code: def test_unshuffled_data():
      target_column = "species"
      df_el, y = load_iris(return_X_y=True, as_frame=True)
      df_el[target_column] = LabelEncoder().fit_transform(y)
  
      features, target = (
          df_el.drop(target_column, axis=1).values,
          df_el[target_column].values,
      )
  
      problem = "classification"
      params = {**TESTS_MAIN_API_DEFAULT_PARAMS, "metric": "f1"}
  
      auto_model = Fedot(problem=problem, seed=42, **params)
      pipeline = auto_model.fit(features=features, target=target)
      assert pipeline is not None
  
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 14 for calling a global function with example usage: test_unshuffled_data() without return types