#!/usr/bin/env python3
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# license-headers: type="bsd-3-clause"

import torch  # pytype: disable=import-error
import torch.nn as nn  # pytype: disable=import-error
import torch.nn.functional as functional  # pytype: disable=import-error


# Network structure is based on original PyTorch MNIST example.
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = functional.relu(x)
        x = self.conv2(x)
        x = functional.relu(x)
        x = functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = functional.log_softmax(x, dim=1)
        return output