class_docstr: torch.Tensor with a variable 0-th dimension, used to describe batched data
class_docstr_tok: ["torch.Tensor","with","a","variable","0-th","dimension",",","used","to","describe","batched","data"]
code: def packb(self) -> bytes:
      obj_dict = asdict(self)
  
      obj_dict["dtype"] = (
          str(self.dtype) if self.dtype is not None else None
      )
      obj_dict["layout"] = (
          str(self.layout) if self.layout is not None else None
      )
  
      device = obj_dict.pop("device")
      device_type, device_index = (
          (device.type, device.index)
          if device is not None
          else (None, None)
      )
      obj_dict.update(
          device_type=device_type,
          device_index=device_index,
      )
  
      return MSGPackSerializer.dumps(obj_dict)
  
imports: from __future__ import annotations
  import warnings
  from dataclasses import asdict, dataclass
  from typing import Tuple
  import numpy
  import torch
  from hivemind.proto.runtime_pb2 import CompressionType
  from hivemind.utils.serializer import MSGPackSerializer
label_desc: Use image node_id 4 for calling the BatchTensorDescriptor obj's underlying member method code with example usage: obj.packb() and returns: MSGPackSerializer