Update some Note comments in numpy.array_api

Original NumPy Commit: 7ed0189477ed4a7d2907d0728b3b8121722d8a2f

Author: asmeurer

array_api_strict/_array_object.py

@@ -177,6 +177,8 @@ def _promote_scalar(self, scalar):
         integer that is too large to fit in a NumPy integer dtype, or
         TypeError when the scalar type is incompatible with the dtype of self.
         """
+        # Note: Only Python scalar types that match the array dtype are
+        # allowed.
         if isinstance(scalar, bool):
             if self.dtype not in _boolean_dtypes:
                 raise TypeError(
@@ -195,6 +197,9 @@ def _promote_scalar(self, scalar):
         else:
             raise TypeError("'scalar' must be a Python scalar")
 
+        # Note: scalars are unconditionally cast to the same dtype as the
+        # array.
+
         # Note: the spec only specifies integer-dtype/int promotion
         # behavior for integers within the bounds of the integer dtype.
         # Outside of those bounds we use the default NumPy behavior (either

array_api_strict/_data_type_functions.py

@@ -56,7 +56,8 @@ def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
         raise TypeError(f"{from_=}, but should be an array_api array or dtype")
     if to not in _all_dtypes:
         raise TypeError(f"{to=}, but should be a dtype")
-    # Note: We avoid np.can_cast() as it has discrepancies with the array API.
+    # Note: We avoid np.can_cast() as it has discrepancies with the array API,
+    # since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
     # See https://github.com/numpy/numpy/issues/20870
     try:
         # We promote `from_` and `to` together. We then check if the promoted