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
      if train_data.task.task_type == TaskTypesEnum.ts_forecasting:
          target_len = train_data.task.task_params.forecast_length
      else:
          target_len = 1
      target_names = train_columns[-target_len:]
  
      models = []
  
      for name in target_names:
          train_columns.remove(name)
      for name in target_names:
          model = H2OAutoML(
              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
              // target_len,
          )
          model.train(
              x=train_columns, y=name, training_frame=train_frame
          )
          models.append(model.leader)
  
      return H2OSerializationWrapper(models)
  
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 H2OAutoMLRegressionStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: H2OSerializationWrapper