code: def test_integration_binary_and_multilabel_inputs(
      n_times, test_data_integration_binary_and_multilabel
  ):
      (
          y_pred,
          y,
          batch_size,
      ) = test_data_integration_binary_and_multilabel
  
      def update_fn(engine, batch):
          idx = (engine.state.iteration - 1) * batch_size
          y_true_batch = np_y[idx : idx + batch_size]
          y_pred_batch = np_y_pred[idx : idx + batch_size]
          return torch.from_numpy(y_pred_batch), torch.from_numpy(
              y_true_batch
          )
  
      engine = Engine(update_fn)
  
      roc_auc_metric = ROC_AUC()
      roc_auc_metric.attach(engine, "roc_auc")
  
      np_y = y.numpy()
      np_y_pred = y_pred.numpy()
  
      np_roc_auc = roc_auc_score(np_y, np_y_pred)
  
      data = list(range(y_pred.shape[0] // batch_size))
      roc_auc = engine.run(data, max_epochs=1).metrics["roc_auc"]
  
      assert isinstance(roc_auc, float)
      assert np_roc_auc == pytest.approx(roc_auc)
  
imports: import os
  from unittest.mock import patch
  import pytest
  import sklearn
  import torch
  from sklearn.metrics import roc_auc_score
  import ignite.distributed
  from ignite.contrib.metrics import ROC_AUC
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics.epoch_metric import EpochMetricWarning
label_desc: Use image node_id 10 for calling a global function with example usage: test_integration_binary_and_multilabel_inputs(n_times, test_data_integration_binary_and_multilabel) and returns: torch,  torch