = 32
        self.conv_layers = nn.Sequential(
            nn.Conv2d(3, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f * 2, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f * 2, number_f, 3, 1, 1, bias=True),
            nn.Conv2d(number_f * 2, 24, 3, 1, 1, bias=True),
        )

        self.maxpool = nn.MaxPool2d(2, stride=2)
        self.upsample = nn.UpsamplingBilinear2d(scale_factor=2)

    def forward(self, x):
        x1 = self.relu(self.conv_layers[0](x))  # 使用Sequential的索引来代替e_conv1
        x2 = self.relu(self.conv_layers[1](x1))
        x3 = self.relu(self.conv_layers[2](x2))
        x4 = self.relu(self.conv_layers[3](x3))
        x5 = self.relu(self.conv_layers[4](torch.cat([x3, x4], 1)))
        x6 = self.relu(self.conv_layers[5](torch.cat([x2, x5], 1)))

        x_r = F.tanh(self.conv_layers[6](torch.cat([x1, x6], 1)))
        r1, r2, r3, r4, r5, r6, r7, r8 = torch.split(x_r, 3, dim=1)

        # 保持原有增强计算逻辑
        x = x + r1 * (torch.pow(x, 2) - x)
        ... (继续其他增强步骤并更新变量名)

        enhance_image_1 = ... (此处根据原代码计算)
        enhance_image = ... (同上)
        r = torch.cat([r1, r2, r3, r4, r5, r6, r7, r8], 1)

        return enhance_image_1, enhance_image, r

这个新的`EnhancedNet`类结合了之前的卷积结构和增强计算逻辑，方便管理和使用。注意，为了简洁起见，我保留了原有的增强计算逻辑，你需要根据实际需求调整这部分代码。