code: def gpd_loglikelihood_minimizer_aligned(price_data):
      result = [False, False]
      DEFAULT_SCALE_PARAM = 1
      DEFAULT_SHAPE_PARAM = 1
      if len(price_data) > 0:
          gpd_loglikelihood_lambda = gpd_loglikelihood_factory(
              price_data
          )
          optimization_results = optimize.minimize(
              gpd_loglikelihood_lambda,
              [DEFAULT_SCALE_PARAM, DEFAULT_SHAPE_PARAM],
              method="Nelder-Mead",
          )
          if optimization_results.success:
              resulting_params = optimization_results.x
              if len(resulting_params) == 2:
                  result[0] = resulting_params[0]
                  result[1] = resulting_params[1]
      return result
  
imports: from __future__ import division
  import math
  import pandas
  import numpy
  from math import pow
  from scipy import stats, optimize
  from six import iteritems
  from sys import float_info
  from .utils import nanmean, nanstd, nanmin, up, down, roll, rolling_window
  from .periods import ANNUALIZATION_FACTORS, APPROX_BDAYS_PER_YEAR
  from .periods import DAILY, WEEKLY, MONTHLY, QUARTERLY, YEARLY
label_desc: Use image node_id 38 for calling a global function with example usage: gpd_loglikelihood_minimizer_aligned(price_data) and returns: result