code: def meta_to_cpu(container, dtype=None):
      if isinstance(container, torch.Tensor):
          return torch.empty(
              *container.shape, dtype=dtype or container.dtype
          )
      elif isinstance(container, tuple):
          return tuple(meta_to_cpu(x, dtype) for x in container)
      elif isinstance(container, dict):
          return dict(
              (k, meta_to_cpu(v, dtype)) for k, v in container.items()
          )
      else:
          raise ValueError(f"Invalid type: {container}")
  
file_path: H2O/h2o_flexgen/benchmark/hf_ds/hf_opt.py
imports: import argparse
  import multiprocessing
  import os
  import pickle
  import time
  import numpy
  from accelerate import infer_auto_device_map, init_empty_weights, load_checkpoint_and_dispatch
  from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
  from transformers import OPTForCausalLM
  import torch
  from flexgen.timer import timers
  from flexgen.utils import GB, project_decode_latency, write_benchmark_log
  from flexgen.opt_config import get_opt_config, disable_torch_init, disable_hf_opt_init
label_desc: Use image node_id 2 for calling a global function with example usage: meta_to_cpu(container, dtype) and returns: torch, tuple, dict