code: def test_lagged_with_multivariate_time_series():
      """
      Checking the correct processing of multivariate time series in the lagged operation
      """
      correct_fit_output = np.array(
          [
              [0.0, 1.0, 10.0, 11.0],
              [1.0, 2.0, 11.0, 12.0],
              [2.0, 3.0, 12.0, 13.0],
              [3.0, 4.0, 13.0, 14.0],
              [4.0, 5.0, 14.0, 15.0],
              [5.0, 6.0, 15.0, 16.0],
              [6.0, 7.0, 16.0, 17.0],
          ]
      )
      correct_predict_output = np.array([[8, 9, 18, 19]])
  
      input_data = get_multivariate_time_series()
      lagged = LaggedTransformationImplementation(
          OperationParameters(window_size=2)
      )
  
      transformed_for_fit = lagged.transform_for_fit(input_data)
      transformed_for_predict = lagged.transform(input_data)
  
      # Check correctness on fit stage
      lagged_features = transformed_for_fit.predict
      assert lagged_features.shape == correct_fit_output.shape
      assert np.all(np.isclose(lagged_features, correct_fit_output))
  
      # Check correctness on predict stage
      lagged_predict = transformed_for_predict.predict
      assert lagged_predict.shape == correct_predict_output.shape
      assert np.all(np.isclose(lagged_predict, correct_predict_output))
  
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 25 for calling a global function with example usage: test_lagged_with_multivariate_time_series() without return types