diff --git a/RELEASE.md b/RELEASE.md
index 49d05bcd69..e902206401 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -18,6 +18,7 @@
 *   Fixes a bug by ensuring feature values are always numpy arrays.
 *   Modifies a ROUGE Test to be compatible with NumPy v2.0.1.
 *   Remove keras_util_test.py which is based on estimator models.
+*   Remove dependency on eval_saved_model encodings.
 
 ## Breaking Changes
 
diff --git a/tensorflow_model_analysis/slicer/slice_accessor_test.py b/tensorflow_model_analysis/slicer/slice_accessor_test.py
index 07551abbee..90f8d949e5 100644
--- a/tensorflow_model_analysis/slicer/slice_accessor_test.py
+++ b/tensorflow_model_analysis/slicer/slice_accessor_test.py
@@ -18,9 +18,10 @@
 import pyarrow as pa
 import tensorflow as tf
 from tensorflow_model_analysis.api import types
-from tensorflow_model_analysis.eval_saved_model import encoding
 from tensorflow_model_analysis.slicer import slice_accessor
 
+_ENCODING_NODE_SUFFIX = 'node'
+
 
 class SliceAccessorTest(tf.test.TestCase, parameterized.TestCase):
 
@@ -78,21 +79,11 @@ def testLegacyAccessFeaturesDict(self):
       ) = sess.run(
           fetches=[sparse, dense, dense_single, dense_multidim, squeeze_needed])
       features_dict = {
-          'sparse': {
-              encoding.NODE_SUFFIX: sparse_value
-          },
-          'dense': {
-              encoding.NODE_SUFFIX: dense_value
-          },
-          'dense_single': {
-              encoding.NODE_SUFFIX: dense_single_value
-          },
-          'squeeze_needed': {
-              encoding.NODE_SUFFIX: squeeze_needed_value
-          },
-          'dense_multidim': {
-              encoding.NODE_SUFFIX: dense_multidim_value
-          },
+          'sparse': {_ENCODING_NODE_SUFFIX: sparse_value},
+          'dense': {_ENCODING_NODE_SUFFIX: dense_value},
+          'dense_single': {_ENCODING_NODE_SUFFIX: dense_single_value},
+          'squeeze_needed': {_ENCODING_NODE_SUFFIX: squeeze_needed_value},
+          'dense_multidim': {_ENCODING_NODE_SUFFIX: dense_multidim_value},
       }
       accessor = slice_accessor.SliceAccessor([features_dict])
       self.assertEqual([b'apple', b'banana'], accessor.get('sparse'))