diff --git a/tests/python-pytest/onnx/mxnet_export_test.py b/tests/python-pytest/onnx/mxnet_export_test.py
index 8e6dc74e01ff..6c81198a8bca 100644
--- a/tests/python-pytest/onnx/mxnet_export_test.py
+++ b/tests/python-pytest/onnx/mxnet_export_test.py
@@ -15,12 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-"""
-Tests for individual operators
-This module contains operator tests which currently do not exist on
-ONNX backend test framework. Once we have PRs on the ONNX repo and get
-those PRs merged, this file will get EOL'ed.
-"""
+
 # pylint: disable=too-many-locals,wrong-import-position,import-error
 from __future__ import absolute_import
 import os
diff --git a/tests/python-pytest/onnx/test_models.py b/tests/python-pytest/onnx/test_models.py
index ab38c065c116..46080c6daecc 100644
--- a/tests/python-pytest/onnx/test_models.py
+++ b/tests/python-pytest/onnx/test_models.py
@@ -15,12 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-"""
-Tests for individual operators
-This module contains operator tests which currently do not exist on
-ONNX backend test framework. Once we have PRs on the ONNX repo and get
-those PRs merged, this file will get EOL'ed.
-"""
+
 # pylint: disable=too-many-locals,wrong-import-position,import-error
 from __future__ import absolute_import
 import sys
diff --git a/tests/python-pytest/onnx/test_node.py b/tests/python-pytest/onnx/test_node.py
index 7498f44425b8..2e7bda8633b4 100644
--- a/tests/python-pytest/onnx/test_node.py
+++ b/tests/python-pytest/onnx/test_node.py
@@ -50,7 +50,7 @@ def get_rnd(shape, low=-1.0, high=1.0, dtype=np.float32):
         return (np.random.uniform(low, high,
                                   np.prod(shape)).reshape(shape).astype(np.float32))
     elif dtype == np.int32:
-        return (np.random.uniform(low, high,
+        return (np.random.randint(low, high,
                                   np.prod(shape)).reshape(shape).astype(np.float32))
     elif dtype == np.bool_:
         return np.random.choice(a=[False, True], size=shape).astype(np.float32)