diff --git a/imperative/python/megengine/functional/tensor.py b/imperative/python/megengine/functional/tensor.py
index e8ff2e6a2da9addf02860e967aeaa3daae2c5dc5..67693ace946a84886d87a7c26ffceddec501f280 100755
--- a/imperative/python/megengine/functional/tensor.py
+++ b/imperative/python/megengine/functional/tensor.py
@@ -240,32 +240,19 @@ def zeros_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
 
 
 def ones_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
-    r"""Returns a ones tensor with the same shape as input tensor.
+    r"""Returns a tensor filled with ones with the same shape and data type as input tensor.
 
     Args:
-        inp: input tensor.
+        inp (Tensor): input tensor.
 
     Return:
-        output tensor.
+        a tensor containing ones.
 
     Examples:
-
-        .. testcode::
-
-            import numpy as np
-            from megengine import tensor
-            import megengine.functional as F
-
-            inp = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3))
-            out = F.ones_like(inp)
-            print(out.numpy())
-
-        Outputs:
-
-        .. testoutput::
-
-            [[1 1 1]
-             [1 1 1]]
+        >>> input = F.arange(6, dtype='int32').reshape(2,3)
+        >>> F.ones_like(input)
+        Tensor([[1 1 1]
+         [1 1 1]], dtype=int32, device=xpux:0)
     """
     return full_like(inp, 1.0)