Restore dummy computation function.

PiperOrigin-RevId: 364388006

research/object_detection/model_lib_v2.py

@@ -351,32 +351,32 @@ def load_fine_tune_checkpoint(
 
   features, labels = iter(input_dataset).next()
 
-#   @tf.function
-#   def _dummy_computation_fn(features, labels):
-#     model._is_training = False  # pylint: disable=protected-access
-#     tf.keras.backend.set_learning_phase(False)
-
-#     labels = model_lib.unstack_batch(
-#         labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
-
-#     return _compute_losses_and_predictions_dicts(
-#         model,
-#         features,
-#         labels)
-
-#   strategy = tf.compat.v2.distribute.get_strategy()
-#   if hasattr(tf.distribute.Strategy, 'run'):
-#     strategy.run(
-#         _dummy_computation_fn, args=(
-#             features,
-#             labels,
-#         ))
-#   else:
-#     strategy.experimental_run_v2(
-#         _dummy_computation_fn, args=(
-#             features,
-#             labels,
-#         ))
+  @tf.function
+  def _dummy_computation_fn(features, labels):
+    model._is_training = False  # pylint: disable=protected-access
+    tf.keras.backend.set_learning_phase(False)
+
+    labels = model_lib.unstack_batch(
+        labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
+
+    return _compute_losses_and_predictions_dicts(
+        model,
+        features,
+        labels)
+
+  strategy = tf.compat.v2.distribute.get_strategy()
+  if hasattr(tf.distribute.Strategy, 'run'):
+    strategy.run(
+        _dummy_computation_fn, args=(
+            features,
+            labels,
+        ))
+  else:
+    strategy.experimental_run_v2(
+        _dummy_computation_fn, args=(
+            features,
+            labels,
+        ))
 
   restore_from_objects_dict = model.restore_from_objects(
       fine_tune_checkpoint_type=checkpoint_type)