diff --git a/imperative/python/megengine/functional/__init__.py b/imperative/python/megengine/functional/__init__.py
index 56f26e6aa9abc0fa6678058a6383a421e951d448..4d70c8a6855f398672d853e1346c90be0882aeb5 100644
--- a/imperative/python/megengine/functional/__init__.py
+++ b/imperative/python/megengine/functional/__init__.py
@@ -13,7 +13,7 @@ from .math import *
 from .nn import *
 from .quantized import conv_bias_activation
 from .tensor import *
-from .utils import accuracy, copy
+from .utils import *
 
 from . import distributed  # isort:skip
 
diff --git a/imperative/python/megengine/functional/utils.py b/imperative/python/megengine/functional/utils.py
index d518b69f21b8d6cfddaa10d521bbc89b980e9987..fa38e8b1fc7ddd24db0cdb77007b9548e2476529 100644
--- a/imperative/python/megengine/functional/utils.py
+++ b/imperative/python/megengine/functional/utils.py
@@ -18,8 +18,13 @@ from ..core.tensor.core import apply
 from .math import topk as _topk
 from .tensor import broadcast_to, transpose
 
+__all__ = [
+    "topk_accuracy",
+    "copy",
+]
 
-def accuracy(
+
+def topk_accuracy(
     logits: Tensor, target: Tensor, topk: Union[int, Iterable[int]] = 1
 ) -> Union[Tensor, Iterable[Tensor]]:
     r"""
@@ -41,7 +46,7 @@ def accuracy(
 
         logits = tensor(np.arange(80, dtype=np.int32).reshape(8,10))
         target = tensor(np.arange(8, dtype=np.int32))
-        top1, top5 = F.accuracy(logits, target, (1, 5))
+        top1, top5 = F.topk_accuracy(logits, target, (1, 5))
         print(top1.numpy(), top5.numpy())
 
     Outputs: