code: def set_seed(seed: int, device_specific: bool = False):
      """
      Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
  
      Args:
          seed (`int`):
              The seed to set.
          device_specific (`bool`, *optional*, defaults to `False`):
              Whether to differ the seed on each device slightly with `self.process_index`.
      """
      if device_specific:
          seed += AcceleratorState().process_index
      random.seed(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      if is_xpu_available():
          torch.xpu.manual_seed_all(seed)
      elif is_npu_available():
          torch.npu.manual_seed_all(seed)
      else:
          torch.cuda.manual_seed_all(seed)
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
          xm.set_rng_state(seed)
  
file_path: accelerate/src/accelerate/utils/random.py
imports: import random
  from typing import List, Optional, Union
  import numpy
  import torch
  from ..state import AcceleratorState
  from .constants import CUDA_DISTRIBUTED_TYPES
  from .dataclasses import DistributedType, RNGType
  from .imports import is_npu_available, is_tpu_available, is_xpu_available
label_desc: Use image node_id 1 for calling a global function with example usage: set_seed(seed, device_specific) without return types