code: def test_get_feature_names_out_from_pipeline(df_enc_binary):
      original_features = ["var_num"]
      input_features = df_enc_binary.columns
  
      tr = Pipeline([("transformer", OneHotEncoder())])
      tr.fit(df_enc_binary)
  
      out = [
          "var_A_A",
          "var_A_B",
          "var_A_C",
          "var_B_A",
          "var_B_B",
          "var_B_C",
          "var_C_AHA",
          "var_C_UHU",
          "var_D_OHO",
          "var_D_EHE",
      ]
      feat_out = original_features + out
  
      assert tr.get_feature_names_out(input_features=None) == feat_out
      assert (
          tr.get_feature_names_out(input_features=input_features)
          == feat_out
      )
  
file_path: feature_engine/tests/test_encoding/test_onehot_encoder.py
imports: import pandas
  import pytest
  from sklearn.pipeline import Pipeline
  from feature_engine.encoding import OneHotEncoder
label_desc: Use image node_id 14 for calling a global function with example usage: test_get_feature_names_out_from_pipeline(df_enc_binary) without return types