import torch.nn as nn
import torch


class LSTMClassifier(nn.Module):

    def __init__(self):
        super(LSTMClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4,
            bias=True)
        self.elu1 = nn.ELU()
        self.bn1 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu2 = nn.ELU()
        self.bn2 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu3 = nn.ELU()
        self.bn3 = nn.BatchNorm2d(128, eps=1e-05, momentum=0, affine=True,
            track_running_stats=False)
        self.res1_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res1_relu1 = nn.ReLU()
        self.res1_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_relu1 = nn.ReLU()
        self.res2_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_relu1 = nn.ReLU()
        self.res3_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_relu1 = nn.ReLU()
        self.res4_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_relu1 = nn.ReLU()
        self.res5_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.deconv1 = nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu1 = nn.ELU()
        self.de_bn1 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv2 = nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu2 = nn.ELU()
        self.de_bn2 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv3 = nn.ConvTranspose2d(32, 3, 9, stride=1, padding=4,
            output_padding=0, groups=1, bias=True)
        self.de_tanh3 = nn.Tanh()

    def forward(self, data):
        temp = self.bn1(self.elu1(self.conv1(data)))
        temp = self.bn2(self.elu2(self.conv2(temp)))
        temp = self.bn3(self.elu3(self.conv3(temp)))
        temp += self.res1_bn2(self.res1_conv2(self.res1_relu1(self.res1_bn1
            (self.res1_conv1(temp)))))
        temp += self.res2_bn2(self.res2_conv2(self.res2_relu1(self.res2_bn1
            (self.res2_conv1(temp)))))
        temp += self.res3_bn2(self.res3_conv2(self.res3_relu1(self.res3_bn1
            (self.res3_conv1(temp)))))
        temp += self.res4_bn2(self.res4_conv2(self.res4_relu1(self.res4_bn1
            (self.res4_conv1(temp)))))
        temp += self.res5_bn2(self.res5_conv2(self.res5_relu1(self.res5_bn1
            (self.res5_conv1(temp)))))
        temp = self.de_bn1(self.de_elu1(self.deconv1(temp)))
        temp = self.de_bn2(self.de_elu2(self.deconv2(temp)))
        temp = self.de_tanh3(self.deconv3(temp))
        temp = (temp + 1) * 127.5
        return temp

feef7e3e7782c96147e8898a6a98d0ea9d2f27c9
<mask token>


class LSTMClassifier(nn.Module):
    <mask token>
    <mask token>

<mask token>


class LSTMClassifier(nn.Module):
    <mask token>

    def forward(self, data):
        temp = self.bn1(self.elu1(self.conv1(data)))
        temp = self.bn2(self.elu2(self.conv2(temp)))
        temp = self.bn3(self.elu3(self.conv3(temp)))
        temp += self.res1_bn2(self.res1_conv2(self.res1_relu1(self.res1_bn1
            (self.res1_conv1(temp)))))
        temp += self.res2_bn2(self.res2_conv2(self.res2_relu1(self.res2_bn1
            (self.res2_conv1(temp)))))
        temp += self.res3_bn2(self.res3_conv2(self.res3_relu1(self.res3_bn1
            (self.res3_conv1(temp)))))
        temp += self.res4_bn2(self.res4_conv2(self.res4_relu1(self.res4_bn1
            (self.res4_conv1(temp)))))
        temp += self.res5_bn2(self.res5_conv2(self.res5_relu1(self.res5_bn1
            (self.res5_conv1(temp)))))
        temp = self.de_bn1(self.de_elu1(self.deconv1(temp)))
        temp = self.de_bn2(self.de_elu2(self.deconv2(temp)))
        temp = self.de_tanh3(self.deconv3(temp))
        temp = (temp + 1) * 127.5
        return temp

<mask token>


class LSTMClassifier(nn.Module):

    def __init__(self):
        super(LSTMClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4,
            bias=True)
        self.elu1 = nn.ELU()
        self.bn1 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu2 = nn.ELU()
        self.bn2 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu3 = nn.ELU()
        self.bn3 = nn.BatchNorm2d(128, eps=1e-05, momentum=0, affine=True,
            track_running_stats=False)
        self.res1_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res1_relu1 = nn.ReLU()
        self.res1_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_relu1 = nn.ReLU()
        self.res2_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_relu1 = nn.ReLU()
        self.res3_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_relu1 = nn.ReLU()
        self.res4_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_relu1 = nn.ReLU()
        self.res5_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.deconv1 = nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu1 = nn.ELU()
        self.de_bn1 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv2 = nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu2 = nn.ELU()
        self.de_bn2 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv3 = nn.ConvTranspose2d(32, 3, 9, stride=1, padding=4,
            output_padding=0, groups=1, bias=True)
        self.de_tanh3 = nn.Tanh()

    def forward(self, data):
        temp = self.bn1(self.elu1(self.conv1(data)))
        temp = self.bn2(self.elu2(self.conv2(temp)))
        temp = self.bn3(self.elu3(self.conv3(temp)))
        temp += self.res1_bn2(self.res1_conv2(self.res1_relu1(self.res1_bn1
            (self.res1_conv1(temp)))))
        temp += self.res2_bn2(self.res2_conv2(self.res2_relu1(self.res2_bn1
            (self.res2_conv1(temp)))))
        temp += self.res3_bn2(self.res3_conv2(self.res3_relu1(self.res3_bn1
            (self.res3_conv1(temp)))))
        temp += self.res4_bn2(self.res4_conv2(self.res4_relu1(self.res4_bn1
            (self.res4_conv1(temp)))))
        temp += self.res5_bn2(self.res5_conv2(self.res5_relu1(self.res5_bn1
            (self.res5_conv1(temp)))))
        temp = self.de_bn1(self.de_elu1(self.deconv1(temp)))
        temp = self.de_bn2(self.de_elu2(self.deconv2(temp)))
        temp = self.de_tanh3(self.deconv3(temp))
        temp = (temp + 1) * 127.5
        return temp

import torch.nn as nn
import torch


class LSTMClassifier(nn.Module):

    def __init__(self):
        super(LSTMClassifier, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4,
            bias=True)
        self.elu1 = nn.ELU()
        self.bn1 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu2 = nn.ELU()
        self.bn2 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True,
            track_running_stats=False)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1,
            bias=True)
        self.elu3 = nn.ELU()
        self.bn3 = nn.BatchNorm2d(128, eps=1e-05, momentum=0, affine=True,
            track_running_stats=False)
        self.res1_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res1_relu1 = nn.ReLU()
        self.res1_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res1_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res2_relu1 = nn.ReLU()
        self.res2_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res2_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res3_relu1 = nn.ReLU()
        self.res3_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res3_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res4_relu1 = nn.ReLU()
        self.res4_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res4_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_conv1 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.res5_relu1 = nn.ReLU()
        self.res5_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.res5_bn2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine
            =True, track_running_stats=False)
        self.deconv1 = nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu1 = nn.ELU()
        self.de_bn1 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv2 = nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1,
            output_padding=0, groups=1, bias=True)
        self.de_elu2 = nn.ELU()
        self.de_bn2 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=
            True, track_running_stats=False)
        self.deconv3 = nn.ConvTranspose2d(32, 3, 9, stride=1, padding=4,
            output_padding=0, groups=1, bias=True)
        self.de_tanh3 = nn.Tanh()

    def forward(self, data):
        temp = self.bn1(self.elu1(self.conv1(data)))
        temp = self.bn2(self.elu2(self.conv2(temp)))
        temp = self.bn3(self.elu3(self.conv3(temp)))
        temp += self.res1_bn2(self.res1_conv2(self.res1_relu1(self.res1_bn1
            (self.res1_conv1(temp)))))
        temp += self.res2_bn2(self.res2_conv2(self.res2_relu1(self.res2_bn1
            (self.res2_conv1(temp)))))
        temp += self.res3_bn2(self.res3_conv2(self.res3_relu1(self.res3_bn1
            (self.res3_conv1(temp)))))
        temp += self.res4_bn2(self.res4_conv2(self.res4_relu1(self.res4_bn1
            (self.res4_conv1(temp)))))
        temp += self.res5_bn2(self.res5_conv2(self.res5_relu1(self.res5_bn1
            (self.res5_conv1(temp)))))
        temp = self.de_bn1(self.de_elu1(self.deconv1(temp)))
        temp = self.de_bn2(self.de_elu2(self.deconv2(temp)))
        temp = self.de_tanh3(self.deconv3(temp))
        temp = (temp + 1) * 127.5
        return temp
