code: def get_trainer_cls(
      cls, backend_config: BackendConfig
  ) -> Tuple[Type[DataParallelTrainer], Dict[str, Any]]:
      if not _ray220:
          from ludwig.distributed._ray_210_compat import (
              HorovodTrainerRay210,
          )
  
          return HorovodTrainerRay210, dict(
              horovod_config=backend_config
          )
  
      return HorovodTrainer, dict(horovod_config=backend_config)
  
imports: import contextlib
  import logging
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TYPE_CHECKING
  import horovod.torch
  import ray
  import torch
  from horovod.torch.optimizer import _DistributedOptimizer
  from packaging import version
  from ray.train.backend import BackendConfig
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.horovod import HorovodTrainer
  from torch import nn
  from torch.optim import Optimizer
  from ludwig.constants import AUTO
  from ludwig.distributed.base import DistributedStrategy
  from ludwig.modules.optimization_modules import create_optimizer
  from ludwig.utils.horovod_utils import gather_all_tensors, is_distributed_available
label_desc: Use image node_id 19 for calling the HorovodStrategy obj's underlying member method code with example usage: obj.get_trainer_cls(cls, backend_config) and returns: HorovodTrainer,  dict, HorovodTrainerRay210,  dict