code: def predict(
      self, trained_operation, predict_data: InputData
  ) -> OutputData:
      res = []
      for model in trained_operation.get_estimators():
          frame = H2OFrame(predict_data.features)
          prediction = model.predict(frame)
          prediction = prediction.as_data_frame().to_numpy()
          res.append(np.ravel(prediction))
      res = np.hstack(res)
      out = self._convert_to_output(res, predict_data)
      return out
  
imports: from typing import Optional
  import numpy
  from h2o import h2o, H2OFrame
  from h2o.automl import H2OAutoML
  from tpot import TPOTClassifier, TPOTRegressor
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.pipelines.automl_wrappers import H2OSerializationWrapper, TPOTRegressionSerializationWrapper
  from fedot.core.repository.tasks import TaskTypesEnum
label_desc: Use image node_id 3 for calling the H2OAutoMLRegressionStrategy obj's underlying member method code with example usage: obj.predict(trained_operation, predict_data) and returns: out