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

class TinyConv(nn.Module):
    def __init__(self, num_classes: int = 10):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 8, 3, padding=1)   # 1x28x28 -> 8x28x28
        self.conv2 = nn.Conv2d(8, 16, 3, padding=1)  # 8x28x28 -> 16x28x28
        self.pool = nn.AdaptiveAvgPool2d((1, 1))     # 16x1x1
        self.fc = nn.Linear(16, num_classes)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = self.pool(x).flatten(1)
        return self.fc(x)