code: def predict(
      self, trained_operation, predict_data: InputData
  ) -> OutputData:
      frame = self._data_transform(predict_data)
      prediction = trained_operation.get_estimators()[0].predict(frame)
      prediction = prediction.as_data_frame().to_numpy()
  
      if self.output_mode == "labels":
          prediction = prediction[:, 0]
      elif self.output_mode in ["probs", "full_probs", "default"]:
          prediction = prediction[:, 1::]
      else:
          raise ValueError(
              f"Output model {self.output_mode} is not supported"
          )
      out = self._convert_to_output(prediction, 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 H2OAutoMLClassificationStrategy obj's underlying member method code with example usage: obj.predict(trained_operation, predict_data) and returns: out