From 0294303683c091b66ded9e7ac2799db407293bb3 Mon Sep 17 00:00:00 2001
From: DBJ <974658390@qq.com>
Date: Tue, 7 Dec 2021 13:41:28 +0800
Subject: [PATCH] docs(mge/functional): update functional.tensor.reshape
 docstring

---
 imperative/python/megengine/functional/tensor.py | 39 ++++++++++++------------
 1 file changed, 19 insertions(+), 20 deletions(-)

diff --git a/imperative/python/megengine/functional/tensor.py b/imperative/python/megengine/functional/tensor.py
index 040a4981..7638a8fd 100755
--- a/imperative/python/megengine/functional/tensor.py
+++ b/imperative/python/megengine/functional/tensor.py
@@ -852,31 +852,30 @@ def transpose(inp: Tensor, pattern: Iterable[int]) -> Tensor:
 
 
 def reshape(inp: Tensor, target_shape: Iterable[int]) -> Tensor:
-    r"""Reshapes a tensor to given target shape; total number of logical elements must
-    remain unchanged
+    r"""Reshapes a tensor without changing its data.
 
     Args:
-        inp: input tensor.
-        target_shape: target shape, it can contain an element of -1 representing ``unspec_axis``.
-
-    Examples:
-
-        .. testcode::
-
-            import numpy as np
-            from megengine import tensor
-            import megengine.functional as F
-            x = tensor(np.arange(12, dtype=np.int32))
-            out = F.reshape(x, (3, 4))
-            print(out.numpy())
+        inp: input tensor to reshape.
+        target_shape: target shape compatible with the original shape. One shape dimension is allowed 
+             to be `-1` . When a shape dimension is `-1` , the corresponding output tensor shape dimension 
+             must be inferred from the length of the tensor and the remaining dimensions.
 
-        Outputs:
+    Returns:
+        an output tensor having the same data type, elements, and underlying element order as `inp` .
 
-        .. testoutput::
+    Examples:
 
-            [[ 0  1  2  3]
-             [ 4  5  6  7]
-             [ 8  9 10 11]]
+            >>> x = F.arange(12)
+            >>> x
+            Tensor([ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11.], device=xpux:0)
+            >>> F.reshape(x, (3, 4))
+            Tensor([[ 0.  1.  2.  3.]
+             [ 4.  5.  6.  7.]
+             [ 8.  9. 10. 11.]], device=xpux:0)
+            >>> F.reshape(x, (2, -1))
+            Tensor([[ 0.  1.  2.  3.  4.  5.]
+             [ 6.  7.  8.  9. 10. 11.]], device=xpux:0)
+            
     """
     return inp.reshape(target_shape)