code: def test_encode_complete_keywords():
      df = pd.DataFrame(
          {
              "var_A": ["A"] * 5
              + ["B"] * 11
              + ["C"] * 4
              + ["D"] * 9
              + ["E"] * 2
              + ["F"] * 2
              + ["G"] * 7,
              "var_B": ["A"] * 11
              + ["B"] * 7
              + ["C"] * 4
              + ["D"] * 9
              + ["E"] * 2
              + ["F"] * 2
              + ["G"] * 5,
              "var_C": ["A"] * 4
              + ["B"] * 5
              + ["C"] * 11
              + ["D"] * 9
              + ["E"] * 2
              + ["F"] * 2
              + ["G"] * 7,
          }
      )
  
      encoder = StringSimilarityEncoder(
          keywords={"var_A": ["X"], "var_B": ["Y"], "var_C": ["Z"]}
      )
      X = encoder.fit_transform(df)
  
      # test fit attr
      transf = {
          "var_A_X": 0,
          "var_B_Y": 0,
          "var_C_Z": 0,
      }
  
      # test fit attr
      assert encoder.variables_ == ["var_A", "var_B", "var_C"]
      assert encoder.n_features_in_ == 3
      assert encoder.encoder_dict_ == {
          "var_A": ["X"],
          "var_B": ["Y"],
          "var_C": ["Z"],
      }
      # test transform output
      for col in transf.keys():
          assert X[col].sum() == transf[col]
      assert "var_B" not in X.columns
      assert "var_B_F" not in X.columns
  
imports: from difflib import SequenceMatcher
  import pandas
  import pytest
  from feature_engine.encoding import StringSimilarityEncoder
  from feature_engine.encoding.similarity_encoder import _gpm_fast
label_desc: Use image node_id 16 for calling a global function with example usage: test_encode_complete_keywords() without return types