code: def test_transform_different_number_of_columns(df):
      """Test transform on df with different number of features to train set."""
      test = DropHighPSIFeatures()
      test.fit(df)
  
      # Define new dataframe with additional column
      data = df.copy()
      data["A"] = [1] * 1000
  
      with pytest.raises(ValueError):
          test.transform(data)
  
imports: from datetime import date
  import math
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import make_classification
  from feature_engine.selection import DropHighPSIFeatures
label_desc: Use image node_id 29 for calling a global function with example usage: test_transform_different_number_of_columns(df) without return types