给出具有三输入特征三输出的一维卷积神经网络，使用ResNet网络的基于pytorch的代码示例，包含训练和测试部分

以下是一个基于PyTorch的三输入三输出一维卷积神经网络的ResNet示例代码，包含训练和测试部分。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class ThreeInputThreeOutputConv(nn.Module):
    def __init__(self):
        super(ThreeInputThreeOutputConv, self).__init__()
        self.conv1 = nn.Conv1d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(64)
        self.conv3 = nn.Conv1d(64, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn3 = nn.BatchNorm1d(3)
        self.downsample = nn.Sequential(
            nn.Conv1d(3, 64, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm1d(64)
        )
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x1, x2, x3):
        identity = torch.cat((x1, x2, x3), dim=1)
        out = self.conv1(identity)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        identity = self.downsample(identity)
        out += identity
        out = self.relu(out)
        return out

# 定义模型
model = ThreeInputThreeOutputConv()

# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# 定义训练函数
def train(model, dataloader, optimizer, criterion):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(dataloader):
        x1, x2, x3, y1, y2, y3 = data
        optimizer.zero_grad()
        outputs = model(x1, x2, x3)
        loss = criterion(outputs[:,0,:], y1) + criterion(outputs[:,1,:], y2) + criterion(outputs[:,2,:], y3)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(dataloader)

# 定义测试函数
def test(model, dataloader, criterion):
    model.eval()
    running_loss = 0.0
    with torch.no_grad():
        for i, data in enumerate(dataloader):
            x1, x2, x3, y1, y2, y3 = data
            outputs = model(x1, x2, x3)
            loss = criterion(outputs[:,0,:], y1) + criterion(outputs[:,1,:], y2) + criterion(outputs[:,2,:], y3)
            running_loss += loss.item()
    return running_loss / len(dataloader)

# 加载数据，训练和测试模型
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

num_epochs = 10
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, optimizer, criterion)
    test_loss = test(model, test_loader, criterion)
    print('Epoch: {} Train Loss: {:.4f} Test Loss: {:.4f}'.format(epoch+1, train_loss, test_loss))