code: def preprocess_module_inference(request_info):
      json_file = request_info["json"]
      df = request_info["df"]
  
      if "preprocessor" in json_file:
          preprocess = json_file["preprocessor"]
          for element in preprocess:
              if element not in preprocess_vocab:
                  raise Exception(
                      "Your specificed preprocessing technique -- {} -- is not supported".format(
                          element
                      )
                  )
              if element == "custom":
                  sys_path = "/nylon/supplementaries/buffer/"
                  sys.path.insert(1, os.getcwd() + sys_path)
  
                  absolute_path = (
                      os.path.abspath(os.getcwd())
                      + "/nylon/supplementaries/buffer/temp.py"
                  )
  
                  file_name = json_file["preprocessor"]["custom"][
                      "loc"
                  ].rsplit("/")[-1]
                  shutil.copy(
                      json_file["preprocessor"]["custom"]["loc"],
                      absolute_path,
                  )
  
                  mod = import_module("temp")
                  new_func = getattr(
                      mod, json_file["preprocessor"]["custom"]["name"]
                  )
  
                  df = new_func(df, json_file)
                  sys.path.remove(sys_path)
                  os.remove("./buffer/temp.py")
  
              if element == "label-encode":
                  df = handle_label_encode(preprocess, df)
              if element == "dates":
                  df = handle_dates(preprocess, df)
              if element == "clean-text":
                  df = handle_text(preprocess, df)
              if element == "embed":
                  df = handle_embedding(preprocess, df)
              if element == "fill":
                  df = handle_filling(preprocess, df)
              if element == "scale":
                  df = handle_scaling(preprocess, df)
              if element == "min-max":
                  df = handle_min_max(preprocess, df)
              if element == "ordinal":
                  df = handle_ordinal(preprocess, df)
              if element == "importance":
                  df = handle_importance(preprocess, df, json_file)
              if element == "one-hot":
                  df = handle_one_hot(preprocess, df, json_file)
  
      else:
          df, y = initial_preprocessor(df, json_file)
  
      request_info["df"] = df
  
      return request_info
  
imports: from pandas.core.algorithms import mode
  from pandas.io import json
  from sklearn.metrics import accuracy_score
  from sklearn.linear_model import LinearRegression
  from sklearn.ensemble import RandomForestRegressor
  import os
  from sklearn.ensemble import BaggingClassifier
  import sys
  from importlib import import_module
  import shutil
  from sklearn.ensemble import VotingClassifier
  import ssl
  from nylon.preprocessing.preprocessing import initial_preprocessor
  import nltk
  import pandas
  import time
  from sklearn.model_selection import train_test_split
  from sklearn.decomposition import PCA
  from nylon.modeling.modeling import a_svm, nearest_neighbors, a_tree, sgd, gradient_boosting, adaboost, rf, mlp, default_modeling, svm_stroke, ensemble_stroke
  from nylon.preprocessing.preprocessing_methods import handle_scaling, handle_min_max, handle_label_encode, handle_ordinal, handle_filling, handle_importance, handle_one_hot, handle_text, handle_embedding, handle_dates
  from nylon.analysis.analysis import default_analysis, acc_score, cross_score, confusion, precision_calculation, recall_score_helper
  from nylon.data.reader import DataReader
label_desc: Use image node_id 9 for calling a global function with example usage: preprocess_module_inference(request_info) and returns: request_info