code: def test_column_types_process_correctly():
      """Generate table with different types of data per columns.
      After transferring via pipeline, they must be processed correctly
      (added and removed)
      """
  
      data = data_with_mixed_types_in_each_column()
      train_data, test_data = train_test_data_setup(
          data, split_ratio=0.9, stratify=False
      )
  
      # Remove target from test sample
      test_data.target = None
      pipeline = generate_linear_pipeline()
  
      pipeline.fit(train_data)
      predicted = pipeline.predict(test_data)
  
      feature_type_ids = predicted.supplementary_data.col_type_ids[
          "features"
      ]
      assert len(feature_type_ids) == predicted.predict.shape[1]
      # All output values are float
      assert (feature_type_ids == TYPE_TO_ID[float]).all()
  
imports: import numpy
  import pandas
  from golem.core.log import default_log
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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 Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from fedot.preprocessing.data_types import TableTypesCorrector, apply_type_transformation
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from test.unit.preprocessing.test_pipeline_preprocessing import correct_preprocessing_params, data_with_mixed_types_in_each_column
label_desc: Use image node_id 6 for calling a global function with example usage: test_column_types_process_correctly() without return types