code: def findDecision(df: pd.DataFrame, config: dict) -> tuple:
      """
      Find the most dominant decision for a given dataframe
      Args:
          df (pd.DataFrame): (sub) train set's dataframe
          config (dict): configuration
      Returns
          result (tuple): the most dominant feature, data frame's
              number of instances, metric value (e.g. information gain
              for ID3), metric name (e.g. gain ratio for C4.5)
  
      Information about metrics: information gain for id3, gain ratio
      for c4.5, gini for cart,     chi square for chaid and std
      for regression
      """
  
      algorithm = config["algorithm"]
  
      resp_obj = findGains(df, config)
      gains = list(resp_obj["gains"].values())
      entropy = resp_obj["entropy"]
  
      if algorithm == "ID3":
          winner_index = gains.index(max(gains))
          metric_value = entropy
          metric_name = "Entropy"
      elif algorithm == "C4.5":
          winner_index = gains.index(max(gains))
          metric_value = entropy
          metric_name = "Entropy"
      elif algorithm == "CART":
          winner_index = gains.index(min(gains))
          metric_value = min(gains)
          metric_name = "Gini"
      elif algorithm == "CHAID":
          winner_index = gains.index(max(gains))
          metric_value = max(gains)
          metric_name = "ChiSquared"
      elif algorithm == "Regression":
          winner_index = gains.index(max(gains))
          metric_value = max(gains)
          metric_name = "Std"
  
      winner_name = df.columns[winner_index]
  
      return winner_name, df.shape[0], metric_value, metric_name
  
imports: import math
  import uuid
  import json
  import copy
  import gc
  from contextlib import closing
  from typing import Optional
  import psutil
  import numpy
  import pandas
  from chefboost.training import Preprocess
  from chefboost.commons import functions
  from chefboost.commons.logger import Logger
  from chefboost.commons.module import load_module
  from chefboost.commons.daemon import CustomPool
label_desc: Use image node_id 2 for calling a global function with example usage: findDecision(df, config) and returns: winner_name,  df,  metric_value,  metric_name