请写出在jupyter notebook中实现图像分类的Resnet 101 神经网络结构代码

以下是在 Jupyter Notebook 中使用 PyTorch 实现的 ResNet-101 图像分类神经网络结构代码：

import torch.nn as nn

class Bottleneck(nn.Module):
    expansion = 4
    
    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes * self.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, planes * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * self.expansion)
            )
        
    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_planes = 64
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        
    def _make_layer(self, block, planes, blocks, stride=1):
        layers = []
        layers.append(block(self.in_planes, planes, stride))
        self.in_planes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_planes, planes))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def ResNet101(num_classes=1000):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)

其中 `Bottleneck` 是 ResNet 中的一个基本模块，`ResNet` 是整个 ResNet 网络结构的实现，`ResNet101` 是 ResNet-101 的具体实例化，并且可以通过 `num_classes` 参数指定分类的类别数。