给出edgeconnect中使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编码器代码

以下是edgeconnect中使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编码器代码：

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

class Context_Encoder(nn.Module):
    def __init__(self):
        super(Context_Encoder, self).__init__()

        self.conv1 = nn.Conv2d(4, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv6 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv7 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)

        self.norm1 = nn.InstanceNorm2d(64, affine=True)
        self.norm2 = nn.InstanceNorm2d(128, affine=True)
        self.norm3 = nn.InstanceNorm2d(256, affine=True)
        self.norm4 = nn.InstanceNorm2d(512, affine=True)
        self.norm5 = nn.InstanceNorm2d(512, affine=True)
        self.norm6 = nn.InstanceNorm2d(512, affine=True)
        self.norm7 = nn.InstanceNorm2d(512, affine=True)
        self.norm8 = nn.InstanceNorm2d(512, affine=True)

        self.relu = nn.ReLU()

    def forward(self, x):
        conv1 = self.conv1(x)
        norm1 = self.norm1(conv1)
        relu1 = self.relu(norm1)

        conv2 = self.conv2(relu1)
        norm2 = self.norm2(conv2)
        relu2 = self.relu(norm2)

        conv3 = self.conv3(relu2)
        norm3 = self.norm3(conv3)
        relu3 = self.relu(norm3)

        conv4 = self.conv4(relu3)
        norm4 = self.norm4(conv4)
        relu4 = self.relu(norm4)

        conv5 = self.conv5(relu4)
        norm5 = self.norm5(conv5)
        relu5 = self.relu(norm5)

        conv6 = self.conv6(relu5)
        norm6 = self.norm6(conv6)
        relu6 = self.relu(norm6)

        conv7 = self.conv7(relu6)
        norm7 = self.norm7(conv7)
        relu7 = self.relu(norm7)

        conv8 = self.conv8(relu7)
        norm8 = self.norm8(conv8)
        relu8 = self.relu(norm8)

        return relu8