class_docstr: Tests for sharing datasets across jobs using a cross-trainer cache.
code: def testRequiresInfiniteDataset(self, range_):
      cluster = self._create_cluster(num_workers=1)
      dataset = dataset_ops.Dataset.range(range_).map(lambda x: x + 1)
      with self.assertRaisesRegex(
          errors.InvalidArgumentError,
          "Cross-trainer caching requires the input dataset to be infinite.",
      ):
          dataset = dataset.apply(
              data_service_ops.distribute(
                  processing_mode=data_service_ops.ShardingPolicy.OFF,
                  service=cluster.dispatcher.target,
                  job_name="job_name",
                  cross_trainer_cache=data_service_ops.CrossTrainerCache(
                      trainer_id="Trainer ID"
                  ),
              )
          )
          self.getDatasetOutput(dataset)
  
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 14 for calling the CrossTrainerCacheTest obj's underlying member method code with example usage: obj.testRequiresInfiniteDataset(range_) without return types