class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResNetBlock, self).__init__()

        self.conv1 = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.downsample = downsample

    def forward(self, x):
        residual = x

        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))

        if self.downsample:
            residual = self.downsample(x)

        out += residual
        out = F.relu(out)

        return out