请写一段代码并加注释，基于pytorch，建立TCN网络实现对一维时间序列数据的噪声去除

当然，我会为您保密并回答您提出的问题。

以下是基于 PyTorch 建立 TCN 网络实现对一维时间序列数据的噪声去除的代码，并且我已经加注释，方便您理解和使用：

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

# 定义 TCN 网络模型
class TCNNet(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):
        super(TCNNet, self).__init__()
        
        # 定义 TCN 网络的卷积模块
        layers = []
        # 第一层卷积包含 input_size 个输入通道和 num_channels[0] 个输出通道
        layers += [nn.Conv1d(input_size, num_channels[0], kernel_size=kernel_size)]
        # 对每层卷积设置 dilation 不断增大，实现感受野增大的效果
        for i in range(1, len(num_channels)):
            dilation_size = 2 ** i
            in_channels = num_channels[i-1]
            out_channels = num_channels[i]
            layers += [nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, dilation=dilation_size)]
        self.tcn = nn.Sequential(*layers)
        
        # 定义 TCN 网络的全连接层和 dropout 层
        self.linear = nn.Linear(num_channels[-1], output_size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # 将输入数据传递给 TCN 网络模型
        out = self.tcn(x)
        # 对输出数据进行平均池化，保留最后一个时间步
        out = out.mean(dim=2)
        # 在全连接层上进行预测并应用 dropout
        out = self.dropout(self.linear(out))
        
        return out

# 定义训练函数
def train(model, optimizer, criterion, X_train, y_train):
    model.train()
    optimizer.zero_grad()
    y_pred = model(X_train)
    loss = criterion(y_pred, y_train)
    loss.backward()
    optimizer.step()
    return loss.item()

# 定义测试函数
def test(model, criterion, X_test, y_test):
    model.eval()
    y_pred = model(X_test)
    loss = criterion(y_pred, y_test)
    return loss.item()

# 定义主函数
def main():
    # 设置超参数和载入数据
    input_size = 1
    output_size = 1
    num_channels = [64, 64, 64, 64] # 卷积层输出通道数
    kernel_size = 3
    dropout = 0.2
    batch_size = 64
    epochs = 10
    learning_rate = 0.001
    
    # 载入数据为 tensor 类型
    X_train = torch.randn((batch_size, input_size, 100))
    y_train = torch.randn((batch_size, output_size))
    X_test = torch.randn((batch_size, input_size, 100))
    y_test = torch.randn((batch_size, output_size))
    
    # 定义 TCN 网络模型、损失函数和优化器
    model = TCNNet(input_size, output_size, num_channels, kernel_size, dropout)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    
    # 开始训练和测试
    for i in range(epochs):
        train_loss = train(model, optimizer, criterion, X_train, y_train)
        test_loss = test(model, criterion, X_test, y_test)
        print("Epoch: {} | Train Loss: {:.5f} | Test Loss: {:.5f}".format(i+1, train_loss, test_loss))

# 调用主函数开始训练
if __name__ == '__main__':
    main()

希望这段代码能够对您有所帮助。如有疑问，请随时提出。