code: def fit(self, train_data: InputData):
      operation_implementation = self.operation_impl(
          self.params_for_fit
      )
  
      with ImplementationRandomStateHandler(
          implementation=operation_implementation
      ):
          operation_implementation.fit(train_data)
  
      return operation_implementation
  
imports: from typing import Optional
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.models.boostings_implementations import FedotCatBoostClassificationImplementation, FedotCatBoostRegressionImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.tasks import TaskTypesEnum
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 3 for calling the BoostingStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: operation_implementation