给出edge-connect中的InpaintingModel的上下文编解码的网络结构极其相似的生成器代码，要求该生成器可以载入InpaintingModel的预训练模型，正常的进行128x128大小的不规则掩膜图像修复

以下是基于PyTorch框架的生成器代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Generator(nn.Module):
    def __init__(self, input_channels=4, output_channels=3, ngf=64):
        super(Generator, self).__init__()
        
        self.encoder = nn.Sequential(
            nn.Conv2d(input_channels, ngf, kernel_size=4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf, ngf * 2, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 2, ngf * 4, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 4, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.LeakyReLU(0.2, inplace=True)
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 8, ngf * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 8, ngf * 4, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf * 2, ngf, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(ngf),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(ngf, output_channels, kernel_size=4, stride=2, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

其中，输入的`input_channels`为4，代表输入为4通道的图像，包含RGB三通道和掩膜一通道。输出的`output_channels`为3，代表输出为RGB三通道的图像。`ngf`为特征图的通道数，设置为64。

在训练时，可以载入InpaintingModel的预训练模型，对128x128大小的不规则掩膜图像进行修复。