docstr: Setup a model and multiple optimizers together.
  
  Currently only a single optimizer is supported.
  
  Return:
      The model wrapped into a :class:`deepspeed.DeepSpeedEngine` and a list with a single
      deepspeed optimizer.
docstr_tok: ["Setup","a","model","and","multiple","optimizers","together",".","Currently","only","a","single","optimizer","is","supported",".","Return",":","The","model","wrapped","into","a",":","class",":","`","deepspeed.DeepSpeedEngine","`","and","a","list","with","a","single","deepspeed","optimizer","."]
code: def _setup_model_and_optimizers(
      self, model: Module, optimizers: List[Optimizer]
  ) -> Tuple["deepspeed.DeepSpeedEngine", List[Optimizer]]:
      """Setup a model and multiple optimizers together.
  
      Currently only a single optimizer is supported.
  
      Return:
          The model wrapped into a :class:`deepspeed.DeepSpeedEngine` and a list with a single
          deepspeed optimizer.
  
      """
      if len(optimizers) != 1:
          raise ValueError(
              f"Currently only one optimizer is supported with DeepSpeed."
              f" Got {len(optimizers)} optimizers instead."
          )
  
      # train_micro_batch_size_per_gpu is used for throughput logging purposes
      # normally we set this to the batch size, but it is not available here unless the user provides it
      # as part of the config
      assert self.config is not None
      self.config.setdefault("train_micro_batch_size_per_gpu", 1)
      self.model, optimizer = self._setup_model_and_optimizer(
          model, optimizers[0]
      )
      self._set_deepspeed_activation_checkpointing()
      return self.model, [optimizer]
  
imports: import argparse
  import json
  import logging
  import os
  import platform
  from collections import OrderedDict
  from contextlib import contextmanager
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
  import torch
  from torch.nn import Module
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins import ClusterEnvironment
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.strategies.deepspeed import _DEEPSPEED_AVAILABLE, _format_precision_config, _validate_checkpoint_directory, _validate_device_index_selection
  from lightning.fabric.utilities.optimizer import _optimizers_to_device
  from lightning.fabric.utilities.seed import reset_seed
  from lightning.fabric.utilities.types import _PATH, LRScheduler, ReduceLROnPlateau
  from lightning.pytorch.accelerators.cuda import CUDAAccelerator
  from lightning.pytorch.core.optimizer import _init_optimizers_and_lr_schedulers
  from lightning.pytorch.plugins.precision import Precision
  from lightning.pytorch.strategies.ddp import DDPStrategy
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities import GradClipAlgorithmType
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_info, rank_zero_warn
  from lightning.pytorch.utilities.types import LRSchedulerConfig
label_desc: Use image node_id 8 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj._setup_model_and_optimizers(model, optimizers) and returns: self,  list