code: def tost(x, y, bound=1, paired=False, correction=False):
      """Two One-Sided Test (TOST) for equivalence.
  
      Parameters
      ----------
      x, y : array_like
          First and second set of observations. ``x`` and ``y`` should have the
          same units. If ``y`` is a single value (e.g. 0), a one-sample test is
          performed.
      bound : float
          Magnitude of region of similarity (a.k.a epsilon). Note that this
          should be expressed in the same unit as ``x`` and ``y``.
      paired : boolean
          Specify whether the two observations are related (i.e. repeated
          measures) or independent.
      correction : auto or boolean
          Specify whether or not to correct for unequal variances using Welch
          separate variances T-test. This only applies if ``paired`` is False.
  
      Returns
      -------
      stats : :py:class:`pandas.DataFrame`
  
          * ``'bound'``: bound (= epsilon, or equivalence margin)
          * ``'dof'``: degrees of freedom
          * ``'pval'``: TOST p-value
  
      See also
      --------
      ttest
  
      References
      ----------
      .. [1] Schuirmann, D.L. 1981. On hypothesis testing to determine if the
             mean of a normal distribution is contained in a known interval.
             Biometrics 37 617.
  
      .. [2] https://cran.r-project.org/web/packages/equivalence/equivalence.pdf
  
      Examples
      --------
      1. Independent two-sample TOST with a region of similarity of 1 (default)
  
      >>> import pingouin as pg
      >>> a = [4, 7, 8, 6, 3, 2]
      >>> b = [6, 8, 7, 10, 11, 9]
      >>> pg.tost(a, b)
            bound  dof      pval
      TOST      1   10  0.965097
  
      2. Paired TOST with a different region of similarity
  
      >>> pg.tost(a, b, bound=0.5, paired=True)
            bound  dof      pval
      TOST    0.5    5  0.954854
  
      3. One sample TOST
  
      >>> pg.tost(a, y=0, bound=4)
            bound  dof      pval
      TOST      4    5  0.825967
      """
      x = np.asarray(x)
      y = np.asarray(y)
      assert isinstance(
          bound, (int, float)
      ), "bound must be int or float."
  
      # T-tests
      df_a = ttest(
          x + bound,
          y,
          paired=paired,
          correction=correction,
          alternative="greater",
      )
      df_b = ttest(
          x - bound,
          y,
          paired=paired,
          correction=correction,
          alternative="less",
      )
      pval = max(df_a.at["T-test", "p-val"], df_b.at["T-test", "p-val"])
  
      # Create output dataframe
      stats = pd.DataFrame(
          {
              "bound": bound,
              "dof": df_a.at["T-test", "dof"],
              "pval": pval,
          },
          index=["TOST"],
      )
      return _postprocess_dataframe(stats)
  
imports: import numpy
  import pandas
  from pingouin.parametric import ttest
  from pingouin.utils import _postprocess_dataframe
label_desc: Use image node_id 1 for calling a global function with example usage: tost(x, y, bound, paired, correction) and returns: _postprocess_dataframe