code: def test_multiobj_for_api():
      train_data, test_data, _ = get_dataset("classification")
  
      params = {
          **TESTS_MAIN_API_DEFAULT_PARAMS,
          "metric": ["f1", "node_number"],
      }
  
      model = Fedot(problem="classification", **params)
      model.fit(features=train_data)
      prediction = model.predict(features=test_data)
      metric = model.get_metrics()
  
      assert len(prediction) == len(test_data.target)
      assert metric["f1"] > 0
      assert (
          model.best_models is not None and len(model.best_models) > 0
      )
  
imports: import os
  import shutil
  from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from golem.core.dag.graph_utils import graph_structure
  from sklearn.datasets import load_iris
  from sklearn.preprocessing import LabelEncoder
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
  from fedot import Fedot
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.tasks import TsForecastingParams
  from test.data.datasets import get_dataset, get_multimodal_ts_data, load_categorical_unimodal, load_categorical_multidata
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.tasks.test_multi_ts_forecast import get_multi_ts_data
label_desc: Use image node_id 9 for calling a global function with example usage: test_multiobj_for_api() without return types