code: def test_encode_features_handles_pass_columns(pd_es):
      f1 = IdentityFeature(pd_es["log"].ww["product_id"])
      f2 = IdentityFeature(pd_es["log"].ww["value"])
  
      features = [f1, f2]
      cutoff_time = pd.DataFrame(
          {
              "instance_id": range(6),
              "time": pd_es["log"]["datetime"][0:6],
              "label": [i % 2 for i in range(6)],
          },
          columns=["instance_id", "time", "label"],
      )
      feature_matrix = calculate_feature_matrix(
          features, pd_es, cutoff_time
      )
  
      assert "label" in feature_matrix.columns
  
      feature_matrix_encoded, _ = encode_features(
          feature_matrix, features
      )
      feature_matrix_encoded_shape = feature_matrix_encoded.shape
  
      # to_encode should keep product_id as a string, and not create 3 additional columns
      to_encode = []
      feature_matrix_encoded, _ = encode_features(
          feature_matrix,
          features,
          to_encode=to_encode,
      )
      assert (
          feature_matrix_encoded_shape != feature_matrix_encoded.shape
      )
  
      to_encode = ["value"]
      feature_matrix_encoded, _ = encode_features(
          feature_matrix,
          features,
          to_encode=to_encode,
      )
      assert (
          feature_matrix_encoded_shape != feature_matrix_encoded.shape
      )
  
      assert "label" in feature_matrix_encoded.columns
  
file_path: featuretools/featuretools/tests/synthesis/test_encode_features.py
imports: import pandas
  import pytest
  from featuretools import EntitySet, calculate_feature_matrix, dfs
  from featuretools.feature_base import Feature, IdentityFeature
  from featuretools.primitives import NMostCommon
  from featuretools.synthesis import encode_features
label_desc: Use image node_id 5 for calling a global function with example usage: test_encode_features_handles_pass_columns(pd_es) without return types