code: def rms_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:
      """Computes RMS error."""
      return np.sqrt(mean_squared_error(y_true, y_pred))
  
imports: import numpy
  import scipy.stats
  from sklearn.metrics import matthews_corrcoef
  from sklearn.metrics import recall_score
  from sklearn.metrics import cohen_kappa_score
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import precision_score
  from sklearn.metrics import precision_recall_curve
  from sklearn.metrics import auc
  from sklearn.metrics import jaccard_score
  from sklearn.metrics import f1_score
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import balanced_accuracy_score
  from sklearn.metrics import top_k_accuracy_score
label_desc: Use image node_id 6 for calling a global function with example usage: rms_score(y_true, y_pred) and returns: np