code: def fit(self, train_data: InputData):
      ip, port = self._get_h2o_connect_config()
  
      h2o.init(ip=ip, port=port, name="h2o_server")
  
      frame = self._data_transform(train_data)
  
      train_frame, valid_frame = frame.split_frame(ratios=[0.85])
  
      # make sure that your target column is the last one
      train_columns = train_frame.columns
      target_name = train_columns[-1]
      train_columns.remove(target_name)
      train_frame[target_name] = train_frame[target_name].asfactor()
      model = self.operation_impl(
          max_models=self.params_for_fit.get("max_models"),
          seed=self.params_for_fit.get("seed"),
          max_runtime_secs=self.params_for_fit.get("timeout") * 60,
      )
  
      model.train(
          x=train_columns, y=target_name, training_frame=train_frame
      )
      model.leader.classes_ = np.unique(train_data.target)
      return H2OSerializationWrapper([model.leader])
  
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 2 for calling the H2OAutoMLClassificationStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: H2OSerializationWrapper