code: def __call__(self, data):
      d = dict(data)
  
      # attempt to fetch algorithmic parameters from app if present
      self.num_mixtures = d.get("num_mixtures", self.num_mixtures)
  
      # load scribbles idx from labels_info (if available)
      self._set_scribbles_idx_from_labelinfo(d)
  
      # copy affine meta data from image input
      d = self._copy_affine(d, src=self.image, dst=self.post_proc_label)
  
      # read relevant terms from data
      image = self._fetch_data(d, self.image)
      scribbles = self._fetch_data(d, self.scribbles)
  
      # make likelihood image
      post_proc_label = make_likelihood_image_gmm(
          image,
          scribbles,
          scribbles_bg_label=self.scribbles_bg_label,
          scribbles_fg_label=self.scribbles_fg_label,
          num_mixtures=self.num_mixtures,
          return_label=False,
      )
  
      if self.normalise:
          post_proc_label = self._normalise_logits(
              post_proc_label, axis=0
          )
  
      d = self._save_data(d, self.post_proc_label, post_proc_label)
  
      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 MakeLikelihoodFromScribblesGMMd obj's underlying member method code with example usage: obj.__call__(data) and returns: d