code: def _pack_iterator_resource_dtensor(
      datasets: List[Tuple[int, data_types.DatasetV2]],
      layouts: Any,
      mesh: layout_lib.Mesh,
      num_local_devices_per_replica: int,
  ):
      """Creates a DTensor iterator resource for the per-replica datasets.
  
      Given a list of replica ID to tf.data.Dataset mappings, this function creates
      iterators for each device and then packs the underlying iterator resource
      tensors into a single DTensor. This resource tensor is used by the
      IteratorGetNext op to retrieve the next element in the dataset.
  
      Args:
        datasets: a list of tuples of each unique local replica ID to the dataset
          object whose elements will be placed on the devices corresponding to that
          replica.
        layouts: a structure of DTensor layouts to be applied to the elements
          returned by the underlying iterators. This can be a single layout or
          (possibly nested) tuples or dictionaries of layouts, and the structure
          must match the structure of the iterator elements.
        mesh: the DTensor mesh to place the iterator batches on.
        num_local_devices_per_replica: the number of devices in each data-parallel
          replica.
  
      Returns:
        A DTensor of the underlying iterator resource tensors.
      """
      host_mesh_devices = mesh.host_mesh().local_devices()
      device_idx = 0
  
      iterators = []
      for _, dataset in datasets:
          for idx in range(num_local_devices_per_replica):
              with ops.device_v2(host_mesh_devices[device_idx]):
                  device_dataset = dataset.shard(
                      num_shards=num_local_devices_per_replica,
                      index=idx,
                  )
                  iterators.append(iter(device_dataset))
              device_idx += 1
  
      if device_idx != len(host_mesh_devices):
          raise ValueError(
              "The `datasets` argument does not have the correct number of"
              f" underlying datasets, found {device_idx} but expected"
              f" {len(host_mesh_devices)}."
          )
  
      host_layouts = nest.map_structure(
          lambda l: layout_lib.Layout(
              l.sharding_specs, mesh.host_mesh()
          ),
          layouts,
      )
  
      # Pack the iterator resource tensors into a replicated 0-dimensional DTensor
      # and set the element layouts.
      iterator_resources = [
          it._iterator_resource for it in iterators
      ]  # pylint: disable=protected-access
      d_iterator_resource = api.pack(
          iterator_resources,
          layout_lib.Layout.replicated(mesh=mesh.host_mesh(), rank=0),
      )
      api._dtensor_device().set_iterator_element_layouts(  # pylint: disable=protected-access
          d_iterator_resource, nest.flatten(host_layouts)
      )
  
      return d_iterator_resource
  
imports: import dataclasses
  import operator
  from typing import Any, List, Optional, Sequence, Tuple
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import layout
  from tensorflow.python.data.experimental.ops import data_service_ops
  from tensorflow.python.data.experimental.ops import distribute
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  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.framework import 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 math_ops
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling a global function with example usage: _pack_iterator_resource_dtensor(datasets, layouts, mesh, num_local_devices_per_replica) and returns: d_iterator_resource