docstr: Make a transition of the tensor into the next state.
  
  Args:
      tensor (torch.Tensor): a torch Tensor object.
      tensor_state (TensorState): the target state for transition.
docstr_tok: ["Make","a","transition","of","the","tensor","into","the","next","state",".","Args",":","tensor","(","torch.Tensor",")",":","a","torch","Tensor","object",".","tensor_state","(","TensorState",")",":","the","target","state","for","transition","."]
code: def tensor_trans_state(
      self, tensor: torch.Tensor, tensor_state: TensorState
  ) -> None:
      """
      Make a transition of the tensor into the next state.
  
      Args:
          tensor (torch.Tensor): a torch Tensor object.
          tensor_state (TensorState): the target state for transition.
      """
  
      # As the gradient hook can be triggered either before or after post-backward
      # tensor's state can be compute -> hold_after_bwd -> ready_for_reduce
      # or compute -> ready_for_reduce -> hold_after_bwd
      # the second one is invalid, we just ignore ready_for_reduce -> hold_after_bwd
      # this function only apply valid state transformation
      # invalid calls will be ignored and nothing changes
      if (
          self.tensors_info[tensor].state,
          tensor_state,
      ) not in STATE_TRANS:
          return
      self.__update_one_tensor_info(
          self.tensors_info[tensor], tensor_state
      )
  
imports: from dataclasses import dataclass
  from enum import Enum
  from typing import Dict, List, Optional
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from colossalai.utils import get_current_device
  from colossalai.utils.device import IS_NPU_AVAILABLE
label_desc: Use image node_id 17 for calling the Chunk obj's underlying member method code with example usage: obj.tensor_trans_state(tensor, tensor_state) without return types