code: def prepare(
      self,
      model: nn.Module,
      trainer_config: "ECDTrainerConfig",
      base_learning_rate: float,
  ) -> Tuple[nn.Module, Optimizer]:
      return FSDP(model), create_optimizer(
          model, trainer_config.optimizer, base_learning_rate
      )
  
file_path: ludwig/ludwig/distributed/fsdp.py
imports: import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  import torch
  from torch import nn
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.optim import Optimizer
  from ludwig.distributed.ddp import DDPStrategy
  from ludwig.modules.optimization_modules import create_optimizer
label_desc: Use image node_id 2 for calling the FSDPStrategy obj's underlying member method code with example usage: obj.prepare(model, trainer_config, base_learning_rate) and returns: FSDP,  create_optimizer