code: def testMultiClientIter(
      self, mesh_dims, idx_sharding, images_sharding
  ):
      num_batches = 4
      batch_size = 16
      num_elems = num_batches * batch_size
  
      images = stateless_random_ops.stateless_random_uniform(
          [num_elems, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS],
          seed=(1, 2),
          minval=0,
          maxval=255,
          dtype=dtypes.int32,
      )
      dataset = dataset_ops.DatasetV2.from_tensor_slices(images)
  
      # Enumerate the dataset elements to make it easier to identify the batches
      # returned by the DTensorDataset.
      dataset = dataset.enumerate()
  
      # Store a mapping of index to dataset elements which can be looked up later
      # to identify the batches returned by the DTensorDataset.
      all_elems = {idx.numpy(): elem for idx, elem in dataset}
  
      # Write the dataset and shard it among multiple files.
      dataset_paths = self.write_dataset(
          dataset, num_files=8, num_elems=num_elems
      )
  
      # Construct args for starmap.
      args = []
      mesh_dim_names, mesh_dim_sizes = zip(*mesh_dims)
      global_device_ids = test_util.create_device_ids_array(
          mesh_dim_sizes
      )
      device_ids_split = np.split(
          np.ravel(global_device_ids), NUM_CLIENTS
      )
      dtensor_jobs = [
          f"localhost:{self.server_ports[i]}"
          for i in range(NUM_CLIENTS)
      ]
  
      for client_id in range(NUM_CLIENTS):
          # Manually specify DTensor environment variables since we are in a test
          # environment.
          env = {
              config._DT_CLIENT_ID: str(client_id),
              config._DT_JOB_NAME: str(JOB_NAME),
              config._DT_JOBS: ",".join(dtensor_jobs),
          }
  
          local_device_ids = device_ids_split[client_id].tolist()
          local_devices = [
              device_spec.DeviceSpecV2(  # pylint: disable=g-complex-comprehension
                  job=JOB_NAME,
                  replica=0,
                  task=client_id,
                  device_type="CPU",
                  device_index=i,
              )
              for i in range(len(local_device_ids))
          ]
          mesh = Mesh(
              dim_names=mesh_dim_names,
              global_device_ids=global_device_ids,
              local_device_ids=local_device_ids,
              local_devices=local_devices,
          )
          idx_layout = Layout(idx_sharding, mesh)
          images_layout = Layout(images_sharding, mesh)
          batch_dim = (
              MESH_DIM_BATCH
              if MESH_DIM_BATCH in images_sharding
              else None
          )
  
          args.append(
              (
                  client_id,
                  self._testMethodName,
                  env,
                  NUM_DEVICES_PER_CLIENT,
                  self.cluster.dispatcher_address(),
                  self.worker_ports[client_id],
                  batch_size,
                  dataset_paths,
                  mesh,
                  batch_dim,
                  (idx_layout, images_layout),
              )
          )
  
      def get_results():
          # Run the DTensor client processes and get the DTensor dataset components.
          with mp_context.Pool(NUM_CLIENTS) as pool:
              results = pool.starmap(run_client, args)
              pool.close()
              pool.join()
  
          return results
  
      # TODO(b/271162918): fix multi-client use case.
      with self.assertRaises(NotImplementedError):
          results = get_results()
  
      return
      # pylint: disable=unreachable
  
      # Create a mesh on the main test process. The tensor components returned
      # from each DTensor client subprocess will be packed onto this mesh to
      # verify correctness.
      test_mesh = mesh_util.create_mesh(
          mesh_dims=mesh_dims,
          devices=[
              "CPU:%d" % i
              for i in range(NUM_CLIENTS * NUM_DEVICES_PER_CLIENT)
          ],
      )
      test_mesh = self.configTestMesh({"CPU": test_mesh})
      idx_test_layout = Layout(idx_sharding, test_mesh)
      images_test_layout = Layout(images_sharding, test_mesh)
  
      for batch_elems in zip(*results):
          # Collect the tensor components returned from each client.
          idx_components = []
          images_components = []
          for client_id in range(NUM_CLIENTS):
              local_idx, local_images = batch_elems[client_id]
              idx_components.extend(local_idx)
              images_components.extend(local_images)
  
          # Pack the dataset elements into a DTensor on the test mesh.
          d_idx = api.pack(idx_components, idx_test_layout)
          d_images = api.pack(images_components, images_test_layout)
  
          # Get the batch of elements from the original dataset using the element
          # indices.
          batch_stack = []
          for elem_idx in d_idx:
              batch_stack.append(all_elems.pop(elem_idx.numpy()))
          batch = array_ops_stack.stack(batch_stack)
  
          self.assertDTensorEqual(batch, images_test_layout, d_images)
  
      self.assertEmpty(
          all_elems, "Not all batches were returned by DTensorDataset."
      )
  
imports: import os
  from typing import Any, List, Mapping, Optional, Tuple
  from absl import logging
  from absl.testing import parameterized
  import numpy
  from tensorflow.core.example import example_pb2
  from tensorflow.core.example import feature_pb2
  from tensorflow.dtensor.python import accelerator_util
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import input_util
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import mesh_util
  from tensorflow.dtensor.python.tests import multi_client_test_util
  from tensorflow.dtensor.python.tests import test_backend_util
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.python.data.experimental.service import server_lib
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import readers
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import device_spec
  from tensorflow.python.framework import dtypes
  from tensorflow.python.lib.io import tf_record
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import gen_parsing_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.ops import parsing_config
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the MultiClientDTensorDatasetTest obj's underlying member method code with example usage: obj.testMultiClientIter(mesh_dims, idx_sharding, images_sharding) and returns: results