code: def __call__(self, data):
      d = dict(data)
  
      # copy affine meta data from logits input
      self._copy_affine(d, self.logits, self.prob)
  
      # read relevant terms from data
      logits = self._fetch_data(d, self.logits)
  
      # calculate temperate beta for range 0.1 to 0.9
      delta = np.max(logits[1, ...] - logits[0, ...])
      beta = np.log(9) / delta
  
      # normalise using softmax with temperature beta
      prob = softmax(logits * beta, axis=0)
  
      d = self._save_data(d, self.prob, prob)
  
      return d
  
imports: import logging
  from copy import deepcopy
  from typing import Optional
  import numpy
  import torch
  from monai.data import MetaTensor
  from monai.networks.blocks import CRF
  from monai.transforms import Transform
  from scipy.special import softmax
  from .utils import make_iseg_unary, make_likelihood_image_gmm, make_likelihood_image_histogram, maxflow
label_desc: Use image node_id 2 for calling the SoftenProbSoftmax obj's underlying member method code with example usage: obj.__call__(data) and returns: d