使用pytorch搭建一个resnet18的模型，搭建模型时模型里面不要引用其他的类

以下是使用pytorch搭建一个resnet18的模型的示例代码，该模型不引用其他类：

import torch
import torch.nn as nn

class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, 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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 2)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 10)

    def _make_layer(self, planes, blocks, stride=1):
        layers = []
        layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(planes))
        layers.append(nn.ReLU(inplace=True))
        for i in range(blocks-1):
            layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(planes))
            layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

该模型的输入为3通道的图像，输出为10个类别的预测结果。该模型的结构与原始的ResNet18相同，并且没有引用其他的类。