'Apply one linear transformation that captures normalization, gamma scaling and beta shift.'

def _fprop_inference(self, inputs, beta=0.0):

    input_ms = self.be.empty_like(inputs)
    input_ms[:] = (inputs - self.gmean)
    inv_v = self.be.empty_like(self.gvar)
    inv_v[:] = (1.0 / self.be.sqrt((self.gvar + self.eps)))
    xhat = self.be.shift(input_ms, inv_v)
    self.y[:] = (((self.y * beta) + self.be.shift(xhat, self.gamma)) + self.beta)
    return self.outputs
