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: deepspeedexamples/inference/huggingface/zero_inference/utils.py
imports: import torch
  import time
label_desc: Use image node_id 12 for calling a global function with example usage: meta_to_cpu(container, dtype) and returns: torch, tuple, dict