code: def prepare(
      self,
      model: nn.Module,
      trainer_config: "ECDTrainerConfig",
      base_learning_rate: float,
  ) -> Tuple[nn.Module, Optimizer]:
      optimizer = create_optimizer(
          model, trainer_config.optimizer, base_learning_rate
      )
      grad_accum_steps = (
          trainer_config.gradient_accumulation_steps
          if trainer_config.gradient_accumulation_steps != AUTO
          else 1
      )
      dist_optimizer = hvd.DistributedOptimizer(
          optimizer,
          named_parameters=model.named_parameters(),
          backward_passes_per_step=grad_accum_steps,
      )
      return model, dist_optimizer
  
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 2 for calling the HorovodStrategy obj's underlying member method code with example usage: obj.prepare(model, trainer_config, base_learning_rate) and returns: model,  dist_optimizer