code: def testFromFunctionInputSignatureForPerReplicaValuesWithOptions(
      self,
      distribution,
      enable_get_next_as_optional,
      experimental_place_dataset_on_device,
      experimental_fetch_to_device,
  ):
      if (
          experimental_place_dataset_on_device
          and experimental_fetch_to_device
      ):
          self.skipTest(
              "Setting experimental_place_dataset_on_device and "
              "experimental_fetch_to_device to `True` is not "
              "allowed when using "
              "distribute_lib.InputReplicationMode.PER_REPLICA."
          )
  
      fname1 = os.path.join(self.get_temp_dir(), "1.txt")
      _create_text_file(fname1, 5)
      fname2 = os.path.join(self.get_temp_dir(), "2.txt")
      _create_text_file(fname2, 9)
  
      def dataset_fn(input_context):
          dataset = dataset_ops.DatasetV2.from_tensor_slices(
              [fname1, fname2]
          )
          dataset = dataset.shard(
              input_context.num_input_pipelines,
              input_context.input_pipeline_id,
          )
          return (
              readers.TextLineDatasetV2(dataset)
              .map(string_ops.string_to_number)
              .batch(input_context.get_per_replica_batch_size(4))
          )
  
      options = distribute_lib.InputOptions(
          experimental_place_dataset_on_device=(
              experimental_place_dataset_on_device
          ),
          experimental_fetch_to_device=experimental_fetch_to_device,
          experimental_replication_mode=(
              distribute_lib.InputReplicationMode.PER_REPLICA
          ),
      )
  
      distribution.extended.experimental_enable_get_next_as_optional = (
          enable_get_next_as_optional
      )
      ds = distribution.experimental_distribute_datasets_from_function(
          dataset_fn, options
      )
  
      iterator = iter(ds)
      _check_type_spec_structure(iterator)
      spec = iterator._type_spec
      tensor_list = spec._to_components(iterator)
      re_iterator = spec._from_components(tensor_list)
  
      _check_type_spec_structure(iter(ds))
      element_spec = ds.element_spec
      iter_element_spec = iter(ds).element_spec
      nest.assert_same_structure(element_spec, iter_element_spec)
      self.assertAllEqual(
          nest.flatten(element_spec), nest.flatten(iter_element_spec)
      )
      self.assertEqual(
          iterator._input_workers, re_iterator._input_workers
      )
      self.assertAllEqual(iterator._iterators, re_iterator._iterators)
  
      @def_function.function(input_signature=[element_spec])
      def process_inputs(inputs):
          distribution.run(lambda inputs: inputs, args=(inputs,))
  
      for x in ds:
          process_inputs(x)
  
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 4 for calling the InputTypeSpecTest obj's underlying member method code with example usage: obj.testFromFunctionInputSignatureForPerReplicaValuesWithOptions(distribution, enable_get_next_as_optional, experimental_place_dataset_on_device, experimental_fetch_to_device) and returns: readers