basic.py 文件源码

def grad(self, inputs, gout):

        (x, y) = inputs
        (gz,) = gout
        if x.type in complex_types:
            raise NotImplementedError()

        # If the output of this op is discrete, then it
        # it is locally flat everywhere, so the gradient
        # through it is 0.
        # This is different from it not being connected
        # to the output; x/y is still a function of x
        # and y; it's just a step function.
        if all(a.dtype in discrete_types for a in (x, y)):
            return [x.zeros_like(), y.zeros_like()]

        first_part = gz / y

        if y.type in complex_types:
            raise NotImplementedError()

        second_part = -(gz * x) / (y * y)

        return first_part, second_part