code: def predict_for_fit(self, input_data: InputData):
      input_data = copy(input_data)
      forecast_length = input_data.task.task_params.forecast_length
      fitted_values = self.arima.fittedvalues
  
      fitted_values = self._inverse_boxcox(
          predicted=fitted_values, lambda_param=self.lambda_value
      )
      fitted_values = self._inverse_shift(fitted_values)
  
      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=input_data.idx,
          time_series=fitted_values,
          window_size=forecast_length,
      )
  
      new_idx, target_columns = ts_to_table(
          idx=input_data.idx,
          time_series=input_data.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 ARIMAImplementation obj's underlying member method code with example usage: obj.predict_for_fit(input_data) and returns: output_data