code: def test_featurevalidatorget_feat_type_from_columns():
      """
      Makes sure that encoded columns are returned by get_feat_type_from_columns
      whereas numerical columns are not returned
      """
      validator = FeatureValidator()
  
      df = pd.DataFrame(
          [
              {"int": 1, "float": 1.0, "category": "one", "bool": True},
              {
                  "int": 2,
                  "float": 2.0,
                  "category": "two",
                  "bool": False,
              },
          ]
      )
  
      for col in df.columns:
          df[col] = df[col].astype(col)
  
      feature_types = validator.get_feat_type_from_columns(df)
  
      assert feature_types == {
          "int": "numerical",
          "float": "numerical",
          "category": "categorical",
          "bool": "categorical",
      }
  
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 5 for calling a global function with example usage: test_featurevalidatorget_feat_type_from_columns() without return types