}

	duration := ts2.Sub(ts1)
	y1, m1, d1 := ts1.Date()
	y2, m2, d2 := ts2.Date()

	switch timePart {
	case timePartYear:
		dy := int64(y2 - y1)
		if m2 > m1 || (m2 == m1 && d2 >= d1) {
			return FromInt(dy), nil
		}
		return FromInt(dy - 1), nil
	case timePartMonth:
		m1 += time.Month(12 * y1)
		m2 += time.Month(12 * y2)

		return FromInt(int64(m2 - m1)), nil
	case timePartDay:

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will