code: def multi_machine_run(params):
      """
      Function to be invoked when executing data loading pipeline on multiple machines
  
      Parameters:
      -----------
      params : argparser object
          argparser object providing access to command line arguments.
      """
      rank = int(os.environ["RANK"])
  
      # init the gloo process group here.
      dist.init_process_group(
          backend="gloo",
          rank=rank,
          world_size=params.world_size,
          timeout=timedelta(seconds=params.process_group_timeout),
      )
      logging.info(
          f"[Rank: {rank}] Done with process group initialization..."
      )
  
      # invoke the main function here.
      gen_dist_partitions(rank, params.world_size, params)
      logging.info(
          f"[Rank: {rank}] Done with Distributed data processing pipeline processing."
      )
  
imports: import gc
  import logging
  import math
  import os
  import sys
  from datetime import timedelta
  from timeit import default_timer
  import constants
  import dgl
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from convert_partition import create_dgl_object, create_metadata_json
  from dataset_utils import get_dataset
  from dist_lookup import DistLookupService
  from globalids import assign_shuffle_global_nids_edges, assign_shuffle_global_nids_nodes, lookup_shuffle_global_nids_edges
  from gloo_wrapper import allgather_sizes, alltoallv_cpu, gather_metadata_json
  from utils import augment_edge_data, DATA_TYPE_ID, get_edge_types, get_etype_featnames, get_gid_offsets, get_gnid_range_map, get_idranges, get_node_types, get_ntype_counts_map, get_ntype_featnames, map_partid_rank, memory_snapshot, read_json, read_ntype_partition_files, REV_DATA_TYPE_ID, write_dgl_objects, write_metadata_json
label_desc: Use image node_id 11 for calling a global function with example usage: multi_machine_run(params) without return types