code: def test_ts_forecasting_smoothing_data_operation():
      train_input, predict_input, y_test = get_time_series()
  
      model_names = OperationTypesRepository().operations_with_tag(
          tags=["smoothing"]
      )
  
      for smoothing_operation in model_names:
          node_smoothing = PipelineNode(smoothing_operation)
          node_lagged = PipelineNode(
              "lagged", nodes_from=[node_smoothing]
          )
          node_ridge = PipelineNode("ridge", nodes_from=[node_lagged])
          pipeline = Pipeline(node_ridge)
  
          pipeline.fit_from_scratch(train_input)
          predicted_output = pipeline.predict(predict_input)
          predicted = np.ravel(predicted_output.predict)
  
          assert len(predicted) == len(np.ravel(y_test))
  
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 15 for calling a global function with example usage: test_ts_forecasting_smoothing_data_operation() without return types