code: def test_operations_are_fast():
      """
      Test ensures that all operations with fast_train preset meet sustainability expectation.
      Test defines operation complexity as polynomial function of data size.
      If complexity function grows fast, then operation should not have fast_train tag.
      """
  
      data_lengths = tuple(map(int, np.logspace(2.2, 4, 6)))
      reference_operations = ["rf", "rfr"]
      to_skip = [
          "custom",
          "decompose",
          "class_decompose",
          "kmeans",
          "resample",
          "one_hot_encoding",
      ] + reference_operations
      reference_time = (float("inf"),) * len(data_lengths)
      # tries for time measuring
      attempt = 2
  
      for operation in OperationTypesRepository("all")._repo:
          if operation.id in reference_operations:
              perfomance_values = get_operation_perfomance(
                  operation, data_lengths, attempt
              )
              reference_time = tuple(
                  map(min, zip(perfomance_values, reference_time))
              )
  
      for operation in OperationTypesRepository("all")._repo:
          if (
              operation.id not in to_skip
              and operation.presets
              and FAST_TRAIN_PRESET_NAME in operation.presets
          ):
              for _ in range(attempt):
                  perfomance_values = get_operation_perfomance(
                      operation, data_lengths
                  )
                  # if attempt is successful then stop
                  if all(
                      x >= y
                      for x, y in zip(reference_time, perfomance_values)
                  ):
                      break
              else:
                  raise Exception(
                      f"Operation {operation.id} cannot have ``fast-train`` tag"
                  )
  
imports: import pickle
  from copy import deepcopy
  from time import perf_counter
  from typing import Tuple, Optional
  import numpy
  import pytest
  from golem.core.log import default_log
  from sklearn.datasets import make_classification
  from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
  from sklearn.preprocessing import MinMaxScaler
  from fedot.core.constants import FAST_TRAIN_PRESET_NAME
  from fedot.core.data.data import InputData, OutputData
  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_transformations import PCAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.discriminant_analysis import LDAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.naive import NaiveAverageForecastImplementation, RepeatLastValueImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import GLMImplementation
  from fedot.core.operations.model import Model
  from fedot.core.operations.operation_parameters import get_default_params, 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 OperationMetaInfo, OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.data_operations.test_time_series_operations import synthetic_univariate_ts
  from test.unit.tasks.test_forecasting import get_ts_data, get_ts_data_with_dt_idx
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 25 for calling a global function with example usage: test_operations_are_fast() without return types