code: def get_boston_dataset():
      boston = load_boston()
      df_boston = pd.DataFrame(boston.data)
      df_boston.columns = boston.feature_names
      df_boston["MEDV"] = boston["target"]
      df_boston_train, df_boston_test = train_test_split(
          df_boston, test_size=0.2, random_state=42
      )
      return df_boston_train, df_boston_test
  
imports: import csv
  import datetime
  import numbers
  import os
  import pkg_resources
  import numpy
  import pandas
  import scipy
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import column_or_1d
  from auto_ml._version import __version__
label_desc: Use image node_id 6 for calling a global function with example usage: get_boston_dataset() and returns: df_boston_train,  df_boston_test