code: def test_poly_features_on_big_datasets():
      """
      Use a table with a large number of features to run a poly features operation.
      For a large number of features the operation should not greatly increase the
      number of columns.
      """
      test_file_path = str(os.path.dirname(__file__))
      file = os.path.join("../../data", "advanced_classification.csv")
      train_input = InputData.from_csv(
          os.path.join(test_file_path, file),
          task=Task(TaskTypesEnum.classification),
      )
  
      # Take only small number of rows from dataset
      train_input.features = train_input.features[5:20, :]
      train_input.idx = np.arange(len(train_input.features))
      train_input.target = train_input.target[5:20].reshape((-1, 1))
  
      poly_node = Pipeline(PipelineNode("poly_features"))
      poly_node.fit(train_input)
      transformed_features = poly_node.predict(train_input)
  
      n_rows, n_cols = transformed_features.predict.shape
      assert n_cols == 85
  
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 27 for calling a global function with example usage: test_poly_features_on_big_datasets() without return types