code: def get_multivariate_time_series(mutli_ts=False):
      """Generate several time series in one InputData block"""
      ts_1 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape((-1, 1))
      ts_2 = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]).reshape(
          (-1, 1)
      )
      several_ts = np.hstack((ts_1, ts_2))
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=2),
      )
      if mutli_ts:
          data_type = DataTypesEnum.multi_ts
          target = several_ts
      else:
          data_type = DataTypesEnum.ts
          target = np.ravel(ts_1)
      train_input = InputData(
          idx=np.arange(0, len(several_ts)),
          features=several_ts,
          target=target,
          task=task,
          data_type=data_type,
      )
      return train_input
  
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 4 for calling a global function with example usage: get_multivariate_time_series(mutli_ts) and returns: train_input