Add type annotations to the array api submodule function definitions

Some stubs still need to be modified to properly pass mypy type checking.
Also, 'device' is just left as a TypeVar() for now.

1 files changed, 11 insertions, 7 deletions

diff --git a/numpy/_array_api/_statistical_functions.py b/numpy/_array_api/_statistical_functions.py
index 833c47f66..020053896 100644
--- a/numpy/_array_api/_statistical_functions.py
+++ b/numpy/_array_api/_statistical_functions.py
@@ -1,24 +1,28 @@
+from __future__ import annotations
+
+from ._types import Optional, Tuple, Union, array
+
 import numpy as np
 
-def max(x, /, *, axis=None, keepdims=False):
+def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
     return np.max(x, axis=axis, keepdims=keepdims)
 
-def mean(x, /, *, axis=None, keepdims=False):
+def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
     return np.mean(x, axis=axis, keepdims=keepdims)
 
-def min(x, /, *, axis=None, keepdims=False):
+def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
     return np.min(x, axis=axis, keepdims=keepdims)
 
-def prod(x, /, *, axis=None, keepdims=False):
+def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
     return np.prod(x, axis=axis, keepdims=keepdims)
 
-def std(x, /, *, axis=None, correction=0.0, keepdims=False):
+def std(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array:
     # Note: the keyword argument correction is different here
     return np.std(x, axis=axis, ddof=correction, keepdims=keepdims)
 
-def sum(x, /, *, axis=None, keepdims=False):
+def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
     return np.sum(x, axis=axis, keepdims=keepdims)
 
-def var(x, /, *, axis=None, correction=0.0, keepdims=False):
+def var(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array:
     # Note: the keyword argument correction is different here
     return np.var(x, axis=axis, ddof=correction, keepdims=keepdims)