请基于pytorch，分模块编写一个应用于一维时间序列噪声去除的TCN网络代码

好的，以下是基于PyTorch的TCN网络代码，用于一维时间序列噪声去除：

import torch
import torch.nn as nn
from torch.nn.utils import weight_norm

class TemporalConvNet(nn.Module):
    def __init__(self, input_size, output_size, num_channels, kernel_size=2, 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 = input_size if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, dilation=dilation_size, 
                                     padding=(kernel_size-1)*dilation_size, dropout=dropout)]
        self.tcn = nn.Sequential(*layers)
        self.linear = nn.Linear(num_channels[-1], output_size)

    def forward(self, x):
        y = self.tcn(x)
        o = self.linear(y[:, :, -1])
        return o

class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, dilation, padding, dropout):
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, dilation=dilation, padding=padding))
        self.chomp1 = nn.ConstantPad1d((0, 1), 0)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(dropout)

        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, dilation=dilation, padding=padding))
        self.chomp2 = nn.ConstantPad1d((0, 1), 0)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout)

        self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
                                 self.conv2, self.chomp2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()

    def forward(self, x):
        y = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        y += res
        return self.relu(y)

这个代码实现了一个时序卷积网络（Temporal Convolutional Network，TCN），用于一维时间序列噪声去除。这个TCN网络由多个TemporalBlock组成，每个TemporalBlock内含两个卷积层和一些其他层，用空洞卷积（dilated convolution）实现局部连接并且不增加数据量。最后，给一个线性层，输出我们想要的时序去噪信号。这个代码可以通过传递输入数据x作为网络的输入，获取输出o作为去噪后的信号。

请注意，这里的代码块可能不是完整的能够成功运行的程序，使用代码前应该根据自己的需求对代码进行修改和调整。