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，使用([[1,2,3],[2,3,4]])作为输入特征，使用([[1,2,3],[2,3,4]])作为输出，使用([[1,2,3]])作为测试，包含训练和测试部分

以下是基于PyTorch实现的卷积神经网络的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义卷积神经网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv1d(3, 6, 3)
        self.pool = nn.MaxPool1d(2)
        self.conv2 = nn.Conv1d(6, 16, 3)
        self.fc1 = nn.Linear(16 * 2, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 16 * 2)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义ResNet网络
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv1d(3, 6, 3)
        self.pool = nn.MaxPool1d(2)
        self.conv2 = nn.Conv1d(6, 16, 3)
        self.fc1 = nn.Linear(16 * 2, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)
        self.relu = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv1d(16, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(16)
        self.conv4 = nn.Conv1d(16, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(16)

    def forward(self, x):
        identity = x
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(-1, 16 * 2)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        x = x.view(-1, 16, 3)
        x = self.conv3(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.bn2(x)
        x += identity
        x = self.relu(x)
        return x

# 训练模型
model = ResNet()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

inputs = torch.tensor([[1,2,3],[2,3,4]], dtype=torch.float32).unsqueeze(0)
labels = torch.tensor([[1,2,3],[2,3,4]], dtype=torch.float32).unsqueeze(0)

for epoch in range(1000):
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print('Epoch {}, Loss: {:.4f}'.format(epoch, loss.item()))

# 测试模型
test_input = torch.tensor([[1,2,3]], dtype=torch.float32).unsqueeze(0)
test_output = model(test_input)
print('Test Output:', test_output)

这个代码示例实现了一个具有三个输入特征和三个输出的一维卷积神经网络，并使用了ResNet网络结构。在训练部分中，我们使用了MSE损失函数和随机梯度下降优化器来训练模型。在测试部分中，我们使用了一个测试输入来测试模型的输出。