Audited vmap docs

Author: zou3519

functorch/_src/vmap.py

@@ -171,25 +171,16 @@ def _get_name(func: Callable):
 # on BatchedTensors perform the batched operations that the user is asking for.
 def vmap(func: Callable, in_dims: in_dims_t = 0, out_dims: out_dims_t = 0) -> Callable:
     """
-    vmap is the vectorizing map. Returns a new function that maps `func` over some
-    dimension of the inputs. Semantically, vmap pushes the map into PyTorch
-    operations called by `func`, effectively vectorizing those operations.
+    vmap is the vectorizing map; `vmap(func)` returns a new function that maps
+    `func` over some dimension of the inputs. Semantically, vmap pushes the map
+    into PyTorch operations called by `func`, effectively vectorizing those
+    operations.
 
     vmap is useful for handling batch dimensions: one can write a function `func`
     that runs on examples and then lift it to a function that can take batches of
     examples with `vmap(func)`. vmap can also be used to compute batched
     gradients when composed with autograd.
 
-    .. warning::
-        functorch.vmap is an experimental prototype that is subject to
-        change and/or deletion. Please use at your own risk.
-
-    .. note::
-        If you're interested in using vmap for your use case, please
-        `contact us! <https://github.com/pytorch/pytorch/issues/42368>`_
-        We're interested in gathering feedback from early adopters to inform
-        the design.
-
     Args:
         func (function): A Python function that takes one or more arguments.
             Must return one or more Tensors.