给我一个基于多尺度残差卷积神经网络的python代码

以下是一个基于多尺度残差卷积神经网络的python代码示例：

import torch
import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        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.stride = stride

    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)

        if self.stride != 1 or identity.size() != out.size():
            identity = nn.Conv2d(identity.size(1), out.size(1), kernel_size=1, stride=self.stride, bias=False)(identity)

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

        return out

class MultiScaleResNet(nn.Module):
    def __init__(self, num_classes=10):
        super(MultiScaleResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = nn.Sequential(
            ResidualBlock(64, 64),
            ResidualBlock(64, 64)
        )

        self.layer2 = nn.Sequential(
            ResidualBlock(64, 128, stride=2),
            ResidualBlock(128, 128)
        )

        self.layer3 = nn.Sequential(
            ResidualBlock(128, 256, stride=2),
            ResidualBlock(256, 256)
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

这个代码实现了一个多尺度残差卷积神经网络，可以用于图像分类任务。其中，ResidualBlock 是多尺度残差块的实现，MultiScaleResNet 是整个网络的实现。