写一个四层的一维卷积代码

以下是一个四层的一维卷积代码的示例：

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.BatchNorm1d(16),
            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.BatchNorm1d(32),
            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.BatchNorm1d(64),
            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.BatchNorm1d(128),
            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

这是一个使用 PyTorch 框架实现的四层一维卷积神经网络，其中包含了四个卷积层和一个全连接层。每个卷积层都包含了卷积、批量归一化、ReLU 激活和最大池化操作。最后的全连接层将卷积层的输出映射到一个 10 维的向量，用于分类任务。