code: def get_pima_data():
      train_dataset = pima_indians_head.iloc[1:5, :]
      holdout_dataset = pima_indians_head.iloc[[0], :]
  
      train_targets = train_dataset.loc[:, ["class"]]
      train_inputs = train_dataset.drop(["class"], axis=1)
  
      holdout_targets = holdout_dataset.loc[:, ["class"]]
      holdout_inputs = holdout_dataset.drop(["class"], axis=1)
  
      return (
          train_inputs,
          train_targets,
          holdout_inputs,
          holdout_targets,
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter.callbacks.recipes import dataset_recorder
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  from pandas.testing import assert_frame_equal
  import pytest
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: get_pima_data() and returns: train_inputs,  train_targets,  holdout_inputs,  holdout_targets