class_docstr: Class for forecasting time series with mean 
code: def predict_for_fit(self, input_data: InputData) -> OutputData:
      input_data = copy(input_data)
      forecast_length = input_data.task.task_params.forecast_length
      features = input_data.features
      shape = features.shape[0]
  
      window = self._window(features)
      mean_values = np.array(
          [
              np.mean(features[-window - shape + i : i + 1])
              for i in range(shape)
          ]
      )
  
      forecast = np.repeat(
          mean_values.reshape((-1, 1)), forecast_length, axis=1
      )
  
      # Update target
      new_idx, transformed_target = ts_to_table(
          idx=input_data.idx,
          time_series=input_data.target,
          window_size=forecast_length,
      )
  
      input_data.target = transformed_target
      forecast = forecast[new_idx]
      input_data.idx = new_idx
      output_data = self._convert_to_output(
          input_data, predict=forecast, data_type=DataTypesEnum.table
      )
      return output_data
  
imports: from copy import copy
  import numpy
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ts_to_table, transform_features_and_target_into_lagged
  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 5 for calling the NaiveAverageForecastImplementation obj's underlying member method code with example usage: obj.predict_for_fit(input_data) and returns: output_data