code: def minimize(f, num_evals=50, solver_name=None, pmap=map, **kwargs):
      """Basic function minimization routine. Minimizes ``f`` within
      the given box constraints.
  
      :param f: the function to be minimized
      :param num_evals: number of permitted function evaluations
      :param solver_name: name of the solver to use (optional)
      :type solver_name: string
      :param pmap: the map function to use
      :type pmap: callable
      :param kwargs: box constraints, a dict of the following form
          ``{'parameter_name': [lower_bound, upper_bound], ...}``
      :returns: retrieved minimum, extra information and solver info
  
      This function will implicitly choose an appropriate solver and
      its initialization based on ``num_evals`` and the box constraints.
  
      """
      # sanity check on box constraints
      assert all(
          [len(v) == 2 and v[0] < v[1] for v in kwargs.values()]
      ), "Box constraints improperly specified: should be [lb, ub] pairs"
  
      func = _wrap_hard_box_constraints(f, kwargs, sys.float_info.max)
  
      suggestion = suggest_solver(num_evals, solver_name, **kwargs)
      solver = make_solver(**suggestion)
      solution, details = optimize(
          solver, func, maximize=False, max_evals=num_evals, pmap=pmap
      )
      return solution, details, suggestion
  
imports: import timeit
  import sys
  import operator
  from .None import functions
  from .None import solvers
  from .None import search_spaces
  from .solvers import solver_registry
  from .util import DocumentedNamedTuple
  from .constraints import wrap_constraints
label_desc: Use image node_id 6 for calling a global function with example usage: minimize(f, num_evals, solver_name, pmap) and returns: solution,  details,  suggestion