code: def get_time_series(len_forecast=5, length=80):
      """Function returns time series for time series forecasting task"""
      synthetic_ts = generate_synthetic_data(length=length)
  
      train_data = synthetic_ts[:-len_forecast]
      test_data = synthetic_ts[-len_forecast:]
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=len_forecast),
      )
  
      train_input = InputData(
          idx=np.arange(0, len(train_data)),
          features=train_data,
          target=train_data,
          task=task,
          data_type=DataTypesEnum.ts,
      )
  
      start_forecast = len(train_data)
      end_forecast = start_forecast + len_forecast
      predict_input = InputData(
          idx=np.arange(start_forecast, end_forecast),
          features=train_data,
          target=None,
          task=task,
          data_type=DataTypesEnum.ts,
      )
  
      return train_input, predict_input, test_data
  
imports: import os
  import numpy
  import pytest
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from examples.simple.time_series_forecasting.gapfilling import generate_synthetic_data
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_imbalanced_class import ResampleImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import CutImplementation, LaggedTransformationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features
label_desc: Use image node_id 3 for calling a global function with example usage: get_time_series(len_forecast, length) and returns: train_input,  predict_input,  test_data