写一段基于pytorch框架的卷积神经网络

在PyTorch框架中，构建卷积神经网络的代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(in_features=64 * 8 * 8, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=10)
    
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

上面的代码定义了一个简单的卷积神经网络，其中包含两个卷积层，两个全连接层。使用PyTorch的nn模块定义了各个层，并使用forward函数定义了数据的前向传播过程。