code: def pixel_error(y: np.ndarray, y_pred: np.ndarray) -> float:
      """An error metric in case y, y_pred are images.
  
      Defined as 1 - the maximal F-score of pixel similarity, or squared
      Euclidean distance between the original and the result labels.
  
      Parameters
      ----------
      y: np.ndarray
          ground truth array
      y_pred: np.ndarray
          predicted array
  
      Returns
      -------
      score: float
          The pixel-error. A number between 0 and 1.
      """
      return 1 - f1_score(y, 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 4 for calling a global function with example usage: pixel_error(y, y_pred) and returns: unknown