code: def _minimize_powell(
      func,
      x0,
      args=(),
      callback=None,
      xtol=1e-4,
      ftol=1e-4,
      maxiter=None,
      maxfev=None,
      disp=False,
      direc=None,
      return_all=False,
      **unknown_options,
  ):
      """
      Minimization of scalar function of one or more variables using the
      modified Powell algorithm.
  
      Options
      -------
      disp : bool
          Set to True to print convergence messages.
      xtol : float
          Relative error in solution `xopt` acceptable for convergence.
      ftol : float
          Relative error in ``fun(xopt)`` acceptable for convergence.
      maxiter, maxfev : int
          Maximum allowed number of iterations and function evaluations.
          Will default to ``N*1000``, where ``N`` is the number of
          variables, if neither `maxiter` or `maxfev` is set. If both
          `maxiter` and `maxfev` are set, minimization will stop at the
          first reached.
      direc : ndarray
          Initial set of direction vectors for the Powell method.
  
      """
      _check_unknown_options(unknown_options)
      maxfun = maxfev
      retall = return_all
      # we need to use a mutable object here that we can update in the
      # wrapper function
      fcalls, func = wrap_function(func, args)
      x = asarray(x0).flatten()
      if retall:
          allvecs = [x]
      N = len(x)
      # If neither are set, then set both to default
      if maxiter is None and maxfun is None:
          maxiter = N * 1000
          maxfun = N * 1000
      elif maxiter is None:
          # Convert remaining Nones, to np.inf, unless the other is np.inf, in
          # which case use the default to avoid unbounded iteration
          if maxfun == np.inf:
              maxiter = N * 1000
          else:
              maxiter = np.inf
      elif maxfun is None:
          if maxiter == np.inf:
              maxfun = N * 1000
          else:
              maxfun = np.inf
  
      if direc is None:
          direc = eye(N, dtype=float)
      else:
          direc = asarray(direc, dtype=float)
  
      fval = squeeze(func(x))
      x1 = x.copy()
      iter = 0
      ilist = list(range(N))
      while True:
          fx = fval
          bigind = 0
          delta = 0.0
          for i in ilist:
              direc1 = direc[i]
              fx2 = fval
              fval, x, direc1 = _linesearch_powell(
                  func, x, direc1, tol=xtol * 100
              )
              if (fx2 - fval) > delta:
                  delta = fx2 - fval
                  bigind = i
          iter += 1
          if callback is not None:
              callback(x)
          if retall:
              allvecs.append(x)
          bnd = ftol * (numpy.abs(fx) + numpy.abs(fval)) + 1e-20
          if 2.0 * (fx - fval) <= bnd:
              break
          if fcalls[0] >= maxfun:
              break
          if iter >= maxiter:
              break
  
          # Construct the extrapolated point
          direc1 = x - x1
          x2 = 2 * x - x1
          x1 = x.copy()
          fx2 = squeeze(func(x2))
  
          if fx > fx2:
              t = 2.0 * (fx + fx2 - 2.0 * fval)
              temp = fx - fval - delta
              t *= temp * temp
              temp = fx - fx2
              t -= delta * temp * temp
              if t < 0.0:
                  fval, x, direc1 = _linesearch_powell(
                      func, x, direc1, tol=xtol * 100
                  )
                  direc[bigind] = direc[-1]
                  direc[-1] = direc1
  
      warnflag = 0
      if fcalls[0] >= maxfun:
          warnflag = 1
          msg = _status_message["maxfev"]
          if disp:
              print("Warning: " + msg)
      elif iter >= maxiter:
          warnflag = 2
          msg = _status_message["maxiter"]
          if disp:
              print("Warning: " + msg)
      else:
          msg = _status_message["success"]
          if disp:
              print(msg)
              print("         Current function value: %f" % fval)
              print("         Iterations: %d" % iter)
              print("         Function evaluations: %d" % fcalls[0])
  
      x = squeeze(x)
  
      result = OptimizeResult(
          fun=fval,
          direc=direc,
          nit=iter,
          nfev=fcalls[0],
          status=warnflag,
          success=(warnflag == 0),
          message=msg,
          x=x,
      )
      if retall:
          result["allvecs"] = allvecs
      return result
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  import sys
  import numpy
  from scipy._lib.six import callable, xrange
  from numpy import atleast_1d, eye, mgrid, argmin, zeros, shape, squeeze, vectorize, asarray, sqrt, Inf, asfarray, isinf
  import numpy
  from .linesearch import line_search_wolfe1, line_search_wolfe2, line_search_wolfe2, LineSearchWarning
  from scipy._lib._util import getargspec_no_self
label_desc: Use image node_id 31 for calling a global function with example usage: _minimize_powell(func, x0, args, callback, xtol, ftol, maxiter, maxfev, disp, direc, return_all) and returns: result