code: def on_training_start(
      self, worker_group: WorkerGroup, backend_config: BackendConfig
  ):
      worker_group.execute(_set_torch_distributed_env_vars)
  
imports: import logging
  import os
  from dataclasses import dataclass
  from datetime import timedelta
  from typing import Optional
  import torch
  import torch.distributed
  import ray
  from ray.train._internal.utils import get_address_and_port
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import Backend, BackendConfig
  from ray.train.constants import DEFAULT_NCCL_SOCKET_IFNAME
  from ray.util import PublicAPI
label_desc: Use image node_id 3 for calling the _TorchBackend obj's underlying member method code with example usage: obj.on_training_start(worker_group, backend_config) without return types