code: def bonf(pvals, alpha=0.05):
      """P-values correction with Bonferroni method.
  
      Parameters
      ----------
      pvals : array_like
          Array of p-values of the individual tests.
      alpha : float
          Error rate (= alpha level).
  
      Returns
      -------
      reject : array, bool
          True if a hypothesis is rejected, False if not
      pval_corrected : array
          P-values adjusted for multiple hypothesis testing using the Bonferroni
          procedure (= multiplied by the number of tests).
  
      See also
      --------
      holm : Holm-Bonferroni correction
      fdr : Benjamini/Hochberg and Benjamini/Yekutieli FDR correction
  
      Notes
      -----
      From Wikipedia:
  
      Statistical hypothesis testing is based on rejecting the null hypothesis
      if the likelihood of the observed data under the null hypotheses is low.
      If multiple hypotheses are tested, the chance of a rare event increases,
      and therefore, the likelihood of incorrectly rejecting a null hypothesis
      (i.e., making a Type I error) increases.
      The Bonferroni correction compensates for that increase by testing each
      individual hypothesis :math:`p_i` at a significance level of
      :math:`p_i = \\alpha / n` where :math:`\\alpha` is the desired overall
      alpha level and :math:`n` is the number of hypotheses. For example, if a
      trial is testing :math:`n=20` hypotheses with a desired
      :math:`\\alpha=0.05`, then the Bonferroni correction would test each
      individual hypothesis at :math:`\\alpha=0.05/20=0.0025``.
  
      The Bonferroni adjusted p-values are defined as:
  
      .. math::
          \\widetilde {p}_{{(i)}}= n \\cdot p_{{(i)}}
  
      The Bonferroni correction tends to be a bit too conservative.
  
      Note that NaN values are not taken into account in the p-values correction.
  
      References
      ----------
      - Bonferroni, C. E. (1935). Il calcolo delle assicurazioni su gruppi
        di teste. Studi in onore del professore salvatore ortu carboni, 13-60.
  
      - https://en.wikipedia.org/wiki/Bonferroni_correction
  
      Examples
      --------
      >>> import pingouin as pg
      >>> pvals = [.50, .003, .32, .054, .0003]
      >>> reject, pvals_corr = pg.multicomp(pvals, method='bonf', alpha=.05)
      >>> print(reject, pvals_corr)
      [False  True False False  True] [1.     0.015  1.     0.27   0.0015]
      """
      pvals = np.asarray(pvals)
      num_nan = np.isnan(pvals).sum()
      pvals_corrected = pvals * (float(pvals.size) - num_nan)
      pvals_corrected = np.clip(pvals_corrected, None, 1)
      with np.errstate(invalid="ignore"):
          reject = np.less(pvals_corrected, alpha)
      return reject, pvals_corrected
  
imports: import numpy
  from pandas import Series
label_desc: Use image node_id 2 for calling a global function with example usage: bonf(pvals, alpha) and returns: reject,  pvals_corrected