(self, num_features, activation=nn.ReLU(inplace=True), **kwargs)
Creates an Activated Batch Normalization module

        Parameters
        ----------
        num_features : int
            Number of feature channels in the input and output.
        activation : nn.Module
            Module used as an activation function.
        kwargs
            All other arguments are forwarded to the `BatchNorm2d` constructor.
Creates an Activated Batch Normalization module
Creates an Activated Batch Normalization module
def __init__(self, num_features, activation=nn.ReLU(inplace=True), **kwargs):
        """Creates an Activated Batch Normalization module

        Parameters
        ----------
        num_features : int
            Number of feature channels in the input and output.
        activation : nn.Module
            Module used as an activation function.
        kwargs
            All other arguments are forwarded to the `BatchNorm2d` constructor.
        """
        super(ABN, self).__init__(OrderedDict([
            ("bn", nn.BatchNorm2d(num_features, **kwargs)),
            ("act", activation)
        ]))
def __init__ ( self , num_features , activation = nn . ReLU ( inplace = True ) , * * kwargs ) : super ( ABN , self ) . __init__ ( OrderedDict ( [ ( "bn" , nn . BatchNorm2d ( num_features , * * kwargs ) ) , ( "act" , activation ) ] ) )