DOC: changes after gh21853

Author: andyfaff

scipy/optimize/_optimize.py

@@ -1031,7 +1031,7 @@ def approx_fprime(xk, f, epsilon=_epsilon, *args):
                              args=args, f0=f0)
 
 
-@_transition_to_rng("seed", position_num=6, replace_doc=False)
+@_transition_to_rng("seed", position_num=6)
 def check_grad(func, grad, x0, *args, epsilon=_epsilon,
                 direction='all', rng=None):
     r"""Check the correctness of a gradient function by comparing it against a
@@ -1057,35 +1057,15 @@ def check_grad(func, grad, x0, *args, epsilon=_epsilon,
         using `func`. By default it is ``'all'``, in which case, all
         the one hot direction vectors are considered to check `grad`.
         If `func` is a vector valued function then only ``'all'`` can be used.
-    rng : {None, int, `numpy.random.Generator`}, optional
-        If `rng` is passed by keyword, types other than `numpy.random.Generator` are
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
         passed to `numpy.random.default_rng` to instantiate a ``Generator``.
-        If `rng` is already a ``Generator`` instance, then the provided instance is
-        used. Specify `rng` for repeatable function behavior.
-
-        If this argument is passed by position or `seed` is passed by keyword,
-        legacy behavior for the argument `seed` applies:
-
-        - If `seed` is None (or `numpy.random`), the `numpy.random.RandomState`
-          singleton is used.
-        - If `seed` is an int, a new ``RandomState`` instance is used,
-          seeded with `seed`.
-        - If `seed` is already a ``Generator`` or ``RandomState`` instance then
-          that instance is used.
-
-        .. versionchanged:: 1.15.0
-            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
-            transition from use of `numpy.random.RandomState` to
-            `numpy.random.Generator`, this keyword was changed from `seed` to `rng`.
-            For an interim period, both keywords will continue to work, although only
-            one may be specified at a time. After the interim period, function calls
-            using the `seed` keyword will emit warnings. The behavior of both `seed`
-            and `rng` are outlined above, but only the `rng` keyword should be used in
-            new code.
-
-        The random numbers generated with this seed affect the random vector
-        along which gradients are computed to check ``grad``. Note that `seed`
-        is only used when `direction` argument is set to `'random'`.
+
+        The random numbers generated affect the random vector along which gradients
+        are computed to check ``grad``. Note that `rng` is only used when `direction`
+        argument is set to `'random'`.
 
     Returns
     -------