diff --git a/neural_compressor/adaptor/tensorflow.py b/neural_compressor/adaptor/tensorflow.py
index a6660d50e70..51fbe338da7 100644
--- a/neural_compressor/adaptor/tensorflow.py
+++ b/neural_compressor/adaptor/tensorflow.py
@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from csv import writer
 import os
 import copy
 import yaml
@@ -87,27 +88,28 @@ def log_histogram(self, writer, tag, values, step=0, bins=1000):
         values = np.array(values)
 
         # Create histogram using numpy
-        counts, bin_edges = np.histogram(values, bins=bins)
+        # counts, bin_edges = np.histogram(values, bins=bins)
 
         # Fill fields of histogram proto
-        hist = tf.compat.v1.HistogramProto()
-        hist.min = float(np.min(values))
-        hist.max = float(np.max(values))
-        hist.num = int(np.prod(values.shape))
-        hist.sum = float(np.sum(values))
-        hist.sum_squares = float(np.sum(values**2))
+        # hist = tf.compat.v1.HistogramProto()
+        # hist.min = float(np.min(values))
+        # hist.max = float(np.max(values))
+        # hist.num = int(np.prod(values.shape))
+        # hist.sum = float(np.sum(values))
+        # hist.sum_squares = float(np.sum(values**2))
 
-        bin_edges = bin_edges[1:]
+        # bin_edges = bin_edges[1:]
 
-        for edge in bin_edges:
-            hist.bucket_limit.append(edge)
-        for c in counts:
-            hist.bucket.append(c)
+        # for edge in bin_edges:
+        #     hist.bucket_limit.append(edge)
+        # for c in counts:
+        #     hist.bucket.append(c)
 
         # Create and write Summary
-        summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, histo=hist)])
-        writer.add_summary(summary, step)
-        writer.flush()
+        # update using TF2.X API
+        with writer.as_default():
+            tf.summary.histogram(tag, values, step)
+            writer.flush()
 
     def _pre_hook_for_hvd(self, dataloader=None):
         import horovod.tensorflow as hvd
@@ -275,7 +277,10 @@ def evaluate(self, model, dataloader, postprocess=None,
             if os.path.isdir(temp_dir):
                 import shutil
                 shutil.rmtree(temp_dir, ignore_errors=True)
-            writer = tf.compat.v1.summary.FileWriter(temp_dir, model.graph)
+            # Create the writer using TF2.x APIs to handle eager excutions
+            writer = tf.summary.create_file_writer(temp_dir) # pylint: disable=no-member
+            with writer.as_default():
+                tf.summary.graph(model.graph) # pylint: disable=no-member
 
             cur_graph = GraphAnalyzer()
             cur_graph.graph = model.graph_def
@@ -1310,7 +1315,8 @@ def get_optype_wise_ability(self):
     def _pre_eval_hook(self, model):
         return model
 
-    def _post_eval_hook(self, model):
+    # Add keyword arguments unpacking
+    def _post_eval_hook(self, model, **kwargs):
         pass
 
     def save(self, model, path):