code: def test_repartition(self):
      # The following test case has a few partitioned datasets that are either
      # well partitioned relative to the number of workers that the user wants
      # or poorly partitioned. We only want to repartition when the dataset
      # is poorly partitioned so _repartition_needed is true in those instances.
  
      classifier = SparkXGBClassifier(num_workers=self.n_workers)
      basic = self.cls_df_train_distributed
      self.assertTrue(classifier._repartition_needed(basic))
      bad_repartitioned = basic.repartition(self.n_workers + 1)
      self.assertTrue(classifier._repartition_needed(bad_repartitioned))
      good_repartitioned = basic.repartition(self.n_workers)
      self.assertFalse(
          classifier._repartition_needed(good_repartitioned)
      )
  
      # Now testing if force_repartition returns True regardless of whether the data is well partitioned
      classifier = SparkXGBClassifier(
          num_workers=self.n_workers, force_repartition=True
      )
      good_repartitioned = basic.repartition(self.n_workers)
      self.assertTrue(
          classifier._repartition_needed(good_repartitioned)
      )
  
imports: import json
  import logging
  import os
  import random
  import tempfile
  import uuid
  from collections import namedtuple
  import numpy
  import pytest
  import xgboost
  from xgboost import testing
  from xgboost.callback import LearningRateScheduler
  from typing import Generator
  from pyspark.ml.linalg import Vectors
  from pyspark.sql import SparkSession
  from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor
  from xgboost.spark.utils import _get_max_num_concurrent_tasks
  from .utils import SparkLocalClusterTestCase
label_desc: Use image node_id 9 for calling the XgboostLocalClusterTestCase obj's underlying member method code with example usage: obj.test_repartition() without return types