fix: is_tensor now accepts numpy scalars

keras_core/backend/numpy/core.py

@@ -41,7 +41,7 @@ def convert_to_numpy(x):
 
 
 def is_tensor(x):
-    if isinstance(x, np.ndarray):
+    if isinstance(x, (np.generic, np.ndarray)):
         return True
     return False
 

keras_core/backend/numpy/numpy.py

@@ -560,12 +560,7 @@ def var(x, axis=None, keepdims=False):
 
 def sum(x, axis=None, keepdims=False):
     axis = tuple(axis) if isinstance(axis, list) else axis
-    # `np.sum()` function, when applied to an array, returns a scalar
-    # value that represents the sum of all the elements in the array.
-    # This operation reduces the dimensionality of the input and hence
-    # return objects of type `np.<DTYPE>`. To fix this behaviour, we
-    # wrap the result in a list and explicitly reutrns an `np.ndarray`.
-    return np.array([np.sum(x, axis=axis, keepdims=keepdims)])
+    return np.sum(x, axis=axis, keepdims=keepdims)
 
 
 def eye(N, M=None, k=0, dtype="float32"):