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


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(28 * 28, 200)
        self.fc2 = nn.Linear(200, 10)

    def forward(self, x):
        x = x.view((-1, 28 * 28))
        x = F.relu(self.fc(x))
        x = self.fc2(x)
        return x


class Normalize(nn.Module):

    def forward(self, x):
        return (x - 0.1307) / 0.3081

26b21ff7f849dc5cde4ac6d9acf5f0756e251c34
<mask token>


class Normalize(nn.Module):

    def forward(self, x):
        return (x - 0.1307) / 0.3081

<mask token>


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

    def forward(self, x):
        x = x.view((-1, 28 * 28))
        x = F.relu(self.fc(x))
        x = self.fc2(x)
        return x


class Normalize(nn.Module):

    def forward(self, x):
        return (x - 0.1307) / 0.3081

<mask token>


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(28 * 28, 200)
        self.fc2 = nn.Linear(200, 10)

    def forward(self, x):
        x = x.view((-1, 28 * 28))
        x = F.relu(self.fc(x))
        x = self.fc2(x)
        return x


class Normalize(nn.Module):

    def forward(self, x):
        return (x - 0.1307) / 0.3081

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


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(28 * 28, 200)
        self.fc2 = nn.Linear(200, 10)

    def forward(self, x):
        x = x.view((-1, 28 * 28))
        x = F.relu(self.fc(x))
        x = self.fc2(x)
        return x


class Normalize(nn.Module):

    def forward(self, x):
        return (x - 0.1307) / 0.3081
