code: def _data_transform(self, data: InputData) -> H2OFrame:
      concat_data = np.concatenate(
          (data.features, data.target.reshape(-1, 1)), 1
      )
      frame = H2OFrame(python_obj=concat_data)
      return frame
  
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 4 for calling the H2OAutoMLClassificationStrategy obj's underlying member method code with example usage: obj._data_transform(data) and returns: frame