class_docstr: Tests for sharing datasets across jobs using a cross-trainer cache.
code: def testMultipleIterationsForOneDatasetEagerMode(self):
      cluster = self._create_cluster(num_workers=1)
      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"
          ),
      )
      # In the eager mode, each iteration creates a new data service job and does
      # not reuse cached data. We disallow this use case.
      with self.assertRaisesRegex(
          errors.InvalidArgumentError,
          "Cross-trainer caching requires infinite datasets and disallows "
          "multiple repetitions of the same dataset.",
      ):
          self.getDatasetOutput(dataset1.take(10))
          self.getDatasetOutput(dataset1.take(10))
          self.getDatasetOutput(dataset1.take(10))
  
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 15 for calling the CrossTrainerCacheTest obj's underlying member method code with example usage: obj.testMultipleIterationsForOneDatasetEagerMode() without return types