code: def fit(self, train_data: InputData):
      model = self.operation_impl(
          generations=self.params_for_fit.get("generations"),
          population_size=self.params_for_fit.get("population_size"),
          verbosity=2,
          random_state=42,
          max_time_mins=self.params_for_fit.get("timeout", 0.0),
      )
  
      model.fit(
          train_data.features.astype(float),
          train_data.target.astype(int),
      )
  
      return model.fitted_pipeline_
  
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 TPOTAutoMLClassificationStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: model