code: def test_categorical_preprocessing_unidata_predefined():
      train_data, test_data = load_categorical_unimodal()
  
      auto_model = Fedot(
          problem="classification", **TESTS_MAIN_API_DEFAULT_PARAMS
      )
      auto_model.fit(features=train_data, predefined_model="rf")
      prediction = auto_model.predict(features=test_data)
      prediction_proba = auto_model.predict_proba(features=test_data)
  
      assert np.issubdtype(prediction.dtype, np.number)
      assert np.isnan(prediction).sum() == 0
      assert np.issubdtype(prediction_proba.dtype, np.number)
      assert np.isnan(prediction_proba).sum() == 0
  
file_path: FEDOT/test/unit/api/test_main_api.py
imports: import numpy
  import pandas
  import pytest
  from fedot import Fedot
  from fedot.api.api_utils.api_data import ApiDataProcessor
  from fedot.core.data.data import InputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.data.datasets import data_with_binary_features_and_categorical_target, get_dataset, load_categorical_multidata, get_split_data_paths, get_split_data, get_multimodal_ts_data, load_categorical_unimodal
label_desc: Use image node_id 10 for calling a global function with example usage: test_categorical_preprocessing_unidata_predefined() without return types