code: def _update_input(
      pre_history_ts,
      scope_len,
      task,
      data_type: DataTypesEnum = DataTypesEnum.ts,
  ):
      """Method make new InputData object based on the previous part of time
      series
  
      Args:
          pre_history_ts: time series
          scope_len: how many elements to the future can algorithm forecast
          task: time series forecasting task
          data_type: type of data (ts or multi_ts
  
      Returns:
          input_data: updated InputData
      """
      start_forecast = len(pre_history_ts)
      end_forecast = start_forecast + scope_len
      input_data = InputData(
          idx=np.arange(start_forecast, end_forecast),
          features=pre_history_ts,
          target=None,
          task=task,
          data_type=data_type,
      )
  
      return input_data
  
file_path: FEDOT/fedot/core/pipelines/ts_wrappers.py
imports: import math
  from copy import copy
  from typing import Union, Optional
  import numpy
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ts_to_table
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import TaskTypesEnum
label_desc: Use image node_id 6 for calling a global function with example usage: _update_input(pre_history_ts, scope_len, task, data_type) and returns: input_data