'Compute gradients for learning gamma and beta as well as layer weights.
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:'

@Layer.accumulates
def bprop(self, error, alpha=1.0, beta=0.0):

    assert ((alpha == 1.0) and (beta == 0.0))
    if (not self.error_view):
        self.error_view = error.reshape((self.nfm, (-1)))
    self.be.compound_bprop_bn(self.deltas, self.grad_gamma, self.grad_beta, self.error_view, self.inputs, self.xsum, self.xvar, self.gamma, self.eps, binary=self.binary, layer=self.nglayer)
    return self.deltas
