code: def get_impute_zero_score(X_missing, y_missing):
      imputer = SimpleImputer(
          missing_values=np.nan,
          add_indicator=True,
          strategy="constant",
          fill_value=0,
      )
      zero_impute_scores = get_scores_for_imputer(
          imputer, X_missing, y_missing
      )
      return zero_impute_scores.mean(), zero_impute_scores.std()
  
file_path: scikit-learn/examples/impute/plot_missing_values.py
imports: import numpy
  from sklearn.datasets import fetch_california_housing, load_diabetes
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.experimental import enable_iterative_imputer
  from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import make_pipeline
  import matplotlib.pyplot
label_desc: Use image node_id 4 for calling a global function with example usage: get_impute_zero_score(X_missing, y_missing) and returns: zero_impute_scores,  zero_impute_scores