code: def _get_attack_features_and_metadata(
      logits: Iterator[np.ndarray], labels: Iterator[np.ndarray]
  ) -> Tuple[np.ndarray, float, float]:
      """Returns the average rank of tokens per batch of sequences and the loss.
  
      Args:
        logits: Logits returned by a seq2seq model, dim = (num_batches,
          num_sequences, num_tokens, vocab_size).
        labels: Target labels for the seq2seq model, dim = (num_batches,
          num_sequences, num_tokens, 1).
  
      Returns:
        1. An array of average ranks, dim = (num_batches, 1).
        Each average rank is calculated over ranks of tokens in sequences of a
        particular batch.
        2. Loss computed over all logits and labels.
        3. Accuracy computed over all logits and labels.
      """
      ranks = []
      loss = 0.0
      dataset_length = 0.0
      correct_preds = 0
      total_preds = 0
      for batch_logits, batch_labels in zip(logits, labels):
          # Compute average rank for the current batch.
          batch_ranks = _get_batch_ranks(batch_logits, batch_labels)
          ranks.append(np.mean(batch_ranks))
  
          # Update overall loss metrics with metrics of the current batch.
          batch_loss, batch_length = _get_batch_loss_metrics(
              batch_logits, batch_labels
          )
          loss += batch_loss
          dataset_length += batch_length
  
          # Update overall accuracy metrics with metrics of the current batch.
          (
              batch_correct_preds,
              batch_total_preds,
          ) = _get_batch_accuracy_metrics(batch_logits, batch_labels)
          correct_preds += batch_correct_preds
          total_preds += batch_total_preds
  
      # Compute loss and accuracy for the dataset.
      loss = loss / dataset_length
      accuracy = correct_preds / total_preds
  
      return np.array(ranks), loss, accuracy
  
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 2 for calling a global function with example usage: _get_attack_features_and_metadata(logits, labels) and returns: np,  loss,  accuracy