class_docstr: This is the class to record the infomation of each microbatch, and also do some update operation.
  This clase is the base class of `HeadMicroBatchDescription` and `BodyMicroBatchDescription`, for more
  details, please refer to the doc of these two classes blow.
  
  Args:
      inputs_dict (Dict[str, torch.Tensor]): the inputs of current stage. The key should have `input_ids` and `attention_mask`.
      output_dict (Dict[str, torch.Tensor]): the outputs of previous stage. The key should have `hidden_states` and `past_key_values`.
class_docstr_tok: ["This","is","the","class","to","record","the","infomation","of","each","microbatch",",","and","also","do","some","update","operation",".","This","clase","is","the","base","class","of","`","HeadMicroBatchDescription","`","and","`","BodyMicroBatchDescription","`",",","for","more","details",",","please","refer","to","the","doc","of","these","two","classes","blow",".","Args",":","inputs_dict","(","Dict","[","str",",","torch.Tensor","]",")",":","the","inputs","of","current","stage",".","The","key","should","have","`","input_ids","`","and","`","attention_mask","`",".","output_dict","(","Dict","[","str",",","torch.Tensor","]",")",":","the","outputs","of","previous","stage",".","The","key","should","have","`","hidden_states","`","and","`","past_key_values","`","."]
code: def update(self, *args, **kwargs):
      pass
  
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 2 for calling the MicroBatchDescription obj's underlying member method code with example usage: obj.update() without return types