code: def single_machine_run(params):
      """Main function for distributed implementation on a single machine
  
      Parameters:
      -----------
      params : argparser object
          Argument Parser structure with pre-determined arguments as defined
          at the bottom of this file.
      """
      processes = []
      mp.set_start_method("spawn")
  
      # Invoke `target` function from each of the spawned process for distributed
      # implementation
      for rank in range(params.world_size):
          p = mp.Process(
              target=run,
              args=(
                  rank,
                  params.world_size,
                  gen_dist_partitions,
                  params,
              ),
          )
          p.start()
          processes.append(p)
  
      for p in processes:
          p.join()
  
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 9 for calling a global function with example usage: single_machine_run(params) without return types