用pytorch生成一个5层一维卷积神经网络，要求第一层卷积层输入特征数为41，卷积核为3

以下是生成一个5层一维卷积神经网络的代码，其中第一层卷积层输入特征数为41，卷积核为3：

import torch
import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        
        self.layer1 = nn.Sequential(
            nn.Conv1d(41, 32, kernel_size=3),
            nn.BatchNorm1d(32),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2))
        
        self.layer2 = nn.Sequential(
            nn.Conv1d(32, 64, kernel_size=3),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2))
        
        self.layer3 = nn.Sequential(
            nn.Conv1d(64, 128, kernel_size=3),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2))
        
        self.layer4 = nn.Sequential(
            nn.Conv1d(128, 256, kernel_size=3),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2))
        
        self.layer5 = nn.Sequential(
            nn.Conv1d(256, 512, kernel_size=3),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2))
        
        self.fc = nn.Linear(512, 10)
    
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

在这个神经网络中，每一层都包含了一个卷积层、一个批量归一化层、一个ReLU激活函数和一个最大池化层。其中，第一层卷积层的输入特征数为41，卷积核大小为3。最后，有一个全连接层将特征图转换为类别预测。