code: def generate_multi_shift_rule(frequencies, shifts=None, orders=None):
      r"""Computes the parameter shift rule with respect to two parametrized unitaries,
      given their generator's eigenvalue frequency spectrum. This corresponds to a
      shift rule that computes off-diagonal elements of higher order derivative tensors.
      For the second order, this corresponds to the Hessian.
  
      Args:
          frequencies (list[tuple[int or float]]): List of eigenvalue frequencies corresponding
              to the each parametrized unitary.
          shifts (list[tuple[int or float]]): List of shift values corresponding to each parametrized
              unitary. If unspecified, equidistant shifts are assumed. If supplied, the length
              of each tuple in the list must be the same as the length of the corresponding tuple in
              ``frequencies``.
          orders (list[int]): the order of differentiation for each parametrized unitary.
              If unspecified, the first order derivative shift rule is computed for each parametrized
              unitary.
  
      Returns:
          tuple: a tuple of coefficients, shifts for the first parameter, and shifts for the
          second parameter, describing the gradient rule
          for the parameter-shift method.
  
          For parameters :math:`\phi_a` and :math:`\phi_b`, the
          coefficients :math:`c_i` and the shifts :math:`s^{(a)}_i`, :math:`s^{(b)}_i`,
          combine to give a gradient rule of the following form:
  
          .. math::
  
              \frac{\partial^2}{\partial\phi_a \partial\phi_b}f
              = \sum_{i} c_i f(\phi_a + s^{(a)}_i, \phi_b + s^{(b)}_i).
  
          where :math:`f(\phi_a, \phi_b) = \langle 0|U(\phi_a)^\dagger V(\phi_b)^\dagger \hat{O} V(\phi_b) U(\phi_a)|0\rangle`
          for some observable :math:`\hat{O}` and unitaries :math:`U(\phi_a)=e^{iH_a\phi_a}` and :math:`V(\phi_b)=e^{iH_b\phi_b}`.
  
      **Example**
  
      >>> generate_multi_shift_rule([(1,), (1,)])
      array([[ 0.25      ,  1.57079633,  1.57079633],
             [-0.25      ,  1.57079633, -1.57079633],
             [-0.25      , -1.57079633,  1.57079633],
             [ 0.25      , -1.57079633, -1.57079633]])
  
      This corresponds to the gradient rule
  
      .. math::
  
          \frac{\partial^2 f}{\partial x\partial y} &= \frac{1}{4}
          \left[f(x+\pi/2, y+\pi/2) - f(x+\pi/2, y-\pi/2)\\
          &~~~- f(x-\pi/2, y+\pi/2) + f(x-\pi/2, y-\pi/2) \right].
      """
      rules = []
      shifts = shifts or [None] * len(frequencies)
      orders = orders or [1] * len(frequencies)
  
      for f, s, o in zip(frequencies, shifts, orders):
          rule = generate_shift_rule(f, shifts=s, order=o)
          rules.append(process_shifts(rule))
  
      return _combine_shift_rules(rules)
  
imports: import functools
  import itertools
  import numbers
  import warnings
  import numpy
  import pennylane
  from pennylane.measurements import MeasurementProcess
  from pennylane.ops.functions import bind_new_parameters
  from pennylane.tape import QuantumScript
label_desc: Use image node_id 9 for calling a global function with example usage: generate_multi_shift_rule(frequencies, shifts, orders) and returns: _combine_shift_rules