Support the ndarray object in the remaining array API functions

1 files changed, 8 insertions, 7 deletions

diff --git a/numpy/_array_api/_statistical_functions.py b/numpy/_array_api/_statistical_functions.py
index e62410d01..fa3551248 100644
--- a/numpy/_array_api/_statistical_functions.py
+++ b/numpy/_array_api/_statistical_functions.py
@@ -1,28 +1,29 @@
 from __future__ import annotations
 
 from ._types import Optional, Tuple, Union, array
+from ._array_object import ndarray
 
 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 ndarray._new(np.max(x._array, 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 ndarray._new(np.asarray(np.mean(x._array, 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 ndarray._new(np.min(x._array, 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 ndarray._new(np.asarray(np.prod(x._array, 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 ndarray._new(np.asarray(np.std(x._array, 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 ndarray._new(np.asarray(np.sum(x._array, 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 ndarray._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))