code: def analysis_module(request_info):
      json_file = request_info["json"]
      df = request_info["df"]
      model = request_info["model"]
      y = request_info["y"]
  
      analysis_tuple = {}
  
      cv_results = cross_score(json_file, df, model, y)
      analysis_tuple["cross-validation"] = cv_results
  
      if "analysis" in json_file:
          analysis = json_file["analysis"]
      else:
          json_file["analysis"] = {"type": "ALL"}
      if True:
          for element in json_file["analysis"]:
              if element == "type":
                  analysis_group = json_file["analysis"]["type"]
                  if isinstance(analysis_group, str):
                      analysis_group = [analysis_group]
                  for analysis_type in analysis_group:
                      if analysis_type not in analysis_vocab:
                          raise Exception(
                              "Your specific type of analysis -- {} -- is not in the supported formats".format(
                                  analysis_type
                              )
                          )
                      if analysis_type == "acc-score":
                          acc_results = acc_score(model, df, y)
                          analysis_tuple["acc-score"] = acc_results
                      if analysis_type == "confusion":
                          matrix = confusion(model, df, y)
                          analysis_tuple["confusion_matrix"] = matrix
                      if analysis_type == "pr":
                          analysis_tuple[
                              "precision"
                          ] = precision_calculation(model, df, y)
                          analysis_tuple[
                              "recall"
                          ] = recall_score_helper(model, df, y)
                      if analysis_type == "importances":
                          analysis_tuple[
                              "importances"
                          ] = feature_importances(model, df, y)
                      if analysis_type == "ALL":
                          (
                              acc,
                              cv,
                              matrix,
                              precision,
                              recaller,
                          ) = default_analysis(json_file, model, df, y)
                          analysis_tuple["acc-score"] = acc
                          analysis_tuple["cross-validation"] = cv
                          analysis_tuple["confusion_matrix"] = matrix
                          analysis_tuple["precision"] = precision
                          analysis_tuple["recall"] = recaller
              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["analysis"]["custom"][
                      "loc"
                  ].rsplit("/")[-1]
                  shutil.copy(
                      json_file["analysis"]["custom"]["loc"],
                      absolute_path,
                  )
  
                  mod = import_module("temp")
                  new_func = getattr(
                      mod, json_file["analysis"]["custom"]["name"]
                  )
  
                  results, name = new_func(json_file, df, model, y)
                  sys.path.remove(sys_path)
                  os.remove("./buffer/temp.py")
  
                  analysis_tuple[name] = results
  
      request_info["analysis"] = analysis_tuple
      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 5 for calling a global function with example usage: analysis_module(request_info) and returns: request_info