'Apply the backward pass transformation to the input data.
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'

def bprop(self, error, alpha=1.0, beta=0):

    if self.update:
        self.dW[:] = 0
        self.be.compound_bprop_lut(self.nin, self.inputs, error, self.outputs_t, self.dW, self.pad_idx, alpha, beta)
    return self.deltas
