(self, error, alpha=1.0, beta=0.0)
Branch nodes should be skipped in bprop, since their deltas are shared

        Arguments:
            error (Tensor): deltas back propagated from the adjacent higher layer
            alpha (float, optional): scale to apply to input for activation
                                     gradient bprop.  Defaults to 1.0
            beta (float, optional): scale to apply to output activation
                                    gradient bprop.  Defaults to 0.0

        Returns:
            Tensor: deltas to propagate to the adjacent lower layer
Branch nodes should be skipped in bprop, since their deltas are shared
Branch nodes should be skipped in bprop since their deltas are shared
def bprop(self, error, alpha=1.0, beta=0.0):
        """
        Branch nodes should be skipped in bprop, since their deltas are shared

        Arguments:
            error (Tensor): deltas back propagated from the adjacent higher layer
            alpha (float, optional): scale to apply to input for activation
                                     gradient bprop.  Defaults to 1.0
            beta (float, optional): scale to apply to output activation
                                    gradient bprop.  Defaults to 0.0

        Returns:
            Tensor: deltas to propagate to the adjacent lower layer
        """
        pass
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : pass