# So hist_mids[i] = (lower_bound + bin_width / 2) + bin_width * i
    first_mid = self._lower_bound + self._bin_width / 2
    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)

  def _get_dequantized_hist_mids_after_quantize(
      self, quant_min: float, quant_max: float
  ) -> np.ndarray:

}

// trimmer is implemented as a state machine.
// It can be in one of the following states:
const (
	inSpace  = iota // inside space
	inEscape        // inside text bracketed by tabwriter.Escapes
	inText          // inside text
)

func (p *trimmer) resetSpace() {
	p.state = inSpace
	p.space = p.space[0:0]
}

// Design note: It is tempting to eliminate extra blanks occurring in