code: def test_online_batch_consistent():
      # Batch
  
      batch = (
          preprocessing.StandardScaler()
          | multiclass.OneVsRestClassifier(
              linear_model.LogisticRegression()
          )
      )
  
      dataset = datasets.ImageSegments()
  
      batch_metric = metrics.MacroF1()
  
      for i, x in enumerate(pd.read_csv(dataset.path, chunksize=1)):
          y = x.pop("category")
          y_pred = batch.predict_many(x)
          batch.learn_many(x, y)
  
          for yt, yp in zip(y, y_pred):
              if yp is not None:
                  batch_metric.update(yt, yp)
  
          if i == 30:
              break
  
      # Online
  
      online = (
          preprocessing.StandardScaler()
          | multiclass.OneVsRestClassifier(
              linear_model.LogisticRegression()
          )
      )
  
      online_metric = metrics.MacroF1()
  
      X = pd.read_csv(dataset.path)
      Y = X.pop("category")
  
      for i, (x, y) in enumerate(stream.iter_pandas(X, Y)):
          y_pred = online.predict_one(x)
          online.learn_one(x, y)
  
          if y_pred is not None:
              online_metric.update(y, y_pred)
  
          if i == 30:
              break
  
      assert online_metric.get() == batch_metric.get()
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_online_batch_consistent() without return types