Support numpy scalars in Tensor (#102)

Numpy scalars (https://numpy.org/doc/2.2/reference/arrays.scalars.html)
have `__array__` defined but doesn't behave like normal np arrays
sometimes. This change updates the initialization logic in `Tensor` to
detect them and turn them into np.ndarray.

Fixes #101.

---------

Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>

Author: justinchuby

src/onnx_ir/_core.py

@@ -417,6 +417,9 @@ def __init__(
         else:
             self._shape = shape
             self._shape.freeze()
+        if isinstance(value, np.generic):
+            # Turn numpy scalar into a numpy array
+            value = np.array(value)  # type: ignore[assignment]
         if dtype is None:
             if isinstance(value, np.ndarray):
                 self._dtype = _enums.DataType.from_numpy(value.dtype)

src/onnx_ir/_core_test.py

@@ -69,6 +69,17 @@ def test_initialize_with_just_np_array(self):
         tensor = _core.Tensor(array)
         np.testing.assert_array_equal(tensor, array)
 
+    @parameterized.parameterized.expand(
+        [
+            ("bfloat16", ml_dtypes.bfloat16(0.5)),
+            ("float32", np.float32(0.5)),
+            ("bool", np.bool(True)),
+        ]
+    )
+    def test_initialize_with_np_number(self, _: str, number: np.generic):
+        tensor = _core.Tensor(number)
+        np.testing.assert_equal(tensor.numpy(), np.array(number), strict=True)
+
     def test_initialize_raises_when_numpy_dtype_doesnt_match(self):
         array = np.random.rand(1, 2).astype(np.float32)
         with self.assertRaises(TypeError):