docs: def generate_pairwise(params, n_comparisons=10):
      """Generate pairwise comparisons from a Bradley--Terry model.
  
      This function samples comparisons pairs independently and uniformly at
      random over the ``len(params)`` choose 2 possibilities, and samples the
      corresponding comparison outcomes from a Bradley--Terry model parametrized
      by ``params``.
  
      Parameters
      ----------
      params : array_like
          Model parameters.
      n_comparisons : int
          Number of comparisons to be returned.
  
      Returns
      -------
      data : list of (int, int)
         Pairwise-comparison samples (see :ref:`data-pairwise`).
      """
      n = len(params)
      items = tuple(range(n))
      params = np.asarray(params)
      data = list()
      for _ in range(n_comparisons):
          # Pick the pair uniformly at random.
          a, b = random.sample(items, 2)
          if compare((a, b), params) == a:
              data.append((a, b))
          else:
              data.append((b, a))
      return tuple(data)