Add observation_function for full custom control over the observation

Lets a game define how its state becomes an observation as a plain
Python function (module:attr), used identically by GameEnvironment
(training) and TrainedPolicy (inference) instead of two independently
maintained encoding paths. Removes the egocentric/egocentric_player/
egocentric_radius flags — cropping is now something an
observation_function does itself by calling egocentric_board(), and
extras_size is discovered from one sampled observation instead of
being configured via observe_state_sizes.

docs/walkthrough.rst

@@ -127,9 +127,11 @@ The number of exploration turns is controlled by the
 
 The ``[tool.retro-gamer]`` section describes the game. Preprocessing
 options—such as ``spatial`` (whether to use a CNN or MLP, default:
-``false``), ``egocentric``, and ``observe_state``—live in the
-``[preprocessing]`` section of the generated ``config.toml``. You can
-edit them there after running ``retro-gamer create``.
+``false``) and ``observe_state``—live in the ``[preprocessing]`` section of
+the generated ``config.toml``. You can edit them there after running
+``retro-gamer create``. For full control over the observation (for example,
+a cropped/egocentric board), write an ``observation_function`` instead — see :ref:`observation-function` in the
+reference docs for details.
 
 ``observe_state``
 ~~~~~~~~~~~~~~~~~
@@ -408,15 +410,14 @@ checkpoints remain valid:
 game or the shape of the network. The saved model weights are
 incompatible with the new configuration:
 
-- ``actions``, ``reward``, ``character_set``, ``board_size``
-  (``[metadata]``) — These define what the agent perceives and what it
-  can do. Changing them changes the size of the network's input or
-  output layers; the existing weights no longer fit.
-- ``spatial``, ``board``, ``observe_state``, ``observe_state_sizes``,
-  ``egocentric``, ``egocentric_player``, ``egocentric_radius``
-  (``[preprocessing]``) — These control how the observation is
-  constructed. Any change here alters the input shape or meaning and
-  makes existing weights invalid.
+- ``actions``, ``reward``, ``character_set``, ``board_size``,
+  ``observation_function``, ``extras_size`` (``[metadata]``) — These define
+  what the agent perceives and what it can do. Changing them changes the
+  size of the network's input or output layers; the existing weights no
+  longer fit.
+- ``spatial``, ``board``, ``observe_state`` (``[preprocessing]``) — These
+  control how the observation is constructed. Any change here alters the
+  input shape or meaning and makes existing weights invalid.
 - ``hidden_sizes`` (``[model]``) — This defines the network's hidden
   layers. Changing it changes the shape of the network; the existing
   weights no longer fit.