code: def test_one2one_df2list(model):
      """
      if a one-to-one model (detector) is applied to a DataFrame and returns a
      dict, the output dict keys should match the input column names
      """
      if isinstance(model, _Detector):
          df = pd.DataFrame(
              np.arange(300).reshape(100, 3),
              index=pd.date_range(
                  start="2017-1-1", periods=100, freq="D"
              ),
              columns=["A", "B", "C"],
          )
          if isinstance(model, _TrainableModel):
              result = model.fit_detect(df, return_list=True)
          else:
              result = model.detect(df, return_list=True)
          if sys.version_info[1] >= 6:
              assert list(result.keys()) == ["A", "B", "C"]
          else:
              assert set(result.keys()) == {"A", "B", "C"}
  
file_path: adtk/tests/test_series_name.py
imports: import sys
  import numpy
  import pandas
  import pytest
  from sklearn.cluster import KMeans
  from sklearn.linear_model import LinearRegression
  from sklearn.neighbors import LocalOutlierFactor
  import adtk.detector
  import adtk.transformer
  from adtk._base import _TrainableModel
  from adtk._detector_base import _NonTrainableUnivariateDetector, _TrainableMultivariateDetector, _TrainableUnivariateDetector
label_desc: Use image node_id 4 for calling a global function with example usage: test_one2one_df2list(model) without return types