diff --git a/mindspore/numpy/array_ops.py b/mindspore/numpy/array_ops.py
index 53c7e7a72e..9c8fe7efc7 100644
--- a/mindspore/numpy/array_ops.py
+++ b/mindspore/numpy/array_ops.py
@@ -561,7 +561,7 @@ def vstack(tup):
     """
     Stacks tensors in sequence vertically.
     This is equivalent to concatenation along the first axis. 1-D tensors should firstly be reshaped to `(1, N)`,
-        and then be concatenated along the first axis.
+    and then be concatenated along the first axis.
 
     Args:
         tup (Union[Tensor, tuple, list]): A sequence of 1-D or 2-D tensors. The tensors must have the same shape
diff --git a/mindspore/numpy/math_ops.py b/mindspore/numpy/math_ops.py
index 6ffe003c48..4158dc409d 100644
--- a/mindspore/numpy/math_ops.py
+++ b/mindspore/numpy/math_ops.py
@@ -963,11 +963,13 @@ def tensordot(a, b, axes=2):
     non-negative integer_like scalar, `N`; if it is such, then the last `N` dimensions of
     `a` and the first `N` dimensions of `b` are summed over.
     Three common use cases are:
+
         ``axes = 0`` : tensor product
 
         ``axes = 1`` : tensor dot product
 
         ``axes = 2`` : (default) tensor double contraction
+
     When axes is integer_like, the sequence for evaluation will be: first the `-Nth`
     axis in `a` and 0th axis in `b`, and the -1th axis in `a` and `Nth` axis in `b` last.
     When there is more than one axis to sum over - and they are not the last (first)