写一个四层的一维卷积代码，包含了relu和pooling

以下是一个四层的一维卷积代码，包含了relu和pooling：

import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv1d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv1d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(
            nn.Conv1d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer4 = nn.Sequential(
            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.fc = nn.Linear(128, 10)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

希望能对你有所帮助！