class_docstr: 通过该 callback 来测试 Trainer 的 accumulation_steps 是否实现正确；
  
  1. 在每一个 batch 检验模型是否正确地得到了更新（只有每隔 accumulation_steps 模型的参数才应该改变）；
  2. 检验 optimizer 的参数是否只在正确的时刻进行了清零；
code: def __init__(self, accumulation_steps: int):
      self.accumulation_steps = accumulation_steps
      self.last_batch_params = None
  
      self.equal = 0
  
imports: from copy import deepcopy
  from fastNLP.core.callbacks.callback import Callback
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 1 to create a new RecordAccumulationStepsCallback_Torch object from inherited base classes: Callback with example: obj = RecordAccumulationStepsCallback_Torch(accumulation_steps)