Use _implementation on all functions that have it in the array API submodule

That way they only work on actual ndarray inputs, not array-like, which is
more inline with the spec.

1 files changed, 7 insertions, 7 deletions

diff --git a/numpy/_array_api/_statistical_functions.py b/numpy/_array_api/_statistical_functions.py
index e62410d01..79bc125dc 100644
--- a/numpy/_array_api/_statistical_functions.py
+++ b/numpy/_array_api/_statistical_functions.py
@@ -5,24 +5,24 @@ from ._types import Optional, Tuple, Union, array
 import numpy as np
 
 def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
-    return np.max(x, axis=axis, keepdims=keepdims)
+    return np.max._implementation(x, axis=axis, keepdims=keepdims)
 
 def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
-    return np.asarray(np.mean(x, axis=axis, keepdims=keepdims))
+    return np.asarray(np.mean._implementation(x, axis=axis, keepdims=keepdims))
 
 def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
-    return np.min(x, axis=axis, keepdims=keepdims)
+    return np.min._implementation(x, axis=axis, keepdims=keepdims)
 
 def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
-    return np.asarray(np.prod(x, axis=axis, keepdims=keepdims))
+    return np.asarray(np.prod._implementation(x, axis=axis, keepdims=keepdims))
 
 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.asarray(np.std(x, axis=axis, ddof=correction, keepdims=keepdims))
+    return np.asarray(np.std._implementation(x, axis=axis, ddof=correction, keepdims=keepdims))
 
 def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
-    return np.asarray(np.sum(x, axis=axis, keepdims=keepdims))
+    return np.asarray(np.sum._implementation(x, axis=axis, keepdims=keepdims))
 
 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.asarray(np.var(x, axis=axis, ddof=correction, keepdims=keepdims))
+    return np.asarray(np.var._implementation(x, axis=axis, ddof=correction, keepdims=keepdims))