用 Python 的 PyTorch 编写残差神经网络的代码示例，并加上注释

当然，我可以回答您有关编写残差神经网络的代码示例的问题，以下是一些 Python PyTorch 的残差网络代码：

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample

    def forward(self, x):
        residual = x

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

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

        if self.downsample:
            residual = self.downsample(x)

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

        return out

这是一个基本的残差块的结构，可以使用它来搭建整个残差神经网络。请注意，这个代码例子只是一个参考，您可以根据您自己的需要进行修改和适应。