code: def process_allgather(in_tree: Any, tiled: bool = False) -> Any:
      """Gather data from across processes.
  
      Args:
        in_tree: pytree of arrays - each array _must_ have the same shape across the
          hosts.
        tiled: Whether to stack or concat the output. Defaults to False i.e. stack
          into a new positional axis at index 0.
  
      Returns:
        Pytrees of numpy arrays.
          * If the input is a non-fully addressable jax.Array, then the data is
            fully replicated.
          * If the input is numpy array or fully addressable jax.Array, then the
            output shape is dependent on the `tiled` argument.
            If its False, then the output will be stacked else concatenated.
          * If the input is a scalar, then the output will be stacked.
      """
  
      def _pjit(inp):
          return _handle_array_process_allgather(inp, tiled)
  
      return jax.tree_map(_pjit, in_tree)
  
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 6 for calling a global function with example usage: process_allgather(in_tree, tiled) and returns: jax, _handle_array_process_allgather