code: def adaboost(df_1, y, json_file, trained=True):
      n_estimators = 50
      learning_rate = 1
      base_estimator = None
  
      if "params" in json_file:
          parameters = json_file["params"]
          if "n_estimators" in parameters:
              n_estimators = parameters["n_estimators"]
          if "learning_rate" in parameters:
              learning_rate = parameters["learning_rate"]
          if "base_estimator" in parameters:
              base_estimator = parameters["base_estimator"]
  
      """
      adaboost  training function
      :param df_1: dataframe
      :param y: is the target column
      :trained: whether the model is already trained or not
      :return compiled adaboost model
      """
      clf = AdaBoostClassifier(
          n_estimators=n_estimators,
          learning_rate=learning_rate,
          base_estimator=base_estimator,
      )
      if trained:
          clf.fit(df_1, y)
      return clf
  
imports: from sklearn.tree import DecisionTreeClassifier
  from sklearn import svm
  import numpy
  from sklearn.linear_model import SGDClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.metrics import accuracy_score
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  import warnings
label_desc: Use image node_id 7 for calling a global function with example usage: adaboost(df_1, y, json_file, trained) and returns: clf