(self, error, alpha=1.0, beta=0.0)
return self.deltas
Skip nodes just pass back what they got.

        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
Skip nodes just pass back what they got.
Skip nodes just pass back what they got .
def bprop(self, error, alpha=1.0, beta=0.0):
        """
        Skip nodes just pass back what they got.

        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
        """
        # for better performance, mkl do nothing
        # otherwise, convert back and deal with beta and alpha.
        self.be.bprop_skipnode(error, self.deltas, alpha, beta)
        return self.deltas
def bprop ( self , error , alpha = 1.0 , beta = 0.0 ) : # for better performance, mkl do nothing # otherwise, convert back and deal with beta and alpha. self . be . bprop_skipnode ( error , self . deltas , alpha , beta ) return self . deltas