class_docstr: 复原了 paddle 的 launch_collective 函数，将其简化后集成到一个类里
  仅支持每个机器单卡的情况。
class_docstr_tok: ["\u590d\u539f\u4e86","paddle","\u7684","launch_collective","\u51fd\u6570\uff0c\u5c06\u5176\u7b80\u5316\u540e\u96c6\u6210\u5230\u4e00\u4e2a\u7c7b\u91cc","\u4ec5\u652f\u6301\u6bcf\u4e2a\u673a\u5668\u5355\u5361\u7684\u60c5\u51b5\u3002"]
docstr: 用于启动分布式进程。
  首先设置 PaddlePaddle 分布式训练需要设置的环境变量，然后建立新的子进程
docstr_tok: ["\u7528\u4e8e\u542f\u52a8\u5206\u5e03\u5f0f\u8fdb\u7a0b\u3002","\u9996\u5148\u8bbe\u7f6e","PaddlePaddle","\u5206\u5e03\u5f0f\u8bad\u7ec3\u9700\u8981\u8bbe\u7f6e\u7684\u73af\u5883\u53d8\u91cf\uff0c\u7136\u540e\u5efa\u7acb\u65b0\u7684\u5b50\u8fdb\u7a0b"]
code: def launch(self):
      """
      用于启动分布式进程。
      首先设置 PaddlePaddle 分布式训练需要设置的环境变量，然后建立新的子进程
      """
      # 设置环境变量
      self.global_envs = self.get_global_env()
      self.open_subprocess()
  
imports: import os
  import sys
  import __main__
  import tempfile
  import copy
  from typing import List
  from fastNLP.core.drivers.utils import distributed_open_proc
  from fastNLP.envs.env import FASTNLP_DISTRIBUTED_CHECK, FASTNLP_LOG_LEVEL, FASTNLP_GLOBAL_SEED, FASTNLP_GLOBAL_RANK
  from fastNLP.core.utils import get_paddle_device_id
  from .utils import find_free_ports
label_desc: Use image node_id 3 for calling the FleetLauncher obj's underlying member method code with example usage: obj.launch() without return types