def __init__(self):
    super().__init__()
    self.stage1_block = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(in_channels=32, out_channels=1, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Flatten())
def __init__(self):
    super().__init__()
    self.stage1_block = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(in_channels=32, out_channels=1, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Flatten())