class_docstr: Tests for sharing datasets across jobs using a cross-trainer cache.
code: def testSlowClientSkipsData(self):
      cluster = self._create_cluster(
          num_workers=1, cross_trainer_cache_size_bytes=500
      )
      dataset = dataset_ops.Dataset.range(10000000).repeat()
      dataset1 = self.make_distributed_dataset(
          dataset,
          cluster,
          job_name="job",
          cross_trainer_cache=data_service_ops.CrossTrainerCache(
              trainer_id="Trainer 1"
          ),
      )
      self.assertDatasetProduces(dataset1.take(200), list(range(200)))
  
      dataset2 = self.make_distributed_dataset(
          dataset,
          cluster,
          job_name="job",
          cross_trainer_cache=data_service_ops.CrossTrainerCache(
              trainer_id="Trainer 2"
          ),
      )
      dataset2 = dataset2.take(200)
      output = self.getDatasetOutput(dataset2)
      # When the cache is small, the second trainer couldn't read the beginning of
      # the dataset. It can still read 200 elements from the dataset, because the
      # dataset is infinite.
      self.assertGreater(output[0], 0)
      self.assertLen(output, 200)
  
imports: import multiprocessing
  from absl.testing import parameterized
  from tensorflow.python.data.experimental.kernel_tests.service import test_base
  from tensorflow.python.data.experimental.ops import data_service_ops
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import errors
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the CrossTrainerCacheTest obj's underlying member method code with example usage: obj.testSlowClientSkipsData() without return types