Improve qol

src/spox/_function.py

@@ -218,9 +218,9 @@ def init(*args: Var) -> type[Function]:
         def alt_fun(*args: Var) -> Iterable[Union[Var, Optional[Var], Sequence[Var]]]:
             cls = init(*args)
             return [
-                Var(var_info)  # type: ignore
+                Var(var_info)
                 for var_info in cls(cls.Attributes(), cls.Inputs(*unwrap_vars(args)))
-                .outputs.get_fields()
+                .outputs.get_var_infos()
                 .values()
             ]
 

src/spox/_standard.py

@@ -5,8 +5,10 @@
 
 from __future__ import annotations
 
+from collections.abc import Iterable
 from typing import TYPE_CHECKING, Callable
 
+import numpy as np
 import onnx
 import onnx.reference
 import onnx.shape_inference
@@ -121,16 +123,17 @@ def out_value_info(
             elif not isinstance(prop, PropValue) or prop.value is None:
                 continue
             elif isinstance(prop.type, Sequence):
+                assert isinstance(prop.value, Iterable)
                 initializers.extend(
                     [
                         from_array(elem.value, f"{name}_{i}")
-                        for i, elem in enumerate(prop.value)  # type: ignore
+                        for i, elem in enumerate(prop.value)
                         if elem is not None
                     ]
                 )
             else:
-                initializers.append(from_array(prop.value, name))  # type: ignore
-                continue
+                assert isinstance(prop.value, np.ndarray)
+                initializers.append(from_array(prop.value, name))
 
         #  Graph and model
         graph = onnx.helper.make_graph(

tests/test_value_propagation.py

@@ -124,7 +124,7 @@ def test_empty_optional_has_no_element():
 
 @pytest.mark.parametrize("min", [None, 2])
 def test_optional_clip(min):
-    min_var = min if min is None else op.const(min)
+    min_var = None if min is None else op.const(min)
     assert_equal_value(
         op.clip(op.const([1, 2, 3]), min=min_var, max=op.const(3)),
         np.clip([1, 2, 3], a_min=min, a_max=3),