def __init__(self, channels=[16, 32, 32, 32]):
    super().__init__()
    conv_block = (lambda c, h: [torch.nn.BatchNorm2d(h), torch.nn.Conv2d(h, c, 5, 2, 2), torch.nn.ReLU(True)])
    (h, _conv) = (3, [])
    for c in channels:
        _conv += conv_block(c, h)
        h = c
    self._conv = torch.nn.Sequential(*_conv, torch.nn.Conv2d(h, 1, 1))
def __init__(self, channels=[16, 32, 32, 32]):
    super().__init__()
    conv_block = (lambda c, h: [torch.nn.BatchNorm2d(h), torch.nn.Conv2d(h, c, 5, 2, 2), torch.nn.ReLU(True)])
    (h, _conv) = (3, [])
    for c in channels:
        _conv += conv_block(c, h)
        h = c
    self._conv = torch.nn.Sequential(*_conv, torch.nn.Conv2d(h, 1, 1))