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

好的，以下是代码实现：

import torch
import torch.nn as nn
from torch.autograd import Variable

class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding):
        super(TemporalBlock, self).__init__()
        self.conv1 = nn.Conv1d(n_inputs, n_outputs, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.conv2 = nn.Conv1d(n_outputs, n_outputs, kernel_size,
                               stride=stride, padding=padding, dilation=dilation)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()

    def forward(self, inputs):
        residual = inputs
        output = self.conv1(inputs)
        output = self.relu1(output)
        output = self.conv2(output)
        if self.downsample is not None:
            residual = self.downsample(inputs)
        output += residual
        output = self.relu2(output)

        return output

class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=3, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2 ** i
            in_channels = num_inputs if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size,
                                      padding=(kernel_size-1)*dilation_size, dropout=dropout)]

        self.network = nn.Sequential(*layers)

    def forward(self, inputs):
        return self.network(inputs)

class TCN(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size=3, dropout=0.2):
        super(TCN, self).__init__()
        self.tcn = TemporalConvNet(input_size, num_channels, kernel_size=kernel_size, dropout=dropout)
        self.linear = nn.Linear(num_channels[-1], output_size)

    def forward(self, x):
        y1 = self.tcn(x.permute(0, 2, 1))
        y1 = y1.permute(0, 2, 1)
        return self.linear(y1[:, -1, :])

上面代码定义了一系列模块，如 TemporalBlock 表示时间卷积层的基本模块，TemporalConvNet 表示多个TemporalBlock连接的TCN，TCN 是模型的整体架构，可以用于一维时间序列数据的噪声去除。

在使用这个模型时，可以按照以下方式进行：

model = TCN(input_size, output_size, num_channels, kernel_size=kernel_size, dropout=dropout)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = Variable(inputs)
        labels = Variable(labels)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

其中，输入的数据 inputs、labels 可以从 train_loader 中提取出来，自行设置 learning rate、batch size、 epoch、优化器 optimizer 等参数进行训练。