More examples of functional transforms (#1402)

docs/source/transforms.rst

@@ -89,7 +89,9 @@ Functional transforms give you fine-grained control of the transformation pipeli
 As opposed to the transformations above, functional transforms don't contain a random number
 generator for their parameters.
 That means you have to specify/generate all parameters, but you can reuse the functional transform.
-For example, you can apply a functional transform to multiple images like this:
+
+Example:
+you can apply a functional transform with the same parameters to multiple images like this:
 
 .. code:: python
 
@@ -104,5 +106,27 @@ For example, you can apply a functional transform to multiple images like this:
         # more transforms ...
         return image, segmentation
 
+
+Example:
+you can use a functional transform to build transform classes with custom behavior:
+
+.. code:: python
+
+    import torchvision.transforms.functional as TF
+    import random
+
+    class MyRotationTransform:
+        """Rotate by one of the given angles."""
+
+        def __init__(self, angles):
+            self.angles = angles
+
+        def __call__(self, x):
+            angle = random.choice(self.angles)
+            return TF.rotate(x, angle)
+
+    rotation_transform = MyRotationTransform(angles=[-30, -15, 0, 15, 30])
+
+
 .. automodule:: torchvision.transforms.functional
     :members: