Added clarification to apply_fn parameter of apply_to_modules (#2831)

Co-authored-by: Awni Hannun <[email protected]>

Author: yuchaoran2011

python/mlx/nn/layers/base.py

@@ -407,7 +407,10 @@ def apply_to_modules(self, apply_fn: Callable[[str, Module], Any]) -> Module:
         instance).
 
         Args:
-            apply_fn (Callable): The function to apply to the modules.
+            apply_fn (Callable): The function to apply to the modules which
+                takes two parameters. The first parameter is the string path of
+                the module (e.g. ``"model.layers.0.linear"``). The second
+                parameter is the module object.
 
         Returns:
             The module instance after updating submodules.

python/src/transforms.cpp

@@ -1238,8 +1238,18 @@ void init_transforms(nb::module_& m) {
               same in number, shape, and type as the inputs of ``fun`` (i.e. the ``primals``).
 
         Returns:
-            list(array): A list of the Jacobian-vector products which
-            is the same in number, shape, and type of the inputs to ``fun``.
+            tuple(list(array), list(array)): A tuple with the outputs of
+            ``fun`` in the first position and the Jacobian-vector products
+            in the second position.
+
+        Example:
+
+         .. code-block:: python
+
+             import mlx.core as mx
+
+             outs, jvps = mx.jvp(mx.sin, (mx.array(1.0),), (mx.array(1.0),))
+
       )pbdoc");
   m.def(
       "vjp",
@@ -1277,8 +1287,18 @@ void init_transforms(nb::module_& m) {
             same in number, shape, and type as the outputs of ``fun``.
 
         Returns:
-            list(array): A list of the vector-Jacobian products which
-            is the same in number, shape, and type of the outputs of ``fun``.
+            tuple(list(array), list(array)): A tuple with the outputs of
+            ``fun`` in the first position and the vector-Jacobian products
+            in the second position.
+
+        Example:
+
+         .. code-block:: python
+
+             import mlx.core as mx
+
+             outs, vjps = mx.vjp(mx.sin, (mx.array(1.0),), (mx.array(1.0),))
+
       )pbdoc");
   m.def(
       "value_and_grad",