class_docstr: Defines the interfaces required by the init_torch_dist_process_group().
  
  This is modeled after RayTrainerWorker, which allows arbitrary functions
  to be executed on a remote DDP worker.
class_docstr_tok: ["Defines","the","interfaces","required","by","the","init_torch_dist_process_group","(",")",".","This","is","modeled","after","RayTrainerWorker",",","which","allows","arbitrary","functions","to","be","executed","on","a","remote","DDP","worker","."]
docstr: Executes the input function and returns the output.
  
  Args:
      func: The function to execute.
      args, kwargs: The arguments to pass into func.
docstr_tok: ["Executes","the","input","function","and","returns","the","output",".","Args",":","func",":","The","function","to","execute",".","args",",","kwargs",":","The","arguments","to","pass","into","func","."]
code: def execute(self, func: Callable[..., T], *args, **kwargs) -> T:
      """Executes the input function and returns the output.
  
      Args:
          func: The function to execute.
          args, kwargs: The arguments to pass into func.
      """
      return func(*args, **kwargs)
  
file_path: ray/python/ray/air/util/torch_dist.py
imports: from abc import ABC
  from collections import defaultdict
  from datetime import timedelta
  import os
  import torch
  import torch.distributed
  from typing import Callable, List, T
  import ray
  from ray.actor import ActorHandle
  from ray.train._internal.utils import get_address_and_port
  from ray.train.constants import DEFAULT_NCCL_SOCKET_IFNAME
  from ray.air._internal.torch_utils import get_device
label_desc: Use image node_id 1 for calling the TorchDistributedWorker obj's underlying member method code with example usage: obj.execute(func) and returns: func