code: def multicontrast_pvalues(
      tstat, tcorr, df=None, dist="t", alternative="two-sided"
  ):
      """pvalues for simultaneous tests"""
      from statsmodels.sandbox.distributions.multivariate import (
          mvstdtprob,
      )
  
      if (df is None) and (dist == "t"):
          raise ValueError(
              "df has to be specified for the t-distribution"
          )
      tstat = np.asarray(tstat)
      ntests = len(tstat)
      cc = np.abs(tstat)
      pval_global = 1 - mvstdtprob(-cc, cc, tcorr, df)
      pvals = []
      for ti in cc:
          limits = ti * np.ones(ntests)
          pvals.append(1 - mvstdtprob(-cc, cc, tcorr, df))
  
      return pval_global, np.asarray(pvals)
  
imports: from collections import namedtuple
  from statsmodels.compat.python import lzip, lrange
  import copy
  import math
  import numpy
  from numpy.testing import assert_almost_equal, assert_equal
  from scipy import stats, interpolate
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.stats.multitest import multipletests, _ecdf, fdrcorrection, fdrcorrection_twostage
  from statsmodels.graphics import utils
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 27 for calling a global function with example usage: multicontrast_pvalues(tstat, tcorr, df, dist, alternative) and returns: pval_global,  np