class_docstr: This class is used to record the infomation of the stages except the first stage of pipeline, the stages should have attributes `hidden_states` and `past_key_values`,
  
  Args:
      inputs_dict (Dict[str, torch.Tensor]): will always be `None`. Other stages only receive hiddenstates from previous stage.
class_docstr_tok: ["This","class","is","used","to","record","the","infomation","of","the","stages","except","the","first","stage","of","pipeline",",","the","stages","should","have","attributes","`","hidden_states","`","and","`","past_key_values","`",",","Args",":","inputs_dict","(","Dict","[","str",",","torch.Tensor","]",")",":","will","always","be","`","None","`",".","Other","stages","only","receive","hiddenstates","from","previous","stage","."]
code: def __init__(
      self,
      inputs_dict: Dict[str, torch.Tensor],
      max_input_len: int,
      max_output_len: int,
      cache_manager: MemoryManager,
  ) -> None:
      super().__init__(
          inputs_dict, max_input_len, max_output_len, cache_manager
      )
  
imports: from enum import Enum
  from typing import Dict
  import torch
  from ..tensor_parallel.batch_infer_state import BatchInferState
  from ..tensor_parallel.kvcache_manager import MemoryManager
label_desc: Use image node_id 1 to create a new BodyMicroBatchDescription object from inherited base classes: MicroBatchDescription with example: obj = BodyMicroBatchDescription(inputs_dict, max_input_len, max_output_len, cache_manager)