code: def adaboost_model(parameters, X, y):
      """Returns the AdaBoost model"""
      estimator = DecisionTreeClassifier(
          min_samples_leaf=parameters["min_samples_leaf"],
          min_samples_split=parameters["min_samples_split"],
          criterion=parameters["criterion"],
          splitter="best",
      )
      clf = AdaBoostClassifier(
          base_estimator=estimator, n_estimators=100, random_state=0
      ).fit(X, y)
      return clf
  
file_path: doc-parsr/server/src/processing/MlHeadingDetectionModule/train_model/train.py
imports: import argparse
  import os
  import pandas
  import pickle
  from sklearn import metrics
  from sklearn.feature_selection import RFECV
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
  from sklearn_porter import Porter
label_desc: Use image node_id 3 for calling a global function with example usage: adaboost_model(parameters, X, y) and returns: clf