class_docstr: Interface that wraps a distributed training framework (Horovod, DDP).
  
  Distributed strategies modify the model and/or optimizer to coordinate gradient updates among multiple workers
  running in parallel. In most cases, these are using collective communication libraries pass messages between
  processes.
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docstr: Modifies the model to support distributed training and creates the optimizer.
  
  Args:
      model: The model to wrap for distributed training.
      trainer_config: The trainer configuration, which includes optimizer params.
      base_learning_rate: The base learning rate to init the optimizer, which may be scaled by the strategy.
  
  Returns:
      A tuple of the wrapped model and the optimizer.
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code: def prepare(
      self,
      model: nn.Module,
      trainer_config: ECDTrainerConfig,
      base_learning_rate: float,
  ) -> tuple[nn.Module, Optimizer]:
      """Modifies the model to support distributed training and creates the optimizer.
  
      Args:
          model: The model to wrap for distributed training.
          trainer_config: The trainer configuration, which includes optimizer params.
          base_learning_rate: The base learning rate to init the optimizer, which may be scaled by the strategy.
  
      Returns:
          A tuple of the wrapped model and the optimizer.
      """
      pass
  
imports: from __future__ import annotations
  import contextlib
  from abc import ABC, abstractmethod
  from typing import Any, Callable, TYPE_CHECKING
  import torch
  from torch import nn
  from torch.optim import Optimizer
  from ludwig.modules.optimization_modules import create_optimizer
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 1 for calling the DistributedStrategy obj's underlying member method code with example usage: obj.prepare(model, trainer_config, base_learning_rate) without return types