diff --git a/tensorflow_privacy/privacy/membership_inference_attack/seq2seq_mia.py b/tensorflow_privacy/privacy/membership_inference_attack/seq2seq_mia.py
index 87552b9..c60e936 100644
--- a/tensorflow_privacy/privacy/membership_inference_attack/seq2seq_mia.py
+++ b/tensorflow_privacy/privacy/membership_inference_attack/seq2seq_mia.py
@@ -248,9 +248,10 @@ def _get_batch_accuracy_metrics(batch_logits: np.ndarray,
 
 
 def create_seq2seq_attacker_data(attack_input_data: Seq2SeqAttackInputData,
-                                 privacy_report_metadata: PrivacyReportMetadata,
                                  test_fraction: float = 0.25,
-                                 balance: bool = True) -> AttackerData:
+                                 balance: bool = True,
+                                 privacy_report_metadata: PrivacyReportMetadata = PrivacyReportMetadata()
+                                 ) -> AttackerData:
   """Prepares Seq2SeqAttackInputData to train ML attackers.
 
   Uses logits and losses to generate ranks and performs a random train-test
@@ -261,11 +262,11 @@ def create_seq2seq_attacker_data(attack_input_data: Seq2SeqAttackInputData,
 
   Args:
     attack_input_data: Original Seq2SeqAttackInputData
-    privacy_report_metadata: the metadata of the model under attack.
     test_fraction: Fraction of the dataset to include in the test split.
     balance: Whether the training and test sets for the membership inference
       attacker should have a balanced (roughly equal) number of samples from the
       training and test sets used to develop the model under attack.
+    privacy_report_metadata: the metadata of the model under attack.
 
   Returns:
     AttackerData.
@@ -297,7 +298,6 @@ def create_seq2seq_attacker_data(attack_input_data: Seq2SeqAttackInputData,
         features_all, labels_all, test_size=test_fraction, stratify=labels_all)
 
   # Populate accuracy, loss fields in privacy report metadata
-  privacy_report_metadata = privacy_report_metadata or PrivacyReportMetadata()
   privacy_report_metadata.loss_train = loss_train
   privacy_report_metadata.loss_test = loss_test
   privacy_report_metadata.accuracy_train = accuracy_train