code: def test_no_error_when_null_values_in_variable(df_vartypes):
      df_na = df_vartypes.copy()
      df_na.loc[1, "Age"] = np.nan
  
      transformer = MathFeatures(
          variables=["Age", "Marks"],
          func=["sum", "mean"],
          missing_values="ignore",
      )
  
      X = transformer.fit_transform(df_na)
  
      ref = pd.DataFrame.from_dict(
          {
              "Name": ["tom", "nick", "krish", "jack"],
              "City": ["London", "Manchester", "Liverpool", "Bristol"],
              "Age": [20, np.nan, 19, 18],
              "Marks": [0.9, 0.8, 0.7, 0.6],
              "dob": pd.date_range("2020-02-24", periods=4, freq="T"),
              "sum_Age_Marks": [20.9, 0.8, 19.7, 18.6],
              "mean_Age_Marks": [10.45, 0.8, 9.85, 9.3],
          }
      )
      # transform params
      pd.testing.assert_frame_equal(X, ref)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.pipeline import Pipeline
  from feature_engine.creation import MathFeatures
label_desc: Use image node_id 13 for calling a global function with example usage: test_no_error_when_null_values_in_variable(df_vartypes) without return types