Initial exposition of initializer

src/spox/__init__.py

@@ -1,6 +1,6 @@
 import importlib.metadata
 
-from spox._public import argument, build, inline
+from spox._public import argument, build, initializer, inline
 from spox._type_system import Optional, Sequence, Tensor, Type
 from spox._var import Var
 
@@ -11,6 +11,7 @@
     "Sequence",
     "Optional",
     "argument",
+    "initializer",
     "build",
     "inline",
 ]

src/spox/_future.py

@@ -4,11 +4,9 @@
 from typing import Iterable, List, Optional, Union
 
 import numpy as np
-import numpy.typing as npt
 
 import spox._node
 import spox._value_prop
-from spox._graph import initializer as _initializer
 from spox._type_system import Tensor
 from spox._var import Var
 
@@ -42,32 +40,6 @@ def value_prop_backend(backend: ValuePropBackend):
     set_value_prop_backend(prev_backend)
 
 
-def initializer(value: npt.ArrayLike, dtype: npt.DTypeLike = None) -> Var:
-    """
-    Create a Var with a constant value.
-
-    Parameters
-    ----------
-    value
-        Array-like value for the variable.
-    dtype
-        Data type for the given value. If ``None``, it is inferred from the value
-        using numpy rules (``numpy.array(value)``).
-
-    Returns
-    -------
-    Var
-        Variable with the given constant ``value``.
-
-    Notes
-    -----
-    When the model is built, constants created by this function become initializers.
-    As such, they are independent of an opset version and are listed separately
-    in the model. Initializers are also used internally in Spox.
-    """
-    return _initializer(np.array(value, dtype))
-
-
 class _NumpyLikeOperatorDispatcher:
     def __init__(self, op, type_promotion: bool, constant_promotion: bool):
         self.op = op
@@ -210,8 +182,6 @@ def operator_overloading(
     "TypeWarningLevel",
     "set_type_warning_level",
     "type_warning_level",
-    # Initializer-backed constants
-    "initializer",
     # Value propagation backend
     "ValuePropBackend",
     "set_value_prop_backend",

src/spox/_public.py

@@ -5,12 +5,15 @@
 from typing import Dict, List, Optional, Protocol
 
 import numpy as np
+import numpy.typing as npt
 import onnx
 from onnx.numpy_helper import to_array
 
 from . import _internal_op
 from ._attributes import AttrType
-from ._graph import Argument, initializer, results
+from ._graph import Argument
+from ._graph import initializer as _initializer
+from ._graph import results
 from ._inline import _Inline
 from ._standard import _strip_dim_symbol
 from ._type_system import Type
@@ -37,6 +40,32 @@ def argument(typ: Type) -> Var:
     ).outputs.arg
 
 
+def initializer(value: npt.ArrayLike, dtype: npt.DTypeLike = None) -> Var:
+    """
+    Create a Var with a constant value.
+
+    Parameters
+    ----------
+    value
+        Array-like value for the variable.
+    dtype
+        Data type for the given value. If ``None``, it is inferred from the value
+        using numpy rules (``numpy.array(value)``).
+
+    Returns
+    -------
+    Var
+        Variable with the given constant ``value``.
+
+    Notes
+    -----
+    When the model is built, constants created by this function become initializers.
+    As such, they are independent of an opset version and are listed separately
+    in the model. Initializers are also used internally in Spox.
+    """
+    return _initializer(np.array(value, dtype))
+
+
 @contextlib.contextmanager
 def _temporary_renames(**kwargs: Var):
     # The build code can't really special-case variable names that are
@@ -284,4 +313,4 @@ def inline_inner(*args: Var, **kwargs: Var) -> Dict[str, Var]:
     return inline_inner
 
 
-__all__ = ["argument", "build", "inline"]
+__all__ = ["argument", "initializer", "build", "inline"]

tests/test_initializer.py

@@ -4,7 +4,7 @@
 import numpy
 import pytest
 
-from spox._future import initializer
+from spox import initializer
 
 TESTED_INITIALIZER_ROWS: List[List[Any]] = [
     [0, 1, 2],

tests/test_subgraphs.py

@@ -4,9 +4,8 @@
 import pytest
 
 import spox.opset.ai.onnx.v17 as op
-from spox import Var
+from spox import Var, initializer
 from spox._exceptions import BuildError
-from spox._future import initializer
 from spox._graph import arguments, results
 from spox._type_system import Sequence, Tensor