class_docstr: A manager class to manipulate the tensors in chunks.
  
  Args:
      chunk_configuration (Dict[int, Dict]): the configuration dictionary of this chunk manager.
      init_device (torch.device): optional, the device on which the chunk is initialized. The default is None.
class_docstr_tok: ["A","manager","class","to","manipulate","the","tensors","in","chunks",".","Args",":","chunk_configuration","(","Dict","[","int",",","Dict","]",")",":","the","configuration","dictionary","of","this","chunk","manager",".","init_device","(","torch.device",")",":","optional",",","the","device","on","which","the","chunk","is","initialized",".","The","default","is","None","."]
docstr: Transit tensor state according to pre-defined state machine.
docstr_tok: ["Transit","tensor","state","according","to","pre-defined","state","machine","."]
code: def trans_tensor_state(
      self, tensor: torch.Tensor, state: TensorState
  ) -> None:
      """Transit tensor state according to pre-defined state machine."""
      chunk = self.tensor_chunk_map[tensor]
      chunk.tensor_trans_state(tensor, state)
  
imports: from collections import deque
  from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from colossalai.utils import free_storage, get_current_device
  from .chunk import Chunk, ChunkFullError, TensorState
label_desc: Use image node_id 7 for calling the ChunkManager obj's underlying member method code with example usage: obj.trans_tensor_state(tensor, state) without return types