code: def testDatasetAssertWithDynamicBatch(self, distribution):
      # Regression test for github issue 33517.
      def step_fn(data):
          assert_op = control_flow_assert.Assert(
              math_ops.less_equal(math_ops.reduce_max(data), 100.0),
              [data],
          )
          with ops.control_dependencies([assert_op]):
              return math_ops.square(data)
  
      @def_function.function
      def train(dataset):
          results = []
          iterator = iter(dataset)
          # we iterate through the loop 5 times since we have 3 elements and a
          # global batch of 2.
          for _ in range(2):
              elem = next(iterator)
              output = distribution.experimental_local_results(
                  distribution.run(step_fn, args=(elem,))
              )
              results.append(output)
          return results
  
      dataset = dataset_ops.DatasetV2.from_tensor_slices(
          [
              5.0,
              6.0,
              7.0,
          ]
      ).batch(2)
      # TODO(b/138326910): Remove Dataset V1 version once bug resolved.
      if not tf2.enabled():
          dataset = dataset_ops.Dataset.from_tensor_slices(
              [
                  5.0,
                  6.0,
                  7.0,
              ]
          ).batch(2)
      dist_dataset = distribution.experimental_distribute_dataset(
          dataset
      )
      results = train(dist_dataset)
  
      expected_results = [[25.0, 36.0], [49.0]]
      self.assertEqual(len(expected_results), len(results))
  
      # Need to expand results since output will be grouped differently depending
      # on the number of replicas.
      for i, expected_result in enumerate(expected_results):
          final_result = []
          actual_result = results[i]
          for val in actual_result:
              final_result.extend(val.numpy())
          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 12 for calling the InputIterationTest obj's underlying member method code with example usage: obj.testDatasetAssertWithDynamicBatch(distribution) and returns: results, math_ops