code: def predict_for_fit(self, input_data: InputData) -> OutputData:
      parameters = input_data.task.task_params
      forecast_length = parameters.forecast_length
      idx = input_data.idx
      target = input_data.target
      fitted_values = self.model.get_prediction(
          start=idx[0], end=idx[-1]
      ).predicted_mean
      diff = int(self.actual_ts_len) - len(fitted_values)
      # If first elements skipped
      if diff != 0:
          # Fill nans with first values
          first_element = fitted_values[0]
          first_elements = [first_element] * diff
          first_elements.extend(list(fitted_values))
  
          fitted_values = np.array(first_elements)
  
      _, predict = ts_to_table(
          idx=idx,
          time_series=fitted_values,
          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
  from typing import Optional
  import numpy
  from scipy import stats
  from scipy.special import boxcox, inv_boxcox
  from statsmodels.tsa.api import STLForecast
  from statsmodels.tsa.arima.model import ARIMA
  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
  from fedot.utilities.ts_gapfilling import SimpleGapFiller
label_desc: Use image node_id 4 for calling the STLForecastARIMAImplementation obj's underlying member method code with example usage: obj.predict_for_fit(input_data) and returns: output_data