diff --git a/imperative/python/megengine/functional/tensor.py b/imperative/python/megengine/functional/tensor.py
index 040a4981..50027530 100755
--- a/imperative/python/megengine/functional/tensor.py
+++ b/imperative/python/megengine/functional/tensor.py
@@ -6,7 +6,7 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-from typing import Iterable, Optional, Sequence, Union
+from typing import Iterable, Optional, Sequence, Union, Tuple
 
 import numpy as np
 
@@ -148,33 +148,40 @@ def full(
     return broadcast_to(x, shape)
 
 
-def ones(shape, dtype="float32", device=None) -> Tensor:
-    r"""Returns a ones tensor with given shape.
+def ones(
+    shape: Union[int, Tuple[int, ...]], 
+    *,
+    dtype="float32", 
+    device: Optional[CompNode] = None
+) -> Tensor:
+    r"""Returns a new tensor having a specified shape and filled with ones.
 
     Args:
-        shape: a list, tuple or integer defining the shape of the output tensor.
-        dtype: the desired data type of the output tensor. Default: ``float32``.
-        device: the desired device of the output tensor. Default: if ``None``,
-            use the default device (see :func:`~.megengine.get_default_device`).
+        shape (int or sequence of ints): the shape of the output tensor.
+    
+    Keyword args:
+        dtype (:attr:`.Tensor.dtype`): output tensor data type. Default: ``float32``.
+        device (:attr:`.Tensor.device`): device on which to place the created tensor. Default: ``None``.
 
     Returns:
-        output tensor.
+        a tensor containing ones.
 
     Examples:
 
-        .. testcode::
+        >>> megengine.functional.ones(5)
+        Tensor([1. 1. 1. 1. 1.], device=xpux:0)
 
-            import megengine.functional as F
-
-            out = F.ones((2, 1))
-            print(out.numpy())
-
-        Outputs:
-
-        .. testoutput::
+        >>> megengine.functional.ones((5, ), dtype='int32')
+        Tensor([1 1 1 1 1], dtype=int32, device=xpux:0)
+        
+        >>> megengine.functional.ones((2, 2))
+        Tensor([[1. 1.]
+         [1. 1.]], device=xpux:0)
+        
+        >>> megengine.functional.ones([2, 1])
+        Tensor([[1.]
+         [1.]], device=xpux:0)
 
-            [[1.]
-             [1.]]
     """
     return full(shape, 1.0, dtype=dtype, device=device)