code: def _power_equivalence_oneway_emp(
      f_stat, n_groups, nobs, eps, df, alpha=0.05
  ):
      """Empirical power of oneway equivalence test
  
      This only returns post-hoc, empirical power.
  
      Warning: eps is currently effect size margin as defined as in Wellek, and
      not the signal to noise ratio (Cohen's f family).
  
      Parameters
      ----------
      f_stat : float
          F-statistic from oneway anova, used to compute empirical effect size
      n_groups : int
          Number of groups in oneway comparison.
      nobs : ndarray
          Array of number of observations in groups.
      eps : float
          Equivalence margin in terms of effect size given by Wellek's psi.
      df : tuple
          Degrees of freedom for F distribution.
      alpha : float in (0, 1)
          Significance level for the hypothesis test.
  
      Returns
      -------
      pow : float
          Ex-post, post-hoc or empirical power at f-statistic of the equivalence
          test.
      """
  
      res = equivalence_oneway_generic(
          f_stat,
          n_groups,
          nobs,
          eps,
          df,
          alpha=alpha,
          margin_type="wellek",
      )
  
      nobs_mean = nobs.sum() / n_groups
      fn = f_stat  # post-hoc power, empirical power at estimate
      esn = fn * (n_groups - 1) / nobs_mean  # Wellek psi
      pow_ = ncf_cdf(res.crit_f, df[0], df[1], nobs_mean * esn)
  
      return pow_
  
imports: import numpy
  from scipy import stats
  from scipy.special import ncfdtrinc
  from statsmodels.stats.power import ncf_cdf, ncf_ppf
  from statsmodels.stats.robust_compare import TrimmedMean, scale_transform
  from statsmodels.tools.testing import Holder
  from statsmodels.stats.base import HolderTuple
label_desc: Use image node_id 13 for calling a global function with example usage: _power_equivalence_oneway_emp(f_stat, n_groups, nobs, eps, df, alpha) and returns: pow_