code: def prepare(
      self,
      model: nn.Module,
      trainer_config: "ECDTrainerConfig",
      base_learning_rate: float,
  ) -> Tuple[nn.Module, Optimizer]:
      return DDP(model), create_optimizer(
          model, trainer_config.optimizer, base_learning_rate
      )
  
imports: import contextlib
  import logging
  import os
  import socket
  from typing import Any, Callable, Dict, Optional, Tuple, Type, TYPE_CHECKING, Union
  import torch
  import torch.distributed
  from ray.train.backend import BackendConfig
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.torch import TorchTrainer
  from torch import nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Optimizer
  from torchmetrics.utilities.distributed import gather_all_tensors
  from ludwig.distributed.base import DistributedStrategy
  from ludwig.modules.optimization_modules import create_optimizer
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 3 for calling the DDPStrategy obj's underlying member method code with example usage: obj.prepare(model, trainer_config, base_learning_rate) and returns: DDP,  create_optimizer