class_docstr: Tests for sharing datasets across jobs using a cross-trainer cache.
code: def testSmallCache(self):
      cluster = self._create_cluster(
          num_workers=1, cross_trainer_cache_size_bytes=500
      )
      dataset = dataset_ops.Dataset.range(10000000).repeat()
      num_readers = 20
  
      for i in range(num_readers):
          # Even if the cache is small and may discard old data, each trainer can
          # still read the required number of elements because the input dataset is
          # infinite.
          distributed_dataset = self.make_distributed_dataset(
              dataset,
              cluster,
              job_name="job",
              cross_trainer_cache=data_service_ops.CrossTrainerCache(
                  trainer_id=f"Trainer {i}"
              ),
          )
          output = self.getDatasetOutput(distributed_dataset.take(200))
          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 6 for calling the CrossTrainerCacheTest obj's underlying member method code with example usage: obj.testSmallCache() without return types