diff --git a/examples/acm_data.py b/examples/acm_data.py
index 06209b96eb..d6c7912209 100644
--- a/examples/acm_data.py
+++ b/examples/acm_data.py
@@ -30,9 +30,30 @@
 import numpy as np
 import torch as th
 import torch.nn as nn
-from dgl.data.utils import save_graphs
 
-from utils import convert_tensor_to_list_arrays
+
+def convert_tensor_to_list_arrays(tensor):
+    """ Convert Pytorch Tensor to a list of arrays
+    
+    Since a pandas DataFrame cannot save a 2D numpy array in parquet format, it is necessary to
+    convert the tensor (1D or 2D) into a list of lists or a list of array. This converted tensor
+    can then be used to build a pandas DataFrame, which can be saved in parquet format. However,
+    tensor with a dimension greater than or equal to 3D cannot be processed or saved into parquet
+    files.
+
+    Parameters:
+    tensor: Pytorch Tensor
+        The input Pytorch tensor (1D or 2D) to be converted
+    
+    Returns:
+    list_array: list of numpy arrays
+        A list of numpy arrays
+    """
+    
+    np_array = tensor.numpy()
+    list_array = [np_array[i] for i in range(len(np_array))]
+
+    return list_array
 
 
 def create_acm_raw_data(graph,
@@ -336,7 +357,7 @@ def create_acm_dgl_graph(dowload_path='/tmp/ACM.mat',
         # Save DGL graph
         output_graph_file_path = os.path.join(output_path, dataset_name + '.dgl')
         print(f'Saving ACM data to {output_graph_file_path} ......')
-        save_graphs(output_graph_file_path, [graph_acm], None)
+        dgl.save_graphs(output_graph_file_path, [graph_acm], None)
         print(f'{output_graph_file_path} saved.')
         # Save raw node text
         output_text_file_path = os.path.join(output_path, dataset_name + '_text.pkl')
diff --git a/examples/utils.py b/examples/utils.py
deleted file mode 100644
index f2309da359..0000000000
--- a/examples/utils.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""
-    Copyright 2023 Contributors
-
-    Licensed under the Apache License, Version 2.0 (the "License");
-    you may not use this file except in compliance with the License.
-    You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-    Unless required by applicable law or agreed to in writing, software
-    distributed under the License is distributed on an "AS IS" BASIS,
-    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    See the License for the specific language governing permissions and
-    limitations under the License.
-
-    Generate example graph data using built-in datasets for node classifcation,
-    node regression, edge classification and edge regression.
-
-    Utility functions to help create require raw data tables and JSON for using
-    GraphStorm
-"""
-
-
-def convert_tensor_to_list_arrays(tensor):
-    """ Convert Pytorch Tensor to a list of arrays
-    
-    Since a pandas DataFrame cannot save a 2D numpy array in parquet format, it is necessary to
-    convert the tensor (1D or 2D) into a list of lists or a list of array. This converted tensor
-    can then be used to build a pandas DataFrame, which can be saved in parquet format. However,
-    tensor with a dimension greater than or equal to 3D cannot be processed or saved into parquet
-    files.
-
-    Parameters:
-    tensor: Pytorch Tensor
-        The input Pytorch tensor (1D or 2D) to be converted
-    
-    Returns:
-    list_array: list of numpy arrays
-        A list of numpy arrays
-    """
-    
-    np_array = tensor.numpy()
-    list_array = [np_array[i] for i in range(len(np_array))]
-
-    return list_array