diff --git a/TrainingExtensions/tensorflow/test/python/eager/test_batch_norm_fold_keras.py b/TrainingExtensions/tensorflow/test/python/eager/test_batch_norm_fold_keras.py
index a2ec6b629b..d17a546044 100644
--- a/TrainingExtensions/tensorflow/test/python/eager/test_batch_norm_fold_keras.py
+++ b/TrainingExtensions/tensorflow/test/python/eager/test_batch_norm_fold_keras.py
@@ -42,6 +42,7 @@
 import copy
 import json
 import os
+from packaging import version
 import unittest
 import tensorflow as tf
 import numpy as np
@@ -54,12 +55,19 @@
 from aimet_common.defs import QuantScheme
 from aimet_tensorflow.keras.utils.quantizer_utils import get_wrappers_weight_quantizer
 
-np.random.seed(0)
-tf.random.set_seed(0)
-
 
 class TestBatchNormFold(unittest.TestCase):
     """ Test methods for BatchNormFold"""
+    @pytest.fixture(autouse=True)
+    def set_random_seed(self):
+        tf.compat.v1.reset_default_graph()
+        tf.compat.v1.set_random_seed(43)
+        if version.parse(tf.version.VERSION) >= version.parse("2.10"):
+            tf.keras.utils.set_random_seed(43)
+        else:
+            np.random.seed(43)
+        yield
+
 
     def test_bn_replacement_model(self):
 
@@ -569,7 +577,6 @@ def test_modify_bn_params_to_make_as_passthrough(self):
 
         var = tf.constant([[5.0]])
         out = model(var)
-
         self.assertTrue(out.numpy(), 15.0)
 
     def test_find_conv_bn_pairs_combined_model(self):
@@ -1146,6 +1153,16 @@ class TestBatchNormFoldToScale:
     def clear_sessions(self):
         tf.keras.backend.clear_session()
         yield
+    
+    @pytest.fixture(autouse=True)
+    def set_random_seed(self):
+        tf.compat.v1.reset_default_graph()
+        tf.compat.v1.set_random_seed(43)
+        if version.parse(tf.version.VERSION) >= version.parse("2.10"):
+            tf.keras.utils.set_random_seed(43)
+        else:
+            np.random.seed(43)
+        yield
 
     @pytest.fixture(scope="session", autouse=True)
     def cleanup(request):
@@ -1497,7 +1514,6 @@ def test_bn_fold_auto_mode_transposed_conv2d(self):
         folded_pairs = fold_all_batch_norms_to_scale(sim)
         model = sim.model
         output_after_fold = model(random_input)
-
         # Check to make sure rebuild Quantization Sim Model working properly
         sim.export(path="/tmp", filename_prefix="temp_bn_fold_to_scale")