def backward(self, inputs, grad_outputs):
        xp = cuda.get_array_module(*inputs)
        x, t = inputs
        gloss = grad_outputs[0]
        y, = sigmoid.Sigmoid(self.use_cudnn).forward((x,))
        gx = xp.divide(
            gloss * self.ignore_mask * (t - y), self.count,
            dtype=y.dtype)
        return gx, None
dataset/ETHPy150Open pfnet/chainer/chainer/functions/loss/sigmoid_cross_entropy.py/SigmoidCrossEntropy.backward/swapped operands of `-`