diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index 91f976cb0b..070ce68545 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -31,7 +31,8 @@ from mindspore.common.api import _executor
 from mindspore.common import dtype as mstype
 from mindspore._checkparam import check_input_data
 
-__all__ = ["save_checkpoint", "load_checkpoint", "load_param_into_net", "export", "parse_print"]
+__all__ = ["save_checkpoint", "load_checkpoint", "load_param_into_net", "export", "parse_print",
+           "build_searched_strategy", "merge_sliced_parameter"]
 
 tensor_to_ms_type = {"Int8": mstype.int8, "Uint8": mstype.uint8, "Int16": mstype.int16, "Uint16": mstype.uint16,
                      "Int32": mstype.int32, "Uint32": mstype.uint32, "Int64": mstype.int64, "Uint64": mstype.uint64,
@@ -575,19 +576,19 @@ def parse_print(print_file_name):
 
 def _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even):
     """
-        Merge data slices to one tensor with whole data when strategy is not None.
+    Merge data slices to one tensor with whole data when strategy is not None.
 
-        Args:
-            sliced_data (list[numpy.ndarray]): data slices in order of rank_id.
-            parameter_name (str): name of parameter.
-            strategy (dict): parameter slice strategy.
-            is_even (bool): slice manner that True represents slicing evenly and False represents slicing unevenly.
+    Args:
+        sliced_data (list[numpy.ndarray]): Data slices in order of rank_id.
+        parameter_name (str): Name of parameter.
+        strategy (dict): Parameter slice strategy.
+        is_even (bool): Slice manner that True represents slicing evenly and False represents slicing unevenly.
 
-        Returns:
-            Tensor, the merged Tensor which has the whole data.
+    Returns:
+        Tensor, the merged Tensor which has the whole data.
 
-        Raises:
-            ValueError: failed to merge.
+    Raises:
+        ValueError: Failed to merge.
     """
     layout = strategy.get(parameter_name)
     try:
@@ -661,17 +662,17 @@ def _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even):
 
 def build_searched_strategy(strategy_filename):
     """
-    build strategy of every parameter in network.
+    Build strategy of every parameter in network.
 
     Args:
-        strategy_filename (str): name of strategy file.
+        strategy_filename (str): Name of strategy file.
 
     Returns:
         Dictionary, whose key is parameter name and value is slice strategy of this parameter.
 
     Raises:
-        ValueError: strategy file is incorrect.
-        TypeError: strategy_filename is not str.
+        ValueError: Strategy file is incorrect.
+        TypeError: Strategy_filename is not str.
 
     Examples:
         >>> strategy_filename = "./strategy_train.ckpt"
@@ -707,32 +708,32 @@ def build_searched_strategy(strategy_filename):
 
 def merge_sliced_parameter(sliced_parameters, strategy=None):
     """
-        Merge parameter slices to one whole parameter.
-
-        Args:
-            sliced_parameters (list[Parameter]): parameter slices in order of rank_id.
-            strategy (dict): parameter slice strategy. Default: None.
-
-                If strategy is None, just merge parameter slices in 0 axis order.
-                - key (str): parameter name.
-                - value (<class 'node_strategy_pb2.ParallelLayouts'>): slice strategy of this parameter.
-
-        Returns:
-            Parameter, the merged parameter which has the whole data.
-
-        Raises:
-            ValueError: failed to merge.
-            TypeError: the sliced_parameters is incorrect or strategy is not dict.
-            KeyError: the parameter name is not in keys of strategy.
-
-        Examples:
-            >>> strategy = build_searched_strategy("./strategy_train.ckpt")
-            >>> sliced_parameters = [\
-            Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])), "network.embedding_table"), \
-            Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])), "network.embedding_table"), \
-            Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])), "network.embedding_tabel"), \
-            Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])), "network.embedding_table")]
-            >>> merged_parameter = merge_sliced_parameter(sliced_parameters, strategy)
+    Merge parameter slices to one whole parameter.
+
+    Args:
+        sliced_parameters (list[Parameter]): Parameter slices in order of rank_id.
+        strategy (dict): Parameter slice strategy. Default: None.
+                         If strategy is None, just merge parameter slices in 0 axis order.
+
+            - key (str): Parameter name.
+            - value (<class 'node_strategy_pb2.ParallelLayouts'>): Slice strategy of this parameter.
+
+    Returns:
+        Parameter, the merged parameter which has the whole data.
+
+    Raises:
+        ValueError: Failed to merge.
+        TypeError: The sliced_parameters is incorrect or strategy is not dict.
+        KeyError: The parameter name is not in keys of strategy.
+
+    Examples:
+        >>> strategy = build_searched_strategy("./strategy_train.ckpt")
+        >>> sliced_parameters = [\
+        Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])), "network.embedding_table"), \
+        Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])), "network.embedding_table"), \
+        Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])), "network.embedding_tabel"), \
+        Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])), "network.embedding_table")]
+        >>> merged_parameter = merge_sliced_parameter(sliced_parameters, strategy)
     """
     if not isinstance(sliced_parameters, list):
         raise TypeError(f"The sliced_parameters should be list, but got {type(sliced_parameters)}.")