code: def test_object_columns():
      class Dummy:
          def __init__(self, x):
              self.x = x
  
          def __call__(self):
              print(self.x)
  
          def dummy_func(self):
              for i in range(100):
                  print("do something 100 times")
  
      dummy_object = Dummy(1)
      lst = [1, 2, 3]
      array = np.array([1, 2, 3])
      dummy_string = "dummy string"
  
      df = pd.DataFrame(
          {
              "dummy_object": [dummy_object] * 4,
              "dummy_lst": [lst] * 4,
              "dummy_array": [array] * 4,
              "dummy_string": [dummy_string] * 4,
              "type_mix_column": [
                  dummy_string,
                  dummy_object,
                  array,
                  lst,
              ],
              "cat_column": ["a", "b", "a", "b"],
          }
      )
      df["cat_column"] = df["cat_column"].astype("category")
  
      with pytest.warns(
          UserWarning,
          match=r"Input Column dummy_object has "
          r"generic type object. "
          r"Autosklearn will treat "
          r"this column as string. "
          r"Please ensure that this setting "
          r"is suitable for your task.",
      ):
          validator = FeatureValidator()
          feat_type = validator.get_feat_type_from_columns(df)
  
      column_types = {
          "dummy_object": "string",
          "dummy_lst": "string",
          "dummy_array": "string",
          "dummy_string": "string",
          "type_mix_column": "string",
          "cat_column": "categorical",
      }
  
      assert feat_type == column_types
  
imports: import numpy
  import pandas
  import sklearn.datasets
  import sklearn.model_selection
  from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype
  from scipy import sparse
  from autosklearn.data.feature_validator import FeatureValidator
  import pytest
label_desc: Use image node_id 12 for calling a global function with example usage: test_object_columns() without return types