broadcast_arrays et al: change the return type into a tuple

array_api_compat/cupy/_aliases.py

@@ -139,6 +139,11 @@ def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
     return cp.take_along_axis(x, indices, axis=axis)
 
 
+# https://github.com/cupy/cupy/pull/9582
+def broadcast_arrays(*arrays: Array) -> tuple[Array, ...]:
+    return tuple(cp.broadcast_arrays(*arrays))
+
+
 # These functions are completely new here. If the library already has them
 # (i.e., numpy 2.0), use the library version instead of our wrapper.
 if hasattr(cp, 'vecdot'):
@@ -161,7 +166,8 @@ def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
                               'atan2', 'atanh', 'bitwise_left_shift',
                               'bitwise_invert', 'bitwise_right_shift',
                               'bool', 'concat', 'count_nonzero', 'pow', 'sign',
-                              'ceil', 'floor', 'trunc', 'take_along_axis']
+                              'ceil', 'floor', 'trunc', 'take_along_axis',
+                              'broadcast_arrays',]
 
 
 def __dir__() -> list[str]:

array_api_compat/cupy/_info.py

@@ -333,4 +333,4 @@ def devices(self):
         __array_namespace_info__.dtypes
 
         """
-        return [cuda.Device(i) for i in range(cuda.runtime.getDeviceCount())]
+        return tuple(cuda.Device(i) for i in range(cuda.runtime.getDeviceCount()))

array_api_compat/dask/array/_info.py

@@ -379,7 +379,7 @@ def dtypes(
             return res
         raise ValueError(f"unsupported kind: {kind!r}")
 
-    def devices(self) -> list[Device]:
+    def devices(self) -> tuple[Device]:
         """
         The devices supported by Dask.
 
@@ -404,4 +404,4 @@ def devices(self) -> list[Device]:
         ['cpu', DASK_DEVICE]
 
         """
-        return ["cpu", _DASK_DEVICE]
+        return ("cpu", _DASK_DEVICE)

array_api_compat/numpy/_info.py

@@ -332,7 +332,7 @@ def dtypes(
             return res
         raise ValueError(f"unsupported kind: {kind!r}")
 
-    def devices(self) -> list[Device]:
+    def devices(self) -> tuple[Device]:
         """
         The devices supported by NumPy.
 
@@ -357,7 +357,7 @@ def devices(self) -> list[Device]:
         ['cpu']
 
         """
-        return ["cpu"]
+        return ("cpu",)
 
 
 __all__ = ["__array_namespace_info__"]

array_api_compat/torch/_aliases.py

@@ -706,9 +706,9 @@ def astype(
     return x.to(dtype=dtype, copy=copy)
 
 
-def broadcast_arrays(*arrays: Array) -> list[Array]:
+def broadcast_arrays(*arrays: Array) -> tuple[Array, ...]:
     shape = torch.broadcast_shapes(*[a.shape for a in arrays])
-    return [torch.broadcast_to(a, shape) for a in arrays]
+    return tuple(torch.broadcast_to(a, shape) for a in arrays)
 
 # Note that these named tuples aren't actually part of the standard namespace,
 # but I don't see any issue with exporting the names here regardless.
@@ -881,10 +881,11 @@ def sign(x: Array, /) -> Array:
         return out
 
 
-def meshgrid(*arrays: Array, indexing: Literal['xy', 'ij'] = 'xy') -> list[Array]:
-    # enforce the default of 'xy'
-    # TODO: is the return type a list or a tuple
-    return list(torch.meshgrid(*arrays, indexing=indexing))
+def meshgrid(*arrays: Array, indexing: Literal['xy', 'ij'] = 'xy') -> tuple[Array, ...]:
+    # torch <= 2.9 emits a UserWarning: "torch.meshgrid: in an upcoming release, it
+    # will be required to pass the indexing argument."
+    # Thus always pass it explicitly.
+    return torch.meshgrid(*arrays, indexing=indexing)
 
 
 __all__ = ['asarray', 'result_type', 'can_cast',

array_api_compat/torch/_info.py

@@ -366,4 +366,4 @@ def devices(self):
                     break
                 i += 1
 
-        return devices
+        return tuple(devices)