docs: def evaluate(self, verbose=False, decode=True, passes=None, num_threads=1, apply_experimental=True):
          """Evaluates by creating an Index containing evaluated data.
  
          See `LazyResult`
  
          Returns
          -------
          Index
              Index with evaluated data.
  
          """
          if self.start == 0 and self.stop == 0:
              evaluated_data = np.empty(0, dtype=np.int64)
          else:
              evaluated_data = super(Index, self).evaluate(verbose, decode, passes, num_threads, apply_experimental)
  
          return Index(evaluated_data, self.dtype, self.name)