code: def test_pandas_input_for_api():
      train_data, test_data, threshold = get_dataset("classification")
  
      train_features = pd.DataFrame(train_data.features)
      train_target = pd.Series(train_data.target.reshape(-1))
  
      test_features = pd.DataFrame(test_data.features)
      test_target = pd.Series(test_data.target.reshape(-1))
  
      # task selection, initialisation of the framework
      baseline_model = Fedot(problem="classification")
  
      # fit model without optimisation - single XGBoost node is used
      baseline_model.fit(
          features=train_features,
          target=train_target,
          predefined_model="xgboost",
      )
  
      # evaluate the prediction with test data
      prediction = baseline_model.predict(features=test_features)
  
      assert len(prediction) == len(test_target)
  
      # evaluate quality metric for the test sample
      baseline_metrics = baseline_model.get_metrics(
          metric_names="f1", target=test_target
      )
  
      assert baseline_metrics["f1"] > 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 8 for calling a global function with example usage: test_pandas_input_for_api() without return types