Additional updates to parallelism docs

More updates to make API consistent:

1. Showing inputs for class-based action (repeat but good to hammer
   home)
2. Making API consistent

docs/concepts/actions.rst

@@ -30,6 +30,8 @@ There are two APIs for defining actions: class-based and function-based. They ar
 - use the function-based API when you want to write something quick and terse that reads from a fixed set of state variables
 - use the class-based API when you want to leverage inheritance or parameterize the action in more powerful ways
 
+.. _functionbasedactions:
+
 ----------------------
 Function-based actions
 ----------------------
@@ -127,6 +129,9 @@ injected into your Burr Actions. This is done by adding ``__context`` to the act
 Class-Based Actions
 -------------------
 
+.. _classbasedactions:
+
+
 You can define an action by implementing the :py:class:`Action <burr.core.action.Action>` class:
 
 .. code-block:: python

docs/concepts/parallelism.rst

@@ -81,12 +81,12 @@ This looks as follows -- in this case we're running the same LLM over different
 
     class TestMultiplePrompts(MapStates):
 
-        def action(self) -> Action | Callable | RunnableGraph:
+        def action(self, state: State, inputs: Dict[str, Any]) -> Action | Callable | RunnableGraph:
             # make sure to add a name to the action
             # This is not necessary for subgraphs, as actions will already have names
             return query_llm.with_name("query_llm")
 
-        def states(self, state: State) -> Generator[State, None, None]:
+        def states(self, state: State, context: ApplicationContext, inputs: Dict[str, Any]) -> Generator[State, None, None]:
             # You could easily have a list_prompts upstream action that writes to "prompts" in state
             # And loop through those
             # This hardcodes for simplicity
@@ -146,7 +146,7 @@ For case (2) (mapping actions over the same state) you implement the ``MapAction
 
     from burr.core import action, state
     from burr.core.parallelism import MapActions, RunnableGraph
-    from typing import Callable, Generator, List
+    from typing import Callable, Generator, List, Dict, Any
 
     @action(reads=["prompt", "model"], writes=["llm_output"])
     def query_llm(state: State, model: str) -> State:
@@ -155,7 +155,7 @@ For case (2) (mapping actions over the same state) you implement the ``MapAction
 
     class TestMultipleModels(MapActions):
 
-        def actions(self, state: State) -> Generator[Action | Callable | RunnableGraph, None, None]:
+        def actions(self, state: State, context: ApplicationContext, inputs: Dict[str, Any]) -> Generator[Action | Callable | RunnableGraph, None, None]:
             # Make sure to add a name to the action if you use bind() with a function,
             # note that these can be different actions, functions, etc...
             # in this case we're using `.bind()` to create multiple actions, but we can use some mix of
@@ -167,7 +167,7 @@ For case (2) (mapping actions over the same state) you implement the ``MapAction
             ]
                 yield action
 
-        def state(self, state: State) -> State:
+        def state(self, state: State, inputs: Dict[str, Any]) -> State:
             return state.update(prompt="What is the meaning of life?")
 
         def reduce(self, state: State, states: Generator[State, None, None]) -> State:
@@ -314,7 +314,7 @@ This might look as follows -- say we have a simple subflow that takes in a raw p
         def action(self, state: State, inputs: Dict[str, Any]) -> Action | Callable | RunnableGraph:
             return runnable_graph
 
-        def states(self, state: State) -> Generator[State, None, None]:
+        def states(self, state: State, context: ApplicationContext, inputs: Dict[str, Any]) -> Generator[State, None, None]:
             for prompt in [
                 "What is the meaning of life?",
                 "What is the airspeed velocity of an unladen swallow?",
@@ -332,15 +332,11 @@ it can run just as the single prompt we did above. Note this is also doable for
 Passing inputs
 --------------
 
-.. note::
-
-    Should ``MapOverInputs`` be its own class? Or should we have ``bind_from_state(prompt="prompt_field_in_state")`` that allows you to pass it in as
-    state and just use the mapping capabilities?
-
-Each of these can (optionally) produce ``inputs`` by yielding/returning a tuple from the ``states``/``actions`` function.
-
-This is useful if you want to vary the inputs. Note this is the same as passing ``inputs=`` to ``app.run``.
+Parallel actions can accept inputs in the same way that class-based actions do. In order to accept inputs you have to declare them in the class. As we're using the :ref:`class-based API <classbasedactions>`,
+this is done by declaring the ``inputs`` property -- a list of strings that are used in inputs. Note you have to use the superclasses
+inputs as well to ensure it has everything it needs -- we will likely be automating this.
 
+This looks as follows:
 
 .. code-block:: python
 
@@ -379,17 +375,22 @@ This is useful if you want to vary the inputs. Note this is the same as passing
         def action(self) -> Action | Callable | RunnableGraph:
             return runnable_graph
 
-        def states(self, state: State) -> Generator[Tuple[State, dict], None, None]:
-            for prompt in [
-                "What is the meaning of life?",
-                "What is the airspeed velocity of an unladen swallow?",
-                "What is the best way to cook a steak?",
-            ]:
-                yield state.update(prompt=prompt), {"model": "gpt-4"} # pass in the model as an input
+        @property
+        def inputs(self) -> List[str]:
+            return ["prompts"] + super().inputs # make sure to include the superclass inputs
+
+        def states(self, state: State, context: ApplicationContext, inputs: Dict[str, Any]) -> Generator[Tuple[State, dict], None, None]:
+            for prompt in inputs["prompts"]:
+                yield state.update(prompt=prompt)
 
         ... # same as above
 
 
+.. note::
+
+    Should ``MapOverInputs`` be its own class? Or should we have ``bind_from_state(prompt="prompt_field_in_state")`` that allows you to pass it in as
+    state and just use the mapping capabilities? Or are we happy as it currently is because we can pass in inputs through `MapStates`/`MapActions` (as shown above).
+
 
 Lower-level API
 ===============