code: def gpd_loglikelihood_scale_only(scale, price_data):
      n = len(price_data)
      data_sum = price_data.sum()
      result = -1 * float_info.max
      if scale >= 0:
          result = (-n * np.log(scale)) - (data_sum / scale)
      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 44 for calling a global function with example usage: gpd_loglikelihood_scale_only(scale, price_data) and returns: result