code: def testDatasetDistributeEvenlyDivisibleNoDrop(self, distribution):
      # Setting drop_remainder=False on the dataset causes DistributedIterator
      # to use get_next_as_optional(), even if the batched dataset is evenly
      # divisible by the number of workers.
      dataset = get_dataset_from_tensor_slices([5.0, 6.0]).batch(
          2, drop_remainder=False
      )
      input_iterator = iter(
          distribution.experimental_distribute_dataset(dataset)
      )
  
      data = next(input_iterator)
  
      expected_result = [5.0, 6.0]
      final_result = []
      actual_result = distribution.experimental_local_results(data)
      for val in actual_result:
          final_result.extend(val)
      self.assertAllEqual(expected_result, final_result)
  
imports: from absl.testing import parameterized
  from tensorflow.python import tf2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import test_util
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.tpu import tpu
  from tensorflow.python.util import nest
label_desc: Use image node_id 33 for calling the InputIterationTest obj's underlying member method code with example usage: obj.testDatasetDistributeEvenlyDivisibleNoDrop(distribution) without return types