class ResUp(nn.Module):

这是一个PyTorch的神经网络模块，用于上采样图像或特征图。其实现方式为ResNet中的上采样模块，通常用于图像分割或图像生成等任务中。以下是该模块的代码实现：

import torch.nn as nn

class ResUp(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(ResUp, self).__init__()
        self.conv1 = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, output_padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * 4, kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)
        self.upsample = nn.Sequential(
            nn.ConvTranspose2d(in_channels, out_channels * 4, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels * 4)
        )

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        identity = self.upsample(identity)

        out += identity
        out = self.relu(out)

        return out

该模块输入的张量shape为$[batch\_size, in\_channels, height, width]$，输出的张量shape为$[batch\_size, out\_channels * 4, height * stride, width * stride]$。其中，$stride$表示上采样的倍数。