code: def test_param_C_is_float_variables_is_none(
      log_base, exp_age, exp_marks, df_vartypes
  ):
      float_constant = 1.0
      transformer = LogCpTransformer(base=log_base, C=float_constant)
      X = transformer.fit_transform(df_vartypes)
  
      # expected output
      transf_df = df_vartypes.copy()
      transf_df["Age"] = exp_age
      transf_df["Marks"] = exp_marks
  
      # test init params
      assert transformer.base == log_base
      assert transformer.variables is None
      assert transformer.C == float_constant
      # test fit attr
      assert transformer.variables_ == ["Age", "Marks"]
      assert transformer.n_features_in_ == 5
      assert transformer.C_ == float_constant
      # test transform output
      pd.testing.assert_frame_equal(X, transf_df)
  
      # test inverse_transform output
      Xit = transformer.inverse_transform(X)
      # convert numbers to original format.
      Xit["Age"] = Xit["Age"].round().astype("int64")
      Xit["Marks"] = Xit["Marks"].round(1)
  
      pd.testing.assert_frame_equal(Xit, df_vartypes)
  
file_path: feature_engine/tests/test_transformation/test_logcp_transformer.py
imports: import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from feature_engine.transformation import LogCpTransformer
label_desc: Use image node_id 4 for calling a global function with example usage: test_param_C_is_float_variables_is_none(log_base, exp_age, exp_marks, df_vartypes) without return types