code: def testDoesNotTriggerFunctionTracing(
      self, distribution, enable_get_next_as_optional
  ):
      trace_count = [0]
  
      @def_function.function
      def f(iterator):
          trace_count[0] += 1
          counter = np.int64(0)
          for _ in range(5):
              next(iterator)
              counter += 1
          return counter
  
      ctx = distribute_lib.InputContext()
      batch_size = ctx.get_per_replica_batch_size(8)
      # Use 20 which isn't divisible by 8 to test partial batch behavior.
      row_lengths = np.mod(np.arange(50), 4).astype(np.int64)
      ragged_tensor = ragged_tensor_lib.RaggedTensor.from_row_lengths(
          np.repeat(np.arange(50, dtype=np.float32), row_lengths),
          row_lengths,
      )
      dataset = dataset_ops.DatasetV2.from_tensor_slices(
          {
              "dense": ragged_tensor.to_tensor(),
              "ragged": ragged_tensor,
              "sparse": ragged_tensor.to_sparse(),
          }
      )
      dataset = dataset.shard(
          ctx.num_input_pipelines, ctx.input_pipeline_id
      )
      dataset = dataset.batch(batch_size)
  
      distribution.extended.experimental_enable_get_next_as_optional = (
          enable_get_next_as_optional
      )
  
      if isinstance(
          distribution,
          (tpu_strategy.TPUStrategyV2, tpu_strategy.TPUStrategy),
      ):
          # TPUStrategy does not support distributed datasets with device prefetch
          # when using sparse or ragged tensors.
          options = distribute_lib.InputOptions(
              experimental_fetch_to_device=False
          )
      else:
          options = None
  
      dist_dataset = distribution.experimental_distribute_dataset(
          dataset, options
      )
      with distribution.scope():
          for _ in range(3):
              iterator = iter(dist_dataset)
              _check_type_spec_structure(iterator)
              counter = f(iterator)
  
              self.assertEqual(trace_count[0], 1)
              self.assertEqual(counter, 5)
  
imports: import os
  from absl.testing import parameterized
  import numpy
  from tensorflow.python import tf2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import readers
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import test_util
  from tensorflow.python.distribute import tpu_strategy
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the RaggedTensorDistributedIteratorTest obj's underlying member method code with example usage: obj.testDoesNotTriggerFunctionTracing(distribution, enable_get_next_as_optional) and returns: counter