code: def from_meta(cls, meta, local_part, group, device="cuda"):
      assert meta.dtype == torch.long
      dummy = torch.ones(dist.get_world_size(group=group))
      part_obj = cls(tensor=dummy, group=group)
  
      meta = meta.tolist()
  
      # [N, list0, ..., listN-1]
      part_obj.orig_size = meta[1 : (1 + meta[0])]
      meta = meta[1 + meta[0] :]
  
      part_obj.orig_device = device
      part_obj.local_data = local_part.detach()
  
      part_obj.group = group
  
      # Partition is encoded like the rowptr of a CSR matrix:
      # [num_parts, rank, 0, part_1, ..., part_num_parts]
      # TODO: support shuffle between different partition granularities
      assert part_obj.num_parts == meta[0]
      assert part_obj.rank == meta[1]
      part_obj.partition = meta[2:]  # length num_parts+1
  
      return part_obj
  
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 2 for calling the PartitionedTensor obj's underlying member method code with example usage: obj.from_meta(cls, meta, local_part, group, device) and returns: part_obj