code: def predict(self, model, predict_data, args):
      batch_size = args.batch_size
  
      if self.learning_task == LearningTask.REGRESSION:
          predict_fn = model.predict
      else:
          predict_fn = model.predict_proba
  
      with Timer() as t:
          total_size = len(predict_data)
          iterations = total_size // batch_size
  
          if total_size == batch_size:
              self.predictions = predict_fn(predict_data)
          else:
              iterations += 1 if total_size % batch_size > 0 else 0
              iterations = max(1, iterations)
  
              if self.learning_task == LearningTask.CLASSIFICATION:
                  self.predictions = np.empty(
                      [total_size, 2], dtype="f4"
                  )
              if (
                  self.learning_task
                  == LearningTask.MULTICLASS_CLASSIFICATION
              ):
                  self.predictions = np.empty(
                      [total_size, model.n_classes_], dtype="f4"
                  )
              if self.learning_task == LearningTask.REGRESSION:
                  self.predictions = np.empty([total_size], dtype="f4")
  
              for i in range(0, iterations):
                  start = i * batch_size
                  end = min(start + batch_size, total_size)
                  batch = predict_data[start:end]
                  self.predictions[start:end] = predict_fn(batch)
  
      return t.interval
  
imports: from abc import ABC, abstractmethod
  import lightgbm
  import numpy
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import xgboost
  from benchmarks.timer import Timer
  from benchmarks.datasets import LearningTask
label_desc: Use image node_id 5 for calling the TrainEnsembleAlgorithm obj's underlying member method code with example usage: obj.predict(model, predict_data, args) and returns: t