(self, num_features, eps=1e-5, momentum=0.1, affine=True, activation="leaky_relu", slope=0.01)
Creates an InPlace Activated Batch Normalization module

        Parameters
        ----------
        num_features : int
            Number of feature channels in the input and output.
        eps : float
            Small constant to prevent numerical issues.
        momentum : float
            Momentum factor applied to compute running statistics as.
        affine : bool
            If `True` apply learned scale and shift transformation after normalization.
        activation : str
            Name of the activation functions, one of: `leaky_relu`, `elu` or `none`.
        slope : float
            Negative slope for the `leaky_relu` activation.
Creates an InPlace Activated Batch Normalization module
Creates an InPlace Activated Batch Normalization module
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, activation="leaky_relu", slope=0.01):
        """Creates an InPlace Activated Batch Normalization module

        Parameters
        ----------
        num_features : int
            Number of feature channels in the input and output.
        eps : float
            Small constant to prevent numerical issues.
        momentum : float
            Momentum factor applied to compute running statistics as.
        affine : bool
            If `True` apply learned scale and shift transformation after normalization.
        activation : str
            Name of the activation functions, one of: `leaky_relu`, `elu` or `none`.
        slope : float
            Negative slope for the `leaky_relu` activation.
        """
        super(InPlaceABN, self).__init__(num_features, eps, momentum, affine, activation, slope)
def __init__ ( self , num_features , eps = 1e-5 , momentum = 0.1 , affine = True , activation = "leaky_relu" , slope = 0.01 ) : super ( InPlaceABN , self ) . __init__ ( num_features , eps , momentum , affine , activation , slope )