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 __init__(
      self, *instance_size, **kwargs
  ):  # compatibility: allow initializing with *size
      if len(instance_size) == 1 and isinstance(
          instance_size[0], (list, tuple, torch.Size)
      ):
          instance_size = instance_size[
              0
          ]  # we were given size as the only parameter instead of *parameters
      super().__init__((None, *instance_size), **kwargs)
  
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 1 to create a new BatchTensorDescriptor object from inherited base classes: TensorDescriptor with example: obj = BatchTensorDescriptor()