code: def _score_regressor(estimator, X, y, idx):
      name_time = y.dtype.names[1]
      error = (estimator.predict(X).ravel() - y[name_time]) ** 2
      return idx, error
  
imports: import numbers
  from joblib import Parallel, delayed
  import numpy
  from scipy.stats import kendalltau, rankdata, spearmanr
  from sklearn.base import BaseEstimator, clone
  from sklearn.model_selection import check_cv
  from sklearn.utils._param_validation import Interval, StrOptions
  from .base import _fit_and_score
  from .stacking import Stacking
label_desc: Use image node_id 4 for calling a global function with example usage: _score_regressor(estimator, X, y, idx) and returns: idx,  error