code: def bounds_to_space(bounds):
      """
      Takes as input a list of tuples with bounds, and create a dictionary to be processed by the class Design_space. This function
      us used to keep the compatibility with previous versions of GPyOpt in which only bounded continuous optimization was possible
      (and the optimization domain passed as a list of tuples).
      """
      space = []
      for k in range(len(bounds)):
          space += [
              {
                  "name": "var_" + str(k + 1),
                  "type": "continuous",
                  "domain": bounds[k],
                  "dimensionality": 1,
              }
          ]
      return space
  
imports: import numpy
  import itertools
  from copy import deepcopy
  from .variables import create_variable
  from ..errors import InvalidConfigError, InvalidVariableNameError
label_desc: Use image node_id 1 for calling a global function with example usage: bounds_to_space(bounds) and returns: space