code: def old_bound_to_new(bounds):
      """Convert the old bounds representation to the new one.
  
      The new representation is a tuple (lb, ub) and the old one is a list
      containing n tuples, i-th containing lower and upper bound on a i-th
      variable.
      """
      lb, ub = zip(*bounds)
      lb = np.array([x if x is not None else -np.inf for x in lb])
      ub = np.array([x if x is not None else np.inf for x in ub])
      return lb, ub
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from ._hessian_update_strategy import BFGS
  from ._differentiable_functions import VectorFunction, LinearVectorFunction, IdentityVectorFunction
  from .optimize import OptimizeWarning
  from warnings import warn
  from scipy.sparse import issparse
label_desc: Use image node_id 2 for calling a global function with example usage: old_bound_to_new(bounds) and returns: lb,  ub