code: def run_seq2seq_attack(
      attack_input: Seq2SeqAttackInputData,
      privacy_report_metadata: PrivacyReportMetadata = None,
      balance_attacker_training: bool = True,
  ) -> AttackResults:
      """Runs membership inference attacks on a seq2seq model.
  
      Args:
        attack_input: input data for running an attack
        privacy_report_metadata: the metadata of the model under attack.
        balance_attacker_training: 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.
  
      Returns:
        the attack result.
      """
      attack_input.validate()
      (
          attack_input_train,
          loss_train,
          accuracy_train,
      ) = _get_attack_features_and_metadata(
          attack_input.logits_train, attack_input.labels_train
      )
      (
          attack_input_test,
          loss_test,
          accuracy_test,
      ) = _get_attack_features_and_metadata(
          attack_input.logits_test, attack_input.labels_test
      )
  
      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
      privacy_report_metadata.accuracy_test = accuracy_test
  
      # `attack_input_train` and `attack_input_test` contains the rank of the
      # ground-truth label in the logit, so smaller value means an example is
      # more likely a training example.
      return mia.run_attacks(
          AttackInputData(
              loss_train=attack_input_train, loss_test=attack_input_test
          ),
          attack_types=(AttackType.THRESHOLD_ATTACK,),
          privacy_report_metadata=privacy_report_metadata,
          balance_attacker_training=balance_attacker_training,
      )
  
imports: import dataclasses
  from typing import Iterator, List, Optional, Tuple
  import numpy
  from scipy import stats
  import tensorflow
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyReportMetadata
label_desc: Use image node_id 7 for calling a global function with example usage: run_seq2seq_attack(attack_input, privacy_report_metadata, balance_attacker_training) and returns: mia