def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
    conv3d = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, padding=(kernel_size // 2))
    conv3d.weight = nn.Parameter(Normal(0, 1e-05).sample(conv3d.weight.shape))
    conv3d.bias = nn.Parameter(torch.zeros(conv3d.bias.shape))
    super().__init__(conv3d)
def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
    conv3d = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, padding=(kernel_size // 2))
    conv3d.weight = nn.Parameter(Normal(0, 1e-05).sample(conv3d.weight.shape))
    conv3d.bias = nn.Parameter(torch.zeros(conv3d.bias.shape))
    super().__init__(conv3d)