code: def test_column_names_are_numbers(df_numeric_columns):
      encoder = CountFrequencyEncoder(
          encoding_method="frequency",
          variables=[0, 1, 2, 3],
          ignore_format=True,
      )
      X = encoder.fit_transform(df_numeric_columns)
  
      # expected output
      transf_df = {
          0: [0.25, 0.25, 0.25, 0.25],
          1: [0.25, 0.25, 0.25, 0.25],
          2: [0.25, 0.25, 0.25, 0.25],
          3: [0.25, 0.25, 0.25, 0.25],
          4: pd.date_range("2020-02-24", periods=4, freq="T"),
      }
  
      transf_df = pd.DataFrame(transf_df)
  
      # init params
      assert encoder.encoding_method == "frequency"
      assert encoder.variables == [0, 1, 2, 3]
      # fit params
      assert encoder.variables_ == [0, 1, 2, 3]
      assert encoder.n_features_in_ == 5
      # transform params
      pd.testing.assert_frame_equal(X, transf_df)
  
imports: import warnings
  import numpy
  import pandas
  import pytest
  from numpy import nan
  from feature_engine.encoding import CountFrequencyEncoder
label_desc: Use image node_id 13 for calling a global function with example usage: test_column_names_are_numbers(df_numeric_columns) without return types