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 on_train_end(self, trainer):
      print(f"\n equal num: {self.equal}.\n")
      print(f"\ntotal_batch_num: {trainer.n_batches}.\n")
  
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 3 for calling the RecordAccumulationStepsCallback_Torch obj's underlying member method code with example usage: obj.on_train_end(trainer) without return types