code: def get_operation_perfomance(
      operation: OperationMetaInfo,
      data_lengths: Tuple[float, ...],
      times: int = 1,
  ) -> Optional[Tuple[float, ...]]:
      """
      Helper function to check perfomance of only the first valid operation pair (task_type, input_type).
      """
  
      def fit_time_for_operation(
          operation: OperationMetaInfo, data: InputData
      ):
          nodes_from = []
          if task_type is TaskTypesEnum.ts_forecasting:
              if "non_lagged" not in operation.tags:
                  nodes_from = [PipelineNode("lagged")]
          node = PipelineNode(operation.id, nodes_from=nodes_from)
          pipeline = Pipeline(node)
          start_time = perf_counter()
          pipeline.fit(data)
          return perf_counter() - start_time
  
      for task_type in operation.task_type:
          for data_type in operation.input_types:
              perfomance_values = []
              for length in data_lengths:
                  data = get_data_for_testing(
                      task_type,
                      data_type,
                      length=length,
                      features_count=2,
                      random=True,
                  )
                  if data is not None:
                      min_evaluated_time = min(
                          fit_time_for_operation(operation, data)
                          for _ in range(times)
                      )
                      perfomance_values.append(min_evaluated_time)
              if perfomance_values:
                  if len(perfomance_values) != len(data_lengths):
                      raise ValueError(
                          "not all measurements have been proceeded"
                      )
                  return tuple(perfomance_values)
      raise Exception(
          f"Fit time for operation ``{operation.id}`` cannot be measured"
      )
  
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 6 for calling a global function with example usage: get_operation_perfomance(operation, data_lengths, times) and returns: unknown, tuple