class_docstr: Exponential smoothing implementation from statsmodels 
code: def predict_for_fit(self, input_data: InputData) -> OutputData:
      input_data = copy(input_data)
      parameters = input_data.task.task_params
      forecast_length = parameters.forecast_length
      idx = input_data.idx
      target = input_data.target
  
      # Indexing for statsmodels is different
      start_id = idx[0]
      end_id = idx[-1]
      predictions = self.model.predict(start=start_id, end=end_id)
      _, predict = ts_to_table(
          idx=idx, time_series=predictions, window_size=forecast_length
      )
      new_idx, target_columns = ts_to_table(
          idx=idx, time_series=target, window_size=forecast_length
      )
  
      input_data.idx = new_idx
      input_data.target = target_columns
  
      output_data = self._convert_to_output(
          input_data, predict=predict, data_type=DataTypesEnum.table
      )
      return output_data
  
imports: from copy import copy
  import numpy
  import statsmodels.api
  from statsmodels.genmod.families import Gamma, Gaussian, InverseGaussian
  from statsmodels.genmod.families.links import identity, inverse_power, inverse_squared, log
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ts_to_table
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 4 for calling the ExpSmoothingImplementation obj's underlying member method code with example usage: obj.predict_for_fit(input_data) and returns: output_data