docstr: Returns a torch.LongTensor that encodes partitioning information.
  
  Can be used along with ``data()`` to serialize a ``PartitionedTensor`` for
  communication.
  
  Returns:
      torch.LongTensor: a tensor encoding the meta-information for the partitioning
code: def to_meta(self):
      """Returns a torch.LongTensor that encodes partitioning information.
  
      Can be used along with ``data()`` to serialize a ``PartitionedTensor`` for
      communication.
  
      Returns:
          torch.LongTensor: a tensor encoding the meta-information for the partitioning
      """
      meta = []
      meta.append(len(self.orig_size))
      meta += list(self.orig_size)
      meta.append(self.num_parts)
      meta.append(self.rank)
      meta += self.partition
      return torch.LongTensor(data=meta).to(self.orig_device)
  
imports: from collections.abc import Iterable
  from deepspeed.moe.utils import is_moe_param
  import os
  import psutil
  import gc
  from math import sqrt
  from math import floor
  from bisect import bisect_left
  import torch
  from torch import inf
  from deepspeed import comm
  from deepspeed.utils import groups, logger
  from deepspeed.runtime.constants import PIPE_REPLICATED
  from numpy import prod
label_desc: Use image node_id 5 for calling the PartitionedTensor obj's underlying member method code with example usage: obj.to_meta() and returns: torch