code: def bonferroni_correction(pval, alpha=0.05):
      """P-value correction with Bonferroni method.
  
      Parameters
      ----------
      pval : array_like
          Set of p-values of the individual tests.
      alpha : float
          Error rate.
  
      Returns
      -------
      reject : array, bool
          True if a hypothesis is rejected, False if not.
      pval_corrected : array
          P-values adjusted for multiple hypothesis testing to limit FDR.
      """
      pval = np.asarray(pval)
      pval_corrected = pval * float(pval.size)
      # p-values must not be larger than 1.
      pval_corrected = pval_corrected.clip(max=1.0)
      reject = pval_corrected < alpha
      return reject, pval_corrected
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: bonferroni_correction(pval, alpha) and returns: reject,  pval_corrected