code: def initialize(self):
      # Unlike when we use the Ray backend, we need to initialize the `torch.distributed` context so we can
      # broadcast, allgather, etc. before preparing the model within the trainer.
      deepspeed.init_distributed()
      self._distributed = init_dist_strategy(
          self.BACKEND_TYPE,
          zero_optimization=self.zero_optimization,
          fp16=self.fp16,
          bf16=self.bf16,
          compression_training=self.compression_training,
      )
  
file_path: ludwig/ludwig/backend/deepspeed.py
imports: from typing import Any, Dict, Optional, Type
  import deepspeed
  from ludwig.backend.base import DataParallelBackend
  from ludwig.constants import FALLBACK_BATCH_SIZE
  from ludwig.distributed import init_dist_strategy
  from ludwig.utils.batch_size_tuner import BatchSizeEvaluator
label_desc: Use image node_id 2 for calling the DeepSpeedBackend obj's underlying member method code with example usage: obj.initialize() without return types