code: def init_empty_weights(include_buffers: bool = None):
      """
      A context manager under which models are initialized with all parameters on the meta device, therefore creating an
      empty model. Useful when just initializing the model would blow the available RAM.
  
      Args:
          include_buffers (`bool`, *optional*):
              Whether or not to also put all buffers on the meta device while initializing.
  
      Example:
  
      ```python
      import torch.nn as nn
      from accelerate import init_empty_weights
  
      # Initialize a model with 100 billions parameters in no time and without using any RAM.
      with init_empty_weights():
          tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
      ```
  
      <Tip warning={true}>
  
      Any model created under this context manager has no weights. As such you can't do something like
      `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
      Make sure to overwrite the default device_map param for [`load_checkpoint_and_dispatch`], otherwise dispatch is not
      called.
  
      </Tip>
      """
      if include_buffers is None:
          include_buffers = parse_flag_from_env(
              "ACCELERATE_INIT_INCLUDE_BUFFERS", False
          )
      with init_on_device(
          torch.device("meta"), include_buffers=include_buffers
      ) as f:
          yield f
  
file_path: accelerate/src/accelerate/big_modeling.py
imports: import logging
  import os
  from contextlib import contextmanager
  from functools import wraps
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn
  from .hooks import AlignDevicesHook, CpuOffload, UserCpuOffloadHook, add_hook_to_module, attach_align_device_hook, attach_align_device_hook_on_blocks
  from .utils import OffloadedWeightsLoader, check_device_map, extract_submodules_state_dict, find_tied_parameters, get_balanced_memory, infer_auto_device_map, is_npu_available, is_torch_version, load_checkpoint_in_model, offload_state_dict, parse_flag_from_env, retie_parameters
label_desc: Use image node_id 1 for calling a global function with example usage: init_empty_weights(include_buffers) without return types