code: def host_local_array_to_global_array(
      local_inputs: Any, global_mesh: jax.sharding.Mesh, pspecs: Any
  ):
      r"""Converts a host local value to a globally sharded jax.Array.
  
      This function takes host-local data (which might be different
      across hosts), and populates a global array with this data, where each
      device on each host, get the appropriate slice of the data according to
      sharding defined by the global_mesh/pspects.
  
      For example:
  
      >>> global_mesh = jax.sharding.Mesh(jax.devices(), 'x')
      >>> pspecs = jax.sharding.PartitionSpec('x')
      >>> host_id = jax.process_index()
      >>> arr = host_local_array_to_global_array(np.arange(4) * host_id, mesh, pspecs)  # NB: assumes jax.local_device_count() divides 4.   # doctest: +SKIP
  
      The resulting array will have the shape (4 * num_processes) and will
      have distributed value of: (0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9, ... ),
      where each slice np.arange(4) * host_id will be partitioned across the
      corresponding host's devices.
  
      Similarly:
  
      >>> mesh = jax.sharding.Mesh(np.array(jax.devices()).reshape(jax.process_count(), jax.local_device_count()), ['host', 'dev'])
      >>> pspecs = jax.sharding.PartitionSpec('host')
      >>> host_id = jax.process_index()
      >>> arr = host_local_array_to_global_array(np.arange(4) * host_id, mesh, pspecs)  # doctest: +SKIP
  
      will create the same distributed value (0, 1, 2, 3, 0, 2, 4, 6, ...),
      however each slice np.arange(4) * i will be *replicated* across corresponding
      host devices.
  
      On the other hand, if pspecs = PartitionSpec(), which means
      replication across all axes, then this snippet:
  
      >>> pspecs = jax.sharding.PartitionSpec()
      >>> arr = host_local_array_to_global_array(np.arange(4), mesh, pspecs)  # doctest: +SKIP
  
      will have the shape (4,) and the value (0, 1, 2, 3) will be replicated
      across all hosts and devices.
  
      It is an undefined behavior to have not identical local_inputs with pspec
      indicating data replication.
  
      You can use this function to transition to jax.Array. Using jax.Array with
      pjit has the same semantics of using GDA with pjit i.e. all jax.Array
      inputs to pjit should be globally shaped.
  
      If you are currently passing host local values to pjit, you can use this
      function to convert your host local values to global Arrays and then pass that
      to pjit.
  
  
      Example usage.
  
      >>> from jax.experimental import multihost_utils # doctest: +SKIP
      >>>
      >>> global_inputs = multihost_utils.host_local_array_to_global_array(host_local_inputs, global_mesh, in_pspecs) # doctest: +SKIP
      >>>
      >>> with mesh: # doctest: +SKIP
      >>>   global_out = pjitted_fun(global_inputs) # doctest: +SKIP
      >>>
      >>> host_local_output = multihost_utils.global_array_to_host_local_array(global_out, mesh, out_pspecs) # doctest: +SKIP
  
      Please note ths function requires global mesh to be a continuous mesh, meaning
      that  devices that belong to each host should form a subcube in this mesh.
      To move local data to global array with non-continuous mesh use
      jax.make_array_from_callback or jax.make_array_from_single_device_arrays
      instead.
  
      Args:
        local_inputs: A Pytree of host local values.
        global_mesh: A jax.sharding.Mesh object. The mesh must be a contiguous mesh,
        that is all hosts' devices must form a subcube in this mesh.
        pspecs: A Pytree of jax.sharding.PartitionSpec's.
  
      Returns:
        A pytree of global arrays.
      """
      flat_inps, in_tree = tree_flatten(local_inputs)
      in_pspecs = _flatten_pspecs(
          "input pspecs", in_tree, pjit_lib.hashable_pytree(pspecs)
      )
      out_flat = [
          host_local_array_to_global_array_p.bind(
              inp, global_mesh=global_mesh, pspec=in_spec
          )
          for inp, in_spec in safe_zip(flat_inps, in_pspecs)
      ]
      return tree_unflatten(in_tree, out_flat)
  
imports: from __future__ import annotations
  from functools import partial, lru_cache
  from typing import Optional
  import zlib
  from typing import Any
  import jax
  import jax.numpy
  from jax.tree_util import tree_flatten, tree_map, tree_unflatten
  from jax._src import core
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.interpreters import mlir
  from jax._src import array
  from jax._src import sharding_impls
  from jax._src.interpreters import pxla
  from jax.interpreters import xla
  from jax._src import pjit
  from jax.experimental.pjit import pjit
  from jax.sharding import PartitionSpec
  from jax._src import distributed
  from jax._src.util import safe_zip
  import numpy
label_desc: Use image node_id 13 for calling a global function with example usage: host_local_array_to_global_array(local_inputs, global_mesh, pspecs) and returns: tree_unflatten