code: def test_ts_forecasting_with_multiple_series_in_lagged():
      """Test pipeline predict correctly when lagged operation get several time series"""
      horizon = 3
      n_steps = 50
      train_data, test_data = get_ts_data(
          n_steps=n_steps + horizon, forecast_length=horizon
      )
  
      pipeline = get_multiple_ts_pipeline()
      pipeline.fit(train_data)
      predict_output = pipeline.predict(test_data)
  
      assert len(np.ravel(predict_output.predict)) == horizon
  
imports: from copy import deepcopy
  from typing import Optional
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.arima import ARIMAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import AutoRegImplementation
  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.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
label_desc: Use image node_id 17 for calling a global function with example usage: test_ts_forecasting_with_multiple_series_in_lagged() without return types