code: def get_sample_dataset(dataset_properties):
      """Returns sample dataset
  
      Args:
          dataset_properties (dict): Dictionary corresponding to the properties of the dataset
              used to verify the estimator and metric generators.
  
      Returns:
          X (array-like): Features array
  
          y (array-like): Labels array
  
          splits (iterator): This is an iterator that returns train test splits for
              cross-validation purposes on ``X`` and ``y``.
      """
      kwargs = dataset_properties.copy()
      data_type = kwargs.pop("type")
      if data_type == "multiclass":
          try:
              X, y = datasets.make_classification(
                  random_state=8, **kwargs
              )
              splits = model_selection.StratifiedKFold(
                  n_splits=2, random_state=8
              ).split(X, y)
          except Exception as e:
              raise exceptions.UserError(repr(e))
      elif data_type == "iris":
          X, y = datasets.load_iris(return_X_y=True)
          splits = model_selection.StratifiedKFold(
              n_splits=2, random_state=8
          ).split(X, y)
      elif data_type == "mnist":
          X, y = datasets.load_digits(return_X_y=True)
          splits = model_selection.StratifiedKFold(
              n_splits=2, random_state=8
          ).split(X, y)
      elif data_type == "breast_cancer":
          X, y = datasets.load_breast_cancer(return_X_y=True)
          splits = model_selection.StratifiedKFold(
              n_splits=2, random_state=8
          ).split(X, y)
      elif data_type == "boston":
          X, y = datasets.load_boston(return_X_y=True)
          splits = model_selection.KFold(
              n_splits=2, random_state=8
          ).split(X)
      elif data_type == "diabetes":
          X, y = datasets.load_diabetes(return_X_y=True)
          splits = model_selection.KFold(
              n_splits=2, random_state=8
          ).split(X)
      else:
          raise exceptions.UserError(
              "Unknown dataset type {}".format(
                  dataset_properties["type"]
              )
          )
      return X, y, splits
  
imports: from __future__ import absolute_import, print_function, division, unicode_literals
  import imp
  import sys
  import os
  import hashlib
  import json
  import numpy
  from sqlalchemy import create_engine
  from sqlalchemy.orm import Session
  from six import exec_, iteritems
  from sklearn import datasets
  from sklearn import model_selection
  from xcessiv import app, exceptions
label_desc: Use image node_id 9 for calling a global function with example usage: get_sample_dataset(dataset_properties) and returns: X,  y,  splits