code: def _all_gather(
      obj, worker_names=None, timeout: float = UNSET_RPC_TIMEOUT
  ):
      r"""
      This is similar to torch.distributed.all_gather(), but is using RPC. It
      picks the worker with the smallest name (alphabetic order) as the leader.
      Then all followers send their data ``obj`` to the leader. After the leader
      has received all, it will broadcast the results back to all followers. This
      function blocks until all workers have received the gathered results.
      """
      if not worker_names:
          assert (
              _ALL_WORKER_NAMES is not None
          ), "`_ALL_WORKER_NAMES` is not initialized for `def _all_gather`."
          worker_names = _ALL_WORKER_NAMES
      leader_name = min(worker_names)
  
      self_name = _get_current_rpc_agent().get_worker_info().name
  
      with _all_gather_dict_lock:
          concat_names = "".join(sorted(worker_names))
          sequence_num = _all_gather_sequence_id.get(concat_names, 0)
          _all_gather_sequence_id[concat_names] = sequence_num + 1
          sequence_id = concat_names + str(sequence_num)
  
      is_leader = leader_name == self_name
  
      if timeout == UNSET_RPC_TIMEOUT:
          # Timeout is specified by agent for RPC calls
          rpc_timeout = get_rpc_timeout()
          # No timeout for signal
          signal_timeout = None
      elif timeout == DEFAULT_SHUTDOWN_TIMEOUT:
          # No timeout for RPC
          rpc_timeout = timeout
          # No timeout for signal
          signal_timeout = None
      else:
          # Signal and RPC timeout use the same timeout
          signal_timeout = rpc_timeout = timeout
  
      # Phase 1: Followers send it's object to the leader
      if is_leader:
          _gather_to_leader(sequence_id, self_name, obj, worker_names)
      else:
          rpc_sync(
              leader_name,
              _gather_to_leader,
              args=(sequence_id, self_name, obj, worker_names),
              timeout=rpc_timeout,
          )
  
      with _all_gather_dict_lock:
          states = _all_gather_sequence_id_to_states[sequence_id]
  
      # Timeout is either set by function parameter or None (which is indefinite)
      states.proceed_signal.wait(timeout=signal_timeout)
  
      # Phase 2: Leader broadcast gathered results to all followers
      # Leader's signal is the first to be unblocked, after receiving all
      # followers' data objects.
      if is_leader:
          worker_name_to_response_future_dict = {}
          for follower_name in worker_names - {leader_name}:
              fut = rpc_async(
                  follower_name,
                  _broadcast_to_followers,
                  args=(sequence_id, states.gathered_objects),
                  timeout=rpc_timeout,
              )
              worker_name_to_response_future_dict[follower_name] = fut
  
          errors = []
          for (
              follower_name,
              fut,
          ) in worker_name_to_response_future_dict.items():
              try:
                  fut.wait()
              except RuntimeError as ex:
                  errors.append((follower_name, ex))
  
          if errors:
              raise RuntimeError(
                  f"Followers {[e[0] for e in errors]} timed out in _all_gather "
                  f"after {rpc_timeout:.2f} seconds. The first exception is {errors[0][1]}"
              )
  
      # Clean up for the states using the sequence_id
      with _all_gather_dict_lock:
          states = _all_gather_sequence_id_to_states.pop(sequence_id)
      return states.gathered_objects
  
imports: import collections
  import contextlib
  import functools
  import inspect
  import logging
  import threading
  from typing import Dict, Generic, TypeVar, Set, Any, TYPE_CHECKING
  import torch
  from torch.futures import Future
  from torch._C._distributed_rpc import PyRRef, RemoteProfilerManager, WorkerInfo, TensorPipeAgent, get_rpc_timeout, _cleanup_python_rpc_handler, _delete_all_user_and_unforked_owner_rrefs, _destroy_rref_context, _get_current_rpc_agent, _invoke_remote_builtin, _invoke_remote_python_udf, _invoke_remote_torchscript, _invoke_rpc_builtin, _invoke_rpc_python_udf, _invoke_rpc_torchscript, _is_current_rpc_agent_set, _reset_current_rpc_agent, _set_and_start_rpc_agent
  from .internal import PythonUDF, RPCExecMode, _internal_rpc_pickler, _build_rpc_profiling_key
  from .constants import DEFAULT_SHUTDOWN_TIMEOUT, UNSET_RPC_TIMEOUT
  from ._utils import _group_membership_management, _update_group_membership
label_desc: Use image node_id 7 for calling a global function with example usage: _all_gather(obj, worker_names, timeout) and returns: states