Add type hints and move docstring to OpFromGraph constructor

aesara/compile/builders.py

 """Define new Ops from existing Ops"""
 from collections import OrderedDict
 from functools import partial
+from typing import List, Optional
 
 import aesara.tensor as aet
 from aesara.compile.function.pfunc import rebuild_collect_shared
@@ -87,101 +88,6 @@ class OpFromGraph(Op, HasInnerGraph):
 
     Currently does not support ``updates`` or ``givens`` argument.
 
-    Parameters
-    ----------
-
-    inputs: list of :class:`Variable <aesara.graph.basic.Variable>`
-
-    outputs: list of :class:`Variable <aesara.graph.basic.Variable>`
-
-    inline: bool, optional
-        Defaults to ``False``
-
-        ``True`` : Cause the Op's original graph being used during
-        compilation, the Op will not be visible in the compiled
-        graph but rather its internal graph.
-
-        ``False`` : will use a pre-compiled function inside.
-
-    grad_overrides : single or list of {'default', OpFromGraph, callable, Variable with special type}, optional
-        Defaults to ``'default'``.
-        This argument is mutually exclusive with lop_overrides.
-
-        ``'default'`` : Do not override, use default grad() result
-
-        OpFromGraph instance : Override with another OpFromGraph, should
-        accept inputs as the same order and types of ``inputs`` and ``output_grads``
-        arguments as one would specify in grad() method.
-
-        callable : Should take two args: ``inputs`` and ``output_grads``.
-        Each argument is expected to be a list of :class:`Variable <aesara.graph.basic.Variable>`.
-        Must return list of :class:`Variable <aesara.graph.basic.Variable>`.
-
-        Variable :
-            ``NullType() instance`` : Treat as non-differentiable
-            ``DisconnectedType() instance`` : Treat as disconnected gradient, numerically gives zero
-
-        list: Each OpFromGraph/callable must return a single
-        :class:`Variable <aesara.graph.basic.Variable>`. Each list element corresponds to gradient of
-        a specific input, length of list must be equal to number of inputs.
-
-    lop_overrides : single or list of {'default', OpFromGraph, callable, Variable with special type}, optional
-        Defaults to ``'default'``.
-        This argument is mutually exclusive with ``grad_overrides``.
-
-        ``'default'`` : Do not override, use default L_op() result
-
-        OpFromGraph instance : Override with another OpFromGraph, should
-        accept inputs as the same order and types of ``inputs``, ``outputs`` and ``output_grads``
-        arguments as one would specify in grad() method.
-
-        callable : Should take three args: ``inputs``, ``outputs`` and ``output_grads``.
-        Each argument is expected to be a list of :class:`Variable <aesara.graph.basic.Variable>`.
-        Must return list of :class:`Variable <aesara.graph.basic.Variable>`.
-
-        Variable :
-            `NullType` instance: Treat as non-differentiable
-            `DisconnectedType` instance: Treat as disconnected gradient, numerically gives zero
-
-        list: Each OpFromGraph/callable must return a single
-        :class:`Variable <aesara.graph.basic.Variable>`. Each list element corresponds to gradient of
-        a specific input, length of list must be equal to number of inputs.
-
-    rop_overrides : single or list of {'default', OpFromGraph, callable, Variable with special type}, optional
-        Defaults to ``default``.
-
-        ``'default'`` : Do not override, use default R_op() result
-
-        OpFromGraph instance : Override with another OpFromGraph, should
-        accept inputs as the same order and types of ``inputs`` and ``eval_points``
-        arguments as one would specify in R_op() method.
-
-        callable : Should take two args: ``inputs`` and ``eval_points``.
-        Each argument is expected to be a list of :class:`Variable <aesara.graph.basic.Variable>`.
-        Must return list of :class:`Variable <aesara.graph.basic.Variable>`.
-
-        Variable :
-            `NullType` instance: Treat as non-differentiable
-            `DisconnectedType` instance: Treat as zero since DisconnectedType is not yet supported in R_op
-
-        list: Each OpFromGraph/callable must return a single
-        :class:`Variable <aesara.graph.basic.Variable>`. Each list element corresponds
-        to a specific output of R_op, length of list must be equal to number of outputs.
-
-    connection_pattern : list of list
-
-        If not ``None``, this will be used as the connection_pattern
-        for this op.
-
-    name : string, optional
-        A name for debugging purposes
-
-    \*\*kwargs : optional
-        Check
-        :func:`orig_function <aesara.compile.function.types.orig_function>`
-        for more arguments, only works when not inline.
-
-
     .. TODO:
         - examples for a multi-layer mlp. where?
         - __hash__, __eq__ otherwise won't merge, try
@@ -322,16 +228,103 @@ class OpFromGraph(Op, HasInnerGraph):
 
     def __init__(
         self,
-        inputs,
-        outputs,
-        inline=False,
-        lop_overrides="default",
-        grad_overrides="default",
-        rop_overrides="default",
-        connection_pattern=None,
-        name=None,
+        inputs: List[Variable],
+        outputs: List[Variable],
+        inline: bool = False,
+        lop_overrides: str = "default",
+        grad_overrides: str = "default",
+        rop_overrides: str = "default",
+        connection_pattern: Optional[List[List[bool]]] = None,
+        name: Optional[str] = None,
         **kwargs,
     ):
+        """
+        Parameters
+        ----------
+        inputs
+            The inputs to the graph.
+        outputs
+            The outputs to the graph.
+        inline
+            Defaults to ``False``
+
+            ``True`` : Cause the :class:`Op`'s original graph being used during
+            compilation, the :class:`Op` will not be visible in the compiled
+            graph but rather its internal graph.
+
+            ``False`` : will use a pre-compiled function inside.
+        grad_overrides
+            Defaults to ``'default'``.
+            This argument is mutually exclusive with ``lop_overrides``.
+
+            ``'default'`` : Do not override, use default grad() result
+
+            `OpFromGraph`: Override with another `OpFromGraph`, should
+            accept inputs as the same order and types of ``inputs`` and ``output_grads``
+            arguments as one would specify in :meth:`Op.grad`() method.
+
+            `callable`: Should take two args: ``inputs`` and ``output_grads``.
+            Each argument is expected to be a list of :class:`Variable `.
+            Must return list of :class:`Variable `.
+        lop_overrides
+            Defaults to ``'default'``.
+
+            This argument is mutually exclusive with ``grad_overrides``.
+
+            These options are similar to the ``grad_overrides`` above, but for
+            the :meth:`Op.L_op` method.
+
+            ``'default'``: Do not override, use the default :meth:`Op.L_op` result
+
+            `OpFromGraph`: Override with another `OpFromGraph`, should
+            accept inputs as the same order and types of ``inputs``,
+            ``outputs`` and ``output_grads`` arguments as one would specify in
+            :meth:`Op.grad` method.
+
+            `callable`: Should take three args: ``inputs``, ``outputs`` and ``output_grads``.
+            Each argument is expected to be a list of :class:`Variable`.
+            Must return list of :class:`Variable`.
+
+            `NullType` instance: Treat as non-differentiable
+            `DisconnectedType` instance: Treat as disconnected gradient,
+            numerically gives zero
+
+            ``list``: Each `OpFromGraph`/callable must return a single
+            :class:`Variable`. Each list element corresponds to gradient of
+            a specific input, length of list must be equal to number of inputs.
+
+        rop_overrides
+            One of ``{'default', OpFromGraph, callable, Variable}``.
+
+            Defaults to ``'default'``.
+
+            ``'default'``: Do not override, use the default :meth:`Op.R_op` result
+
+            `OpFromGraph`: Override with another `OpFromGraph`, should
+            accept inputs as the same order and types of ``inputs`` and ``eval_points``
+            arguments as one would specify in :meth:`Op.R_op` method.
+
+            `callable`: Should take two args: ``inputs`` and ``eval_points``.
+            Each argument is expected to be a list of :class:`Variable`.  Must
+            return list of :class:`Variable`.
+
+            `NullType` instance: Treat as non-differentiable `DisconnectedType`
+            instance: Treat as zero since `DisconnectedType` is not yet supported
+            in :meth:`Op.R_op`.
+
+            ``list``:
+            Each :class:`OpFromGraph`/callable must return a single
+            :class:`Variable <aesara.graph.basic.Variable>`. Each list element
+            corresponds to a specific output of :meth:`Op.R_op`, length of list
+            must be equal to number of outputs.  connection_pattern If not
+            ``None``, this will be used as the connection_pattern for this
+            :class:`Op`.
+        name
+            A name for debugging purposes.
+        kwargs
+            Check :func:`orig_function` for more arguments, only works when not
+            inline.
+        """
 
         if not (isinstance(inputs, list) and isinstance(outputs, list)):
             raise TypeError("Inputs and outputs must be lists")