code: def process_params_out(model, params, return_str=False):
      """
      This method processes the parameters obtained from the model.
  
      params:
      1) model - the model to process params.
      2) params - json parameters of the model (which we get through cereal).
      3) return_str (bool) - if True then a pretty string version of the
                             params is returned.
      """
      # for pretty printing.
      pp = pprint.PrettyPrinter()
  
      # value_resolver defined later.
      params_dic = json.loads(params, object_pairs_hook=value_resolver)
  
      # remove "cereal_class_version".
      # this stores the cereal_class_version value for all deleted pairs.
      cereal_class_version = []
      ref_path = []
      # 'full_paths' will store the complete path in the dictionary for all
      # occurrences. This will be used during the reversed process, to insert
      # 'cereal_class_version' at the correct places to avoid any errors.
      full_paths = []
  
      scrub(
          params_dic,
          "cereal_class_version",
          cereal_class_version,
          full_paths,
          ref_path,
      )
  
      # storing 'cereal_class_version' occurrences paths and values.
      model.scrubbed_params["cereal_class_version"] = {
          "values": cereal_class_version,
          "full_paths": full_paths,
      }
  
      # convert armadillo dictionary to numpy array.
      arma_to_np(params_dic)
  
      if return_str:
          return params_dic, pp.pformat(params_dic)
      else:
          return params_dic
  
file_path: mlpack/src/mlpack/bindings/python/mlpack/preprocess_json_params.py
imports: import numpy
  import json
  import pprint
  from copy import deepcopy
  from collections import OrderedDict
label_desc: Use image node_id 1 for calling a global function with example usage: process_params_out(model, params, return_str) and returns: params_dic,  pp, params_dic