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 make_zeros(self, *batch_size: int, **kwargs) -> torch.Tensor:
      assert (
          self.shape[0] is None
      ), "Make sure 0-th dimension is not specified (set to None)"
      return super().make_zeros(
          size=(*batch_size, *self.shape[1:]), **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 3 for calling the BatchTensorDescriptor obj's underlying member method code with example usage: obj.make_zeros() and returns: super