message: Improve documentation for tf.custom_gradient.
  Fixes
diff: @@ -82,11 +82,10 @@ def custom_gradient(f):
  scope must be using `ResourceVariable`s.
  Args:
  - f: function `f(x)` that returns a tuple `(y, grad_fn)` where:
  - - `x` is a `Tensor` or sequence of `Tensor` inputs to the function.
  + f: function `f(*x)` that returns a tuple `(y, grad_fn)` where:
  + - `x` is a sequence of `Tensor` inputs to the function.
  - `y` is a `Tensor` or sequence of `Tensor` outputs of applying
  - TensorFlow
  - operations in `f` to `x`.
  + TensorFlow operations in `f` to `x`.
  - `grad_fn` is a function with the signature `g(*grad_ys)` which returns
  a list of `Tensor`s - the derivatives of `Tensor`s in `y` with respect
  to the `Tensor`s in `x`. `grad_ys` is a `Tensor` or sequence of
  @@ -96,7 +95,8 @@ def custom_gradient(f):
  signature `g(*grad_ys, variables=None)`, where `variables` is a list of
  the `Variable`s, and return a 2-tuple `(grad_xs, grad_vars)`, where
  `grad_xs` is the same as above, and `grad_vars` is a `list<Tensor>`
  - with the derivatives of `Tensor`s in `y` with respect to the variables.
  + with the derivatives of `Tensor`s in `y` with respect to the variables
  + (that is, grad_vars has one Tensor per variable in variables).
  Returns:
  A function `h(x)` which returns the same value as `f(x)[0]` and whose
  