code: def prepare(
      self,
      model: nn.Module,
      trainer_config: "ECDTrainerConfig",
      base_learning_rate: float,
  ) -> Tuple[nn.Module, Optimizer]:
      # If `batch_size=auto`, we set to MIN_POSSIBLE_BATCH_SIZE temporarily until auto-tuning adjusts it`
      # We can really set it to be whatever we want, as it will be overridden by the auto-tuning.
      batch_size = (
          trainer_config.batch_size
          if isinstance(trainer_config.batch_size, int)
          else MIN_POSSIBLE_BATCH_SIZE
      )
      # Paged and 8-bit optimizers are not supported by Deepspeed - just whatever is supported
      # by torch.optim.Optimizer. https://www.deepspeed.ai/docs/config-json/#optimizer-parameters.
      if (
          trainer_config.optimizer.is_paged
          or trainer_config.optimizer.is_8bit
      ):
          raise ValueError(
              "Cannot use a paged or 8-bit optimizer with DeepSpeed."
          )
      optimizer_cls, optimizer_kwargs = get_optimizer_class_and_kwargs(
          trainer_config.optimizer, base_learning_rate
      )
      ds_config = {
          "amp": {
              "enabled": trainer_config.use_mixed_precision,
          },
          "optimizer": {
              "type": optimizer_cls.__name__,
              "params": optimizer_kwargs,
          },
          "zero_optimization": self.zero_optimization,
          "gradient_clipping": trainer_config.gradient_clipping.clipglobalnorm,
          "train_micro_batch_size_per_gpu": batch_size,
          "gradient_accumulation_steps": trainer_config.gradient_accumulation_steps,
          "steps_per_print": trainer_config.steps_per_checkpoint
          or 10000,
      }
  
      # DeepSpeed doesn't like passing these params as None values
      if self.fp16 is not None:
          ds_config["fp16"] = self.fp16
      if self.bf16 is not None:
          ds_config["bf16"] = self.bf16
      if self.compression_training is not None:
          ds_config["compression_training"] = self.compression_training
  
      model_engine, optimizer, _, _ = deepspeed.initialize(
          model=model,
          model_parameters=model.parameters(),
          lr_scheduler=None,  # Don't let DeepSpeed manage the learning rate scheduler
          config=ds_config,
          dist_init_required=False,
      )
  
      if hasattr(optimizer, "optimizer"):
          # Zero-3 wraps the optimizer
          optimizer = optimizer.optimizer
  
      return model_engine, optimizer
  
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, List, Mapping, Optional, Tuple, TYPE_CHECKING, Union
  import deepspeed
  import deepspeed.comm
  import torch
  from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
  from packaging import version
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from ludwig.constants import MIN_POSSIBLE_BATCH_SIZE
  from ludwig.distributed.ddp import DDPStrategy
  from ludwig.modules.optimization_modules import get_optimizer_class_and_kwargs
  from ludwig.utils.checkpoint_utils import Checkpoint
  from ludwig.utils.model_utils import extract_tensors, replace_tensors
label_desc: Use image node_id 3 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj.prepare(model, trainer_config, base_learning_rate) and returns: model_engine,  optimizer