code: def pearsonr(y: np.ndarray, y_pred: np.ndarray) -> float:
      """Computes Pearson correlation coefficient.
  
      Parameters
      ----------
      y: np.ndarray
          ground truth array
      y_pred: np.ndarray
          predicted array
  
      Returns
      -------
      float
          The Pearson correlation coefficient.
      """
      return scipy.stats.pearsonr(y, y_pred)[0]
  
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 1 for calling a global function with example usage: pearsonr(y, y_pred) and returns: scipy