diff --git a/mindspore/nn/metrics/metric.py b/mindspore/nn/metrics/metric.py
index 39a188b8b24..8bb7242cf94 100644
--- a/mindspore/nn/metrics/metric.py
+++ b/mindspore/nn/metrics/metric.py
@@ -23,7 +23,30 @@ _eval_types = {'classification', 'multilabel'}
 
 def rearrange_inputs(func):
     """
-    This decorator is used to rearrange the inputs according to its indexes.
+    This decorator is used to rearrange the inputs according to its `_indexes` attributes
+    which is specified by the `set_indexes` method.
+
+    Examples:
+        >>> class RearrangeInputsExample:
+        ...     def __init__(self):
+        ...         self._indexes = None
+        ...
+        ...     @property
+        ...     def indexes(self):
+        ...         return getattr(self, '_indexes', None)
+        ...
+        ...     def set_indexes(self, indexes):
+        ...         self._indexes = indexes
+        ...         return self
+        ...
+        ...     @rearrange_inputs
+        ...     def update(self, *inputs):
+        ...         return inputs
+        >>>
+        >>> rearrange_inputs_example = RearrangeInputsExample().set_indexes([1, 0])
+        >>> outs = rearrange_inputs_example.update(5, 9)
+        >>> print(outs)
+        >>> (9, 5)
 
     Args:
         func (Callable): A candidate function to be wrapped whose input will be rearranged.
@@ -43,9 +66,8 @@ class Metric(metaclass=ABCMeta):
     """
     Base class of metric.
 
-
     Note:
-        For examples of subclasses, please refer to the definition of class `MAE`, 'Recall' etc.
+        For examples of subclasses, please refer to the definition of class `MAE`, `Recall` etc.
     """
     def __init__(self):
         self._indexes = None
@@ -117,9 +139,42 @@ class Metric(metaclass=ABCMeta):
 
     @property
     def indexes(self):
+        """The `_indexes` is a private attributes, and you can retrieve it by `self.indexes`.
+        """
         return getattr(self, '_indexes', None)
 
     def set_indexes(self, indexes):
+        """
+        The `_indexes` is a private attributes, and you can modify it by this function.
+        This allows you to determine the order of logits and labels to be calculated in the
+        inputs, specially when you call the method `update` within this metrics.
+
+        Note:
+            It has been applied in subclass of Metric, eg. `Accuracy`, `BleuScore`, `ConfusionMatrix`,
+            `CosineSimilarity`, `MAE`, and `MSE`.
+
+        Args:
+            indexes (List(int)): The order of logits and labels to be rearranged.
+
+        Outputs:
+            :class:`Metric`, its original Class instance.
+
+        Examples:
+            >>> from mindspore import Tensor
+            >>> from mindspore.nn import Accuracy
+            >>>
+            >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
+            >>> y = Tensor(np.array([1, 0, 1]))
+            >>> y2 = Tensor(np.array([0, 0, 1]))
+            >>> metric = Accuracy('classification').set_indexes([0, 2])
+            >>> metric.clear()
+            >>> metric.update(x, y, y2)
+            >>> accuracy = metric.eval()
+            >>> print(accuracy)
+            0.3333333333333333
+        """
+        if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes):
+            raise ValueError("The indexes should be a list and all its elements should be int")
         self._indexes = indexes
         return self
 
diff --git a/tests/ut/python/metrics/test_accuracy.py b/tests/ut/python/metrics/test_accuracy.py
index f3cc8816e19..bb4aa1a46dc 100644
--- a/tests/ut/python/metrics/test_accuracy.py
+++ b/tests/ut/python/metrics/test_accuracy.py
@@ -47,6 +47,12 @@ def test_classification_accuracy_indexes_awareness():
     assert math.isclose(accuracy, 1 / 3)
 
 
+@pytest.mark.parametrize('indexes', [0, [0., 2.], [0., 1], ['1', '0']])
+def test_set_indexes(indexes):
+    with pytest.raises(ValueError, match="indexes should be a list and all its elements should be int"):
+        _ = Accuracy('classification').set_indexes(indexes)
+
+
 def test_multilabel_accuracy():
     x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
     y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 1]]))