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 _encode_str(
      x: str, device: torch.device, size: int
  ) -> torch.Tensor:
      name = torch.tensor(bytearray(x, "utf-8")).to(device)
      padded_x = torch.zeros(size + 1, device=device, dtype=torch.long)
      padded_x[: len(name)] = name
      padded_x[-1] = len(name)
      # output is tensor of shape (1, size + 1)
      return padded_x.unsqueeze(0)
  
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 16 for calling the ComputationModel obj's underlying member method code with example usage: obj._encode_str(x, device, size) and returns: padded_x