def __init__(self, layers=[16, 32, 32, 32], n_output_channels=2, kernel_size=3):
    super().__init__()
    L = []
    c = 3
    for l in layers:
        L.append(self.Block(c, l, kernel_size, 2))
        c = l
    self.network = torch.nn.Sequential(*L)
    self.classifier = torch.nn.Linear(c, n_output_channels)
def __init__(self, layers=[16, 32, 32, 32], n_output_channels=2, kernel_size=3):
    super().__init__()
    L = []
    c = 3
    for l in layers:
        L.append(self.Block(c, l, kernel_size, 2))
        c = l
    self.network = torch.nn.Sequential(*L)
    self.classifier = torch.nn.Linear(c, n_output_channels)