class_docstr: Result of `scipy.stats.tukey_hsd`.
  
  Attributes
  ----------
  statistic : float ndarray
      The computed statistic of the test for each comparison. The element
      at index ``(i, j)`` is the statistic for the comparison between groups
      ``i`` and ``j``.
  pvalue : float ndarray
      The associated p-value from the studentized range distribution. The
      element at index ``(i, j)`` is the p-value for the comparison
      between groups ``i`` and ``j``.
  
  Notes
  -----
  The string representation of this object displays the most recently
  calculated confidence interval, and if none have been previously
  calculated, it will evaluate ``confidence_interval()``.
  
  References
  ----------
  .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "7.4.7.1. Tukey's
         Method."
         https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm,
         28 November 2020.
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code: def __str__(self):
      # Note: `__str__` prints the confidence intervals from the most
      # recent call to `confidence_interval`. If it has not been called,
      # it will be called with the default CL of .95.
      if self._ci is None:
          self.confidence_interval(confidence_level=0.95)
      s = (
          "Tukey's HSD Pairwise Group Comparisons"
          f" ({self._ci_cl*100:.1f}% Confidence Interval)\n"
      )
      s += "Comparison  Statistic  p-value  Lower CI  Upper CI\n"
      for i in range(self.pvalue.shape[0]):
          for j in range(self.pvalue.shape[0]):
              if i != j:
                  s += (
                      f" ({i} - {j}) {self.statistic[i, j]:>10.3f}"
                      f"{self.pvalue[i, j]:>10.3f}"
                      f"{self._ci.low[i, j]:>10.3f}"
                      f"{self._ci.high[i, j]:>10.3f}\n"
                  )
      return s
  
imports: from collections import namedtuple
  from dataclasses import dataclass
  from math import comb
  import numpy
  import warnings
  from itertools import combinations
  import scipy.stats
  from scipy.optimize import shgo
  from .None import distributions
  from ._common import ConfidenceInterval
  from ._continuous_distns import chi2, norm
  from scipy.special import gamma, kv, gammaln
  from scipy.fft import ifft
  from ._stats_pythran import _a_ij_Aij_Dij2
  from ._stats_pythran import _concordant_pairs, _discordant_pairs
  from ._axis_nan_policy import _axis_nan_policy_factory
  from scipy.stats import _stats_py
label_desc: Use image node_id 2 for calling the TukeyHSDResult obj's underlying member method code with example usage: obj.__str__() and returns: s