code: def save_accelerator_state(
      output_dir: str,
      model_states: List[dict],
      optimizers: list,
      schedulers: list,
      dataloaders: list,
      process_index: int,
      scaler: GradScaler = None,
      save_on_each_node: bool = False,
      safe_serialization: bool = True,
  ):
      """
      Saves the current states of the models, optimizers, scaler, and RNG generators to a given directory.
  
      <Tip>
  
      If `safe_serialization` is `True`, models will be saved with `safetensors` while the rest are saved using native
      `pickle`.
  
      </Tip>
  
      Args:
          output_dir (`str` or `os.PathLike`):
              The name of the folder to save all relevant weights and states.
          model_states (`List[torch.nn.Module]`):
              A list of model states
          optimizers (`List[torch.optim.Optimizer]`):
              A list of optimizer instances
          schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`):
              A list of learning rate schedulers
          dataloaders (`List[torch.utils.data.DataLoader]`):
              A list of dataloader instances to save their sampler states
          process_index (`int`):
              The current process index in the Accelerator state
          scaler (`torch.cuda.amp.GradScaler`, *optional*):
              An optional gradient scaler instance to save
          save_on_each_node (`bool`, *optional*):
              Whether to save on every node, or only the main node.
          safe_serialization (`bool`, *optional*, defaults to `True`):
              Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
      """
      output_dir = Path(output_dir)
      # Model states
      for i, state in enumerate(model_states):
          weights_name = (
              WEIGHTS_NAME
              if not safe_serialization
              else SAFE_WEIGHTS_NAME
          )
          if i > 0:
              weights_name = weights_name.replace(".", f"_{i}.")
          output_model_file = output_dir.joinpath(weights_name)
          save(
              state,
              output_model_file,
              save_on_each_node=save_on_each_node,
              safe_serialization=safe_serialization,
          )
          logger.info(f"Model weights saved in {output_model_file}")
      # Optimizer states
      for i, opt in enumerate(optimizers):
          state = opt.state_dict()
          optimizer_name = (
              f"{OPTIMIZER_NAME}.bin"
              if i == 0
              else f"{OPTIMIZER_NAME}_{i}.bin"
          )
          output_optimizer_file = output_dir.joinpath(optimizer_name)
          save(
              state,
              output_optimizer_file,
              save_on_each_node=save_on_each_node,
              safe_serialization=False,
          )
          logger.info(
              f"Optimizer state saved in {output_optimizer_file}"
          )
      # Scheduler states
      for i, scheduler in enumerate(schedulers):
          state = scheduler.state_dict()
          scheduler_name = (
              f"{SCHEDULER_NAME}.bin"
              if i == 0
              else f"{SCHEDULER_NAME}_{i}.bin"
          )
          output_scheduler_file = output_dir.joinpath(scheduler_name)
          save(
              state,
              output_scheduler_file,
              save_on_each_node=save_on_each_node,
              safe_serialization=False,
          )
          logger.info(
              f"Scheduler state saved in {output_scheduler_file}"
          )
      # DataLoader states
      for i, dataloader in enumerate(dataloaders):
          sampler_name = (
              f"{SAMPLER_NAME}.bin"
              if i == 0
              else f"{SAMPLER_NAME}_{i}.bin"
          )
          output_sampler_file = output_dir.joinpath(sampler_name)
          # Only save if we have our custom sampler
          from .data_loader import (
              IterableDatasetShard,
              SeedableRandomSampler,
          )
  
          if isinstance(dataloader.dataset, IterableDatasetShard):
              sampler = dataloader.sampler.sampler
  
              if isinstance(sampler, SeedableRandomSampler):
                  save(
                      sampler,
                      output_sampler_file,
                      save_on_each_node=save_on_each_node,
                      safe_serialization=False,
                  )
          logger.info(
              f"Sampler state for dataloader {i} saved in {output_sampler_file}"
          )
  
      # GradScaler state
      if scaler is not None:
          state = scaler.state_dict()
          output_scaler_file = output_dir.joinpath(SCALER_NAME)
          torch.save(state, output_scaler_file)
          logger.info(
              f"Gradient scaler state saved in {output_scaler_file}"
          )
      # Random number generator states
      states = {}
      states_name = f"{RNG_STATE_NAME}_{process_index}.pkl"
      states["random_state"] = random.getstate()
      states["numpy_random_seed"] = np.random.get_state()
      states["torch_manual_seed"] = torch.get_rng_state()
      if is_xpu_available():
          states[
              "torch_xpu_manual_seed"
          ] = torch.xpu.get_rng_state_all()
      else:
          states[
              "torch_cuda_manual_seed"
          ] = torch.cuda.get_rng_state_all()
      if is_tpu_available():
          states["xm_seed"] = xm.get_rng_state()
      output_states_file = output_dir.joinpath(states_name)
      torch.save(states, output_states_file)
      logger.info(f"Random states saved in {output_states_file}")
      return output_dir
  
file_path: accelerate/src/accelerate/checkpointing.py
imports: import random
  from pathlib import Path
  from typing import List
  import numpy
  import torch
  from safetensors.torch import load_file
  from torch.cuda.amp import GradScaler
  from .utils import MODEL_NAME, OPTIMIZER_NAME, RNG_STATE_NAME, SAFE_MODEL_NAME, SAFE_WEIGHTS_NAME, SAMPLER_NAME, SCALER_NAME, SCHEDULER_NAME, WEIGHTS_NAME, get_pretty_name, is_tpu_available, is_xpu_available, save
  from .logging import get_logger
  from .state import PartialState
label_desc: Use image node_id 1 for calling a global function with example usage: save_accelerator_state(output_dir, model_states, optimizers, schedulers, dataloaders, process_index, scaler, save_on_each_node, safe_serialization) and returns: output_dir