code: def testDatasetIterationInFunction(self, distribution):
      with distribution.scope():
          a = variables.Variable(
              1.0,
              aggregation=variables.VariableAggregation.ONLY_FIRST_REPLICA,
          )
  
      def train_step(_):
          a.assign_add(1.0)
  
      @def_function.function
      def f_train_step(dist_dataset):
          number_of_steps = constant_op.constant(0.0)
          product_of_means = constant_op.constant(2.0)
          for (
              x
          ) in dist_dataset:  # loop with values modified each iteration
              number_of_steps += 1
              product_of_means *= math_ops.cast(
                  distribution.reduce("MEAN", x, axis=0),
                  product_of_means.dtype,
              )
  
          for y in dist_dataset:  # loop with no intermediate state
              distribution.run(train_step, args=(y,))
  
          return number_of_steps, product_of_means
  
      dataset = get_dataset_from_tensor_slices(
          [5.0, 6.0, 7.0, 8.0]
      ).batch(2)
      dist_dataset = distribution.experimental_distribute_dataset(
          dataset
      )
  
      number_of_steps, product_of_means = f_train_step(dist_dataset)
      self.assertEqual(2, number_of_steps.numpy())
      self.assertNear(
          (2 * (5 + 6) / 2 * (7 + 8) / 2),
          product_of_means.numpy(),
          1e-3,
      )
  
      # We set the initial value of `a` to 1 and iterate through the dataset 2
      # times(4/2 where 4 is the number of dataset elements and 2 is the batch
      # size). Hence the final result is 3.
      self.assertEqual(3.0, (a.numpy()))
  
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 11 for calling the InputIterationTest obj's underlying member method code with example usage: obj.testDatasetIterationInFunction(distribution) and returns: number_of_steps,  product_of_means