code: def test_ts_forecasting_pipeline_with_poly_features():
      """Test pipeline with polynomial features in ts forecasting task"""
      lagged_node = PipelineNode("lagged")
      poly_node = PipelineNode(
          "poly_features", nodes_from=[lagged_node]
      )
      ridge_node = PipelineNode("ridge", nodes_from=[poly_node])
      pipeline = Pipeline(ridge_node)
  
      train_data, test_data = get_ts_data(n_steps=25, forecast_length=5)
  
      pipeline.fit(train_data)
      prediction = pipeline.predict(test_data)
      assert prediction is not None
  
imports: import datetime
  import os
  import platform
  import time
  from copy import deepcopy
  from multiprocessing import set_start_method
  import numpy
  import pytest
  from sklearn.datasets import load_iris
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import probs_to_labels
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.integration.composer.test_composer import to_categorical_codes
  from test.integration.models.test_model import classification_dataset_with_redundant_features
  from test.unit.dag.test_graph_operator import get_pipeline
  from test.unit.pipelines.test_pipeline_comparison import pipeline_first
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 20 for calling a global function with example usage: test_ts_forecasting_pipeline_with_poly_features() without return types