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

以下是一个基于pytorch的ResNet网络的代码示例，实现了具有三个输入特征和三个输出的一维卷积神经网络，输入和输出都是([[1,2,3],[2,3,4]])，并使用([[1,2,3]])作为测试数据。

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

class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResNetBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)
        self.stride = stride
        self.downsample = None
        if stride != 1 or in_channels != out_channels:
            self.downsample = nn.Sequential(
                nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(out_channels)
            )

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, layers):
        super(ResNet, self).__init__()
        self.in_channels = 3
        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.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512, 3)

    def make_layer(self, block, out_channels, blocks, stride=1):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        for _ in range(1, blocks):
            layers.append(block(out_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

def train_model(model, train_loader, optimizer, criterion):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

def test_model(model, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    accuracy = correct / len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
        test_loss, correct, len(test_loader.dataset), 100. * accuracy))

if __name__ == '__main__':
    train_data = torch.tensor([[1,2,3],[2,3,4]]) # 作为训练数据
    train_target = torch.tensor([[1,2,3],[2,3,4]]) # 作为训练目标
    test_data = torch.tensor([[1,2,3]]) # 作为测试数据
    test_target = torch.tensor([1,2,3]) # 作为测试目标

    train_dataset = torch.utils.data.TensorDataset(train_data, train_target)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True)
    test_dataset = torch.utils.data.TensorDataset(test_data, test_target)
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)

    model = ResNet(ResNetBlock, [2, 2, 2, 2])
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()

    for epoch in range(10):
        train_model(model, train_loader, optimizer, criterion)
        test_model(model, test_loader, criterion)

在这个代码示例中，我们定义了一个ResNet模型，其中包含四个ResNet块，每个块包含2个卷积层，最终输出3个类别。我们还定义了一个训练函数和一个测试函数，用于训练和测试模型，在训练和测试过程中，我们使用交叉熵损失函数和Adam优化器来优化模型。