code: def low_memory_init():
      init_contexts = []
      init_contexts.extend(
          [init_empty_weights(), DisableTorchAllocTensor()]
      )
      # Load everything except Tensors' parameters
      init_contexts.append(LazyLoadTensors())
      # As we have muted the `torch.load`, this will trigger a key missing warning in hf
      # but this matters not for we will load again later.
      init_contexts.append(
          MuteHFLogger(logger=transformers.modeling_utils.logger)
      )
      with ContextManagers(init_contexts):
          yield
  
imports: import torch
  import os
  import json
  from .transformers import ggml_convert_low_bit
  from torch.nn.modules import Module
  from torch.nn.modules.module import _IncompatibleKeys
  from accelerate import init_empty_weights
  from accelerate.utils import set_module_tensor_to_device
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.utils import extract_local_archive_file, get_local_shard_files
  import transformers
  import warnings
  from transformers import PreTrainedModel
  from .utils.common import MuteHFLogger
  from .utils.lazy_load_torch import LazyLoadTensors
  from contextlib import ExitStack, contextmanager
label_desc: Use image node_id 3 for calling a global function with example usage: low_memory_init() without return types