docs(mge/functional): update functional.tensor.reshape docstring

imperative/python/megengine/functional/tensor.py

@@ -851,34 +851,33 @@ def transpose(inp: Tensor, pattern: Iterable[int]) -> Tensor:
     return inp.transpose(list(-1 if _ == "x" else _ for _ in pattern))
 
 
-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
+def reshape(inp: Tensor, shape: Iterable[int]) -> Tensor:
+    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:
+        inp (Tensor): input tensor to reshape.
+        shape (sequence of ints): 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.
 
-        .. 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())
+    Returns:
+        an output tensor having the same data type, elements, and underlying element order as ``inp``.
 
-        Outputs:
+    Examples:
 
-        .. testoutput::
+        >>> 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)
             
-            [[ 0  1  2  3]
-             [ 4  5  6  7]
-             [ 8  9 10 11]]
     """
-    return inp.reshape(target_shape)
+    return inp.reshape(shape)
 
 
 def flatten(inp: Tensor, start_axis: int = 0, end_axis: int = -1) -> Tensor: