code: def test_data_with_mixed_types_per_column_processed_correctly():
      """
      Check if dataset with columns, which contain several data types can be
      processed correctly.
      """
      input_data = data_with_mixed_types_in_each_column()
      train_data, test_data = train_test_data_setup(
          input_data, split_ratio=0.9, stratify=False
      )
  
      pipeline = Pipeline(PipelineNode("dt"))
      pipeline = correct_preprocessing_params(
          pipeline, categorical_max_uniques_th=5
      )
      pipeline.fit(train_data)
      predicted = pipeline.predict(test_data)
  
      importances = pipeline.nodes[
          0
      ].operation.fitted_operation.feature_importances_
  
      # Finally, seven features were used to give a forecast
      assert len(importances) == 7
      # Target must contain 4 labels
      assert predicted.predict.shape[-1] == 4
  
imports: import numpy
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  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.tasks import TaskTypesEnum, Task
  from fedot.preprocessing.categorical import BinaryCategoricalPreprocessor
  from fedot.preprocessing.data_types import TableTypesCorrector
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from test.unit.data_operations.test_data_operations_implementations import get_mixed_data
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features, data_with_too_much_nans, data_with_spaces_and_nans_in_features, data_with_nans_in_target_column, data_with_nans_in_multi_target, data_with_categorical_target
label_desc: Use image node_id 12 for calling a global function with example usage: test_data_with_mixed_types_per_column_processed_correctly() without return types