class_docstr: Base class for distributed computation models and defines interface methods.
  This class is public and should be used for other custom derived distributed models.
class_docstr_tok: ["Base","class","for","distributed","computation","models","and","defines","interface","methods",".","This","class","is","public","and","should","be","used","for","other","custom","derived","distributed","models","."]
code: def _decode_as_placeholder(
      encoded_msg: List[int], device: torch.device
  ) -> Union[torch.Tensor, float, str]:
      if encoded_msg[0] == 0:
          len_shape = encoded_msg[1]
          le = 2 + len_shape
          shape = encoded_msg[2:le] if len_shape > 0 else []
          len_dtype = encoded_msg[le]
          dtype_str = bytearray(
              encoded_msg[le + 1 : le + 1 + len_dtype]
          ).decode("utf-8")
          dtype = eval(dtype_str)
          return torch.empty(shape, device=device, dtype=dtype)
      elif encoded_msg[0] == 1:
          return 0.0
      elif encoded_msg[0] == 2:
          return ""
      else:
          raise RuntimeError(
              f"Internal error: unhandled dtype {encoded_msg[0]}, encoded_msg={encoded_msg}"
          )
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Number
  from typing import Any, Callable, cast, List, Optional, Union
  import torch
  from packaging.version import Version
label_desc: Use image node_id 19 for calling the ComputationModel obj's underlying member method code with example usage: obj._decode_as_placeholder(encoded_msg, device) and returns: torch, int, str